system

Abstract

We provide the system. [Solution] A means for collecting and analyzing large-scale biomedical information, A recommendation system that acquires patient information and recommends treatment appropriate for the patient, A compound discovery method that searches existing drug information and discovers new indications, An integration tool to combine research results from around the world and build a knowledge base, A matching method for selecting participants in clinical trials based on genetic information and medical history, A system that includes this.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a method for controlling a persona chatbot, which is 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 character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 In the development of therapeutic drugs for rare diseases and intractable diseases, the difficulties of clinical trials due to the small number of patients, the high development costs, and the unclear disease mechanisms have become barriers to the conventional drug discovery process. Therefore, there is a need to improve the efficiency of development and reduce costs, but the current methods have not fully overcome these problems. 【Means for Solving the Problems】 【0005】 This invention solves the above problems by providing a system equipped with analytical means for collecting and analyzing large-scale biomedical information, and also with recommendation means for recommending appropriate treatment based on patient information. Furthermore, it dramatically improves the efficiency of the entire drug discovery process by combining compound discovery means for exploring existing drug information to discover new indications, integration means for integrating research results from around the world, and matching means for efficiently selecting clinical trial participants based on genetic information and medical history. 【0006】 "Large-scale biomedical information" refers to vast amounts of data related to medical research and treatment, such as patients' genetic information, medical records, and drug response data. 【0007】 "Analysis tools" refer to a collection of software and algorithms used to process and analyze collected data to derive useful insights. 【0008】 "Patient information" refers to information about individual patients, and includes clinical data, genetic information, medical history, etc. 【0009】 "Recommendation methods" refer to functions and processes that recommend the most suitable treatment or medication to a patient based on analysis results. 【0010】 "Existing drug information" refers to information about the characteristics, indications, and side effects of drugs that are available on the market. 【0011】 "Compound discovery methods" refer to search and analysis methods used to find new indications for drugs. 【0012】 "Integration methods" refer to techniques for combining data collected from different sources to build a consistent knowledge base. 【0013】 "Matching method" refers to an algorithm or process for selecting suitable clinical trial participants based on the criteria of the trial. [Brief explanation of the drawing] 【0014】 [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined. 【MODE FOR CARRYING OUT THE INVENTION】 【0015】 An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings. 【0016】 First, the terms used in the following description will be explained. 【0017】 In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like. 【0018】 In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0019】 In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc. 【0020】 In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark). 【0021】 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or." 【0022】 [First Embodiment] 【0023】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0024】 As shown in Figure 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. 【0025】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network). 【0026】 The smart device 14 comprises a computer 36, a reception 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 reception device 38, output device 40, and camera 42 are also connected to the bus 52. 【0027】 The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input. 【0028】 The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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. 【0029】 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. 【0030】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0031】 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. 【0032】 The 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. 【0033】 In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0034】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0035】 The system for implementing the present invention functions through the cooperation of three parties: a server, a terminal, and a user. The system includes the following elements: 【0036】 A server acts as the central hub for collecting large amounts of biomedical information. This includes patient genetic information, medical records, and drug response data transmitted from research and healthcare institutions. This data is analyzed on the server, and machine learning algorithms are applied to reveal specific interactions and disease patterns. 【0037】 Based on the analysis results, the server matches them with patient information and develops an individualized treatment plan. This recommendation system suggests the most effective treatment based on the patient's genetic information and medical history. 【0038】 Furthermore, the server searches a database of existing drugs to explore new indications for those drugs. Compound discovery methods identify the potential of off-patent drugs and unknown drugs, and reposition them for the treatment of specific diseases. 【0039】 The server integrates medical research findings from around the world, strengthening the knowledge base. This integration method allows for cross-referencing of diverse information sources, facilitating the development of new research hypotheses and a better understanding of disease mechanisms. 【0040】 Users, such as medical researchers and clinicians, can access the server through their terminals to gain insights that support their daily clinical decisions. For example, they can use the recommendations provided by the server to optimize treatment plans for patients with specific genetic abnormalities. 【0041】 Furthermore, the device identifies suitable clinical participants using patient matching methods based on the clinical trial conditions. This can improve the success rate of clinical trials and accelerate the development of new drugs for rare and intractable diseases. 【0042】 As described above, the present invention can significantly streamline the process of medical research and drug development for the treatment of rare and intractable diseases, thereby enhancing the sustainability of research and development in terms of both time and cost. 【0043】 The following describes the processing flow. 【0044】 Step 1: 【0045】 The server receives large amounts of biomedical information transmitted from various research and medical institutions. This includes patient genetic information, medical records, and drug response data. 【0046】 Step 2: 【0047】 The server preprocesses the received data. It handles missing values, normalizes the data, and converts it into a format suitable for analysis. 【0048】 Step 3: 【0049】 The server applies machine learning algorithms to large datasets and performs analyses to identify new drug interactions and disease patterns. 【0050】 Step 4: 【0051】 The server accumulates these analysis results and uses a recommendation system to propose the most suitable treatment based on specific symptoms and genetic information. 【0052】 Step 5: 【0053】 The server searches existing drug databases and executes compound discovery methods to find drug candidates that can be repositioned. 【0054】 Step 6: 【0055】 The user reviews the analysis results using the device and decides on a treatment plan based on the insights gained. The device presents the user with the best treatment options based on the patient's medical history and genetic information. 【0056】 Step 7: 【0057】 The terminal inputs patient information for the clinical trial, and the server uses matching means to identify suitable clinical trial participants based on the patient's genetic information and medical history. 【0058】 Step 8: 【0059】 Based on the obtained clinical trial participant information, the server transmits the data to the research institution and prepares to support the implementation of the trial. 【0060】 (Example 1) 【0061】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0062】 Currently, the decentralization of data and the complexity of analysis in the medical field make it difficult to propose efficient treatments and select appropriate participants for clinical trials. Furthermore, there is a need for efficient methods to find new applications for existing drugs. These problems hinder the progress of medical research and create barriers to providing optimal care to patients. 【0063】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0064】 In this invention, the server includes an information processing means for collecting and analyzing large-scale biomedical information; a treatment suggestion means for acquiring user information and recommending individualized treatments; a drug evaluation means for exploring information on existing drugs and discovering new indications; an information integration means for compiling research results from around the world and forming a knowledge base; a candidate selection means for selecting participants for clinical trials based on genetic information and medical history; and a user terminal that enables the visual display of information. This makes it possible to provide personalized treatment suggestions, discover new drug indications, and select appropriate clinical trial participants through efficient and effective data analysis. 【0065】 "Information processing means" refers to devices that collect large amounts of biomedical information and have the function of extracting specific patterns and interactions by analyzing that information. 【0066】 A "treatment suggestion tool" is an algorithm or system that individually recommends the most suitable treatment method based on the user's genetic information and medical history. 【0067】 "Drug evaluation methods" include data mining and evaluation processes that analyze existing drug information to discover new indications. 【0068】 An "information integration tool" is a system that integrates research results from around the world and forms a knowledge base to provide the latest medical knowledge. 【0069】 "Candidate selection methods" refer to methods and systems for selecting the most suitable participants for a clinical trial, taking into account genetic information and medical history. 【0070】 A "user terminal" is hardware or an interface that allows a user to visually view and interact with information from a server. 【0071】 This invention is a medical information processing system in which a server, terminals, and users work together. The server centrally collects large amounts of biomedical information and analyzes it using information processing means. This includes genetic information, medical records, and drug response data transmitted from research and medical institutions. To collect and process this data, the server imports the information into a database via an API and performs analysis using machine learning algorithms such as TENSORFLOW® and PyTorch. 【0072】 The server uses treatment suggestion tools to develop personalized treatment plans based on the patient's genetic information and medical history. This plan development utilizes a generative AI model and prompts such as, "Which medication is suitable for patient X?". Furthermore, drug evaluation tools allow for the exploration and consideration of new indications for expired patents and existing medications. This enables repositioning for specific diseases. 【0073】 The information integration mechanism involves a server that collects medical research findings from around the world and uses it to strengthen the knowledge base. Specifically, the server collects information from sources such as PubMed, uses natural language processing technology to perform cross-referencing, and aims to promote the understanding of new research hypotheses and mechanisms. 【0074】 Medical researchers and clinicians, as users, can access the server through their terminals and use the insights gained to support their daily clinical decisions. For example, they can leverage recommendations to optimize treatments for patients with specific genetic abnormalities. The terminals can also use candidate selection tools to identify participants in clinical trials and improve the success rate of those trials. This process accelerates the development of new drugs for rare and intractable diseases, improving the efficiency of research and development in terms of both time and cost. 【0075】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0076】 Step 1: 【0077】 The server receives genetic information, medical records, and drug response data from research and medical institutions via APIs. This data is collected and stored in a large-scale biomedical information database. The input consists of raw data from each institution, which is then processed to a format suitable for storage in the central database. 【0078】 Step 2: 【0079】 The server analyzes the collected data using machine learning algorithms (e.g., TensorFlow and PyTorch). The input consists of structured genetic information and medical records, which the server analyzes to identify disease patterns and drug interactions. Through data processing, specific genetic tendencies and chemical reaction patterns are obtained as output. 【0080】 Step 3: 【0081】 Based on the analysis results, the server utilizes a generative AI model to generate personalized treatment suggestions using the prompt "Which medication is suitable for patient X?". By using the analysis results and patient information as input, it outputs a treatment plan suitable for a specific patient. This output is provided to medical researchers and clinicians. 【0082】 Step 4: 【0083】 The server searches a database of existing drugs using compound discovery tools to find new indications. This process involves analyzing data on expired drugs and performing data calculations to identify potential new indications. The output is a list of re-evaluated drugs. 【0084】 Step 5: 【0085】 The server uses information integration tools to collect medical research information from around the world and enhance knowledge. Inputs include various academic papers and research data, and by organizing and integrating this information using natural language processing, new research hypotheses and insights into disease understanding can be obtained. The output becomes an enhanced knowledge base. 【0086】 Step 6: 【0087】 Users review the information provided through the terminal and use it to support their daily clinical decisions. They enter prompts to view treatment suggestions and analysis results from the server and decide on specific patient actions. In this process, the terminal outputs visualized information on the screen. 【0088】 Step 7: 【0089】 The terminal utilizes a candidate selection method to select participants for a clinical trial. Using the candidate's genetic information and medical history as input, data calculations are performed to identify patients who meet the criteria. The output is a list of suitable clinical trial participants. 【0090】 (Application Example 1) 【0091】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0092】 In modern medical and nursing care settings, there is a growing demand for personalized care that utilizes each patient's individual biomedical information. However, the sheer volume of data, the complexity of data centralization, and the intricacies of analysis make it extremely difficult to develop treatment and care plans optimized for each patient. Furthermore, the discovery of new indications for existing drugs and the efficient conduct of clinical trials have not progressed sufficiently, leading to a stagnation in the development of treatments for rare diseases in particular. There is a need for solutions to these challenges and to realize more efficient and effective medical and nursing care. 【0093】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0094】 In this invention, the server includes analytical means for collecting and analyzing large-scale biological data, recommendation means for acquiring subject information and recommending treatments suitable for the subject, and proposal means for generating personalized care plans. This enables the efficient formulation of treatment methods and care plans tailored to each patient's condition through centralized data management and advanced analysis. Furthermore, these means can support the development of new treatments for rare and intractable diseases and improve the quality of medical care and nursing. 【0095】 "Large-scale biological data" refers to a collection of diverse information gathered from research institutions and medical facilities, including genetic information, medical records, and drug response data. 【0096】 "Analysis methods" refer to the process of analyzing collected biological data using machine learning algorithms and other methods to reveal specific interactions and disease patterns. 【0097】 A "recommendation method" is a mechanism that proposes the optimal treatment method based on the individual health condition and genetic information of each subject, based on the analyzed data. 【0098】 A "compound discovery method" is a method that, based on existing medical information, discovers new indications and enables drug redistribution. 【0099】 An "integration method" is a method for building knowledge resources by aggregating research results from around the world and cross-referencing that information. 【0100】 "Matching methods" refer to the process of selecting participants suitable for a particular clinical trial based on their genetic information and medical history. 【0101】 The "proposed method" is a system that generates individualized care plans based on the subject's biomedical information and provides specific care guidelines to caregivers and medical professionals. 【0102】 This invention is implemented as a system for developing personalized medical and care plans using biological data. The system primarily operates through the cooperation of a server, terminals, and users. The server collects large amounts of biological data from research and medical institutions and centrally manages this data. The server, accessed through terminals, utilizes machine learning algorithms as an analytical tool. Specifically, it uses libraries such as TensorFlow and PyTorch to identify specific interactions and disease patterns from the collected data. 【0103】 The server also functions as a recommendation system, suggesting the optimal treatment method based on artificial intelligence technology. These recommendations are provided to the device using frameworks such as React Native and Flutter®, based on the subject's genetic information and health status. Furthermore, as a suggestion system, the server generates personalized care plans integrated into smartphone applications, providing guidelines to the subject and their caregivers. 【0104】 One of the key features of this system is that it provides elderly diabetic patients with customized advice on dietary restrictions and exercise plans tailored to their daily blood glucose levels. 【0105】 An example of a prompt to input into the generating AI model would be: "Based on a 70-year-old female with diabetes and past blood glucose data, please create a care plan for tomorrow." This would provide guidance for implementing specific and effective care based on the data. 【0106】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0107】 Step 1: 【0108】 The server acquires large amounts of biological data from research and medical institutions. This data includes genetic information, medical records, and drug response data. The server converts this data into a standardized format and stores it in a database. At this stage, the data is appropriately anonymized and privacy is protected. 【0109】 Step 2: 【0110】 The server performs analysis using machine learning algorithms on the stored data. This analysis utilizes TensorFlow and PyTorch to identify interactions and disease patterns within the data. The input to this process is the biological data stored on the server, and the output is a list of identified interactions and patterns. 【0111】 Step 3: 【0112】 The server executes recommendation procedures based on the analysis results. Here, artificial intelligence technology is used to propose the most suitable treatment option for each subject. The input is the analysis results obtained by machine learning and the patient's genetic information, and the output is an individualized treatment plan. 【0113】 Step 4: 【0114】 Users (healthcare professionals and caregivers) receive personalized care plans provided by the server via a terminal. The terminal uses an application developed with React Native or Flutter to present information to the user in an easy-to-understand GUI. The input here is care plan information from the server, and the output is care guidelines in a format that the user can view. 【0115】 Step 5: 【0116】 Users incorporate the provided care plan into their daily work. A specific example is implementing appropriate dietary restrictions and exercise for managing blood glucose levels in elderly diabetic patients. Users can also provide feedback, which the system uses to collect more data for future analysis and recommendations. 【0117】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0118】 The system implementing this invention is composed of three main components: a server, a terminal, and a user. By incorporating an emotion engine, it enables the provision of detailed medical information tailored to the user's emotional state. 【0119】 First, the server analyzes large amounts of biomedical information obtained from hospitals and research institutions. This analysis utilizes machine learning algorithms to elucidate the relationship between specific drugs and diseases. The analysis results are then compared with existing data using delta matching techniques to gain new scientific insights. 【0120】 Furthermore, the server uses these new insights to match patient information and recommend the most effective treatment in a personalized manner. In this process, an emotion engine is integrated into the user interface, analyzing user input and responses to recognize emotions. For example, if a user is experiencing anxiety or stress, the emotion engine detects this, and the server presents information in a flexible communication style. 【0121】 The device records the user's reaction when appropriate treatment options are presented and analyzes it using an emotion engine. This analysis is sent to a server and used to improve future treatment recommendations. Specifically, if the previous presentation of medical information caused anxiety, the system can select a more reassuring method of presentation for subsequent consultations. 【0122】 This system enables the provision of optimal and efficient medical information while taking into consideration the patient's own feelings. Furthermore, it possesses compound discovery capabilities, allowing for the early detection of new indications beyond existing drugs, thereby expanding treatment options. 【0123】 Thus, this invention provides more individualized and optimized medical information, particularly in supporting patients in medical settings by considering their psychological state. Furthermore, since emotional state is also considered in the selection of clinical trial participants, it is anticipated that the efficiency of conducting trials will be further improved. 【0124】 The following describes the processing flow. 【0125】 Step 1: 【0126】 The server collects biomedical data transmitted from healthcare and research institutions. This data includes genetic information, medical history, and drug responses. 【0127】 Step 2: 【0128】 The server uses machine learning algorithms to analyze the collected data and discover new drug interactions and disease patterns. These results are then stored in a knowledge base. 【0129】 Step 3: 【0130】 After obtaining patient information, the server performs calculations to recommend the optimal treatment based on that information. This calculation uses the patient's genetic information and medical history. 【0131】 Step 4: 【0132】 The device presents the user with information recommending treatment. Here, the emotion engine analyzes the user's input and responses in real time to evaluate the user's emotional state. 【0133】 Step 5: 【0134】 When a user reacts to the information presented, the device processes that reaction using an emotion engine and sends it to the server. The emotional state is recorded and influences the next information presentation. 【0135】 Step 6: 【0136】 The server analyzes the user's emotional data received from the emotion engine and determines how to present information next. For example, if the user is feeling anxious, it will choose an information presentation method that provides more reassurance. 【0137】 Step 7: 【0138】 The server considers emotional state in addition to genetic information and medical history during the clinical trial participant selection process. This process includes evaluation using machine learning. 【0139】 Step 8: 【0140】 The device provides users with final clinical trial participant information and supports the preparation and progress of the trial. It also monitors participants' emotional states and provides feedback as needed. 【0141】 (Example 2) 【0142】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0143】 Conventional medical information systems have faced challenges in providing information tailored to each patient's individual emotional state, thus failing to alleviate patients' psychological burden. Furthermore, they have limitations in optimizing medical care due to the inability to effectively select new treatments or trial participants. 【0144】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0145】 In this invention, the server includes information processing means for handling large amounts of biomedical information and analyzing that data, suggestion means for recommending the optimal treatment method based on individual patient information, and psychological analysis means for evaluating emotional states and adjusting the information provision method. This enables efficient and flexible provision of medical information that takes into account the emotions of individual patients. 【0146】 "Information processing means" refers to technologies or devices for analyzing large-scale biomedical information and extracting specific interrelationships or relationships. 【0147】 "Proposal means" refers to a technology or device for deriving and recommending the optimal treatment method based on individual patient information. 【0148】 "Chemical substance discovery methods" refer to technologies or devices that analyze existing drug information and use it to discover new therapeutic applications. 【0149】 "Integration means" refers to technologies or devices for integrating multiple research results and information to build a consistent knowledge base. 【0150】 "Selection method" refers to a technique or device for selecting participants in a study based on genetic information or medical history. 【0151】 "Psychological analysis tools" are techniques or devices used to evaluate a subject's emotional state and adjust the method of providing information. 【0152】 A "learning tool" is a technology or device that analyzes response feedback and uses it to improve the information provided in the future. 【0153】 This invention is an advanced medical information provision system realized through sophisticated collaboration between users, servers, and terminals. First, the server acquires large amounts of biomedical information from hospitals and research institutions and analyzes it using machine learning algorithms. This analysis uses libraries such as Scikit-learn and TensorFlow in Python to reveal the relationship between specific drugs and diseases. The server also compares the obtained analysis results with existing data using delta matching to generate new insights. 【0154】 Next, the server uses this knowledge to compare it with the user's health information and proposes a personalized treatment plan. In this process, artificial intelligence contributes to the development of the personalized treatment plan. A terminal equipped with an emotion engine evaluates the user's emotional state in real time and adjusts the way information is presented according to the user. This system analyzes emotions using voice and facial recognition technology. 【0155】 Furthermore, the device records the user's reaction to the presented treatment and sends this emotional data to the server. Based on this feedback, the server continuously updates its machine learning model to optimize future information delivery. This process enables efficient and flexible information delivery that takes into account the individual emotions of each patient. 【0156】 For example, if a user enters cold symptoms and the server analysis recommends a specific medication, and anxiety is detected, the terminal can provide flexible information, including relaxation suggestions. An example of a prompt message would be: "The user entered cold symptoms. The emotion engine detected anxiety. What information should be provided next?" 【0157】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0158】 Step 1: 【0159】 The server acquires large amounts of biomedical information from hospitals and research institutions. As input, it downloads electronic medical records, research papers, and clinical trial information from databases. Based on this data, machine learning algorithms are applied to explore the relationship between specific drugs and diseases. For data processing, natural language processing is used to clean the text data and extract necessary information. The output is a list of drugs associated with specific diseases. 【0160】 Step 2: 【0161】 The server compares the analysis results obtained in the previous step with existing data using delta matching. The input to this process is the elucidated relationship information and historical datasets. Difference analysis is performed, and data calculations are carried out to extract new insights. The output is newly discovered scientific knowledge. 【0162】 Step 3: 【0163】 The server develops personalized treatment plans based on the patient's health status and medical history. Inputs include individual information obtained from the user and newly acquired insights. The suggestion engine uses artificial intelligence technology to process the data and recommend the optimal treatment plan. The output is a treatment plan optimized for the patient. 【0164】 Step 4: 【0165】 The device analyzes user feedback and input using an emotion engine. Input includes voice, facial expressions, and handwritten replies. It uses emotion analysis technology to evaluate the user's emotions and processes the data to adjust how information is presented. The output is customized information presentation tailored to the emotion. 【0166】 Step 5: 【0167】 The terminal records the user's reactions and sends this information to the server. The input is user response data to treatment suggestions, and the output is feedback data for optimizing future information delivery. The server updates its model using learning mechanisms based on the data sent from the terminal to improve the next information delivery. 【0168】 Step 6: 【0169】 The server analyzes feedback data and continuously updates its model to optimize future medical information delivery. The input is user emotional response data, and through data computation, it generates insights to improve future interactions. The output is a more appropriate and reassuring way of delivering information. 【0170】 (Application Example 2) 【0171】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal". 【0172】 In the modern healthcare environment, the lack of information provision that takes into account the emotional state of patients makes them more susceptible to anxiety and stress, which can affect their acceptance of medical information and their treatment choices. Furthermore, individualized approaches are often lacking in the discovery of new treatments and medications. To address this challenge, there is a need to provide information in accordance with patients' emotions and to personalize the exploration and proposal of new treatments. 【0173】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0174】 In this invention, the server includes processing means for collecting and analyzing large-scale biomedical data; instruction means for acquiring user information and recommending medical prescriptions suitable for the user; drug search means for searching existing drug data and discovering new indications; and emotion analysis means for analyzing the user's emotional state in real time and providing information in accordance with that emotion. This enables the provision of medical information that takes the user's emotions into consideration and the search for personalized treatment methods. 【0175】 "Large-scale biomedical data" refers to biological and medical information collected from a wide range of research institutions and medical facilities. 【0176】 A "processing device" is a component that has the function of analyzing collected data and extracting useful information from it. 【0177】 "User information" refers to data about individual users receiving medical treatment, including medical history and genetic information. 【0178】 A "prescription tool" is a component that has the function of suggesting appropriate medical prescriptions and treatment plans based on the user's information. 【0179】 "Drug data" refers to a collection of information about existing pharmaceuticals, including their ingredients, effects, and side effects. 【0180】 A "drug discovery tool" is a component that has the function of investigating existing drug data in order to discover new indications. 【0181】 "Emotional analysis methods" refer to technologies that analyze a user's facial expressions and behavior and recognize their emotional state based on this analysis. 【0182】 The system for implementing this invention primarily consists of three components: a server, a terminal, and a user. The server collects large-scale biomedical data and uses machine learning-based processing methods to analyze it. Machine learning libraries such as TensorFlow are used for data analysis to extract specific interactions and new knowledge. 【0183】 The terminal collects user information and transmits it to the server. On the terminal, emotion analysis APIs such as Affectiva are used to analyze the user's emotions in real time. This emotion analysis data is sent to the server and used to present optimal medical information tailored to the user's emotions. 【0184】 The information users obtain through their devices is processed into personalized medical prescriptions based on instructions on the server. At the same time, a drug discovery system for identifying new indications also operates, expanding the range of medical information available to users. More effective information presentation becomes possible through prompts to the generative AI model, such as, "Generate feedback suggesting relaxing music and care methods for anxious elderly individuals." 【0185】 This system allows users to receive medical information in an emotionally sensitive manner, enabling a personalized medical experience. For example, if an elderly person in a nursing home is feeling anxious, the terminal analyzes that information, and the server provides reassuring care and prompts to reduce the user's stress. 【0186】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0187】 Step 1: 【0188】 The user logs into the device and answers a questionnaire about their health and emotions. The device retrieves the user's response data and uses an emotion analysis API to analyze the user's emotional state in real time. The input for this analysis includes the user's questionnaire responses and facial expression data, and the output is the analyzed emotional state. 【0189】 Step 2: 【0190】 The device transmits user information, including analyzed emotional and health status, to the server using a secure communication protocol. Input includes analyzed emotional data and user health information, which are then sent to the server. 【0191】 Step 3: 【0192】 The server searches a large biomedical database based on the user information it receives. Here, machine learning algorithms are used to generate personalized medical prescriptions and care plans for the user. The input is user information, and the output includes personalized medical prescriptions and care plans. 【0193】 Step 4: 【0194】 The server uses a drug discovery tool to review data on drugs recommended to the user and investigate whether there are any new indications. The input includes existing drug data, and the output provides a list of potential new indications and drugs. 【0195】 Step 5: 【0196】 The server generates prompts for the generative AI model to provide information tailored to the user's emotions. For example, it might send the prompt, "Generate feedback suggesting relaxing music and care methods for an elderly person experiencing anxiety." The input includes emotional state and medical information, and the output generates user-appropriate feedback. 【0197】 Step 6: 【0198】 The terminal displays medical prescriptions and feedback received from the server to the user. The user then decides on their next medical action based on this information. The output includes information that the user sees on the terminal. 【0199】 Step 7: 【0200】 After the user takes action based on the information they have obtained, the device records the results and reactions and sends feedback to the server for future use. The input is the user's reactions and actions, and the output is the server's learning data being updated. 【0201】 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. 【0202】 Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0203】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14. 【0204】 [Second Embodiment] 【0205】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0206】 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. 【0207】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network). 【0208】 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. 【0209】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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. 【0210】 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0211】 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. 【0212】 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 using the processor 28. The storage 32 stores the specific processing program 56. 【0213】 The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30. 【0214】 The 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. 【0215】 In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0216】 Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0217】 The system for implementing the present invention functions through the cooperation of three parties: a server, a terminal, and a user. The system includes the following elements: 【0218】 A server acts as the central hub for collecting large amounts of biomedical information. This includes patient genetic information, medical records, and drug response data transmitted from research and healthcare institutions. This data is analyzed on the server, and machine learning algorithms are applied to reveal specific interactions and disease patterns. 【0219】 Based on the analysis results, the server matches them with patient information and develops an individualized treatment plan. This recommendation system suggests the most effective treatment based on the patient's genetic information and medical history. 【0220】 Furthermore, the server searches a database of existing drugs to explore new indications for those drugs. Compound discovery methods identify the potential of off-patent drugs and unknown drugs, and reposition them for the treatment of specific diseases. 【0221】 The server integrates medical research findings from around the world, strengthening the knowledge base. This integration method allows for cross-referencing of diverse information sources, facilitating the development of new research hypotheses and a better understanding of disease mechanisms. 【0222】 Users, such as medical researchers and clinicians, can access the server through their terminals to gain insights that support their daily clinical decisions. For example, they can use the recommendations provided by the server to optimize treatment plans for patients with specific genetic abnormalities. 【0223】 Furthermore, the device identifies suitable clinical participants using patient matching methods based on the clinical trial conditions. This can improve the success rate of clinical trials and accelerate the development of new drugs for rare and intractable diseases. 【0224】 As described above, the present invention can significantly streamline the process of medical research and drug development for the treatment of rare and intractable diseases, thereby enhancing the sustainability of research and development in terms of both time and cost. 【0225】 The following describes the processing flow. 【0226】 Step 1: 【0227】 The server receives large amounts of biomedical information transmitted from various research and medical institutions. This includes patient genetic information, medical records, and drug response data. 【0228】 Step 2: 【0229】 The server preprocesses the received data. It handles missing values, normalizes the data, and converts it into a format suitable for analysis. 【0230】 Step 3: 【0231】 The server applies machine learning algorithms to large datasets and performs analyses to identify new drug interactions and disease patterns. 【0232】 Step 4: 【0233】 The server accumulates these analysis results and uses a recommendation system to propose the most suitable treatment based on specific symptoms and genetic information. 【0234】 Step 5: 【0235】 The server searches existing drug databases and executes compound discovery methods to find drug candidates that can be repositioned. 【0236】 Step 6: 【0237】 The user reviews the analysis results using the device and decides on a treatment plan based on the insights gained. The device presents the user with the best treatment options based on the patient's medical history and genetic information. 【0238】 Step 7: 【0239】 The terminal inputs patient information for the clinical trial, and the server uses matching means to identify suitable clinical trial participants based on the patient's genetic information and medical history. 【0240】 Step 8: 【0241】 Based on the obtained clinical trial participant information, the server transmits the data to the research institution and prepares to support the implementation of the trial. 【0242】 (Example 1) 【0243】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0244】 Currently, the decentralization of data and the complexity of analysis in the medical field make it difficult to propose efficient treatments and select appropriate participants for clinical trials. Furthermore, there is a need for efficient methods to find new applications for existing drugs. These problems hinder the progress of medical research and create barriers to providing optimal care to patients. 【0245】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0246】 In this invention, the server includes an information processing means for collecting and analyzing large-scale biomedical information; a treatment suggestion means for acquiring user information and recommending individualized treatments; a drug evaluation means for exploring information on existing drugs and discovering new indications; an information integration means for compiling research results from around the world and forming a knowledge base; a candidate selection means for selecting participants for clinical trials based on genetic information and medical history; and a user terminal that enables the visual display of information. This makes it possible to provide personalized treatment suggestions, discover new drug indications, and select appropriate clinical trial participants through efficient and effective data analysis. 【0247】 "Information processing means" refers to devices that collect large amounts of biomedical information and have the function of extracting specific patterns and interactions by analyzing that information. 【0248】 A "treatment suggestion tool" is an algorithm or system that individually recommends the most suitable treatment method based on the user's genetic information and medical history. 【0249】 "Drug evaluation methods" include data mining and evaluation processes that analyze existing drug information to discover new indications. 【0250】 An "information integration tool" is a system that integrates research results from around the world and forms a knowledge base to provide the latest medical knowledge. 【0251】 "Candidate selection methods" refer to methods and systems for selecting the most suitable participants for a clinical trial, taking into account genetic information and medical history. 【0252】 A "user terminal" is hardware or an interface that allows a user to visually view and interact with information from a server. 【0253】 This invention is a medical information processing system in which a server, terminals, and users work together. The server centrally collects large amounts of biomedical information and analyzes it using information processing means. This includes genetic information, medical records, and drug response data transmitted from research and medical institutions. To collect and process this data, the server ingests the information into a database via an API and performs analysis using machine learning algorithms such as TensorFlow and PyTorch. 【0254】 The server uses treatment suggestion tools to develop personalized treatment plans based on the patient's genetic information and medical history. This plan development utilizes a generative AI model and prompts such as, "Which medication is suitable for patient X?". Furthermore, drug evaluation tools allow for the exploration and consideration of new indications for expired patents and existing medications. This enables repositioning for specific diseases. 【0255】 The information integration mechanism involves a server that collects medical research findings from around the world and uses it to strengthen the knowledge base. Specifically, the server collects information from sources such as PubMed, uses natural language processing technology to perform cross-referencing, and aims to promote the understanding of new research hypotheses and mechanisms. 【0256】 Medical researchers and clinicians, as users, can access the server through their terminals and use the insights gained to support their daily clinical decisions. For example, they can leverage recommendations to optimize treatments for patients with specific genetic abnormalities. The terminals can also use candidate selection tools to identify participants in clinical trials and improve the success rate of those trials. This process accelerates the development of new drugs for rare and intractable diseases, improving the efficiency of research and development in terms of both time and cost. 【0257】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0258】 Step 1: 【0259】 The server receives genetic information, medical records, and drug response data from research and medical institutions via APIs. This data is collected and stored in a large-scale biomedical information database. The input consists of raw data from each institution, which is then processed to a format suitable for storage in the central database. 【0260】 Step 2: 【0261】 The server analyzes the collected data using machine learning algorithms (e.g., TensorFlow and PyTorch). The input consists of structured genetic information and medical records, which the server analyzes to identify disease patterns and drug interactions. Through data processing, specific genetic tendencies and chemical reaction patterns are obtained as output. 【0262】 Step 3: 【0263】 Based on the analysis results, the server utilizes a generative AI model to generate personalized treatment suggestions using the prompt "Which medication is suitable for patient X?". By using the analysis results and patient information as input, it outputs a treatment plan suitable for a specific patient. This output is provided to medical researchers and clinicians. 【0264】 Step 4: 【0265】 The server searches a database of existing drugs using compound discovery tools to find new indications. This process involves analyzing data on expired drugs and performing data calculations to identify potential new indications. The output is a list of re-evaluated drugs. 【0266】 Step 5: 【0267】 The server uses information integration tools to collect medical research information from around the world and enhance knowledge. Inputs include various academic papers and research data, and by organizing and integrating this information using natural language processing, new research hypotheses and insights into disease understanding can be obtained. The output becomes an enhanced knowledge base. 【0268】 Step 6: 【0269】 Users review the information provided through the terminal and use it to support their daily clinical decisions. They enter prompts to view treatment suggestions and analysis results from the server and decide on specific patient actions. In this process, the terminal outputs visualized information on the screen. 【0270】 Step 7: 【0271】 The terminal utilizes a candidate selection method to select participants for a clinical trial. Using the candidate's genetic information and medical history as input, data calculations are performed to identify patients who meet the criteria. The output is a list of suitable clinical trial participants. 【0272】 (Application Example 1) 【0273】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0274】 In modern medical and nursing care settings, there is a growing demand for personalized care that utilizes each patient's individual biomedical information. However, the sheer volume of data, the complexity of data centralization, and the intricacies of analysis make it extremely difficult to develop treatment and care plans optimized for each patient. Furthermore, the discovery of new indications for existing drugs and the efficient conduct of clinical trials have not progressed sufficiently, leading to a stagnation in the development of treatments for rare diseases in particular. There is a need for solutions to these challenges and to realize more efficient and effective medical and nursing care. 【0275】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0276】 In this invention, the server includes analytical means for collecting and analyzing large-scale biological data, recommendation means for acquiring subject information and recommending treatments suitable for the subject, and proposal means for generating personalized care plans. This enables the efficient formulation of treatment methods and care plans tailored to each patient's condition through centralized data management and advanced analysis. Furthermore, these means can support the development of new treatments for rare and intractable diseases and improve the quality of medical care and nursing. 【0277】 "Large-scale biological data" refers to a collection of diverse information gathered from research institutions and medical facilities, including genetic information, medical records, and drug response data. 【0278】 "Analysis methods" refer to the process of analyzing collected biological data using machine learning algorithms and other methods to reveal specific interactions and disease patterns. 【0279】 A "recommendation method" is a mechanism that proposes the optimal treatment method based on the individual health condition and genetic information of each subject, based on the analyzed data. 【0280】 A "compound discovery method" is a method that, based on existing medical information, discovers new indications and enables drug redistribution. 【0281】 The "integration means" is a method for constructing knowledge resources by aggregating research results worldwide and cross-referencing the information. 【0282】 The "matching means" is a process of selecting participants suitable for a specific clinical trial based on the genetic information and medical history of the subjects. 【0283】 The "proposal means" is a mechanism that generates an individualized care plan based on the biomedical information of the subject and provides specific care guidelines to caregivers and medical staff. 【0284】 This invention is implemented as a system for formulating individualized medical and care plans using biological data. The system mainly operates under the cooperation of a server, a terminal, and a user. The server collects a large amount of biological data from research institutions and medical institutions and centrally manages this data. The server accessed through the terminal utilizes machine learning algorithms as analysis means. Specifically, libraries such as TensorFlow and PyTorch are used to clarify specific interactions and disease patterns from the collected data. 【0285】 The server also has a function as a recommendation means and proposes an optimal treatment method based on artificial intelligence technology. This recommendation is provided to the terminal using frameworks such as React Native and Flutter based on the genetic information and health status of the subject. In addition, the server, in the form incorporated into a smartphone application as a proposal means, generates an individualized care plan and presents guidelines to the subject and the caregiver. 【0286】 As a main characteristic example of this system, it can be mentioned that customized advice on diet restrictions and exercise plans according to the daily fluctuations of blood glucose levels is notified to elderly diabetic patients. 【0287】 An example of a prompt to input into the generating AI model would be: "Based on a 70-year-old female with diabetes and past blood glucose data, please create a care plan for tomorrow." This would provide guidance for implementing specific and effective care based on the data. 【0288】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0289】 Step 1: 【0290】 The server acquires large amounts of biological data from research and medical institutions. This data includes genetic information, medical records, and drug response data. The server converts this data into a standardized format and stores it in a database. At this stage, the data is appropriately anonymized and privacy is protected. 【0291】 Step 2: 【0292】 The server performs analysis using machine learning algorithms on the stored data. This analysis utilizes TensorFlow and PyTorch to identify interactions and disease patterns within the data. The input to this process is the biological data stored on the server, and the output is a list of identified interactions and patterns. 【0293】 Step 3: 【0294】 The server executes recommendation procedures based on the analysis results. Here, artificial intelligence technology is used to propose the most suitable treatment option for each subject. The input is the analysis results obtained by machine learning and the patient's genetic information, and the output is an individualized treatment plan. 【0295】 Step 4: 【0296】 Users (healthcare professionals and caregivers) receive personalized care plans provided by the server via a terminal. The terminal uses an application developed with React Native or Flutter to present information to the user in an easy-to-understand GUI. The input here is care plan information from the server, and the output is care guidelines in a format that the user can view. 【0297】 Step 5: 【0298】 Users incorporate the provided care plan into their daily work. A specific example is implementing appropriate dietary restrictions and exercise for managing blood glucose levels in elderly diabetic patients. Users can also provide feedback, which the system uses to collect more data for future analysis and recommendations. 【0299】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0300】 The system implementing this invention is composed of three main components: a server, a terminal, and a user. By incorporating an emotion engine, it enables the provision of detailed medical information tailored to the user's emotional state. 【0301】 First, the server analyzes large amounts of biomedical information obtained from hospitals and research institutions. This analysis utilizes machine learning algorithms to elucidate the relationship between specific drugs and diseases. The analysis results are then compared with existing data using delta matching techniques to gain new scientific insights. 【0302】 Furthermore, the server uses these new findings to match with patient information and recommend individualized and most effective treatment methods. At this time, the emotion engine is integrated into the interface with the user, analyzes the input and reactions from the user to recognize emotions. As a specific example, when the user feels anxious or stressed, the emotion engine detects it, and the server presents information in a flexible communication style. 【0303】 When an appropriate treatment method is shown to the user, the terminal records the user's reaction and analyzes it with the emotion engine. The analysis results are sent to the server and reflected in the treatment recommendation presentation for the next and subsequent times. Specifically, if the previous medical information presentation induced anxiety, a more reassuring information presentation method can be selected for the next time. 【0304】 With this system, it is possible to always provide optimal and efficient medical information while considering the emotions of the patients themselves. In addition, it has a compound exploration means, can discover new indications earlier than existing drugs, and expand the treatment options. 【0305】 In this way, the present invention makes the provision of medical information more individualized and optimized, and realizes support considering the psychological aspects of patients especially in the medical field. Also, since the emotional state is considered in the selection of clinical trial participants, it is expected that the implementation efficiency of the trial will be further improved. 【0306】 The processing flow will be described below. 【0307】 Step 1: 【0308】 The server collects biomedical data transmitted from medical institutions and research institutions. This data includes genetic information, medical history, drug reactions, etc. 【0309】 Step 2: 【0310】 The server uses machine learning algorithms to analyze the collected data and discover new drug interactions and disease patterns. These results are then stored in a knowledge base. 【0311】 Step 3: 【0312】 After obtaining patient information, the server performs calculations to recommend the optimal treatment based on that information. This calculation uses the patient's genetic information and medical history. 【0313】 Step 4: 【0314】 The device presents the user with information recommending treatment. Here, the emotion engine analyzes the user's input and responses in real time to evaluate the user's emotional state. 【0315】 Step 5: 【0316】 When a user reacts to the information presented, the device processes that reaction using an emotion engine and sends it to the server. The emotional state is recorded and influences the next information presentation. 【0317】 Step 6: 【0318】 The server analyzes the user's emotional data received from the emotion engine and determines how to present information next. For example, if the user is feeling anxious, it will choose an information presentation method that provides more reassurance. 【0319】 Step 7: 【0320】 The server considers emotional state in addition to genetic information and medical history during the clinical trial participant selection process. This process includes evaluation using machine learning. 【0321】 Step 8: 【0322】 The device provides users with final clinical trial participant information and supports the preparation and progress of the trial. It also monitors participants' emotional states and provides feedback as needed. 【0323】 (Example 2) 【0324】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0325】 Conventional medical information systems have faced challenges in providing information tailored to each patient's individual emotional state, thus failing to alleviate patients' psychological burden. Furthermore, they have limitations in optimizing medical care due to the inability to effectively select new treatments or trial participants. 【0326】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0327】 In this invention, the server includes information processing means for handling large amounts of biomedical information and analyzing that data, suggestion means for recommending the optimal treatment method based on individual patient information, and psychological analysis means for evaluating emotional states and adjusting the information provision method. This enables efficient and flexible provision of medical information that takes into account the emotions of individual patients. 【0328】 "Information processing means" refers to technologies or devices for analyzing large-scale biomedical information and extracting specific interrelationships or relationships. 【0329】 "Proposal means" refers to a technology or device for deriving and recommending the optimal treatment method based on individual patient information. 【0330】 "Chemical substance discovery methods" refer to technologies or devices that analyze existing drug information and use it to discover new therapeutic applications. 【0331】 "Integration means" refers to technologies or devices for integrating multiple research results and information to build a consistent knowledge base. 【0332】 "Selection method" refers to a technique or device for selecting participants in a study based on genetic information or medical history. 【0333】 "Psychological analysis tools" are techniques or devices used to evaluate a subject's emotional state and adjust the method of providing information. 【0334】 A "learning tool" is a technology or device that analyzes response feedback and uses it to improve the information provided in the future. 【0335】 This invention is an advanced medical information provision system realized through sophisticated collaboration between users, servers, and terminals. First, the server acquires large amounts of biomedical information from hospitals and research institutions and analyzes it using machine learning algorithms. This analysis uses libraries such as Scikit-learn and TensorFlow in Python to reveal the relationship between specific drugs and diseases. The server also compares the obtained analysis results with existing data using delta matching to generate new insights. 【0336】 Next, the server uses this knowledge to compare it with the user's health information and proposes a personalized treatment plan. In this process, artificial intelligence contributes to the development of the personalized treatment plan. A terminal equipped with an emotion engine evaluates the user's emotional state in real time and adjusts the way information is presented according to the user. This system analyzes emotions using voice and facial recognition technology. 【0337】 Furthermore, the device records the user's reaction to the presented treatment and sends this emotional data to the server. Based on this feedback, the server continuously updates its machine learning model to optimize future information delivery. This process enables efficient and flexible information delivery that takes into account the individual emotions of each patient. 【0338】 For example, if a user enters cold symptoms and the server analysis recommends a specific medication, and anxiety is detected, the terminal can provide flexible information, including relaxation suggestions. An example of a prompt message would be: "The user entered cold symptoms. The emotion engine detected anxiety. What information should be provided next?" 【0339】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0340】 Step 1: 【0341】 The server acquires large amounts of biomedical information from hospitals and research institutions. As input, it downloads electronic medical records, research papers, and clinical trial information from databases. Based on this data, machine learning algorithms are applied to explore the relationship between specific drugs and diseases. For data processing, natural language processing is used to clean the text data and extract necessary information. The output is a list of drugs associated with specific diseases. 【0342】 Step 2: 【0343】 The server compares the analysis results obtained in the previous step with existing data using delta matching. The input to this process is the elucidated relationship information and historical datasets. Difference analysis is performed, and data calculations are carried out to extract new insights. The output is newly discovered scientific knowledge. 【0344】 Step 3: 【0345】 The server develops personalized treatment plans based on the patient's health status and medical history. Inputs include individual information obtained from the user and newly acquired insights. The suggestion engine uses artificial intelligence technology to process the data and recommend the optimal treatment plan. The output is a treatment plan optimized for the patient. 【0346】 Step 4: 【0347】 The device analyzes user feedback and input using an emotion engine. Input includes voice, facial expressions, and handwritten replies. It uses emotion analysis technology to evaluate the user's emotions and processes the data to adjust how information is presented. The output is customized information presentation tailored to the emotion. 【0348】 Step 5: 【0349】 The terminal records the user's reactions and sends this information to the server. The input is user response data to treatment suggestions, and the output is feedback data for optimizing future information delivery. The server updates its model using learning mechanisms based on the data sent from the terminal to improve the next information delivery. 【0350】 Step 6: 【0351】 The server analyzes feedback data and continuously updates its model to optimize future medical information delivery. The input is user emotional response data, and through data computation, it generates insights to improve future interactions. The output is a more appropriate and reassuring way of delivering information. 【0352】 (Application Example 2) 【0353】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0354】 In the modern healthcare environment, the lack of information provision that takes into account the emotional state of patients makes them more susceptible to anxiety and stress, which can affect their acceptance of medical information and their treatment choices. Furthermore, individualized approaches are often lacking in the discovery of new treatments and medications. To address this challenge, there is a need to provide information in accordance with patients' emotions and to personalize the exploration and proposal of new treatments. 【0355】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0356】 In this invention, the server includes processing means for collecting and analyzing large-scale biomedical data; instruction means for acquiring user information and recommending medical prescriptions suitable for the user; drug search means for searching existing drug data and discovering new indications; and emotion analysis means for analyzing the user's emotional state in real time and providing information in accordance with that emotion. This enables the provision of medical information that takes the user's emotions into consideration and the search for personalized treatment methods. 【0357】 "Large-scale biomedical data" refers to biological and medical information collected from a wide range of research institutions and medical facilities. 【0358】 A "processing device" is a component that has the function of analyzing collected data and extracting useful information from it. 【0359】 "User information" refers to data about individual users receiving medical treatment, including medical history and genetic information. 【0360】 A "prescription tool" is a component that has the function of suggesting appropriate medical prescriptions and treatment plans based on the user's information. 【0361】 "Drug data" refers to a collection of information about existing pharmaceuticals, including their ingredients, effects, and side effects. 【0362】 A "drug discovery tool" is a component that has the function of investigating existing drug data in order to discover new indications. 【0363】 "Emotional analysis methods" refer to technologies that analyze a user's facial expressions and behavior and recognize their emotional state based on this analysis. 【0364】 The system for implementing this invention primarily consists of three components: a server, a terminal, and a user. The server collects large-scale biomedical data and uses machine learning-based processing methods to analyze it. Machine learning libraries such as TensorFlow are used for data analysis to extract specific interactions and new knowledge. 【0365】 The terminal collects user information and transmits it to the server. On the terminal, emotion analysis APIs such as Affectiva are used to analyze the user's emotions in real time. This emotion analysis data is sent to the server and used to present optimal medical information tailored to the user's emotions. 【0366】 The information users obtain through their devices is processed into personalized medical prescriptions based on instructions on the server. At the same time, a drug discovery system for identifying new indications also operates, expanding the range of medical information available to users. More effective information presentation becomes possible through prompts to the generative AI model, such as, "Generate feedback suggesting relaxing music and care methods for anxious elderly individuals." 【0367】 This system allows users to receive medical information in an emotionally sensitive manner, enabling a personalized medical experience. For example, if an elderly person in a nursing home is feeling anxious, the terminal analyzes that information, and the server provides reassuring care and prompts to reduce the user's stress. 【0368】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0369】 Step 1: 【0370】 The user logs into the device and answers a questionnaire about their health and emotions. The device retrieves the user's response data and uses an emotion analysis API to analyze the user's emotional state in real time. The input for this analysis includes the user's questionnaire responses and facial expression data, and the output is the analyzed emotional state. 【0371】 Step 2: 【0372】 The device transmits user information, including analyzed emotional and health status, to the server using a secure communication protocol. Input includes analyzed emotional data and user health information, which are then sent to the server. 【0373】 Step 3: 【0374】 The server searches a large biomedical database based on the user information it receives. Here, machine learning algorithms are used to generate personalized medical prescriptions and care plans for the user. The input is user information, and the output includes personalized medical prescriptions and care plans. 【0375】 Step 4: 【0376】 The server uses a drug discovery tool to review data on drugs recommended to the user and investigate whether there are any new indications. The input includes existing drug data, and the output provides a list of potential new indications and drugs. 【0377】 Step 5: 【0378】 The server generates prompts for the generative AI model to provide information tailored to the user's emotions. For example, it might send the prompt, "Generate feedback suggesting relaxing music and care methods for an elderly person experiencing anxiety." The input includes emotional state and medical information, and the output generates user-appropriate feedback. 【0379】 Step 6: 【0380】 The terminal displays medical prescriptions and feedback received from the server to the user. The user then decides on their next medical action based on this information. The output includes information that the user sees on the terminal. 【0381】 Step 7: 【0382】 After the user takes action based on the information they have obtained, the device records the results and reactions and sends feedback to the server for future use. The input is the user's reactions and actions, and the output is the server's learning data being updated. 【0383】 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. 【0384】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0385】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214. 【0386】 [Third Embodiment] 【0387】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0388】 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. 【0389】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network). 【0390】 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. 【0391】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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. 【0392】 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0393】 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. 【0394】 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. 【0395】 The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30. 【0396】 The 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. 【0397】 In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0398】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal". 【0399】 The system for implementing the present invention functions through the cooperation of three parties: a server, a terminal, and a user. The system includes the following elements: 【0400】 A server acts as the central hub for collecting large amounts of biomedical information. This includes patient genetic information, medical records, and drug response data transmitted from research and healthcare institutions. This data is analyzed on the server, and machine learning algorithms are applied to reveal specific interactions and disease patterns. 【0401】 Based on the analysis results, the server matches them with patient information and develops an individualized treatment plan. This recommendation system suggests the most effective treatment based on the patient's genetic information and medical history. 【0402】 Furthermore, the server searches a database of existing drugs to explore new indications for those drugs. Compound discovery methods identify the potential of off-patent drugs and unknown drugs, and reposition them for the treatment of specific diseases. 【0403】 The server integrates medical research findings from around the world, strengthening the knowledge base. This integration method allows for cross-referencing of diverse information sources, facilitating the development of new research hypotheses and a better understanding of disease mechanisms. 【0404】 Users, such as medical researchers and clinicians, can access the server through their terminals to gain insights that support their daily clinical decisions. For example, they can use the recommendations provided by the server to optimize treatment plans for patients with specific genetic abnormalities. 【0405】 Furthermore, the device identifies suitable clinical participants using patient matching methods based on the clinical trial conditions. This can improve the success rate of clinical trials and accelerate the development of new drugs for rare and intractable diseases. 【0406】 As described above, the present invention can significantly streamline the process of medical research and drug development for the treatment of rare and intractable diseases, thereby enhancing the sustainability of research and development in terms of both time and cost. 【0407】 The following describes the processing flow. 【0408】 Step 1: 【0409】 The server receives large amounts of biomedical information transmitted from various research and medical institutions. This includes patient genetic information, medical records, and drug response data. 【0410】 Step 2: 【0411】 The server preprocesses the received data. It handles missing values, normalizes the data, and converts it into a format suitable for analysis. 【0412】 Step 3: 【0413】 The server applies machine learning algorithms to large datasets and performs analyses to identify new drug interactions and disease patterns. 【0414】 Step 4: 【0415】 The server accumulates these analysis results and uses a recommendation system to propose the most suitable treatment based on specific symptoms and genetic information. 【0416】 Step 5: 【0417】 The server searches existing drug databases and executes compound discovery methods to find drug candidates that can be repositioned. 【0418】 Step 6: 【0419】 The user reviews the analysis results using the device and decides on a treatment plan based on the insights gained. The device presents the user with the best treatment options based on the patient's medical history and genetic information. 【0420】 Step 7: 【0421】 The terminal inputs patient information for the clinical trial, and the server uses matching means to identify suitable clinical trial participants based on the patient's genetic information and medical history. 【0422】 Step 8: 【0423】 Based on the obtained clinical trial participant information, the server transmits the data to the research institution and prepares to support the implementation of the trial. 【0424】 (Example 1) 【0425】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0426】 Currently, the decentralization of data and the complexity of analysis in the medical field make it difficult to propose efficient treatments and select appropriate participants for clinical trials. Furthermore, there is a need for efficient methods to find new applications for existing drugs. These problems hinder the progress of medical research and create barriers to providing optimal care to patients. 【0427】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0428】 In this invention, the server includes an information processing means for collecting and analyzing large-scale biomedical information; a treatment suggestion means for acquiring user information and recommending individualized treatments; a drug evaluation means for exploring information on existing drugs and discovering new indications; an information integration means for compiling research results from around the world and forming a knowledge base; a candidate selection means for selecting participants for clinical trials based on genetic information and medical history; and a user terminal that enables the visual display of information. This makes it possible to provide personalized treatment suggestions, discover new drug indications, and select appropriate clinical trial participants through efficient and effective data analysis. 【0429】 "Information processing means" refers to devices that collect large amounts of biomedical information and have the function of extracting specific patterns and interactions by analyzing that information. 【0430】 A "treatment suggestion tool" is an algorithm or system that individually recommends the most suitable treatment method based on the user's genetic information and medical history. 【0431】 "Drug evaluation methods" include data mining and evaluation processes that analyze existing drug information to discover new indications. 【0432】 An "information integration tool" is a system that integrates research results from around the world and forms a knowledge base to provide the latest medical knowledge. 【0433】 "Candidate selection methods" refer to methods and systems for selecting the most suitable participants for a clinical trial, taking into account genetic information and medical history. 【0434】 A "user terminal" is hardware or an interface that allows a user to visually view and interact with information from a server. 【0435】 This invention is a medical information processing system in which a server, terminals, and users work together. The server centrally collects large amounts of biomedical information and analyzes it using information processing means. This includes genetic information, medical records, and drug response data transmitted from research and medical institutions. To collect and process this data, the server ingests the information into a database via an API and performs analysis using machine learning algorithms such as TensorFlow and PyTorch. 【0436】 The server uses treatment suggestion tools to develop personalized treatment plans based on the patient's genetic information and medical history. This plan development utilizes a generative AI model and prompts such as, "Which medication is suitable for patient X?". Furthermore, drug evaluation tools allow for the exploration and consideration of new indications for expired patents and existing medications. This enables repositioning for specific diseases. 【0437】 The information integration mechanism involves a server that collects medical research findings from around the world and uses it to strengthen the knowledge base. Specifically, the server collects information from sources such as PubMed, uses natural language processing technology to perform cross-referencing, and aims to promote the understanding of new research hypotheses and mechanisms. 【0438】 Medical researchers and clinicians, as users, can access the server through their terminals and use the insights gained to support their daily clinical decisions. For example, they can leverage recommendations to optimize treatments for patients with specific genetic abnormalities. The terminals can also use candidate selection tools to identify participants in clinical trials and improve the success rate of those trials. This process accelerates the development of new drugs for rare and intractable diseases, improving the efficiency of research and development in terms of both time and cost. 【0439】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0440】 Step 1: 【0441】 The server receives genetic information, medical records, and drug response data from research and medical institutions via APIs. This data is collected and stored in a large-scale biomedical information database. The input consists of raw data from each institution, which is then processed to a format suitable for storage in the central database. 【0442】 Step 2: 【0443】 The server analyzes the collected data using machine learning algorithms (e.g., TensorFlow and PyTorch). The input consists of structured genetic information and medical records, which the server analyzes to identify disease patterns and drug interactions. Through data processing, specific genetic tendencies and chemical reaction patterns are obtained as output. 【0444】 Step 3: 【0445】 Based on the analysis results, the server utilizes a generative AI model to generate personalized treatment suggestions using the prompt "Which medication is suitable for patient X?". By using the analysis results and patient information as input, it outputs a treatment plan suitable for a specific patient. This output is provided to medical researchers and clinicians. 【0446】 Step 4: 【0447】 The server searches a database of existing drugs using compound discovery tools to find new indications. This process involves analyzing data on expired drugs and performing data calculations to identify potential new indications. The output is a list of re-evaluated drugs. 【0448】 Step 5: 【0449】 The server uses information integration tools to collect medical research information from around the world and enhance knowledge. Inputs include various academic papers and research data, and by organizing and integrating this information using natural language processing, new research hypotheses and insights into disease understanding can be obtained. The output becomes an enhanced knowledge base. 【0450】 Step 6: 【0451】 Users review the information provided through the terminal and use it to support their daily clinical decisions. They enter prompts to view treatment suggestions and analysis results from the server and decide on specific patient actions. In this process, the terminal outputs visualized information on the screen. 【0452】 Step 7: 【0453】 The terminal utilizes a candidate selection method to select participants for a clinical trial. Using the candidate's genetic information and medical history as input, data calculations are performed to identify patients who meet the criteria. The output is a list of suitable clinical trial participants. 【0454】 (Application Example 1) 【0455】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0456】 In modern medical and nursing care settings, there is a growing demand for personalized care that utilizes each patient's individual biomedical information. However, the sheer volume of data, the complexity of data centralization, and the intricacies of analysis make it extremely difficult to develop treatment and care plans optimized for each patient. Furthermore, the discovery of new indications for existing drugs and the efficient conduct of clinical trials have not progressed sufficiently, leading to a stagnation in the development of treatments for rare diseases in particular. There is a need for solutions to these challenges and to realize more efficient and effective medical and nursing care. 【0457】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0458】 In this invention, the server includes analytical means for collecting and analyzing large-scale biological data, recommendation means for acquiring subject information and recommending treatments suitable for the subject, and proposal means for generating personalized care plans. This enables the efficient formulation of treatment methods and care plans tailored to each patient's condition through centralized data management and advanced analysis. Furthermore, these means can support the development of new treatments for rare and intractable diseases and improve the quality of medical care and nursing. 【0459】 "Large-scale biological data" refers to a collection of diverse information gathered from research institutions and medical facilities, including genetic information, medical records, and drug response data. 【0460】 "Analysis methods" refer to the process of analyzing collected biological data using machine learning algorithms and other methods to reveal specific interactions and disease patterns. 【0461】 A "recommendation method" is a mechanism that proposes the optimal treatment method based on the individual health condition and genetic information of each subject, based on the analyzed data. 【0462】 A "compound discovery method" is a method that, based on existing medical information, discovers new indications and enables drug redistribution. 【0463】 An "integration method" is a method for building knowledge resources by aggregating research results from around the world and cross-referencing that information. 【0464】 "Matching methods" refer to the process of selecting participants suitable for a particular clinical trial based on their genetic information and medical history. 【0465】 The "proposed method" is a system that generates individualized care plans based on the subject's biomedical information and provides specific care guidelines to caregivers and medical professionals. 【0466】 This invention is implemented as a system for developing personalized medical and care plans using biological data. The system primarily operates through the cooperation of a server, terminals, and users. The server collects large amounts of biological data from research and medical institutions and centrally manages this data. The server, accessed through terminals, utilizes machine learning algorithms as an analytical tool. Specifically, it uses libraries such as TensorFlow and PyTorch to identify specific interactions and disease patterns from the collected data. 【0467】 The server also functions as a recommendation system, suggesting the optimal treatment based on artificial intelligence technology. These recommendations are provided to the device using frameworks such as React Native and Flutter, based on the subject's genetic information and health status. Furthermore, as a suggestion system, the server generates personalized care plans integrated into smartphone applications, providing guidelines to both the subject and their caregivers. 【0468】 One of the key features of this system is that it provides elderly diabetic patients with customized advice on dietary restrictions and exercise plans tailored to their daily blood glucose levels. 【0469】 An example of a prompt to input into the generating AI model would be: "Based on a 70-year-old female with diabetes and past blood glucose data, please create a care plan for tomorrow." This would provide guidance for implementing specific and effective care based on the data. 【0470】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0471】 Step 1: 【0472】 The server acquires large amounts of biological data from research and medical institutions. This data includes genetic information, medical records, and drug response data. The server converts this data into a standardized format and stores it in a database. At this stage, the data is appropriately anonymized and privacy is protected. 【0473】 Step 2: 【0474】 The server performs analysis using machine learning algorithms on the stored data. This analysis utilizes TensorFlow and PyTorch to identify interactions and disease patterns within the data. The input to this process is the biological data stored on the server, and the output is a list of identified interactions and patterns. 【0475】 Step 3: 【0476】 The server executes recommendation procedures based on the analysis results. Here, artificial intelligence technology is used to propose the most suitable treatment option for each subject. The input is the analysis results obtained by machine learning and the patient's genetic information, and the output is an individualized treatment plan. 【0477】 Step 4: 【0478】 Users (healthcare professionals and caregivers) receive personalized care plans provided by the server via a terminal. The terminal uses an application developed with React Native or Flutter to present information to the user in an easy-to-understand GUI. The input here is care plan information from the server, and the output is care guidelines in a format that the user can view. 【0479】 Step 5: 【0480】 Users incorporate the provided care plan into their daily work. A specific example is implementing appropriate dietary restrictions and exercise for managing blood glucose levels in elderly diabetic patients. Users can also provide feedback, which the system uses to collect more data for future analysis and recommendations. 【0481】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0482】 The system implementing this invention is composed of three main components: a server, a terminal, and a user. By incorporating an emotion engine, it enables the provision of detailed medical information tailored to the user's emotional state. 【0483】 First, the server analyzes large amounts of biomedical information obtained from hospitals and research institutions. This analysis utilizes machine learning algorithms to elucidate the relationship between specific drugs and diseases. The analysis results are then compared with existing data using delta matching techniques to gain new scientific insights. 【0484】 Furthermore, the server uses these new insights to match patient information and recommend the most effective treatment in a personalized manner. In this process, an emotion engine is integrated into the user interface, analyzing user input and responses to recognize emotions. For example, if a user is experiencing anxiety or stress, the emotion engine detects this, and the server presents information in a flexible communication style. 【0485】 The device records the user's reaction when appropriate treatment options are presented and analyzes it using an emotion engine. This analysis is sent to a server and used to improve future treatment recommendations. Specifically, if the previous presentation of medical information caused anxiety, the system can select a more reassuring method of presentation for subsequent consultations. 【0486】 This system enables the provision of optimal and efficient medical information while taking into consideration the patient's own feelings. Furthermore, it possesses compound discovery capabilities, allowing for the early detection of new indications beyond existing drugs, thereby expanding treatment options. 【0487】 Thus, this invention provides more individualized and optimized medical information, particularly in supporting patients in medical settings by considering their psychological state. Furthermore, since emotional state is also considered in the selection of clinical trial participants, it is anticipated that the efficiency of conducting trials will be further improved. 【0488】 The following describes the processing flow. 【0489】 Step 1: 【0490】 The server collects biomedical data transmitted from healthcare and research institutions. This data includes genetic information, medical history, and drug responses. 【0491】 Step 2: 【0492】 The server uses machine learning algorithms to analyze the collected data and discover new drug interactions and disease patterns. These results are then stored in a knowledge base. 【0493】 Step 3: 【0494】 After obtaining patient information, the server performs calculations to recommend the optimal treatment based on that information. This calculation uses the patient's genetic information and medical history. 【0495】 Step 4: 【0496】 The device presents the user with information recommending treatment. Here, the emotion engine analyzes the user's input and responses in real time to evaluate the user's emotional state. 【0497】 Step 5: 【0498】 When a user reacts to the information presented, the device processes that reaction using an emotion engine and sends it to the server. The emotional state is recorded and influences the next information presentation. 【0499】 Step 6: 【0500】 The server analyzes the user's emotional data received from the emotion engine and determines how to present information next. For example, if the user is feeling anxious, it will choose an information presentation method that provides more reassurance. 【0501】 Step 7: 【0502】 The server considers emotional state in addition to genetic information and medical history during the clinical trial participant selection process. This process includes evaluation using machine learning. 【0503】 Step 8: 【0504】 The device provides users with final clinical trial participant information and supports the preparation and progress of the trial. It also monitors participants' emotional states and provides feedback as needed. 【0505】 (Example 2) 【0506】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0507】 Conventional medical information systems have faced challenges in providing information tailored to each patient's individual emotional state, thus failing to alleviate patients' psychological burden. Furthermore, they have limitations in optimizing medical care due to the inability to effectively select new treatments or trial participants. 【0508】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0509】 In this invention, the server includes information processing means for handling large amounts of biomedical information and analyzing that data, suggestion means for recommending the optimal treatment method based on individual patient information, and psychological analysis means for evaluating emotional states and adjusting the information provision method. This enables efficient and flexible provision of medical information that takes into account the emotions of individual patients. 【0510】 "Information processing means" refers to technologies or devices for analyzing large-scale biomedical information and extracting specific interrelationships or relationships. 【0511】 "Proposal means" refers to a technology or device for deriving and recommending the optimal treatment method based on individual patient information. 【0512】 "Chemical substance discovery methods" refer to technologies or devices that analyze existing drug information and use it to discover new therapeutic applications. 【0513】 "Integration means" refers to technologies or devices for integrating multiple research results and information to build a consistent knowledge base. 【0514】 "Selection method" refers to a technique or device for selecting participants in a study based on genetic information or medical history. 【0515】 "Psychological analysis tools" are techniques or devices used to evaluate a subject's emotional state and adjust the method of providing information. 【0516】 A "learning tool" is a technology or device that analyzes response feedback and uses it to improve the information provided in the future. 【0517】 This invention is an advanced medical information provision system realized through sophisticated collaboration between users, servers, and terminals. First, the server acquires large amounts of biomedical information from hospitals and research institutions and analyzes it using machine learning algorithms. This analysis uses libraries such as Scikit-learn and TensorFlow in Python to reveal the relationship between specific drugs and diseases. The server also compares the obtained analysis results with existing data using delta matching to generate new insights. 【0518】 Next, the server uses this knowledge to compare it with the user's health information and proposes a personalized treatment plan. In this process, artificial intelligence contributes to the development of the personalized treatment plan. A terminal equipped with an emotion engine evaluates the user's emotional state in real time and adjusts the way information is presented according to the user. This system analyzes emotions using voice and facial recognition technology. 【0519】 Furthermore, the device records the user's reaction to the presented treatment and sends this emotional data to the server. Based on this feedback, the server continuously updates its machine learning model to optimize future information delivery. This process enables efficient and flexible information delivery that takes into account the individual emotions of each patient. 【0520】 For example, if a user enters cold symptoms and the server analysis recommends a specific medication, and anxiety is detected, the terminal can provide flexible information, including relaxation suggestions. An example of a prompt message would be: "The user entered cold symptoms. The emotion engine detected anxiety. What information should be provided next?" 【0521】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0522】 Step 1: 【0523】 The server acquires large amounts of biomedical information from hospitals and research institutions. As input, it downloads electronic medical records, research papers, and clinical trial information from databases. Based on this data, machine learning algorithms are applied to explore the relationship between specific drugs and diseases. For data processing, natural language processing is used to clean the text data and extract necessary information. The output is a list of drugs associated with specific diseases. 【0524】 Step 2: 【0525】 The server compares the analysis results obtained in the previous step with existing data using delta matching. The input to this process is the elucidated relationship information and historical datasets. Difference analysis is performed, and data calculations are carried out to extract new insights. The output is newly discovered scientific knowledge. 【0526】 Step 3: 【0527】 The server develops personalized treatment plans based on the patient's health status and medical history. Inputs include individual information obtained from the user and newly acquired insights. The suggestion engine uses artificial intelligence technology to process the data and recommend the optimal treatment plan. The output is a treatment plan optimized for the patient. 【0528】 Step 4: 【0529】 The device analyzes user feedback and input using an emotion engine. Input includes voice, facial expressions, and handwritten replies. It uses emotion analysis technology to evaluate the user's emotions and processes the data to adjust how information is presented. The output is customized information presentation tailored to the emotion. 【0530】 Step 5: 【0531】 The terminal records the user's reactions and sends this information to the server. The input is user response data to treatment suggestions, and the output is feedback data for optimizing future information delivery. The server updates its model using learning mechanisms based on the data sent from the terminal to improve the next information delivery. 【0532】 Step 6: 【0533】 The server analyzes feedback data and continuously updates its model to optimize future medical information delivery. The input is user emotional response data, and through data computation, it generates insights to improve future interactions. The output is a more appropriate and reassuring way of delivering information. 【0534】 (Application Example 2) 【0535】 Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0536】 In the modern healthcare environment, the lack of information provision that takes into account the emotional state of patients makes them more susceptible to anxiety and stress, which can affect their acceptance of medical information and their treatment choices. Furthermore, individualized approaches are often lacking in the discovery of new treatments and medications. To address this challenge, there is a need to provide information in accordance with patients' emotions and to personalize the exploration and proposal of new treatments. 【0537】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0538】 In this invention, the server includes processing means for collecting and analyzing large-scale biomedical data; instruction means for acquiring user information and recommending medical prescriptions suitable for the user; drug search means for searching existing drug data and discovering new indications; and emotion analysis means for analyzing the user's emotional state in real time and providing information in accordance with that emotion. This enables the provision of medical information that takes the user's emotions into consideration and the search for personalized treatment methods. 【0539】 "Large-scale biomedical data" refers to biological and medical information collected from a wide range of research institutions and medical facilities. 【0540】 A "processing device" is a component that has the function of analyzing collected data and extracting useful information from it. 【0541】 "User information" refers to data about individual users receiving medical treatment, including medical history and genetic information. 【0542】 A "prescription tool" is a component that has the function of suggesting appropriate medical prescriptions and treatment plans based on the user's information. 【0543】 "Drug data" refers to a collection of information about existing pharmaceuticals, including their ingredients, effects, and side effects. 【0544】 A "drug discovery tool" is a component that has the function of investigating existing drug data in order to discover new indications. 【0545】 "Emotional analysis methods" refer to technologies that analyze a user's facial expressions and behavior and recognize their emotional state based on this analysis. 【0546】 The system for implementing this invention primarily consists of three components: a server, a terminal, and a user. The server collects large-scale biomedical data and uses machine learning-based processing methods to analyze it. Machine learning libraries such as TensorFlow are used for data analysis to extract specific interactions and new knowledge. 【0547】 The terminal collects user information and transmits it to the server. On the terminal, emotion analysis APIs such as Affectiva are used to analyze the user's emotions in real time. This emotion analysis data is sent to the server and used to present optimal medical information tailored to the user's emotions. 【0548】 The information users obtain through their devices is processed into personalized medical prescriptions based on instructions on the server. At the same time, a drug discovery system for identifying new indications also operates, expanding the range of medical information available to users. More effective information presentation becomes possible through prompts to the generative AI model, such as, "Generate feedback suggesting relaxing music and care methods for anxious elderly individuals." 【0549】 This system allows users to receive medical information in an emotionally sensitive manner, enabling a personalized medical experience. For example, if an elderly person in a nursing home is feeling anxious, the terminal analyzes that information, and the server provides reassuring care and prompts to reduce the user's stress. 【0550】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0551】 Step 1: 【0552】 The user logs into the device and answers a questionnaire about their health and emotions. The device retrieves the user's response data and uses an emotion analysis API to analyze the user's emotional state in real time. The input for this analysis includes the user's questionnaire responses and facial expression data, and the output is the analyzed emotional state. 【0553】 Step 2: 【0554】 The device transmits user information, including analyzed emotional and health status, to the server using a secure communication protocol. Input includes analyzed emotional data and user health information, which are then sent to the server. 【0555】 Step 3: 【0556】 The server searches a large biomedical database based on the user information it receives. Here, machine learning algorithms are used to generate personalized medical prescriptions and care plans for the user. The input is user information, and the output includes personalized medical prescriptions and care plans. 【0557】 Step 4: 【0558】 The server uses a drug discovery tool to review data on drugs recommended to the user and investigate whether there are any new indications. The input includes existing drug data, and the output provides a list of potential new indications and drugs. 【0559】 Step 5: 【0560】 The server generates prompts for the generative AI model to provide information tailored to the user's emotions. For example, it might send the prompt, "Generate feedback suggesting relaxing music and care methods for an elderly person experiencing anxiety." The input includes emotional state and medical information, and the output generates user-appropriate feedback. 【0561】 Step 6: 【0562】 The terminal displays medical prescriptions and feedback received from the server to the user. The user then decides on their next medical action based on this information. The output includes information that the user sees on the terminal. 【0563】 Step 7: 【0564】 After the user takes action based on the information they have obtained, the device records the results and reactions and sends feedback to the server for future use. The input is the user's reactions and actions, and the output is the server's learning data being updated. 【0565】 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. 【0566】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0567】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314. 【0568】 [Fourth Embodiment] 【0569】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0570】 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. 【0571】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network). 【0572】 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. 【0573】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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. 【0574】 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0575】 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. 【0576】 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes. 【0577】 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. 【0578】 The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30. 【0579】 The 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. 【0580】 In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0581】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0582】 The system for implementing the present invention functions through the cooperation of three parties: a server, a terminal, and a user. The system includes the following elements: 【0583】 A server acts as the central hub for collecting large amounts of biomedical information. This includes patient genetic information, medical records, and drug response data transmitted from research and healthcare institutions. This data is analyzed on the server, and machine learning algorithms are applied to reveal specific interactions and disease patterns. 【0584】 Based on the analysis results, the server matches them with patient information and develops an individualized treatment plan. This recommendation system suggests the most effective treatment based on the patient's genetic information and medical history. 【0585】 Furthermore, the server searches a database of existing drugs to explore new indications for those drugs. Compound discovery methods identify the potential of off-patent drugs and unknown drugs, and reposition them for the treatment of specific diseases. 【0586】 The server integrates medical research findings from around the world, strengthening the knowledge base. This integration method allows for cross-referencing of diverse information sources, facilitating the development of new research hypotheses and a better understanding of disease mechanisms. 【0587】 Users, such as medical researchers and clinicians, can access the server through their terminals to gain insights that support their daily clinical decisions. For example, they can use the recommendations provided by the server to optimize treatment plans for patients with specific genetic abnormalities. 【0588】 Furthermore, the device identifies suitable clinical participants using patient matching methods based on the clinical trial conditions. This can improve the success rate of clinical trials and accelerate the development of new drugs for rare and intractable diseases. 【0589】 As described above, the present invention can significantly streamline the process of medical research and drug development for the treatment of rare and intractable diseases, thereby enhancing the sustainability of research and development in terms of both time and cost. 【0590】 The following describes the processing flow. 【0591】 Step 1: 【0592】 The server receives large amounts of biomedical information transmitted from various research and medical institutions. This includes patient genetic information, medical records, and drug response data. 【0593】 Step 2: 【0594】 The server preprocesses the received data. It handles missing values, normalizes the data, and converts it into a format suitable for analysis. 【0595】 Step 3: 【0596】 The server applies machine learning algorithms to large datasets and performs analyses to identify new drug interactions and disease patterns. 【0597】 Step 4: 【0598】 The server accumulates these analysis results and uses a recommendation system to propose the most suitable treatment based on specific symptoms and genetic information. 【0599】 Step 5: 【0600】 The server searches existing drug databases and executes compound discovery methods to find drug candidates that can be repositioned. 【0601】 Step 6: 【0602】 The user reviews the analysis results using the device and decides on a treatment plan based on the insights gained. The device presents the user with the best treatment options based on the patient's medical history and genetic information. 【0603】 Step 7: 【0604】 The terminal inputs patient information for the clinical trial, and the server uses matching means to identify suitable clinical trial participants based on the patient's genetic information and medical history. 【0605】 Step 8: 【0606】 Based on the obtained clinical trial participant information, the server transmits the data to the research institution and prepares to support the implementation of the trial. 【0607】 (Example 1) 【0608】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0609】 Currently, the decentralization of data and the complexity of analysis in the medical field make it difficult to propose efficient treatments and select appropriate participants for clinical trials. Furthermore, there is a need for efficient methods to find new applications for existing drugs. These problems hinder the progress of medical research and create barriers to providing optimal care to patients. 【0610】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0611】 In this invention, the server includes an information processing means for collecting and analyzing large-scale biomedical information; a treatment suggestion means for acquiring user information and recommending individualized treatments; a drug evaluation means for exploring information on existing drugs and discovering new indications; an information integration means for compiling research results from around the world and forming a knowledge base; a candidate selection means for selecting participants for clinical trials based on genetic information and medical history; and a user terminal that enables the visual display of information. This makes it possible to provide personalized treatment suggestions, discover new drug indications, and select appropriate clinical trial participants through efficient and effective data analysis. 【0612】 "Information processing means" refers to devices that collect large amounts of biomedical information and have the function of extracting specific patterns and interactions by analyzing that information. 【0613】 A "treatment suggestion tool" is an algorithm or system that individually recommends the most suitable treatment method based on the user's genetic information and medical history. 【0614】 "Drug evaluation methods" include data mining and evaluation processes that analyze existing drug information to discover new indications. 【0615】 An "information integration tool" is a system that integrates research results from around the world and forms a knowledge base to provide the latest medical knowledge. 【0616】 "Candidate selection methods" refer to methods and systems for selecting the most suitable participants for a clinical trial, taking into account genetic information and medical history. 【0617】 A "user terminal" is hardware or an interface that allows a user to visually view and interact with information from a server. 【0618】 This invention is a medical information processing system in which a server, terminals, and users work together. The server centrally collects large amounts of biomedical information and analyzes it using information processing means. This includes genetic information, medical records, and drug response data transmitted from research and medical institutions. To collect and process this data, the server ingests the information into a database via an API and performs analysis using machine learning algorithms such as TensorFlow and PyTorch. 【0619】 The server uses treatment suggestion tools to develop personalized treatment plans based on the patient's genetic information and medical history. This plan development utilizes a generative AI model and prompts such as, "Which medication is suitable for patient X?". Furthermore, drug evaluation tools allow for the exploration and consideration of new indications for expired patents and existing medications. This enables repositioning for specific diseases. 【0620】 The information integration mechanism involves a server that collects medical research findings from around the world and uses it to strengthen the knowledge base. Specifically, the server collects information from sources such as PubMed, uses natural language processing technology to perform cross-referencing, and aims to promote the understanding of new research hypotheses and mechanisms. 【0621】 Medical researchers and clinicians, as users, can access the server through their terminals and use the insights gained to support their daily clinical decisions. For example, they can leverage recommendations to optimize treatments for patients with specific genetic abnormalities. The terminals can also use candidate selection tools to identify participants in clinical trials and improve the success rate of those trials. This process accelerates the development of new drugs for rare and intractable diseases, improving the efficiency of research and development in terms of both time and cost. 【0622】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0623】 Step 1: 【0624】 The server receives genetic information, medical records, and drug response data from research and medical institutions via APIs. This data is collected and stored in a large-scale biomedical information database. The input consists of raw data from each institution, which is then processed to a format suitable for storage in the central database. 【0625】 Step 2: 【0626】 The server analyzes the collected data using machine learning algorithms (e.g., TensorFlow and PyTorch). The input consists of structured genetic information and medical records, which the server analyzes to identify disease patterns and drug interactions. Through data processing, specific genetic tendencies and chemical reaction patterns are obtained as output. 【0627】 Step 3: 【0628】 Based on the analysis results, the server utilizes a generative AI model to generate personalized treatment suggestions using the prompt "Which medication is suitable for patient X?". By using the analysis results and patient information as input, it outputs a treatment plan suitable for a specific patient. This output is provided to medical researchers and clinicians. 【0629】 Step 4: 【0630】 The server searches a database of existing drugs using compound discovery tools to find new indications. This process involves analyzing data on expired drugs and performing data calculations to identify potential new indications. The output is a list of re-evaluated drugs. 【0631】 Step 5: 【0632】 The server uses information integration tools to collect medical research information from around the world and enhance knowledge. Inputs include various academic papers and research data, and by organizing and integrating this information using natural language processing, new research hypotheses and insights into disease understanding can be obtained. The output becomes an enhanced knowledge base. 【0633】 Step 6: 【0634】 Users review the information provided through the terminal and use it to support their daily clinical decisions. They enter prompts to view treatment suggestions and analysis results from the server and decide on specific patient actions. In this process, the terminal outputs visualized information on the screen. 【0635】 Step 7: 【0636】 The terminal utilizes a candidate selection method to select participants for a clinical trial. Using the candidate's genetic information and medical history as input, data calculations are performed to identify patients who meet the criteria. The output is a list of suitable clinical trial participants. 【0637】 (Application Example 1) 【0638】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0639】 In modern medical and nursing care settings, there is a growing demand for personalized care that utilizes each patient's individual biomedical information. However, the sheer volume of data, the complexity of data centralization, and the intricacies of analysis make it extremely difficult to develop treatment and care plans optimized for each patient. Furthermore, the discovery of new indications for existing drugs and the efficient conduct of clinical trials have not progressed sufficiently, leading to a stagnation in the development of treatments for rare diseases in particular. There is a need for solutions to these challenges and to realize more efficient and effective medical and nursing care. 【0640】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0641】 In this invention, the server includes analytical means for collecting and analyzing large-scale biological data, recommendation means for acquiring subject information and recommending treatments suitable for the subject, and proposal means for generating personalized care plans. This enables the efficient formulation of treatment methods and care plans tailored to each patient's condition through centralized data management and advanced analysis. Furthermore, these means can support the development of new treatments for rare and intractable diseases and improve the quality of medical care and nursing. 【0642】 "Large-scale biological data" refers to a collection of diverse information gathered from research institutions and medical facilities, including genetic information, medical records, and drug response data. 【0643】 "Analysis methods" refer to the process of analyzing collected biological data using machine learning algorithms and other methods to reveal specific interactions and disease patterns. 【0644】 A "recommendation method" is a mechanism that proposes the optimal treatment method based on the individual health condition and genetic information of each subject, based on the analyzed data. 【0645】 A "compound discovery method" is a method that, based on existing medical information, discovers new indications and enables drug redistribution. 【0646】 An "integration method" is a method for building knowledge resources by aggregating research results from around the world and cross-referencing that information. 【0647】 "Matching methods" refer to the process of selecting participants suitable for a particular clinical trial based on their genetic information and medical history. 【0648】 The "proposed method" is a system that generates individualized care plans based on the subject's biomedical information and provides specific care guidelines to caregivers and medical professionals. 【0649】 This invention is implemented as a system for developing personalized medical and care plans using biological data. The system primarily operates through the cooperation of a server, terminals, and users. The server collects large amounts of biological data from research and medical institutions and centrally manages this data. The server, accessed through terminals, utilizes machine learning algorithms as an analytical tool. Specifically, it uses libraries such as TensorFlow and PyTorch to identify specific interactions and disease patterns from the collected data. 【0650】 The server also functions as a recommendation system, suggesting the optimal treatment based on artificial intelligence technology. These recommendations are provided to the device using frameworks such as React Native and Flutter, based on the subject's genetic information and health status. Furthermore, as a suggestion system, the server generates personalized care plans integrated into smartphone applications, providing guidelines to both the subject and their caregivers. 【0651】 One of the key features of this system is that it provides elderly diabetic patients with customized advice on dietary restrictions and exercise plans tailored to their daily blood glucose levels. 【0652】 An example of a prompt to input into the generating AI model would be: "Based on a 70-year-old female with diabetes and past blood glucose data, please create a care plan for tomorrow." This would provide guidance for implementing specific and effective care based on the data. 【0653】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0654】 Step 1: 【0655】 The server acquires large amounts of biological data from research and medical institutions. This data includes genetic information, medical records, and drug response data. The server converts this data into a standardized format and stores it in a database. At this stage, the data is appropriately anonymized and privacy is protected. 【0656】 Step 2: 【0657】 The server performs analysis using machine learning algorithms on the stored data. This analysis utilizes TensorFlow and PyTorch to identify interactions and disease patterns within the data. The input to this process is the biological data stored on the server, and the output is a list of identified interactions and patterns. 【0658】 Step 3: 【0659】 The server executes recommendation procedures based on the analysis results. Here, artificial intelligence technology is used to propose the most suitable treatment option for each subject. The input is the analysis results obtained by machine learning and the patient's genetic information, and the output is an individualized treatment plan. 【0660】 Step 4: 【0661】 Users (healthcare professionals and caregivers) receive personalized care plans provided by the server via a terminal. The terminal uses an application developed with React Native or Flutter to present information to the user in an easy-to-understand GUI. The input here is care plan information from the server, and the output is care guidelines in a format that the user can view. 【0662】 Step 5: 【0663】 Users incorporate the provided care plan into their daily work. A specific example is implementing appropriate dietary restrictions and exercise for managing blood glucose levels in elderly diabetic patients. Users can also provide feedback, which the system uses to collect more data for future analysis and recommendations. 【0664】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0665】 The system implementing this invention is composed of three main components: a server, a terminal, and a user. By incorporating an emotion engine, it enables the provision of detailed medical information tailored to the user's emotional state. 【0666】 First, the server analyzes large amounts of biomedical information obtained from hospitals and research institutions. This analysis utilizes machine learning algorithms to elucidate the relationship between specific drugs and diseases. The analysis results are then compared with existing data using delta matching techniques to gain new scientific insights. 【0667】 Furthermore, the server uses these new insights to match patient information and recommend the most effective treatment in a personalized manner. In this process, an emotion engine is integrated into the user interface, analyzing user input and responses to recognize emotions. For example, if a user is experiencing anxiety or stress, the emotion engine detects this, and the server presents information in a flexible communication style. 【0668】 The device records the user's reaction when appropriate treatment options are presented and analyzes it using an emotion engine. This analysis is sent to a server and used to improve future treatment recommendations. Specifically, if the previous presentation of medical information caused anxiety, the system can select a more reassuring method of presentation for subsequent consultations. 【0669】 This system enables the provision of optimal and efficient medical information while taking into consideration the patient's own feelings. Furthermore, it possesses compound discovery capabilities, allowing for the early detection of new indications beyond existing drugs, thereby expanding treatment options. 【0670】 Thus, this invention provides more individualized and optimized medical information, particularly in supporting patients in medical settings by considering their psychological state. Furthermore, since emotional state is also considered in the selection of clinical trial participants, it is anticipated that the efficiency of conducting trials will be further improved. 【0671】 The following describes the processing flow. 【0672】 Step 1: 【0673】 The server collects biomedical data transmitted from healthcare and research institutions. This data includes genetic information, medical history, and drug responses. 【0674】 Step 2: 【0675】 The server uses machine learning algorithms to analyze the collected data and discover new drug interactions and disease patterns. These results are then stored in a knowledge base. 【0676】 Step 3: 【0677】 After obtaining patient information, the server performs calculations to recommend the optimal treatment based on that information. This calculation uses the patient's genetic information and medical history. 【0678】 Step 4: 【0679】 The device presents the user with information recommending treatment. Here, the emotion engine analyzes the user's input and responses in real time to evaluate the user's emotional state. 【0680】 Step 5: 【0681】 When a user reacts to the information presented, the device processes that reaction using an emotion engine and sends it to the server. The emotional state is recorded and influences the next information presentation. 【0682】 Step 6: 【0683】 The server analyzes the user's emotional data received from the emotion engine and determines how to present information next. For example, if the user is feeling anxious, it will choose an information presentation method that provides more reassurance. 【0684】 Step 7: 【0685】 The server considers emotional state in addition to genetic information and medical history during the clinical trial participant selection process. This process includes evaluation using machine learning. 【0686】 Step 8: 【0687】 The device provides users with final clinical trial participant information and supports the preparation and progress of the trial. It also monitors participants' emotional states and provides feedback as needed. 【0688】 (Example 2) 【0689】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0690】 Conventional medical information systems have faced challenges in providing information tailored to each patient's individual emotional state, thus failing to alleviate patients' psychological burden. Furthermore, they have limitations in optimizing medical care due to the inability to effectively select new treatments or trial participants. 【0691】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0692】 In this invention, the server includes information processing means for handling large amounts of biomedical information and analyzing that data, suggestion means for recommending the optimal treatment method based on individual patient information, and psychological analysis means for evaluating emotional states and adjusting the information provision method. This enables efficient and flexible provision of medical information that takes into account the emotions of individual patients. 【0693】 "Information processing means" refers to technologies or devices for analyzing large-scale biomedical information and extracting specific interrelationships or relationships. 【0694】 "Proposal means" refers to a technology or device for deriving and recommending the optimal treatment method based on individual patient information. 【0695】 "Chemical substance discovery methods" refer to technologies or devices that analyze existing drug information and use it to discover new therapeutic applications. 【0696】 "Integration means" refers to technologies or devices for integrating multiple research results and information to build a consistent knowledge base. 【0697】 "Selection method" refers to a technique or device for selecting participants in a study based on genetic information or medical history. 【0698】 "Psychological analysis tools" are techniques or devices used to evaluate a subject's emotional state and adjust the method of providing information. 【0699】 A "learning tool" is a technology or device that analyzes response feedback and uses it to improve the information provided in the future. 【0700】 This invention is an advanced medical information provision system realized through sophisticated collaboration between users, servers, and terminals. First, the server acquires large amounts of biomedical information from hospitals and research institutions and analyzes it using machine learning algorithms. This analysis uses libraries such as Scikit-learn and TensorFlow in Python to reveal the relationship between specific drugs and diseases. The server also compares the obtained analysis results with existing data using delta matching to generate new insights. 【0701】 Next, the server uses this knowledge to compare it with the user's health information and proposes a personalized treatment plan. In this process, artificial intelligence contributes to the development of the personalized treatment plan. A terminal equipped with an emotion engine evaluates the user's emotional state in real time and adjusts the way information is presented according to the user. This system analyzes emotions using voice and facial recognition technology. 【0702】 Furthermore, the device records the user's reaction to the presented treatment and sends this emotional data to the server. Based on this feedback, the server continuously updates its machine learning model to optimize future information delivery. This process enables efficient and flexible information delivery that takes into account the individual emotions of each patient. 【0703】 For example, if a user enters cold symptoms and the server analysis recommends a specific medication, and anxiety is detected, the terminal can provide flexible information, including relaxation suggestions. An example of a prompt message would be: "The user entered cold symptoms. The emotion engine detected anxiety. What information should be provided next?" 【0704】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0705】 Step 1: 【0706】 The server acquires large amounts of biomedical information from hospitals and research institutions. As input, it downloads electronic medical records, research papers, and clinical trial information from databases. Based on this data, machine learning algorithms are applied to explore the relationship between specific drugs and diseases. For data processing, natural language processing is used to clean the text data and extract necessary information. The output is a list of drugs associated with specific diseases. 【0707】 Step 2: 【0708】 The server compares the analysis results obtained in the previous step with existing data using delta matching. The input to this process is the elucidated relationship information and historical datasets. Difference analysis is performed, and data calculations are carried out to extract new insights. The output is newly discovered scientific knowledge. 【0709】 Step 3: 【0710】 The server develops personalized treatment plans based on the patient's health status and medical history. Inputs include individual information obtained from the user and newly acquired insights. The suggestion engine uses artificial intelligence technology to process the data and recommend the optimal treatment plan. The output is a treatment plan optimized for the patient. 【0711】 Step 4: 【0712】 The device analyzes user feedback and input using an emotion engine. Input includes voice, facial expressions, and handwritten replies. It uses emotion analysis technology to evaluate the user's emotions and processes the data to adjust how information is presented. The output is customized information presentation tailored to the emotion. 【0713】 Step 5: 【0714】 The terminal records the user's reactions and sends this information to the server. The input is user response data to treatment suggestions, and the output is feedback data for optimizing future information delivery. The server updates its model using learning mechanisms based on the data sent from the terminal to improve the next information delivery. 【0715】 Step 6: 【0716】 The server analyzes feedback data and continuously updates its model to optimize future medical information delivery. The input is user emotional response data, and through data computation, it generates insights to improve future interactions. The output is a more appropriate and reassuring way of delivering information. 【0717】 (Application Example 2) 【0718】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0719】 In the modern healthcare environment, the lack of information provision that takes into account the emotional state of patients makes them more susceptible to anxiety and stress, which can affect their acceptance of medical information and their treatment choices. Furthermore, individualized approaches are often lacking in the discovery of new treatments and medications. To address this challenge, there is a need to provide information in accordance with patients' emotions and to personalize the exploration and proposal of new treatments. 【0720】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0721】 In this invention, the server includes processing means for collecting and analyzing large-scale biomedical data; instruction means for acquiring user information and recommending medical prescriptions suitable for the user; drug search means for searching existing drug data and discovering new indications; and emotion analysis means for analyzing the user's emotional state in real time and providing information in accordance with that emotion. This enables the provision of medical information that takes the user's emotions into consideration and the search for personalized treatment methods. 【0722】 "Large-scale biomedical data" refers to biological and medical information collected from a wide range of research institutions and medical facilities. 【0723】 A "processing device" is a component that has the function of analyzing collected data and extracting useful information from it. 【0724】 "User information" refers to data about individual users receiving medical treatment, including medical history and genetic information. 【0725】 A "prescription tool" is a component that has the function of suggesting appropriate medical prescriptions and treatment plans based on the user's information. 【0726】 "Drug data" refers to a collection of information about existing pharmaceuticals, including their ingredients, effects, and side effects. 【0727】 A "drug discovery tool" is a component that has the function of investigating existing drug data in order to discover new indications. 【0728】 "Emotional analysis methods" refer to technologies that analyze a user's facial expressions and behavior and recognize their emotional state based on this analysis. 【0729】 The system for implementing this invention primarily consists of three components: a server, a terminal, and a user. The server collects large-scale biomedical data and uses machine learning-based processing methods to analyze it. Machine learning libraries such as TensorFlow are used for data analysis to extract specific interactions and new knowledge. 【0730】 The terminal collects user information and transmits it to the server. On the terminal, emotion analysis APIs such as Affectiva are used to analyze the user's emotions in real time. This emotion analysis data is sent to the server and used to present optimal medical information tailored to the user's emotions. 【0731】 The information users obtain through their devices is processed into personalized medical prescriptions based on instructions on the server. At the same time, a drug discovery system for identifying new indications also operates, expanding the range of medical information available to users. More effective information presentation becomes possible through prompts to the generative AI model, such as, "Generate feedback suggesting relaxing music and care methods for anxious elderly individuals." 【0732】 This system allows users to receive medical information in an emotionally sensitive manner, enabling a personalized medical experience. For example, if an elderly person in a nursing home is feeling anxious, the terminal analyzes that information, and the server provides reassuring care and prompts to reduce the user's stress. 【0733】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0734】 Step 1: 【0735】 The user logs into the device and answers a questionnaire about their health and emotions. The device retrieves the user's response data and uses an emotion analysis API to analyze the user's emotional state in real time. The input for this analysis includes the user's questionnaire responses and facial expression data, and the output is the analyzed emotional state. 【0736】 Step 2: 【0737】 The device transmits user information, including analyzed emotional and health status, to the server using a secure communication protocol. Input includes analyzed emotional data and user health information, which are then sent to the server. 【0738】 Step 3: 【0739】 The server searches a large biomedical database based on the user information it receives. Here, machine learning algorithms are used to generate personalized medical prescriptions and care plans for the user. The input is user information, and the output includes personalized medical prescriptions and care plans. 【0740】 Step 4: 【0741】 The server uses a drug discovery tool to review data on drugs recommended to the user and investigate whether there are any new indications. The input includes existing drug data, and the output provides a list of potential new indications and drugs. 【0742】 Step 5: 【0743】 The server generates prompts for the generative AI model to provide information tailored to the user's emotions. For example, it might send the prompt, "Generate feedback suggesting relaxing music and care methods for an elderly person experiencing anxiety." The input includes emotional state and medical information, and the output generates user-appropriate feedback. 【0744】 Step 6: 【0745】 The terminal displays medical prescriptions and feedback received from the server to the user. The user then decides on their next medical action based on this information. The output includes information that the user sees on the terminal. 【0746】 Step 7: 【0747】 After the user takes action based on the information they have obtained, the device records the results and reactions and sends feedback to the server for future use. The input is the user's reactions and actions, and the output is the server's learning data being updated. 【0748】 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. 【0749】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0750】 In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414. 【0751】 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. 【0752】 Figure 9 shows an 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. 【0753】 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. 【0754】 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. 【0755】 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, motorcycles, etc., 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, for example, based 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. 【0756】 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." 【0757】 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. 【0758】 The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format. 【0759】 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data. 【0760】 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. 【0761】 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. 【0762】 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. 【0763】 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. 【0764】 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. 【0765】 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. 【0766】 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. 【0767】 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 the like 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. 【0768】 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 as being incorporated by reference. 【0769】 The following is further disclosed regarding the embodiments described above. 【0770】 (Claim 1) 【0771】 A means for collecting and analyzing large-scale biomedical information, 【0772】 A recommendation system that acquires patient information and recommends treatment appropriate for the patient, 【0773】 A compound discovery method that searches existing drug information and discovers new indications, 【0774】 An integration tool to combine research results from around the world and build a knowledge base, 【0775】 A matching method for selecting participants in clinical trials based on genetic information and medical history, 【0776】 A system that includes this. 【0777】 (Claim 2) 【0778】 The system according to claim 1, characterized in that the analysis means identifies specific interactions from the data using a machine learning algorithm. 【0779】 (Claim 3) 【0780】 The system according to claim 1, characterized in that the recommendation means uses artificial intelligence technology to create an individualized treatment plan. 【0781】 "Example 1" 【0782】 (Claim 1) 【0783】 Information processing means for collecting and analyzing large-scale biomedical information, 【0784】 A treatment suggestion tool that acquires user information and recommends individualized treatments, 【0785】 A drug evaluation method that explores information on existing drugs and discovers new indications, 【0786】 Information integration means that combine research results from around the world and form a knowledge base, 【0787】 A candidate selection method for selecting participants in clinical trials based on genetic information and medical history, 【0788】 A user terminal that enables the visual display of information, 【0789】 A system that includes this. 【0790】 (Claim 2) 【0791】 The system according to claim 1, characterized in that the information processing means identifies a specific pattern from the data using a machine learning model. 【0792】 (Claim 3) 【0793】 The system according to claim 1, characterized in that the treatment suggestion means creates an individualized medical plan using generative AI technology. 【0794】 "Application Example 1" 【0795】 (Claim 1) 【0796】 A means for collecting large-scale biological data and analyzing the information, 【0797】 A recommendation system that acquires subject information and recommends treatment appropriate for the subject, 【0798】 A compound discovery method that searches existing pharmaceutical information and discovers new indications, 【0799】 An integrative means to combine research results from around the world and build knowledge resources, 【0800】 A matching method for selecting test participants based on genetic information and history, 【0801】 A proposal method for generating individualized care plans, 【0802】 A system that includes this. 【0803】 (Claim 2) 【0804】 The system according to claim 1, characterized in that the analysis means identifies specific interactions from the data using a machine learning algorithm. 【0805】 (Claim 3) 【0806】 The system according to claim 1, characterized in that the recommendation means creates an individualized treatment plan using artificial intelligence technology, and the suggestion means further provides care guidelines according to the subject's health condition. 【0807】 "Example 2 of combining an emotion engine" 【0808】 (Claim 1) 【0809】 Information processing means for handling large-scale biomedical information and analyzing that data, 【0810】 A suggestion method that recommends the optimal treatment method based on individual patient information, 【0811】 A chemical discovery method that examines existing drug information and finds new treatments, 【0812】 A means of aggregating multiple research results to form an integrated knowledge base, 【0813】 A selection method for selecting trial participants based on genetic information and medical history, 【0814】 Psychological analysis tools for evaluating emotional states and adjusting information delivery methods, 【0815】 A learning method that analyzes response feedback and reflects it in future information provision, 【0816】 A system that includes this. 【0817】 (Claim 2) 【0818】 The system according to claim 1, characterized in that the information processing means and the psychological analysis means extract specific relationships using machine learning technology. 【0819】 (Claim 3) 【0820】 The system according to claim 1, characterized in that the proposed means generates a treatment plan tailored to individual characteristics using artificial intelligence. 【0821】 "Application example 2 when combining with an emotional engine" 【0822】 (Claim 1) 【0823】 A processing means for collecting and analyzing large-scale biomedical data, 【0824】 A means of obtaining user information and recommending medical prescriptions suitable for the user, 【0825】 A drug discovery method that searches existing drug data and discovers new indications, 【0826】 A means of aggregation to integrate research results from around the world and build a knowledge base, 【0827】 A selection method for clinical trial participants based on genetic information and medical history, 【0828】 An emotion analysis method that analyzes the user's emotional state in real time and presents information according to that emotion, 【0829】 A system that includes this. 【0830】 (Claim 2) 【0831】 The system according to claim 1, characterized in that the processing means identifies specific relationships from data using machine learning techniques. 【0832】 (Claim 3) 【0833】 The system according to claim 1, characterized in that the instruction means uses artificial intelligence technology to create an individualized medical plan. [Explanation of symbols] 【0834】 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

[Claim 1] A means for collecting and analyzing large-scale biomedical information, A recommendation system that acquires patient information and recommends treatment appropriate for the patient, A compound discovery method that searches existing drug information and discovers new indications, An integration tool to combine research results from around the world and build a knowledge base, A matching method for selecting participants in clinical trials based on genetic information and medical history, A system that includes this. [Claim 2] The system according to claim 1, characterized in that the analysis means identifies specific interactions from the data using a machine learning algorithm. [Claim 3] The system according to claim 1, characterized in that the recommendation means uses artificial intelligence technology to create an individualized treatment plan.

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