system

Abstract

The system according to this embodiment aims to comprehensively analyze text data and medical image data contained in multiple electronic medical records. [Solution] The system according to this embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects multiple electronic medical records. The analysis unit comprehensively analyzes the text data and medical image data contained in the multiple electronic medical records collected by the collection unit. The provision unit provides the analysis results from the analysis unit.

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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the text data and medical image data included in a plurality of electronic medical records have not been sufficiently analyzed integrally, and there is room for improvement.

[0005] The system according to an embodiment aims to integrally analyze the text data and medical image data included in a plurality of electronic medical records.

Means for Solving the Problems

[0006] The system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects a plurality of electronic medical records. The analysis unit integrally analyzes the text data and medical image data included in the plurality of electronic medical records collected by the collection unit. The provision unit provides the analysis result by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can comprehensively analyze text data and medical image data contained in multiple electronic medical records. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. 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).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The medical mining system according to an embodiment of the present invention is a system that thoroughly analyzes medical big data using generation AI to improve the quality and efficiency of medical care. The medical mining system collects multiple electronic medical records and comprehensively analyzes the text data and medical image data contained in the collected electronic medical records. This analysis uses the latest natural language processing technology and image recognition technology. Next, it provides the analysis results to support the doctor's diagnosis. Furthermore, it accepts information indicating the patient's symptoms and searches for similar cases in the medical database. The search results are provided through the provision unit to complement the doctor's diagnosis. In addition, a system has been built that provides real-time diagnostic support during examinations, estimating the patient's condition by taking into account real-time video of the patient. The system continuously learns new data and continuously improves diagnostic accuracy. It constantly reflects the latest medical knowledge and treatment methods and provides cutting-edge medical support. For example, the medical mining system collects multiple electronic medical records. For example, this includes electronic medical records in text format, image format, audio format, etc. Next, the medical mining system comprehensively analyzes the text data and medical image data contained in the collected electronic medical records. For example, this includes data preprocessing methods and types of analysis algorithms. Next, the medical mining system provides analysis results. For example, it provides analysis results to support a doctor's diagnosis. Next, the medical mining system accepts information indicating the patient's symptoms. For example, this includes information such as text input and multiple-choice questions. Next, the medical mining system searches medical databases for cases similar to the patient's symptoms that were accepted. For example, this includes the degree of symptom matching and past diagnostic results. Next, the medical mining system provides search results. For example, it provides search results to complement a doctor's diagnosis. Next, the medical mining system provides real-time diagnostic support during consultations. For example, it estimates the patient's condition by taking into account real-time video of the patient. Next, the medical mining system continuously learns from new data. For example, it learns from new data to continuously improve diagnostic accuracy. Next, the medical mining system constantly reflects the latest medical knowledge and treatments.For example, to provide cutting-edge medical support, the latest medical knowledge and treatments are reflected. This allows medical mining systems to efficiently collect, analyze, and provide medical big data, thereby improving the quality and efficiency of healthcare.

[0029] The medical mining system according to this embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects multiple electronic medical records. The collection unit can collect electronic medical records in, for example, text format, image format, or audio format. The collection unit can collect, for example, electronic medical records in text format. The collection unit can also collect, for example, electronic medical records in image format. The collection unit can also collect, for example, electronic medical records in audio format. The analysis unit comprehensively analyzes the text data and medical image data contained in the multiple electronic medical records collected by the collection unit. The analysis unit can comprehensively analyze the text data and medical image data using, for example, a data preprocessing method or a type of analysis algorithm. The analysis unit can comprehensively analyze the text data and medical image data using, for example, a data preprocessing method. The analysis unit can also comprehensively analyze the text data and medical image data using, for example, a type of analysis algorithm. The analysis unit can also comprehensively analyze the text data and medical image data using, for example, a generative AI. The provision unit provides the analysis results from the analysis unit. The provisioning unit can, for example, provide analysis results to support a physician's diagnosis. The provisioning unit can, for example, provide analysis results to a physician. The provisioning unit can, for example, provide analysis results to a patient. The provisioning unit can, for example, provide analysis results to a medical institution. As a result, the medical mining system according to the embodiment can improve the quality and efficiency of medical care by efficiently collecting, analyzing, and providing medical big data. Some or all of the above-described processes in the collection unit may be performed using, for example, AI, or not using AI. For example, the collection unit can collect electronic medical records using an AI model. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can have generative AI perform an integrated analysis of text data and medical image data. Some or all of the above-described processes in the provisioning unit may be performed using, for example, AI, or not using AI. For example, the provisioning unit can provide analysis results using an AI model.

[0030] The data collection unit collects multiple electronic medical records (EMRs). The unit can collect EMRs in various formats, such as text, image, and audio. Specifically, text-format EMRs include detailed information such as patient medical records, prescriptions, and test results. This text data is analyzed using natural language processing technology to extract important medical information. Image-format EMRs include medical images such as X-ray images, MRI images, and CT scan images. This image data is analyzed using image recognition technology and used for detecting abnormalities and supporting diagnosis. Audio-format EMRs include conversations between doctors and patients and audio recordings during consultations. This audio data is converted to text using speech recognition technology and further analyzed. The data collection unit centrally collects these diverse formats of EMRs and stores them in a database. The data collection unit can automate EMR collection using AI models. For example, the AI ​​model integrates data from different medical institutions, removes duplicate data, and standardizes data formats. This allows the data collection unit to collect EMRs efficiently and accurately, improving the overall data quality of the system.

[0031] The analysis department comprehensively analyzes text data and medical image data contained in multiple electronic medical records collected by the collection department. Specifically, it comprehensively analyzes text data and medical image data using data preprocessing methods and types of analysis algorithms. Data preprocessing methods include cleaning, normalization, tokenization, and stop word removal for text data. Preprocessing methods for medical image data include resizing, noise reduction, and contrast adjustment. By performing these preprocessing steps, the quality of the data can be improved and the accuracy of the analysis can be increased. Machine learning algorithms and deep learning algorithms are used as analysis algorithms. For example, natural language processing technology is used to analyze text data and extract important medical information. Image recognition technology is used to analyze medical image data to detect abnormalities and support diagnosis. Furthermore, generative AI can also be used to comprehensively analyze text data and medical image data. Generative AI, for example, combines patient medical records and medical images to generate more detailed diagnostic information. As a result, the analysis department can comprehensively analyze the collected data and provide information that is useful in supporting diagnosis and treatment in medical settings.

[0032] The service provider provides the analysis results from the analysis provider. Specifically, it can provide analysis results to support physicians' diagnoses. For example, providing analysis results to physicians can improve the accuracy of diagnoses. Based on the provided analysis results, physicians can gain a detailed understanding of the patient's symptoms and medical history and make appropriate diagnoses. The service provider can also provide the analysis results to patients. Patients can deepen their understanding of their health condition and treatment plan, and communication with physicians can be facilitated. Furthermore, the service provider can provide the analysis results to medical institutions. Based on the provided analysis results, medical institutions can formulate measures to improve the quality of medical services. The service provider can automate the provision of analysis results using AI models. For example, the AI ​​model can convert the analysis results into an appropriate format and provide them quickly to physicians, patients, and medical institutions. The service provider can also collect user feedback and continuously improve the accuracy and effectiveness of the provided content. As a result, the service provider can efficiently support diagnosis and treatment in medical settings, achieving improved quality and efficiency in medical care.

[0033] The symptom reception unit can receive information describing a patient's symptoms. The symptom reception unit can receive information in various formats, such as text input or multiple-choice. For example, the symptom reception unit can receive a patient's symptoms in text input format. The symptom reception unit can also receive a patient's symptoms in multiple-choice format. The symptom reception unit can also receive a patient's symptoms in voice input format. The search unit can search a medical database for cases similar to the patient's symptoms received by the symptom reception unit. The search unit can search for similar cases based on factors such as the degree of symptom matching or past diagnostic results. For example, the search unit can search for similar cases based on the degree of symptom matching. The search unit can also search for similar cases based on past diagnostic results. The search unit can also search for similar cases using generative AI. The provision unit can provide the search results from the search unit. For example, the provision unit can provide search results to complement a doctor's diagnosis. For example, the provision unit can provide search results to a doctor. For example, the provision unit can provide search results to a patient. The provisioning unit can, for example, provide search results to medical institutions. This allows for the search of similar cases based on the patient's symptoms, supporting the doctor's diagnosis. Some or all of the above-described processes in the symptom reception unit may be performed using AI, for example, or without AI. For example, the symptom reception unit can receive patient symptoms using an AI model. Some or all of the above-described processes in the search unit may be performed using generative AI, for example, or without generative AI. For example, the search unit can have generative AI perform the search for similar cases. Some or all of the above-described processes in the provisioning unit may be performed using AI, for example, or without AI. For example, the provisioning unit can provide search results using an AI model.

[0034] The information reception unit can receive patient information. The information reception unit can receive information such as age, gender, and medical history. For example, the information reception unit can receive age. For example, the information reception unit can also receive gender. For example, the information reception unit can also receive medical history. The support unit can provide diagnostic support based on multiple electronic medical records collected by the collection unit and patient information received by the information reception unit. The support unit can provide diagnostic support using, for example, diagnostic algorithms and types of support. For example, the support unit provides diagnostic support using diagnostic algorithms. For example, the support unit can provide diagnostic support using types of support. For example, the support unit can provide diagnostic support using generative AI. This allows for diagnostic support based on collected electronic medical records and patient information, supporting the physician's diagnosis. Some or all of the above processing in the information reception unit may be performed using, for example, AI, or not using AI. For example, the information reception unit can receive patient information using an AI model. Some or all of the above-described processes in the support unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the support unit can have a generative AI perform diagnostic support.

[0035] The update unit can update the medical database based on the analysis results from the analysis unit. For example, the update unit can update the medical database based on the analysis results. For example, the update unit can add new data based on the analysis results. For example, the update unit can also modify existing data based on the analysis results. For example, the update unit can update the medical database using a generative AI. This allows the medical database to be updated based on the analysis results and reflect the latest information. Some or all of the above-described processes in the update unit may be performed using a generative AI, or not. For example, the update unit can have a generative AI perform the update of the medical database.

[0036] The data collection unit can analyze past data collection history and select an efficient data collection method. For example, the data collection unit can select the most efficient data collection method from past data collection history. For example, the data collection unit can optimize the data collection frequency based on past data collection history. For example, the data collection unit can analyze past data collection history and select a method to shorten the time required for data collection. This enables the selection of the optimal data collection method based on past data collection history, thereby achieving efficient data collection. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI perform the analysis of past data collection history.

[0037] The data collection unit can filter electronic medical records based on the patient's current medical condition and treatment status. For example, the data collection unit can prioritize the collection of highly relevant electronic medical records based on the patient's current medical condition. For example, the data collection unit can filter and collect only the necessary information based on the patient's treatment status. For example, the data collection unit can adjust the range of electronic medical records to be collected according to the patient's medical condition and treatment status. This allows for efficient data collection by filtering the necessary information according to the patient's medical condition and treatment status. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI perform filtering based on the patient's medical condition and treatment status.

[0038] The data collection unit can prioritize the collection of highly relevant medical records by considering the patient's geographical location information when collecting electronic medical records. For example, the data collection unit can prioritize the collection of electronic medical records from medical institutions close to the patient's current location. For example, the data collection unit can collect highly relevant electronic medical records from a region based on the patient's geographical location information. For example, the data collection unit can select highly relevant electronic medical records by considering the patient's travel history. This enables efficient data collection by prioritizing the collection of highly relevant medical records by considering the patient's geographical location information. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI perform the priority determination of medical records based on the patient's geographical location information.

[0039] The data collection unit can analyze patients' social media activity and collect relevant records when collecting electronic medical records. For example, the data collection unit can collect information about relevant medical conditions and treatments from patients' social media posts. For example, the data collection unit can analyze patients' social media activity and prioritize the collection of relevant electronic medical records. For example, the data collection unit can adjust the scope of electronic medical records to be collected based on patients' social media data. This enables efficient data collection by analyzing patients' social media activity and collecting relevant records. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI perform the analysis of patients' social media activity.

[0040] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on highly important data. For example, the analysis unit can perform a simplified analysis on less important data. The analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the data. This allows for efficient data analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can have the generative AI perform the adjustment of the level of detail of the analysis based on the importance of the data.

[0041] The analysis unit can apply an efficient analysis algorithm according to the data category during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data. For example, the analysis unit can apply an image recognition algorithm to medical image data. The analysis unit can select the optimal analysis algorithm according to the data category. This enables efficient data analysis by applying the optimal analysis algorithm according to the data category. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can have the generative AI perform the application of an analysis algorithm according to the data category.

[0042] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit can prioritize the analysis of the most recent data. For example, the analysis unit can postpone the analysis of older data. For example, the analysis unit can dynamically adjust the analysis priority according to the data submission date. This enables efficient data analysis by determining the analysis priority according to the data submission date. Some or all of the above processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can have generative AI perform the determination of analysis priority based on the data submission date.

[0043] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can postpone the analysis of less relevant data. For example, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data. This allows for efficient data analysis by adjusting the order of analysis according to the relevance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can have the generative AI perform the adjustment of the order of analysis based on the relevance of the data.

[0044] The information delivery unit can adjust the level of detail provided based on the importance of the analysis results at the time of delivery. For example, the information delivery unit can provide detailed information for analysis results of high importance. For example, the information delivery unit can provide simplified information for analysis results of low importance. The information delivery unit can dynamically adjust the level of detail provided according to the importance of the analysis results. This allows for efficient information delivery by adjusting the level of detail provided according to the importance of the analysis results. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can have AI perform the adjustment of the level of detail provided based on the importance of the analysis results.

[0045] The delivery unit can apply different delivery methods depending on the category of the analysis results at the time of delivery. For example, the delivery unit can provide analysis results of text data in text format. For example, the delivery unit can provide analysis results of medical image data in image format. The delivery unit can select the optimal delivery method depending on the category of the analysis results. This enables efficient information provision by applying the optimal delivery method according to the category of the analysis results. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can have AI perform the application of a delivery method according to the category of the analysis results.

[0046] The service provider can adjust the order of delivery based on the submission date of the analysis results. For example, the service provider can prioritize the delivery of the most recent analysis results. For example, the service provider can postpone the delivery of older analysis results. For example, the service provider can dynamically adjust the order of delivery according to the submission date of the analysis results. This allows for efficient information delivery by adjusting the order of delivery according to the submission date of the analysis results. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can have AI perform the adjustment of the delivery order based on the submission date of the analysis results.

[0047] The delivery unit can adjust the order of delivery based on the relevance of the analysis results at the time of delivery. For example, the delivery unit can prioritize the delivery of highly relevant analysis results. For example, the delivery unit can postpone the delivery of less relevant analysis results. For example, the delivery unit can dynamically adjust the order of delivery according to the relevance of the analysis results. This allows for efficient information delivery by adjusting the order of delivery according to the relevance of the analysis results. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can have AI perform the adjustment of the delivery order based on the relevance of the analysis results.

[0048] The symptom reception unit can select an efficient reception method by referring to the patient's past symptom history when receiving a symptom. For example, the symptom reception unit can select the optimal reception method based on the patient's past symptom history. For example, the symptom reception unit can select a method to shorten the reception time by referring to the patient's past symptom history. For example, the symptom reception unit can analyze the patient's past symptom history and select the most efficient reception method. This enables efficient symptom reception by selecting the optimal reception method based on the patient's past symptom history. Some or all of the above processing in the symptom reception unit may be performed using AI, for example, or without AI. For example, the symptom reception unit can have AI perform the referencing of past symptom history.

[0049] The symptom reception unit can select an efficient reception method when symptoms are received, taking into account the patient's geographical location. For example, the symptom reception unit can prioritize the selection of a reception method for a medical institution close to the patient's current location. For example, the symptom reception unit can select a reception method for a highly relevant region based on the patient's geographical location. For example, the symptom reception unit can select the optimal reception method by considering the patient's travel history. This enables efficient symptom reception by selecting the optimal reception method while considering the patient's geographical location. Some or all of the above processing in the symptom reception unit may be performed using AI, for example, or without AI. For example, the symptom reception unit can have AI perform the selection of a reception method based on the patient's geographical location.

[0050] The search unit can select the optimal search method by referring to past search history during a search. For example, the search unit can select the most efficient search method from past search history. For example, the search unit can optimize the search frequency based on past search history. For example, the search unit can analyze past search history and select a method to shorten the time it takes to search. This enables efficient searching by selecting the optimal search method based on past search history. Some or all of the above processes in the search unit may be performed using AI, for example, or without AI. For example, the search unit can have AI perform the referencing of past search history.

[0051] The search unit can select an efficient search method by considering the patient's geographical location information during a search. For example, the search unit can prioritize searching for information on medical institutions close to the patient's current location. For example, the search unit can search for information on highly relevant regions based on the patient's geographical location information. For example, the search unit can select the optimal search method by considering the patient's travel history. This enables efficient searching by selecting the optimal search method by considering the patient's geographical location information. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can have AI perform the selection of a search method based on the patient's geographical location information.

[0052] The information reception unit can select the optimal reception method by referring to the patient's past information history when receiving information. For example, the information reception unit can select the optimal reception method based on the patient's past information history. For example, the information reception unit can select a method to shorten the time required for reception by referring to the patient's past information history. For example, the information reception unit can analyze the patient's past information history and select the most efficient reception method. This makes it possible to select the optimal reception method based on the patient's past information history and achieve efficient information reception. Some or all of the above processing in the information reception unit may be performed using AI, for example, or without using AI. For example, the information reception unit can have AI perform the referencing of past information history.

[0053] The information reception unit can select an efficient reception method when receiving information, taking into account the patient's geographical location. For example, the information reception unit can prioritize receiving information from medical institutions close to the patient's current location. For example, the information reception unit can receive information from highly relevant regions based on the patient's geographical location. For example, the information reception unit can select the optimal reception method by taking into account the patient's travel history. This enables efficient information reception by selecting the optimal reception method while considering the patient's geographical location. Some or all of the above processing in the information reception unit may be performed using AI, for example, or without AI. For example, the information reception unit can have AI perform the selection of a reception method based on the patient's geographical location.

[0054] The support unit can select an efficient support method by referring to the patient's past diagnostic history during support. For example, the support unit can select the optimal support method based on the patient's past diagnostic history. For example, the support unit can select a method to shorten the time required for support by referring to the patient's past diagnostic history. For example, the support unit can analyze the patient's past diagnostic history and select the most efficient support method. This enables the selection of the optimal support method based on the patient's past diagnostic history, thereby achieving efficient support. Some or all of the above processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can have AI perform the referencing of past diagnostic history.

[0055] The support unit can select an efficient support method by considering the patient's geographical location information during support. For example, the support unit can prioritize providing information on medical institutions close to the patient's current location. For example, the support unit can provide information on highly relevant regions based on the patient's geographical location information. For example, the support unit can select the optimal support method by considering the patient's travel history. This enables efficient support by selecting the optimal support method while considering the patient's geographical location information. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can have AI perform the selection of a support method based on the patient's geographical location information.

[0056] The update unit can select an efficient update method by referring to past update history during an update. For example, the update unit can select the most efficient update method from past update history. For example, the update unit can optimize the update frequency based on past update history. For example, the update unit can analyze past update history and select a method to shorten the time required for updates. This enables efficient database updates by selecting the optimal update method based on past update history. Some or all of the above processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can have AI perform the task of referring to past update history.

[0057] The update unit can weight the updated data based on the data submission date during the update process. For example, the update unit can prioritize updating the most recent data. For example, the update unit can postpone updating older data. For example, the update unit can dynamically adjust the weighting of the updated data according to the data submission date. This enables efficient database updates by weighting the updated data according to the data submission date. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can have AI perform the weighting of updated data based on the data submission date.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] Medical mining systems can also collect and analyze patients' lifestyle data. For example, they can collect data on patients' diet, exercise, and sleep patterns, and integrate this data with medical data for analysis. This allows for an assessment of the impact of patients' lifestyles on their medical condition, providing more accurate diagnostic support. Furthermore, specific lifestyle improvement suggestions can be made to patients based on their lifestyle data. In addition, lifestyle data can be monitored over the long term to track changes in patients' health.

[0060] Medical mining systems can also collect and analyze patients' genetic information. For example, they can collect patients' genetic data and integrate it with medical data for analysis. This allows for an assessment of the impact of genetic factors on disease progression and provides more personalized diagnostic support. Furthermore, genetic information can be used to suggest preventive medical care to patients. It is also possible to monitor genetic information over the long term and track changes in genetic risk.

[0061] Medical mining systems can further enhance diagnostic support by considering the patient's socioeconomic background. For example, they can collect data such as the patient's income, education level, and occupation, and integrate and analyze this data with medical data. This allows for an assessment of the impact of socioeconomic factors on the patient's condition and provides more appropriate diagnostic support. Furthermore, specific support measures can be proposed based on the patient's socioeconomic background. Additionally, it is possible to monitor socioeconomic data over the long term and track changes in the patient's living environment.

[0062] Medical mining systems can also collect and analyze patients' environmental data. For example, they can collect data on patients' living environment, work environment, and climate conditions, and integrate and analyze this data with medical data. This allows for an assessment of the impact of environmental factors on disease symptoms and provides more accurate diagnostic support. Furthermore, based on environmental data, specific suggestions for environmental improvements can be made to patients. In addition, it is possible to monitor environmental data over the long term and track changes in patients' health status.

[0063] Medical mining systems can also collect and analyze patient healthcare expense data. For example, they can collect patient healthcare expenditure data and integrate it with other medical data for analysis. This allows for an assessment of the impact of healthcare costs on a patient's condition and provides more appropriate diagnostic support. Furthermore, based on healthcare expense data, specific healthcare cost reduction measures can be proposed to patients. Additionally, healthcare expense data can be monitored over the long term to track changes in patients' financial situations.

[0064] Medical mining systems can also collect and analyze patient health goal data. For example, they can collect patients' health goals (such as weight loss, blood pressure management, and diabetes management) and integrate and analyze this data with medical data. This allows for the provision of specific support measures to help patients achieve their health goals. Furthermore, based on the health goal data, it is possible to propose measures to improve patient motivation. In addition, it is possible to monitor health goal data over the long term and track the patient's progress.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The collection unit collects multiple electronic medical records. The collection unit can collect electronic medical records in various formats, such as text, image, and audio. For example, the collection unit can collect electronic medical records in text format. The collection unit can also collect electronic medical records in image format. The collection unit can also collect electronic medical records in audio format. Step 2: The analysis unit comprehensively analyzes the text data and medical image data contained in multiple electronic medical records collected by the collection unit. The analysis unit can comprehensively analyze the text data and medical image data using, for example, data preprocessing methods and types of analysis algorithms. The analysis unit can also comprehensively analyze the text data and medical image data using, for example, generative AI. Step 3: The service provider provides the analysis results from the analysis provider. The service provider can provide the analysis results, for example, to support a physician's diagnosis. The service provider can provide the analysis results to a physician, for example. The service provider can also provide the analysis results to a patient, for example. The service provider can also provide the analysis results to a healthcare institution, for example.

[0067] (Example of form 2) The medical mining system according to an embodiment of the present invention is a system that thoroughly analyzes medical big data using generation AI to improve the quality and efficiency of medical care. The medical mining system collects multiple electronic medical records and comprehensively analyzes the text data and medical image data contained in the collected electronic medical records. This analysis uses the latest natural language processing technology and image recognition technology. Next, it provides the analysis results to support the doctor's diagnosis. Furthermore, it accepts information indicating the patient's symptoms and searches for similar cases in the medical database. The search results are provided through the provision unit to complement the doctor's diagnosis. In addition, a system has been built that provides real-time diagnostic support during examinations, estimating the patient's condition by taking into account real-time video of the patient. The system continuously learns new data and continuously improves diagnostic accuracy. It constantly reflects the latest medical knowledge and treatment methods and provides cutting-edge medical support. For example, the medical mining system collects multiple electronic medical records. For example, this includes electronic medical records in text format, image format, audio format, etc. Next, the medical mining system comprehensively analyzes the text data and medical image data contained in the collected electronic medical records. For example, this includes data preprocessing methods and types of analysis algorithms. Next, the medical mining system provides analysis results. For example, it provides analysis results to support a doctor's diagnosis. Next, the medical mining system accepts information indicating the patient's symptoms. For example, this includes information such as text input and multiple-choice questions. Next, the medical mining system searches medical databases for cases similar to the patient's symptoms that were accepted. For example, this includes the degree of symptom matching and past diagnostic results. Next, the medical mining system provides search results. For example, it provides search results to complement a doctor's diagnosis. Next, the medical mining system provides real-time diagnostic support during consultations. For example, it estimates the patient's condition by taking into account real-time video of the patient. Next, the medical mining system continuously learns from new data. For example, it learns from new data to continuously improve diagnostic accuracy. Next, the medical mining system constantly reflects the latest medical knowledge and treatments.For example, to provide cutting-edge medical support, the latest medical knowledge and treatments are reflected. This allows medical mining systems to efficiently collect, analyze, and provide medical big data, thereby improving the quality and efficiency of healthcare.

[0068] The medical mining system according to this embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects multiple electronic medical records. The collection unit can collect electronic medical records in, for example, text format, image format, or audio format. The collection unit can collect, for example, electronic medical records in text format. The collection unit can also collect, for example, electronic medical records in image format. The collection unit can also collect, for example, electronic medical records in audio format. The analysis unit comprehensively analyzes the text data and medical image data contained in the multiple electronic medical records collected by the collection unit. The analysis unit can comprehensively analyze the text data and medical image data using, for example, a data preprocessing method or a type of analysis algorithm. The analysis unit can comprehensively analyze the text data and medical image data using, for example, a data preprocessing method. The analysis unit can also comprehensively analyze the text data and medical image data using, for example, a type of analysis algorithm. The analysis unit can also comprehensively analyze the text data and medical image data using, for example, a generative AI. The provision unit provides the analysis results from the analysis unit. The provisioning unit can, for example, provide analysis results to support a physician's diagnosis. The provisioning unit can, for example, provide analysis results to a physician. The provisioning unit can, for example, provide analysis results to a patient. The provisioning unit can, for example, provide analysis results to a medical institution. As a result, the medical mining system according to the embodiment can improve the quality and efficiency of medical care by efficiently collecting, analyzing, and providing medical big data. Some or all of the above-described processes in the collection unit may be performed using, for example, AI, or not using AI. For example, the collection unit can collect electronic medical records using an AI model. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can have generative AI perform an integrated analysis of text data and medical image data. Some or all of the above-described processes in the provisioning unit may be performed using, for example, AI, or not using AI. For example, the provisioning unit can provide analysis results using an AI model.

[0069] The data collection unit collects multiple electronic medical records (EMRs). The unit can collect EMRs in various formats, such as text, image, and audio. Specifically, text-format EMRs include detailed information such as patient medical records, prescriptions, and test results. This text data is analyzed using natural language processing technology to extract important medical information. Image-format EMRs include medical images such as X-ray images, MRI images, and CT scan images. This image data is analyzed using image recognition technology and used for detecting abnormalities and supporting diagnosis. Audio-format EMRs include conversations between doctors and patients and audio recordings during consultations. This audio data is converted to text using speech recognition technology and further analyzed. The data collection unit centrally collects these diverse formats of EMRs and stores them in a database. The data collection unit can automate EMR collection using AI models. For example, the AI ​​model integrates data from different medical institutions, removes duplicate data, and standardizes data formats. This allows the data collection unit to collect EMRs efficiently and accurately, improving the overall data quality of the system.

[0070] The analysis department comprehensively analyzes text data and medical image data contained in multiple electronic medical records collected by the collection department. Specifically, it comprehensively analyzes text data and medical image data using data preprocessing methods and types of analysis algorithms. Data preprocessing methods include cleaning, normalization, tokenization, and stop word removal for text data. Preprocessing methods for medical image data include resizing, noise reduction, and contrast adjustment. By performing these preprocessing steps, the quality of the data can be improved and the accuracy of the analysis can be increased. Machine learning algorithms and deep learning algorithms are used as analysis algorithms. For example, natural language processing technology is used to analyze text data and extract important medical information. Image recognition technology is used to analyze medical image data to detect abnormalities and support diagnosis. Furthermore, generative AI can also be used to comprehensively analyze text data and medical image data. Generative AI, for example, combines patient medical records and medical images to generate more detailed diagnostic information. As a result, the analysis department can comprehensively analyze the collected data and provide information that is useful in supporting diagnosis and treatment in medical settings.

[0071] The service provider provides the analysis results from the analysis provider. Specifically, it can provide analysis results to support physicians' diagnoses. For example, providing analysis results to physicians can improve the accuracy of diagnoses. Based on the provided analysis results, physicians can gain a detailed understanding of the patient's symptoms and medical history and make appropriate diagnoses. The service provider can also provide the analysis results to patients. Patients can deepen their understanding of their health condition and treatment plan, and communication with physicians can be facilitated. Furthermore, the service provider can provide the analysis results to medical institutions. Based on the provided analysis results, medical institutions can formulate measures to improve the quality of medical services. The service provider can automate the provision of analysis results using AI models. For example, the AI ​​model can convert the analysis results into an appropriate format and provide them quickly to physicians, patients, and medical institutions. The service provider can also collect user feedback and continuously improve the accuracy and effectiveness of the provided content. As a result, the service provider can efficiently support diagnosis and treatment in medical settings, achieving improved quality and efficiency in medical care.

[0072] The symptom reception unit can receive information describing a patient's symptoms. The symptom reception unit can receive information in various formats, such as text input or multiple-choice. For example, the symptom reception unit can receive a patient's symptoms in text input format. The symptom reception unit can also receive a patient's symptoms in multiple-choice format. The symptom reception unit can also receive a patient's symptoms in voice input format. The search unit can search a medical database for cases similar to the patient's symptoms received by the symptom reception unit. The search unit can search for similar cases based on factors such as the degree of symptom matching or past diagnostic results. For example, the search unit can search for similar cases based on the degree of symptom matching. The search unit can also search for similar cases based on past diagnostic results. The search unit can also search for similar cases using generative AI. The provision unit can provide the search results from the search unit. For example, the provision unit can provide search results to complement a doctor's diagnosis. For example, the provision unit can provide search results to a doctor. For example, the provision unit can provide search results to a patient. The provisioning unit can, for example, provide search results to medical institutions. This allows for the search of similar cases based on the patient's symptoms, supporting the doctor's diagnosis. Some or all of the above-described processes in the symptom reception unit may be performed using AI, for example, or without AI. For example, the symptom reception unit can receive patient symptoms using an AI model. Some or all of the above-described processes in the search unit may be performed using generative AI, for example, or without generative AI. For example, the search unit can have generative AI perform the search for similar cases. Some or all of the above-described processes in the provisioning unit may be performed using AI, for example, or without AI. For example, the provisioning unit can provide search results using an AI model.

[0073] The information reception unit can receive patient information. The information reception unit can receive information such as age, gender, and medical history. For example, the information reception unit can receive age. For example, the information reception unit can also receive gender. For example, the information reception unit can also receive medical history. The support unit can provide diagnostic support based on multiple electronic medical records collected by the collection unit and patient information received by the information reception unit. The support unit can provide diagnostic support using, for example, diagnostic algorithms and types of support. For example, the support unit provides diagnostic support using diagnostic algorithms. For example, the support unit can provide diagnostic support using types of support. For example, the support unit can provide diagnostic support using generative AI. This allows for diagnostic support based on collected electronic medical records and patient information, supporting the physician's diagnosis. Some or all of the above processing in the information reception unit may be performed using, for example, AI, or not using AI. For example, the information reception unit can receive patient information using an AI model. Some or all of the above-described processes in the support unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the support unit can have a generative AI perform diagnostic support.

[0074] The update unit can update the medical database based on the analysis results from the analysis unit. For example, the update unit can update the medical database based on the analysis results. For example, the update unit can add new data based on the analysis results. For example, the update unit can also modify existing data based on the analysis results. For example, the update unit can update the medical database using a generative AI. This allows the medical database to be updated based on the analysis results and reflect the latest information. Some or all of the above-described processes in the update unit may be performed using a generative AI, or not. For example, the update unit can have a generative AI perform the update of the medical database.

[0075] The data collection unit can estimate the user's emotions and adjust the timing of electronic medical record collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing and wait until the user is relaxed. For example, if the user is relaxed, the data collection unit can immediately collect the electronic medical record and respond quickly. For example, if the user is in a hurry, the data collection unit can advance the collection timing to quickly obtain the electronic medical record. This allows for efficient data collection by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have a generative AI perform the estimation of the user's emotions.

[0076] The data collection unit can analyze past data collection history and select an efficient data collection method. For example, the data collection unit can select the most efficient data collection method from past data collection history. For example, the data collection unit can optimize the data collection frequency based on past data collection history. For example, the data collection unit can analyze past data collection history and select a method to shorten the time required for data collection. This enables the selection of the optimal data collection method based on past data collection history, thereby achieving efficient data collection. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI perform the analysis of past data collection history.

[0077] The data collection unit can filter electronic medical records based on the patient's current medical condition and treatment status. For example, the data collection unit can prioritize the collection of highly relevant electronic medical records based on the patient's current medical condition. For example, the data collection unit can filter and collect only the necessary information based on the patient's treatment status. For example, the data collection unit can adjust the range of electronic medical records to be collected according to the patient's medical condition and treatment status. This allows for efficient data collection by filtering the necessary information according to the patient's medical condition and treatment status. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI perform filtering based on the patient's medical condition and treatment status.

[0078] The data collection unit can estimate the user's emotions and determine the priority of electronic medical records to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can postpone collecting less important electronic medical records. For example, if the user is relaxed, the data collection unit can prioritize collecting more important electronic medical records. For example, if the user is in a hurry, the data collection unit can prioritize collecting electronic medical records that can be collected quickly. This allows for efficient data collection by determining the priority of electronic medical records to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have a generative AI perform the estimation of the user's emotions.

[0079] The data collection unit can prioritize the collection of highly relevant medical records by considering the patient's geographical location information when collecting electronic medical records. For example, the data collection unit can prioritize the collection of electronic medical records from medical institutions close to the patient's current location. For example, the data collection unit can collect highly relevant electronic medical records from a region based on the patient's geographical location information. For example, the data collection unit can select highly relevant electronic medical records by considering the patient's travel history. This enables efficient data collection by prioritizing the collection of highly relevant medical records by considering the patient's geographical location information. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI perform the priority determination of medical records based on the patient's geographical location information.

[0080] The data collection unit can analyze patients' social media activity and collect relevant records when collecting electronic medical records. For example, the data collection unit can collect information about relevant medical conditions and treatments from patients' social media posts. For example, the data collection unit can analyze patients' social media activity and prioritize the collection of relevant electronic medical records. For example, the data collection unit can adjust the scope of electronic medical records to be collected based on patients' social media data. This enables efficient data collection by analyzing patients' social media activity and collecting relevant records. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI perform the analysis of patients' social media activity.

[0081] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a simple and easy-to-understand analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is in a hurry, the analysis unit can provide a concise analysis result. This allows the presentation of the analysis to be adjusted according to the user's emotions, providing an easy-to-understand analysis result. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, for example, or without a generative AI. For example, the analysis unit can have a generative AI perform the estimation of the user's emotions.

[0082] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on highly important data. For example, the analysis unit can perform a simplified analysis on less important data. The analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the data. This allows for efficient data analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can have the generative AI perform the adjustment of the level of detail of the analysis based on the importance of the data.

[0083] The analysis unit can apply an efficient analysis algorithm according to the data category during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data. For example, the analysis unit can apply an image recognition algorithm to medical image data. The analysis unit can select the optimal analysis algorithm according to the data category. This enables efficient data analysis by applying the optimal analysis algorithm according to the data category. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can have the generative AI perform the application of an analysis algorithm according to the data category.

[0084] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. For example, if the user is relaxed, the analysis unit can provide a longer analysis with detailed explanations. For example, if the user is excited, the analysis unit can provide an analysis with visually stimulating effects. This allows for efficient data analysis by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can have a generative AI perform the estimation of the user's emotions.

[0085] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit can prioritize the analysis of the most recent data. For example, the analysis unit can postpone the analysis of older data. For example, the analysis unit can dynamically adjust the analysis priority according to the data submission date. This enables efficient data analysis by determining the analysis priority according to the data submission date. Some or all of the above processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can have generative AI perform the determination of analysis priority based on the data submission date.

[0086] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can postpone the analysis of less relevant data. For example, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data. This allows for efficient data analysis by adjusting the order of analysis according to the relevance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can have the generative AI perform the adjustment of the order of analysis based on the relevance of the data.

[0087] The service provider can estimate the user's emotions and adjust the delivery method based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and easy-to-understand delivery method. For example, if the user is relaxed, the service provider can provide a delivery method that includes detailed information. For example, if the user is in a hurry, the service provider can provide a delivery method that gets straight to the point. This allows the service provider to adjust the delivery method according to the user's emotions and achieve highly easy-to-understand information delivery. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can have a generative AI perform the estimation of the user's emotions.

[0088] The information delivery unit can adjust the level of detail provided based on the importance of the analysis results at the time of delivery. For example, the information delivery unit can provide detailed information for analysis results of high importance. For example, the information delivery unit can provide simplified information for analysis results of low importance. The information delivery unit can dynamically adjust the level of detail provided according to the importance of the analysis results. This allows for efficient information delivery by adjusting the level of detail provided according to the importance of the analysis results. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can have AI perform the adjustment of the level of detail provided based on the importance of the analysis results.

[0089] The delivery unit can apply different delivery methods depending on the category of the analysis results at the time of delivery. For example, the delivery unit can provide analysis results of text data in text format. For example, the delivery unit can provide analysis results of medical image data in image format. The delivery unit can select the optimal delivery method depending on the category of the analysis results. This enables efficient information provision by applying the optimal delivery method according to the category of the analysis results. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can have AI perform the application of a delivery method according to the category of the analysis results.

[0090] The service provider can estimate the user's emotions and determine the priority of services based on the estimated emotions. For example, if the user is stressed, the service provider can postpone less important services. For example, if the user is relaxed, the service provider can prioritize more important services. For example, if the user is in a hurry, the service provider can prioritize information that can be delivered quickly. This allows for efficient information delivery by determining the priority of services according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can have a generative AI perform the estimation of the user's emotions.

[0091] The service provider can adjust the order of delivery based on the submission date of the analysis results. For example, the service provider can prioritize the delivery of the most recent analysis results. For example, the service provider can postpone the delivery of older analysis results. For example, the service provider can dynamically adjust the order of delivery according to the submission date of the analysis results. This allows for efficient information delivery by adjusting the order of delivery according to the submission date of the analysis results. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can have AI perform the adjustment of the delivery order based on the submission date of the analysis results.

[0092] The delivery unit can adjust the order of delivery based on the relevance of the analysis results at the time of delivery. For example, the delivery unit can prioritize the delivery of highly relevant analysis results. For example, the delivery unit can postpone the delivery of less relevant analysis results. For example, the delivery unit can dynamically adjust the order of delivery according to the relevance of the analysis results. This allows for efficient information delivery by adjusting the order of delivery according to the relevance of the analysis results. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can have AI perform the adjustment of the delivery order based on the relevance of the analysis results.

[0093] The symptom reception unit can estimate the user's emotions and adjust the symptom reception method based on the estimated emotions. For example, if the user is nervous, the symptom reception unit can provide a simple and highly visible reception method. For example, if the user is relaxed, the symptom reception unit can provide a reception method that includes detailed information. For example, if the user is in a hurry, the symptom reception unit can provide a reception method that gets straight to the point. This allows for efficient symptom reception by adjusting the symptom reception method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the symptom reception unit may be performed using AI, for example, or without AI. For example, the symptom reception unit can have a generative AI perform the estimation of the user's emotions.

[0094] The symptom reception unit can select an efficient reception method by referring to the patient's past symptom history when receiving a symptom. For example, the symptom reception unit can select the optimal reception method based on the patient's past symptom history. For example, the symptom reception unit can select a method to shorten the reception time by referring to the patient's past symptom history. For example, the symptom reception unit can analyze the patient's past symptom history and select the most efficient reception method. This enables efficient symptom reception by selecting the optimal reception method based on the patient's past symptom history. Some or all of the above processing in the symptom reception unit may be performed using AI, for example, or without AI. For example, the symptom reception unit can have AI perform the referencing of past symptom history.

[0095] The symptom reception unit can estimate the user's emotions and determine the priority of symptoms based on the estimated emotions. For example, if the user is stressed, the symptom reception unit can postpone less important symptoms. For example, if the user is relaxed, the symptom reception unit can prioritize receiving more important symptoms. For example, if the user is in a hurry, the symptom reception unit can prioritize symptoms that can be addressed quickly. This allows for efficient symptom reception by determining the priority of symptoms according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the symptom reception unit may be performed using AI, for example, or without AI. For example, the symptom reception unit can have a generative AI perform the estimation of the user's emotions.

[0096] The symptom reception unit can select an efficient reception method when symptoms are received, taking into account the patient's geographical location. For example, the symptom reception unit can prioritize the selection of a reception method for a medical institution close to the patient's current location. For example, the symptom reception unit can select a reception method for a highly relevant region based on the patient's geographical location. For example, the symptom reception unit can select the optimal reception method by considering the patient's travel history. This enables efficient symptom reception by selecting the optimal reception method while considering the patient's geographical location. Some or all of the above processing in the symptom reception unit may be performed using AI, for example, or without AI. For example, the symptom reception unit can have AI perform the selection of a reception method based on the patient's geographical location.

[0097] The search unit can estimate the user's emotions and adjust the search method based on the estimated emotions. For example, if the user is nervous, the search unit can provide a simple and highly visible search method. For example, if the user is relaxed, the search unit can provide a search method that includes detailed information. For example, if the user is in a hurry, the search unit can provide a concise search method. This allows for efficient searching by adjusting the search method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can have a generative AI perform the estimation of the user's emotions.

[0098] The search unit can select the optimal search method by referring to past search history during a search. For example, the search unit can select the most efficient search method from past search history. For example, the search unit can optimize the search frequency based on past search history. For example, the search unit can analyze past search history and select a method to shorten the time it takes to search. This enables efficient searching by selecting the optimal search method based on past search history. Some or all of the above processes in the search unit may be performed using AI, for example, or without AI. For example, the search unit can have AI perform the referencing of past search history.

[0099] The search unit can estimate the user's emotions and determine search priorities based on the estimated emotions. For example, if the user is stressed, the search unit can postpone less important searches. For example, if the user is relaxed, the search unit can prioritize more important searches. For example, if the user is in a hurry, the search unit can prioritize searches that can be answered quickly. This allows for efficient searching by determining search priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can have a generative AI perform the estimation of the user's emotions.

[0100] The search unit can select an efficient search method by considering the patient's geographical location information during a search. For example, the search unit can prioritize searching for information on medical institutions close to the patient's current location. For example, the search unit can search for information on highly relevant regions based on the patient's geographical location information. For example, the search unit can select the optimal search method by considering the patient's travel history. This enables efficient searching by selecting the optimal search method by considering the patient's geographical location information. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can have AI perform the selection of a search method based on the patient's geographical location information.

[0101] The information receiving unit can estimate the user's emotions and adjust the information receiving method based on the estimated emotions. For example, if the user is nervous, the information receiving unit can provide a simple and highly visible information receiving method. For example, if the user is relaxed, the information receiving unit can provide an information receiving method that includes detailed information. For example, if the user is in a hurry, the information receiving unit can provide an information receiving method that gets straight to the point. This allows for efficient information receiving by adjusting the information receiving method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information receiving unit may be performed using AI, for example, or without AI. For example, the information receiving unit can have a generative AI perform the estimation of the user's emotions.

[0102] The information reception unit can select the optimal reception method by referring to the patient's past information history when receiving information. For example, the information reception unit can select the optimal reception method based on the patient's past information history. For example, the information reception unit can select a method to shorten the time required for reception by referring to the patient's past information history. For example, the information reception unit can analyze the patient's past information history and select the most efficient reception method. This makes it possible to select the optimal reception method based on the patient's past information history and achieve efficient information reception. Some or all of the above processing in the information reception unit may be performed using AI, for example, or without using AI. For example, the information reception unit can have AI perform the referencing of past information history.

[0103] The information receiving unit can estimate the user's emotions and determine the priority of information based on the estimated emotions. For example, if the user is stressed, the information receiving unit can postpone processing less important information. For example, if the user is relaxed, the information receiving unit can prioritize processing more important information. For example, if the user is in a hurry, the information receiving unit can prioritize information that can be handled quickly. This allows for efficient information receiving by determining the priority of information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information receiving unit may be performed using AI, for example, or without AI. For example, the information receiving unit can have a generative AI perform the estimation of the user's emotions.

[0104] The information reception unit can select an efficient reception method when receiving information, taking into account the patient's geographical location. For example, the information reception unit can prioritize receiving information from medical institutions close to the patient's current location. For example, the information reception unit can receive information from highly relevant regions based on the patient's geographical location. For example, the information reception unit can select the optimal reception method by taking into account the patient's travel history. This enables efficient information reception by selecting the optimal reception method while considering the patient's geographical location. Some or all of the above processing in the information reception unit may be performed using AI, for example, or without AI. For example, the information reception unit can have AI perform the selection of a reception method based on the patient's geographical location.

[0105] The support unit can estimate the user's emotions and adjust its support methods based on the estimated emotions. For example, if the user is nervous, the support unit can provide a simple and easily understandable support method. For example, if the user is relaxed, the support unit can provide a support method that includes detailed information. For example, if the user is in a hurry, the support unit can provide a support method that gets straight to the point. This allows for efficient support by adjusting the support method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can have a generative AI perform the estimation of the user's emotions.

[0106] The support unit can select an efficient support method by referring to the patient's past diagnostic history during support. For example, the support unit can select the optimal support method based on the patient's past diagnostic history. For example, the support unit can select a method to shorten the time required for support by referring to the patient's past diagnostic history. For example, the support unit can analyze the patient's past diagnostic history and select the most efficient support method. This enables the selection of the optimal support method based on the patient's past diagnostic history, thereby achieving efficient support. Some or all of the above processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can have AI perform the referencing of past diagnostic history.

[0107] The support unit can estimate the user's emotions and determine the priority of support based on the estimated emotions. For example, if the user is stressed, the support unit can postpone less important support. For example, if the user is relaxed, the support unit can prioritize more important support. For example, if the user is in a hurry, the support unit can prioritize support that can be addressed quickly. This allows for efficient support by determining the priority of support according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can have a generative AI perform the estimation of the user's emotions.

[0108] The support unit can select an efficient support method by considering the patient's geographical location information during support. For example, the support unit can prioritize providing information on medical institutions close to the patient's current location. For example, the support unit can provide information on highly relevant regions based on the patient's geographical location information. For example, the support unit can select the optimal support method by considering the patient's travel history. This enables efficient support by selecting the optimal support method while considering the patient's geographical location information. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can have AI perform the selection of a support method based on the patient's geographical location information.

[0109] The update unit can estimate the user's emotions and adjust the database update method based on the estimated user emotions. For example, if the user is tense, the update unit can provide a simple and highly visible update method. For example, if the user is relaxed, the update unit can provide an update method that includes detailed information. For example, if the user is in a hurry, the update unit can provide a concise update method. This allows for efficient database updates by adjusting the database update method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can have a generative AI perform the estimation of the user's emotions.

[0110] The update unit can select an efficient update method by referring to past update history during an update. For example, the update unit can select the most efficient update method from past update history. For example, the update unit can optimize the update frequency based on past update history. For example, the update unit can analyze past update history and select a method to shorten the time required for updates. This enables efficient database updates by selecting the optimal update method based on past update history. Some or all of the above processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can have AI perform the task of referring to past update history.

[0111] The update unit can estimate the user's emotions and adjust the database update frequency based on the estimated emotions. For example, if the user is stressed, the update unit can lower the update frequency. For example, if the user is relaxed, the update unit can increase the update frequency. For example, if the user is in a hurry, the update unit can adjust the update frequency to allow for quick updates. This allows for efficient database updates by adjusting the database update frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can have a generative AI perform the estimation of the user's emotions.

[0112] The update unit can weight the updated data based on the data submission date during the update process. For example, the update unit can prioritize updating the most recent data. For example, the update unit can postpone updating older data. For example, the update unit can dynamically adjust the weighting of the updated data according to the data submission date. This enables efficient database updates by weighting the updated data according to the data submission date. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can have AI perform the weighting of updated data based on the data submission date.

[0113] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0114] Medical mining systems can also collect and analyze patients' lifestyle data. For example, they can collect data on patients' diet, exercise, and sleep patterns, and integrate this data with medical data for analysis. This allows for an assessment of the impact of patients' lifestyles on their medical condition, providing more accurate diagnostic support. Furthermore, specific lifestyle improvement suggestions can be made to patients based on their lifestyle data. In addition, lifestyle data can be monitored over the long term to track changes in patients' health.

[0115] Medical mining systems can also collect and analyze patients' genetic information. For example, they can collect patients' genetic data and integrate it with medical data for analysis. This allows for an assessment of the impact of genetic factors on disease progression and provides more personalized diagnostic support. Furthermore, genetic information can be used to suggest preventive medical care to patients. It is also possible to monitor genetic information over the long term and track changes in genetic risk.

[0116] The medical mining system can further estimate the patient's emotions and adjust the diagnostic support based on those emotions. For example, if a patient is feeling anxious, it can provide reassuring diagnostic support. If a patient is relaxed, it can provide diagnostic support that includes detailed explanations. If a patient is in a hurry, it can provide concise diagnostic support that gets straight to the point. This allows for the provision of diagnostic support tailored to the patient's emotions, thereby improving patient satisfaction.

[0117] Medical mining systems can further enhance diagnostic support by considering the patient's socioeconomic background. For example, they can collect data such as the patient's income, education level, and occupation, and integrate and analyze this data with medical data. This allows for an assessment of the impact of socioeconomic factors on the patient's condition and provides more appropriate diagnostic support. Furthermore, specific support measures can be proposed based on the patient's socioeconomic background. Additionally, it is possible to monitor socioeconomic data over the long term and track changes in the patient's living environment.

[0118] The medical mining system can further estimate the patient's emotions and adjust how medical data is displayed based on those emotions. For example, if the patient is anxious, a simple and easy-to-read display can be provided. If the patient is relaxed, a display with detailed information can be provided. If the patient is in a hurry, a display that focuses on the essentials can be provided. This allows for a display method tailored to the patient's emotions, facilitating their understanding of the information.

[0119] Medical mining systems can also collect and analyze patients' environmental data. For example, they can collect data on patients' living environment, work environment, and climate conditions, and integrate and analyze this data with medical data. This allows for an assessment of the impact of environmental factors on disease symptoms and provides more accurate diagnostic support. Furthermore, based on environmental data, specific suggestions for environmental improvements can be made to patients. In addition, it is possible to monitor environmental data over the long term and track changes in patients' health status.

[0120] Medical mining systems can further estimate a patient's emotions and adjust the method of collecting medical data based on those estimated emotions. For example, if a patient is stressed, the collection method can be changed to avoid burdening the patient. If the patient is relaxed, detailed data can be collected. If the patient is in a hurry, a method that allows for rapid collection can be selected. This provides a collection method that is tailored to the patient's emotions, enabling efficient data collection.

[0121] Medical mining systems can also collect and analyze patient healthcare expense data. For example, they can collect patient healthcare expenditure data and integrate it with other medical data for analysis. This allows for an assessment of the impact of healthcare costs on a patient's condition and provides more appropriate diagnostic support. Furthermore, based on healthcare expense data, specific healthcare cost reduction measures can be proposed to patients. Additionally, healthcare expense data can be monitored over the long term to track changes in patients' financial situations.

[0122] Medical mining systems can further estimate a patient's emotions and adjust the analysis of medical data based on those estimated emotions. For example, if a patient is feeling anxious, the system can provide a reassuring analysis. If a patient is relaxed, a detailed analysis can be performed. If a patient is in a hurry, a concise analysis focusing on the key points can be performed. This allows for an analysis tailored to the patient's emotions, thereby improving patient satisfaction.

[0123] Medical mining systems can also collect and analyze patient health goal data. For example, they can collect patients' health goals (such as weight loss, blood pressure management, and diabetes management) and integrate and analyze this data with medical data. This allows for the provision of specific support measures to help patients achieve their health goals. Furthermore, based on the health goal data, it is possible to propose measures to improve patient motivation. In addition, it is possible to monitor health goal data over the long term and track the patient's progress.

[0124] The following briefly describes the processing flow for example form 2.

[0125] Step 1: The collection unit collects multiple electronic medical records. The collection unit can collect electronic medical records in various formats, such as text, image, and audio. For example, the collection unit can collect electronic medical records in text format. The collection unit can also collect electronic medical records in image format. The collection unit can also collect electronic medical records in audio format. Step 2: The analysis unit comprehensively analyzes the text data and medical image data contained in multiple electronic medical records collected by the collection unit. The analysis unit can comprehensively analyze the text data and medical image data using, for example, data preprocessing methods and types of analysis algorithms. The analysis unit can also comprehensively analyze the text data and medical image data using, for example, generative AI. Step 3: The service provider provides the analysis results from the analysis provider. The service provider can provide the analysis results, for example, to support a physician's diagnosis. The service provider can provide the analysis results to a physician, for example. The service provider can also provide the analysis results to a patient, for example. The service provider can also provide the analysis results to a healthcare institution, for example.

[0126] 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.

[0127] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0128] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] For example, the collection unit can be implemented in either the data processing unit 12 or the smart device 14. For example, the specific processing unit 290 of the data processing unit 12 can perform the process of collecting multiple electronic medical records. For example, the control unit 46A of the smart device 14 can collect electronic medical records in text format, image format, audio format, etc. For example, the analysis unit can perform integrated analysis of the collected text data and medical image data using the specific processing unit 290 of the data processing unit 12. For example, the provision unit can provide the analysis results to the physician using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0130] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0131] 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.

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

[0133] 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.

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

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] 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.

[0137] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] 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.

[0143] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] For example, the data collection unit can be implemented by either the data processing unit 12 or the smart glasses 214. For example, the specific processing unit 290 of the data processing unit 12 can perform the process of collecting multiple electronic medical records. For example, the control unit 46A of the smart glasses 214 can collect electronic medical records in text format, image format, audio format, etc. For example, the analysis unit can perform an integrated analysis of the collected text data and medical image data using the specific processing unit 290 of the data processing unit 12. For example, the provision unit can provide the analysis results to the physician using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0146] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0147] 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.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] 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.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] 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.

[0153] 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.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] 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.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] For example, the data collection unit can be implemented in either the data processing unit 12 or the headset terminal 314. For example, the specific processing unit 290 of the data processing unit 12 can perform the process of collecting multiple electronic medical records. For example, the control unit 46A of the headset terminal 314 can collect electronic medical records in text format, image format, audio format, etc. For example, the analysis unit can perform integrated analysis of the collected text data and medical image data using the specific processing unit 290 of the data processing unit 12. For example, the provision unit can provide the analysis results to the physician using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0162] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0163] 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.

[0164] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0165] 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.

[0166] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0167] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0168] 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.

[0169] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0170] 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.

[0171] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0172] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0173] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0174] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0175] 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.

[0176] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0177] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0178] For example, the collection unit can be implemented by either the data processing unit 12 or the robot 414. For example, the specific processing unit 290 of the data processing unit 12 can perform the process of collecting multiple electronic medical records. For example, the control unit 46A of the robot 414 can collect electronic medical records in text format, image format, audio format, etc. For example, the analysis unit can perform integrated analysis of the collected text data and medical image data using the specific processing unit 290 of the data processing unit 12. For example, the provision unit can provide the analysis results to the physician using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0179] 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.

[0180] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0181] 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.

[0182] 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.

[0183] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0184] 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."

[0185] 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.

[0186] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0195] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0196] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0197] (Note 1) A collection unit that collects multiple electronic medical records, An analysis unit that comprehensively analyzes the text data and medical image data contained in the multiple electronic medical records collected by the collection unit, The system comprises a providing unit that provides the analysis results from the aforementioned analysis unit. A system characterized by the following features. (Note 2) A symptom reception department that receives information indicating the patient's symptoms, A search unit that searches a medical database for cases similar to the symptoms of the patient received by the symptom reception unit, The system comprises: a providing unit that provides search results from the search unit; The system described in Appendix 1, characterized by the features described herein. (Note 3) The information reception department receives patient information, The system includes a support unit that provides diagnostic support based on the plurality of electronic medical records collected by the collection unit and the patient information received by the information reception unit. The system described in Appendix 1, characterized by the features described herein. (Note 4) The system includes an update unit that updates the medical database based on the analysis results from the aforementioned analysis unit. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is We will estimate the user's emotions and determine how to adjust the timing of electronic medical record collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Analyze past collection history and select the most efficient collection method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting electronic medical records, filtering is performed based on the patient's current medical condition and treatment status. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Determine how to estimate user emotions and how to prioritize the electronic medical records to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting electronic medical records, prioritize the collection of highly relevant records by considering the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting electronic medical records, analyze patients' social media activity and collect relevant medical records. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is Determine how to estimate the user's emotions and adjust the presentation of the analysis based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During analysis, apply an efficient analytical algorithm according to the data category. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is We estimate the user's emotions and determine how to adjust the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, We estimate the user's emotions and determine how to adjust the delivery method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing the data, we will adjust the level of detail based on the importance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing the results, different delivery methods will be applied depending on the category of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, Determine how to estimate user sentiment and how to prioritize offerings based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing the data, we will adjust the order of delivery based on when the analysis results were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing the data, we will adjust the order of delivery based on the relevance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned symptom reception unit is We estimate the user's emotions and determine how to adjust the symptom reception method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 24) The aforementioned symptom reception unit is When receiving a symptom report, the system selects an efficient reporting method by referring to the patient's past symptom history. The system described in Appendix 2, characterized by the features described herein. (Note 25) The aforementioned symptom reception unit is Determine how to estimate the user's emotions and how to prioritize symptoms based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned symptom reception unit is When receiving symptom reports, the most efficient registration method will be selected, taking into account the patient's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned search unit, We estimate the user's sentiment and determine how to adjust the search method based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned search unit, When performing a search, the system selects the optimal search method by referring to past search history. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned search unit, Determine how to estimate user sentiment and how to prioritize searches based on that estimated sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned search unit, When searching, the most efficient search method is selected, taking into account the patient's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned information receiving unit is We estimate the user's emotions and determine how to adjust how information is received based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned information receiving unit is When receiving information, the system will refer to the patient's past information history to select the most appropriate registration method. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned information receiving unit is Determine how to estimate user sentiment and prioritize information based on that estimated sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned information receiving unit is When receiving patient information, an efficient registration method will be selected, taking into account the patient's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned support unit, We estimate the user's emotions and determine how to adjust the support method based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned support unit, When providing support, refer to the patient's past diagnostic history to select the most efficient support method. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned support unit, Determine how to estimate the user's emotions and how to prioritize support based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned support unit, When providing support, consider the patient's geographical location to select the most efficient support method. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned update unit is We estimate the user's emotions and determine how to adjust database updates based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned update unit is During updates, the system will refer to past update history to select the most efficient update method. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned update unit is We estimate user sentiment and determine how to adjust the database update frequency based on the estimated user sentiment. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned update unit is When updating, the updated data is weighted based on when it was submitted. The system described in Appendix 4, characterized by the features described herein. [Explanation of symbols]

[0198] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects multiple electronic medical records, An analysis unit that comprehensively analyzes the text data and medical image data contained in the multiple electronic medical records collected by the collection unit, The system comprises a providing unit that provides the analysis results from the aforementioned analysis unit. A system characterized by the following features.

2. A symptom reception department that receives information indicating the patient's symptoms, A search unit that searches a medical database for cases similar to the symptoms of the patient received by the symptom reception unit, The system comprises: a providing unit that provides search results from the search unit; The system according to feature 1.

3. The information reception department receives patient information, The system includes a support unit that provides diagnostic support based on the plurality of electronic medical records collected by the collection unit and the patient information received by the information reception unit. The system according to feature 1.

4. The system includes an update unit that updates the medical database based on the analysis results from the aforementioned analysis unit. The system according to feature 2.

5. The aforementioned collection unit is We will estimate the user's emotions and determine how to adjust the timing of electronic medical record collection based on the estimated user emotions. The system according to feature 1.

6. The aforementioned collection unit is Analyze past collection history and select the most efficient collection method. The system according to feature 1.

7. The aforementioned collection unit is When collecting electronic medical records, filtering is performed based on the patient's current medical condition and treatment status. The system according to feature 1.

8. The aforementioned collection unit is Determine how to estimate user emotions and how to prioritize the electronic medical records to collect based on those estimated emotions. The system according to feature 1.

9. The aforementioned collection unit is When collecting electronic medical records, prioritize the collection of highly relevant records by considering the patient's geographical location. The system according to feature 1.

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