system

Abstract

The system according to this embodiment aims to efficiently estimate the cause of a disease and provide this information to healthcare professionals. [Solution] The system according to this embodiment comprises an acquisition unit, an analysis unit, and a provision unit. The acquisition unit automatically acquires basic patient information, statistical information, and related information such as research papers. The analysis unit analyzes the information acquired by the acquisition unit and estimates the cause of the disease. The provision unit provides the cause of the disease estimated by the analysis unit to medical professionals.

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 in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that a lot of time and expertise are required to identify the cause of a disease, and it is difficult to perform efficiently.

[0005] The system according to an embodiment aims to efficiently estimate the cause of a disease and provide it to medical staff.

Means for Solving the Problems

[0006] The system according to an embodiment includes an acquisition unit, an analysis unit, and a provision unit. The acquisition unit automatically acquires relevant information such as basic information of a patient, statistical information, and papers. The analysis unit analyzes the information acquired by the acquisition unit and estimates the cause of the disease. The provision unit provides the cause of the disease estimated by the analysis unit to medical staff. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently estimate the cause of a disease and provide this information to healthcare professionals. [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 disease cause estimation system according to an embodiment of the present invention is a system that quickly and accurately estimates the cause of a disease in hospitals where specialist physicians are absent or understaffed. This system automatically acquires and analyzes relevant information such as basic patient information, statistical information, and research papers, estimates the cause of the disease, and provides it to healthcare professionals. For example, the disease cause estimation system acquires the patient's medical history, test results, and image data. For example, the disease cause estimation system collects information required by specialists in urology or ophthalmology. Next, the disease cause estimation system analyzes the acquired information and estimates the cause of the disease. The disease cause estimation system possesses advanced knowledge equivalent to that of a specialist physician and identifies the cause of the disease based on the acquired information. For example, when estimating the cause of a urological disease, the disease cause estimation system analyzes statistical information and research papers related to urology to estimate the cause of the disease. Finally, the disease cause estimation system provides the estimated cause of the disease to healthcare professionals. Healthcare professionals can select appropriate treatment methods based on the cause estimation results provided by the disease cause estimation system. This makes it possible to quickly and accurately estimate the cause of a disease even in hospitals where specialist physicians are absent or understaffed. This enables disease cause estimation systems to quickly and accurately estimate disease causes, even in hospitals where specialist doctors are absent or understaffed.

[0029] The disease cause estimation system according to this embodiment comprises an acquisition unit, an analysis unit, and a provision unit. The acquisition unit automatically acquires relevant information such as basic patient information, statistical information, and research papers. The acquisition unit acquires, for example, the patient's medical history, test results, and image data. For example, the acquisition unit can acquire the patient's medical history from an electronic medical record. The acquisition unit can also acquire test results from a hospital's testing system. Furthermore, the acquisition unit can acquire image data from a medical imaging system. For example, the acquisition unit automatically acquires the patient's medical history from an electronic medical record and provides it to the analysis unit. The acquisition unit automatically acquires test results from a hospital's testing system and provides them to the analysis unit. The acquisition unit automatically acquires image data from a medical imaging system and provides it to the analysis unit. The analysis unit analyzes the information acquired by the acquisition unit and estimates the cause of the disease. For example, the analysis unit estimates the cause of the disease based on the acquired information. For example, the analysis unit can perform symptom pattern analysis to estimate the cause of the disease. The analysis unit can also perform statistical estimation to estimate the cause of the disease. Furthermore, the analysis unit can also estimate the cause of the disease using machine learning algorithms. For example, the analysis unit performs symptom pattern analysis to estimate the cause of the disease. The analysis unit performs statistical estimation to estimate the cause of the disease. The analysis unit estimates the cause of the disease using machine learning algorithms. The provision unit provides the cause of the disease estimated by the analysis unit to healthcare providers. The provision unit provides the estimated cause of the disease to healthcare providers, for example. The provision unit can provide the estimated cause of the disease to healthcare providers, for example, via email. The provision unit can also provide the estimated cause of the disease to healthcare providers using a dashboard display. Furthermore, the provision unit can also provide the estimated cause of the disease to healthcare providers using a mobile app. For example, the provision unit provides the estimated cause of the disease to healthcare providers via email. The provision unit provides the estimated cause of the disease to healthcare providers using a dashboard display. The provision unit provides the estimated cause of the disease to healthcare providers using a mobile app. As a result, the disease cause estimation system according to this embodiment enables rapid and accurate estimation of disease causes even in hospitals where specialist doctors are absent or understaffed.

[0030] The data acquisition unit automatically retrieves basic patient information, statistical information, and relevant information such as publications. Specifically, it acquires patient medical history, test results, and image data. Patient medical history is automatically acquired from the electronic medical record system and includes past medical records, prescription history, and allergy information. Test results are acquired from the hospital's laboratory system and include detailed data such as blood tests, urine tests, and genetic tests. Image data is acquired from the medical imaging system and includes images such as X-rays, CT scans, and MRIs. This data is collected in real time by the data acquisition unit and provided to the analysis unit. By automating the data collection process, the data acquisition unit reduces the burden on medical staff and ensures the accuracy and consistency of the data. The data acquisition unit can also acquire relevant information from external medical databases and academic paper databases. This enables comprehensive data collection that incorporates the latest research findings and statistical information. Furthermore, the data acquisition unit implements encryption technology and access control to ensure data privacy and security. This protects patients' personal information and prevents unauthorized access and leakage of data. Through these functions, the data acquisition unit provides high-quality data that forms the basis of the disease cause estimation system, improving the accuracy and reliability of the analysis unit.

[0031] The analysis unit analyzes the information acquired by the acquisition unit to estimate the cause of the disease. Specifically, it uses symptom pattern analysis, statistical estimation, and machine learning algorithms to estimate the cause of the disease based on the acquired information. In symptom pattern analysis, the patient's symptoms and test results are analyzed in detail and similar cases are identified by comparing them with past data. This makes it possible to estimate the cause of the disease based on specific symptom patterns. In statistical estimation, a statistical model is built based on a large amount of data to evaluate the probability of disease occurrence and risk factors. For example, the cause of the disease is estimated by considering the incidence rate of the disease in a specific age group or gender. In machine learning algorithms, new patient data is analyzed using a model trained on past diagnostic data to estimate the cause of the disease. Advanced algorithms such as deep learning and support vector machines are used for this. The analysis unit combines these methods to perform more accurate estimations of disease causes. Furthermore, the analysis unit can process data in real time and provide results quickly. This allows healthcare professionals to make diagnoses quickly and start appropriate treatment. In addition, the analysis unit can always perform analyses based on the latest knowledge by incorporating past data and the latest research findings. As a result, the analysis unit, as the core of the disease cause estimation system, enables accurate and rapid estimation of disease causes.

[0032] The service provider will provide healthcare professionals with disease causes estimated by the analysis department. Specifically, estimated disease causes will be provided to healthcare professionals via email, dashboard displays, and mobile apps. When using email, the estimated results will be sent to healthcare professionals in the form of a detailed report. The report will include the estimated disease cause, related symptoms and test results, and the data supporting the estimation. When using the dashboard display, healthcare professionals can access a dedicated web portal to view the estimated results in real time. The dashboard includes visual displays using graphs and charts, allowing for intuitive understanding of the information. When using the mobile app, healthcare professionals can view the estimated results anytime, anywhere via their smartphones or tablets. The app is equipped with a push notification function, which will immediately notify them when new estimated results are generated. Through these means, the service provider will provide healthcare professionals with rapid and accurate information. Furthermore, the service provider can collect feedback from healthcare professionals and continuously improve the accuracy and usability of the system. For example, when healthcare professionals comment on or correct the estimated results, the system learns from that feedback and improves the accuracy of future estimations. This allows the service provider to support healthcare professionals in making quick and appropriate diagnoses and treatments, and to maximize the effectiveness of the disease cause estimation system.

[0033] The acquisition unit can acquire patient medical history, test results, image data, etc. For example, the acquisition unit can acquire patient medical history from electronic medical records. For example, the acquisition unit can acquire test results from the hospital's laboratory system. For example, the acquisition unit can acquire image data from a medical imaging system. By acquiring detailed patient information, the accuracy of disease cause estimation is improved. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit automatically acquires patient medical history from electronic medical records and provides it to the analysis unit.

[0034] The analysis unit can estimate the cause of the disease based on the acquired information. For example, the analysis unit can perform symptom pattern analysis to estimate the cause of the disease. The analysis unit can also perform statistical estimation to estimate the cause of the disease. The analysis unit can also use machine learning algorithms to estimate the cause of the disease. This enables accurate diagnosis by estimating the cause of the disease based on the acquired information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit performs symptom pattern analysis to estimate the cause of the disease.

[0035] The service provider can provide healthcare professionals with the estimated cause of the disease. For example, the service provider may provide the estimated cause of the disease to healthcare professionals via email. Alternatively, the service provider may provide the estimated cause of the disease to healthcare professionals using a dashboard display. The service provider may also provide the estimated cause of the disease to healthcare professionals using a mobile app. This allows healthcare professionals to select appropriate treatments by providing them with the estimated cause of the disease. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider may provide the estimated cause of the disease to healthcare professionals via email.

[0036] The analysis unit can analyze statistical information and papers related to urology and ophthalmology to estimate the causes of diseases. For example, the analysis unit can analyze statistical information related to urology to estimate the causes of diseases. The analysis unit can also analyze statistical information related to ophthalmology to estimate the causes of diseases. The analysis unit can also analyze papers related to urology to estimate the causes of diseases. This improves the accuracy of disease cause estimation by analyzing specialized statistical information and papers. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can analyze statistical information related to urology to estimate the causes of diseases.

[0037] The data acquisition unit can analyze the patient's past medical history and select the optimal data acquisition method. For example, the data acquisition unit can prioritize the acquisition of specific test results from the patient's past medical history. For example, the data acquisition unit can also select necessary image data based on the patient's past medical history. For example, the data acquisition unit can analyze the patient's past medical history and acquire relevant research papers. This allows the optimal information acquisition method to be selected by analyzing the patient's past medical history. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit analyzes the patient's past medical history and selects the optimal data acquisition method.

[0038] The data acquisition unit can filter information based on the patient's current living situation and areas of interest when acquiring it. For example, the data acquisition unit considers the patient's current living situation and prioritizes acquiring relevant information. The data acquisition unit can also filter necessary information based on the patient's areas of interest. For example, the data acquisition unit can determine the priority of information to acquire based on the patient's living situation and areas of interest. This allows for the acquisition of highly relevant information by filtering information based on the patient's living situation and areas of interest. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit determines the priority of information to acquire based on the patient's current living situation and areas of interest.

[0039] The acquisition unit can prioritize the acquisition of highly relevant information by considering the patient's geographical location when acquiring information. For example, the acquisition unit can prioritize the acquisition of information on region-specific diseases based on the patient's geographical location. The acquisition unit can also prioritize the acquisition of information on nearby medical institutions by considering the patient's geographical location. The acquisition unit can also prioritize the acquisition of information on local medical resources based on the patient's geographical location. This allows for the priority acquisition of information on region-specific diseases by considering the patient's geographical location. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit prioritizes the acquisition of information on region-specific diseases based on the patient's geographical location.

[0040] The acquisition unit can analyze the patient's social media activity and acquire relevant information when acquiring information. For example, the acquisition unit can analyze the patient's social media activity and acquire information based on health-related posts. For example, the acquisition unit can also acquire information on health topics of interest from the patient's social media activity. For example, the acquisition unit can acquire relevant medical information based on the patient's social media activity. This allows for the acquisition of information on health topics of interest by analyzing the patient's social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit analyzes the patient's social media activity and acquires relevant information.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the disease during the analysis. For example, the analysis unit performs a detailed analysis for diseases of high importance. For example, the analysis unit can also perform a simplified analysis for diseases of low importance. The analysis unit can also adjust the level of detail of the analysis in stages according to the importance of the disease. This allows for the provision of appropriate analysis results by adjusting the level of detail of the analysis according to the importance of the disease. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit performs a detailed analysis for diseases of high importance.

[0042] The analysis unit can apply different analysis algorithms depending on the disease category during analysis. For example, in the case of a urological disease, the analysis unit applies an analysis algorithm specifically for urology. For example, in the case of an ophthalmic disease, the analysis unit can also apply an analysis algorithm specifically for ophthalmology. The analysis unit can also select the optimal analysis algorithm depending on the disease category. By applying the optimal analysis algorithm according to the disease category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, in the case of a urological disease, the analysis unit applies an analysis algorithm specifically for urology.

[0043] The analysis unit can determine the priority of analysis based on the onset time of the disease during the analysis. For example, the analysis unit will prioritize analysis if the onset time of the disease is recent. The analysis unit can also postpone analysis if the onset time of the disease is older. The analysis unit can also adjust the priority of analysis in stages according to the onset time of the disease. This enables rapid analysis by determining the priority of analysis according to the onset time of the disease. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit will prioritize analysis if the onset time of the disease is recent.

[0044] The analysis unit can adjust the order of analysis based on the relevance of diseases during the analysis. For example, the analysis unit can prioritize the analysis of diseases with a high relevance. For example, the analysis unit can postpone the analysis of diseases with a low relevance. The analysis unit can also adjust the order of analysis step by step according to the relevance of diseases. This allows for efficient analysis by adjusting the order of analysis according to the relevance of diseases. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit prioritizes the analysis of diseases with a high relevance.

[0045] The delivery unit can select the optimal delivery method by referring to the medical professional's past medical history at the time of delivery. For example, the delivery unit selects the optimal delivery method based on the medical professional's past medical history. For example, the delivery unit can also prioritize the provision of relevant information from the medical professional's medical history. For example, the delivery unit can customize the delivery method by referring to the medical professional's medical history. This allows the delivery unit to select the optimal delivery method by referring to the medical professional's past medical history. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without using AI. For example, the delivery unit selects the optimal delivery method based on the medical professional's past medical history.

[0046] The information delivery unit can customize the means of delivery based on the healthcare provider's current treatment status at the time of delivery. For example, the information delivery unit can consider the healthcare provider's current treatment status and select the optimal means of delivery. The information delivery unit can also determine the priority of the information to be delivered based on the healthcare provider's treatment status. The information delivery unit can also customize the means of delivery based on the healthcare provider's treatment status. This makes it possible to provide information efficiently by customizing the means of delivery based on the healthcare provider's current treatment status. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without using AI. For example, the information delivery unit considers the healthcare provider's current treatment status and selects the optimal means of delivery.

[0047] The service provider can select the optimal service delivery method at the time of delivery, taking into account the geographical location information of healthcare providers. For example, the service provider can provide region-specific medical information based on the geographical location information of healthcare providers. The service provider can also provide information on nearby medical institutions, taking into account the geographical location information of healthcare providers. The service provider can also provide information on local medical resources, taking into account the geographical location information of healthcare providers. This allows for the provision of region-specific medical information by considering the geographical location information of healthcare providers. Some or all of the above-described processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide region-specific medical information based on the geographical location information of healthcare providers.

[0048] The service provider can analyze the social media activities of healthcare professionals and propose means of delivery at the time of delivery. For example, the service provider can analyze the social media activities of healthcare professionals and provide relevant medical information. For example, the service provider can also provide information on medical topics of interest based on the social media activities of healthcare professionals. For example, the service provider can propose means of delivery based on the social media activities of healthcare professionals. This allows for the provision of information on medical topics of interest by analyzing the social media activities of healthcare professionals. 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 analyze the social media activities of healthcare professionals and provide relevant medical information.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The acquisition unit can acquire the patient's genetic information and provide it to the analysis unit. For example, the acquisition unit can acquire the patient's DNA sequencing data and provide it to the analysis unit. The acquisition unit can also acquire information to assess genetic risk based on the patient's family history, for example. The acquisition unit can also acquire information on the patient's gene mutations and provide it to the analysis unit. This enables more accurate diagnosis by estimating the cause of the disease based on the genetic information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI. For example, the acquisition unit acquires the patient's genetic information and provides it to the analysis unit.

[0051] The analysis unit can analyze a patient's lifestyle data and estimate the cause of their illness. For example, the analysis unit can analyze a patient's dietary records to assess the risk of nutritional deficiencies or excesses. The analysis unit can also analyze a patient's exercise habits to assess the effects of insufficient or excessive exercise. The analysis unit can also analyze a patient's sleep patterns to assess the risk of sleep deprivation or excessive sleep. This allows for a more comprehensive diagnosis by estimating the cause of illness based on lifestyle data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit analyzes a patient's lifestyle data to estimate the cause of their illness.

[0052] The acquisition unit can acquire environmental data of patients and provide it to the analysis unit. For example, the acquisition unit can acquire data about the patient's living environment and provide it to the analysis unit. The acquisition unit can also acquire data about the patient's work environment. The acquisition unit can also acquire data about the air quality and water quality around the patient and provide it to the analysis unit. This makes it possible to evaluate health risks due to environmental factors by estimating the cause of disease based on the environmental data. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI. For example, the acquisition unit acquires environmental data of patients and provides it to the analysis unit.

[0053] The information provider can customize the information it offers based on the medical professional's area of ​​expertise. For example, the provider will prioritize providing urological information to a urologist. Similarly, it can prioritize providing ophthalmological information to an ophthalmologist. The provider can also provide relevant, up-to-date research papers and statistical data according to the medical professional's area of ​​expertise. This allows for more appropriate information to be provided by customizing the information based on the medical professional's area of ​​expertise. Some or all of the processing described above in the information provider may be performed using AI, for example, or without AI. For example, the provider customizes the information it offers based on the medical professional's area of ​​expertise.

[0054] The analysis unit can perform analyses while considering the patient's socioeconomic background. For example, the analysis unit can assess the risk of disease by considering the patient's income and education level. The analysis unit can also estimate the cause of disease by considering, for example, the patient's occupation and living environment. The analysis unit can also assess the risk of disease progression by considering, for example, the patient's social support network. This allows for a more comprehensive estimation of the cause of disease by considering socioeconomic background. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit performs analyses while considering the patient's socioeconomic background.

[0055] The acquisition unit can acquire vital sign data from a patient and provide it to the analysis unit. For example, the acquisition unit can acquire data such as the patient's heart rate, blood pressure, and body temperature and provide it to the analysis unit. The acquisition unit can also acquire data such as the patient's respiratory rate and oxygen saturation. The acquisition unit can also acquire data such as the patient's blood glucose level and cholesterol level and provide it to the analysis unit. This makes it possible to estimate the cause of the disease based on the vital sign data, thereby enabling a more accurate diagnosis. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI. For example, the acquisition unit acquires vital sign data from a patient and provides it to the analysis unit.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The acquisition unit automatically acquires basic patient information, statistical information, and relevant information such as research papers. For example, it acquires the patient's medical history from the electronic medical record, test results from the hospital's laboratory system, and image data from the medical imaging system. All of this information is acquired automatically and provided to the analysis unit. Step 2: The analysis unit analyzes the information acquired by the acquisition unit and estimates the cause of the disease. For example, it estimates the cause of the disease using symptom pattern analysis, statistical estimation, and machine learning algorithms. This allows for highly accurate estimation of the cause of the disease based on the acquired information. Step 3: The provisioning unit provides healthcare professionals with the disease cause estimated by the analysis unit. For example, the estimated disease cause is provided to healthcare professionals via email, a dashboard display, or a mobile app. This allows healthcare professionals to quickly and accurately understand the cause of the disease.

[0058] (Example of form 2) The disease cause estimation system according to an embodiment of the present invention is a system that quickly and accurately estimates the cause of a disease in hospitals where specialist physicians are absent or understaffed. This system automatically acquires and analyzes relevant information such as basic patient information, statistical information, and research papers, estimates the cause of the disease, and provides it to healthcare professionals. For example, the disease cause estimation system acquires the patient's medical history, test results, and image data. For example, the disease cause estimation system collects information required by specialists in urology or ophthalmology. Next, the disease cause estimation system analyzes the acquired information and estimates the cause of the disease. The disease cause estimation system possesses advanced knowledge equivalent to that of a specialist physician and identifies the cause of the disease based on the acquired information. For example, when estimating the cause of a urological disease, the disease cause estimation system analyzes statistical information and research papers related to urology to estimate the cause of the disease. Finally, the disease cause estimation system provides the estimated cause of the disease to healthcare professionals. Healthcare professionals can select appropriate treatment methods based on the cause estimation results provided by the disease cause estimation system. This makes it possible to quickly and accurately estimate the cause of a disease even in hospitals where specialist physicians are absent or understaffed. This enables disease cause estimation systems to quickly and accurately estimate disease causes, even in hospitals where specialist doctors are absent or understaffed.

[0059] The disease cause estimation system according to this embodiment comprises an acquisition unit, an analysis unit, and a provision unit. The acquisition unit automatically acquires relevant information such as basic patient information, statistical information, and research papers. The acquisition unit acquires, for example, the patient's medical history, test results, and image data. For example, the acquisition unit can acquire the patient's medical history from an electronic medical record. The acquisition unit can also acquire test results from a hospital's testing system. Furthermore, the acquisition unit can acquire image data from a medical imaging system. For example, the acquisition unit automatically acquires the patient's medical history from an electronic medical record and provides it to the analysis unit. The acquisition unit automatically acquires test results from a hospital's testing system and provides them to the analysis unit. The acquisition unit automatically acquires image data from a medical imaging system and provides it to the analysis unit. The analysis unit analyzes the information acquired by the acquisition unit and estimates the cause of the disease. For example, the analysis unit estimates the cause of the disease based on the acquired information. For example, the analysis unit can perform symptom pattern analysis to estimate the cause of the disease. The analysis unit can also perform statistical estimation to estimate the cause of the disease. Furthermore, the analysis unit can also estimate the cause of the disease using machine learning algorithms. For example, the analysis unit performs symptom pattern analysis to estimate the cause of the disease. The analysis unit performs statistical estimation to estimate the cause of the disease. The analysis unit estimates the cause of the disease using machine learning algorithms. The provision unit provides the cause of the disease estimated by the analysis unit to healthcare providers. The provision unit provides the estimated cause of the disease to healthcare providers, for example. The provision unit can provide the estimated cause of the disease to healthcare providers, for example, via email. The provision unit can also provide the estimated cause of the disease to healthcare providers using a dashboard display. Furthermore, the provision unit can also provide the estimated cause of the disease to healthcare providers using a mobile app. For example, the provision unit provides the estimated cause of the disease to healthcare providers via email. The provision unit provides the estimated cause of the disease to healthcare providers using a dashboard display. The provision unit provides the estimated cause of the disease to healthcare providers using a mobile app. As a result, the disease cause estimation system according to this embodiment enables rapid and accurate estimation of disease causes even in hospitals where specialist doctors are absent or understaffed.

[0060] The data acquisition unit automatically retrieves basic patient information, statistical information, and relevant information such as publications. Specifically, it acquires patient medical history, test results, and image data. Patient medical history is automatically acquired from the electronic medical record system and includes past medical records, prescription history, and allergy information. Test results are acquired from the hospital's laboratory system and include detailed data such as blood tests, urine tests, and genetic tests. Image data is acquired from the medical imaging system and includes images such as X-rays, CT scans, and MRIs. This data is collected in real time by the data acquisition unit and provided to the analysis unit. By automating the data collection process, the data acquisition unit reduces the burden on medical staff and ensures the accuracy and consistency of the data. The data acquisition unit can also acquire relevant information from external medical databases and academic paper databases. This enables comprehensive data collection that incorporates the latest research findings and statistical information. Furthermore, the data acquisition unit implements encryption technology and access control to ensure data privacy and security. This protects patients' personal information and prevents unauthorized access and leakage of data. Through these functions, the data acquisition unit provides high-quality data that forms the basis of the disease cause estimation system, improving the accuracy and reliability of the analysis unit.

[0061] The analysis unit analyzes the information acquired by the acquisition unit to estimate the cause of the disease. Specifically, it uses symptom pattern analysis, statistical estimation, and machine learning algorithms to estimate the cause of the disease based on the acquired information. In symptom pattern analysis, the patient's symptoms and test results are analyzed in detail and similar cases are identified by comparing them with past data. This makes it possible to estimate the cause of the disease based on specific symptom patterns. In statistical estimation, a statistical model is built based on a large amount of data to evaluate the probability of disease occurrence and risk factors. For example, the cause of the disease is estimated by considering the incidence rate of the disease in a specific age group or gender. In machine learning algorithms, new patient data is analyzed using a model trained on past diagnostic data to estimate the cause of the disease. Advanced algorithms such as deep learning and support vector machines are used for this. The analysis unit combines these methods to perform more accurate estimations of disease causes. Furthermore, the analysis unit can process data in real time and provide results quickly. This allows healthcare professionals to make diagnoses quickly and start appropriate treatment. In addition, the analysis unit can always perform analyses based on the latest knowledge by incorporating past data and the latest research findings. As a result, the analysis unit, as the core of the disease cause estimation system, enables accurate and rapid estimation of disease causes.

[0062] The service provider will provide healthcare professionals with disease causes estimated by the analysis department. Specifically, estimated disease causes will be provided to healthcare professionals via email, dashboard displays, and mobile apps. When using email, the estimated results will be sent to healthcare professionals in the form of a detailed report. The report will include the estimated disease cause, related symptoms and test results, and the data supporting the estimation. When using the dashboard display, healthcare professionals can access a dedicated web portal to view the estimated results in real time. The dashboard includes visual displays using graphs and charts, allowing for intuitive understanding of the information. When using the mobile app, healthcare professionals can view the estimated results anytime, anywhere via their smartphones or tablets. The app is equipped with a push notification function, which will immediately notify them when new estimated results are generated. Through these means, the service provider will provide healthcare professionals with rapid and accurate information. Furthermore, the service provider can collect feedback from healthcare professionals and continuously improve the accuracy and usability of the system. For example, when healthcare professionals comment on or correct the estimated results, the system learns from that feedback and improves the accuracy of future estimations. This allows the service provider to support healthcare professionals in making quick and appropriate diagnoses and treatments, and to maximize the effectiveness of the disease cause estimation system.

[0063] The acquisition unit can acquire patient medical history, test results, image data, etc. For example, the acquisition unit can acquire patient medical history from electronic medical records. For example, the acquisition unit can acquire test results from the hospital's laboratory system. For example, the acquisition unit can acquire image data from a medical imaging system. By acquiring detailed patient information, the accuracy of disease cause estimation is improved. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit automatically acquires patient medical history from electronic medical records and provides it to the analysis unit.

[0064] The analysis unit can estimate the cause of the disease based on the acquired information. For example, the analysis unit can perform symptom pattern analysis to estimate the cause of the disease. The analysis unit can also perform statistical estimation to estimate the cause of the disease. The analysis unit can also use machine learning algorithms to estimate the cause of the disease. This enables accurate diagnosis by estimating the cause of the disease based on the acquired information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit performs symptom pattern analysis to estimate the cause of the disease.

[0065] The service provider can provide healthcare professionals with the estimated cause of the disease. For example, the service provider may provide the estimated cause of the disease to healthcare professionals via email. Alternatively, the service provider may provide the estimated cause of the disease to healthcare professionals using a dashboard display. The service provider may also provide the estimated cause of the disease to healthcare professionals using a mobile app. This allows healthcare professionals to select appropriate treatments by providing them with the estimated cause of the disease. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider may provide the estimated cause of the disease to healthcare professionals via email.

[0066] The analysis unit can analyze statistical information and papers related to urology and ophthalmology to estimate the causes of diseases. For example, the analysis unit can analyze statistical information related to urology to estimate the causes of diseases. The analysis unit can also analyze statistical information related to ophthalmology to estimate the causes of diseases. The analysis unit can also analyze papers related to urology to estimate the causes of diseases. This improves the accuracy of disease cause estimation by analyzing specialized statistical information and papers. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can analyze statistical information related to urology to estimate the causes of diseases.

[0067] The acquisition unit can estimate the patient's emotions and adjust the timing of information acquisition based on the estimated emotions. For example, if the patient is feeling anxious, the acquisition unit may delay information acquisition until the patient is relaxed. For example, if the patient is relaxed, the acquisition unit may acquire information quickly. For example, if the patient is tense, the acquisition unit may acquire information in stages to reduce the burden. This reduces the burden on the patient by adjusting the timing of information acquisition according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit estimates the patient's emotions and adjusts the timing of information acquisition based on the estimated emotions.

[0068] The data acquisition unit can analyze the patient's past medical history and select the optimal data acquisition method. For example, the data acquisition unit can prioritize the acquisition of specific test results from the patient's past medical history. For example, the data acquisition unit can also select necessary image data based on the patient's past medical history. For example, the data acquisition unit can analyze the patient's past medical history and acquire relevant research papers. This allows the optimal information acquisition method to be selected by analyzing the patient's past medical history. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit analyzes the patient's past medical history and selects the optimal data acquisition method.

[0069] The data acquisition unit can filter information based on the patient's current living situation and areas of interest when acquiring it. For example, the data acquisition unit considers the patient's current living situation and prioritizes acquiring relevant information. The data acquisition unit can also filter necessary information based on the patient's areas of interest. For example, the data acquisition unit can determine the priority of information to acquire based on the patient's living situation and areas of interest. This allows for the acquisition of highly relevant information by filtering information based on the patient's living situation and areas of interest. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit determines the priority of information to acquire based on the patient's current living situation and areas of interest.

[0070] The information acquisition unit can estimate the patient's emotions and determine the priority of information to acquire based on the estimated emotions. For example, if the patient is feeling anxious, the information acquisition unit will prioritize acquiring information that provides reassurance. For example, if the patient is relaxed, the information acquisition unit may also prioritize acquiring detailed information. For example, if the patient is tense, the information acquisition unit may also prioritize acquiring concise information. This allows for the acquisition of optimal information for the patient by prioritizing information according to their 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, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information acquisition unit may be performed using AI, for example, or without AI. For example, the information acquisition unit estimates the patient's emotions and determines the priority of information to acquire based on the estimated emotions.

[0071] The acquisition unit can prioritize the acquisition of highly relevant information by considering the patient's geographical location when acquiring information. For example, the acquisition unit can prioritize the acquisition of information on region-specific diseases based on the patient's geographical location. The acquisition unit can also prioritize the acquisition of information on nearby medical institutions by considering the patient's geographical location. The acquisition unit can also prioritize the acquisition of information on local medical resources based on the patient's geographical location. This allows for the priority acquisition of information on region-specific diseases by considering the patient's geographical location. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit prioritizes the acquisition of information on region-specific diseases based on the patient's geographical location.

[0072] The acquisition unit can analyze the patient's social media activity and acquire relevant information when acquiring information. For example, the acquisition unit can analyze the patient's social media activity and acquire information based on health-related posts. For example, the acquisition unit can also acquire information on health topics of interest from the patient's social media activity. For example, the acquisition unit can acquire relevant medical information based on the patient's social media activity. This allows for the acquisition of information on health topics of interest by analyzing the patient's social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit analyzes the patient's social media activity and acquires relevant information.

[0073] The analysis unit can estimate the patient's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the patient is feeling anxious, the analysis unit may use a reassuring presentation. For example, if the patient is relaxed, the analysis unit may provide detailed analysis results. For example, if the patient is tense, the analysis unit may use a concise and easy-to-understand presentation. By adjusting the presentation of the analysis according to the patient's emotions, the analysis results can be provided in a way that is easy for the patient to understand. 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 AI, for example, or without AI. For example, the analysis unit estimates the patient's emotions and adjusts the presentation of the analysis based on the estimated emotions.

[0074] The analysis unit can adjust the level of detail of the analysis based on the importance of the disease during the analysis. For example, the analysis unit performs a detailed analysis for diseases of high importance. For example, the analysis unit can also perform a simplified analysis for diseases of low importance. The analysis unit can also adjust the level of detail of the analysis in stages according to the importance of the disease. This allows for the provision of appropriate analysis results by adjusting the level of detail of the analysis according to the importance of the disease. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit performs a detailed analysis for diseases of high importance.

[0075] The analysis unit can apply different analysis algorithms depending on the disease category during analysis. For example, in the case of a urological disease, the analysis unit applies an analysis algorithm specifically for urology. For example, in the case of an ophthalmic disease, the analysis unit can also apply an analysis algorithm specifically for ophthalmology. The analysis unit can also select the optimal analysis algorithm depending on the disease category. By applying the optimal analysis algorithm according to the disease category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, in the case of a urological disease, the analysis unit applies an analysis algorithm specifically for urology.

[0076] The analysis unit can estimate the patient's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the patient is feeling anxious, the analysis unit can provide a short, concise analysis result. For example, if the patient is relaxed, the analysis unit can also provide a detailed analysis result. For example, if the patient is tense, the analysis unit can also provide a concise and easy-to-understand analysis result. By adjusting the length of the analysis according to the patient's emotions, the optimal analysis result for the patient can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit estimates the patient's emotions and adjusts the length of the analysis based on the estimated emotions.

[0077] The analysis unit can determine the priority of analysis based on the onset time of the disease during the analysis. For example, the analysis unit will prioritize analysis if the onset time of the disease is recent. The analysis unit can also postpone analysis if the onset time of the disease is older. The analysis unit can also adjust the priority of analysis in stages according to the onset time of the disease. This enables rapid analysis by determining the priority of analysis according to the onset time of the disease. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit will prioritize analysis if the onset time of the disease is recent.

[0078] The analysis unit can adjust the order of analysis based on the relevance of diseases during the analysis. For example, the analysis unit can prioritize the analysis of diseases with a high relevance. For example, the analysis unit can postpone the analysis of diseases with a low relevance. The analysis unit can also adjust the order of analysis step by step according to the relevance of diseases. This allows for efficient analysis by adjusting the order of analysis according to the relevance of diseases. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit prioritizes the analysis of diseases with a high relevance.

[0079] The service provider can estimate the patient's emotions and adjust the delivery method based on the estimated emotions. For example, if the patient is feeling anxious, the service provider will deliver information in a reassuring way. For example, if the patient is relaxed, the service provider may also deliver detailed information. For example, if the patient is tense, the service provider may also deliver information in a concise and easy-to-understand way. By adjusting the delivery method according to the patient's emotions, it becomes possible to provide the most appropriate information to the patient. 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 or not using AI. For example, the service provider estimates the patient's emotions and adjusts the delivery method based on the estimated emotions.

[0080] The delivery unit can select the optimal delivery method by referring to the medical professional's past medical history at the time of delivery. For example, the delivery unit selects the optimal delivery method based on the medical professional's past medical history. For example, the delivery unit can also prioritize the provision of relevant information from the medical professional's medical history. For example, the delivery unit can customize the delivery method by referring to the medical professional's medical history. This allows the delivery unit to select the optimal delivery method by referring to the medical professional's past medical history. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without using AI. For example, the delivery unit selects the optimal delivery method based on the medical professional's past medical history.

[0081] The information delivery unit can customize the means of delivery based on the healthcare provider's current treatment status at the time of delivery. For example, the information delivery unit can consider the healthcare provider's current treatment status and select the optimal means of delivery. The information delivery unit can also determine the priority of the information to be delivered based on the healthcare provider's treatment status. The information delivery unit can also customize the means of delivery based on the healthcare provider's treatment status. This makes it possible to provide information efficiently by customizing the means of delivery based on the healthcare provider's current treatment status. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without using AI. For example, the information delivery unit considers the healthcare provider's current treatment status and selects the optimal means of delivery.

[0082] The information provider can estimate the patient's emotions and determine the priority of information delivery based on the estimated emotions. For example, if the patient is feeling anxious, the information provider will prioritize providing reassuring information. For example, if the patient is relaxed, the information provider may also prioritize providing detailed information. For example, if the patient is tense, the information provider may also prioritize providing concise information. This allows for the provision of optimal information to the patient by determining the priority of information delivery according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI or not using AI. For example, the information provider estimates the patient's emotions and determines the priority of information delivery based on the estimated emotions.

[0083] The service provider can select the optimal service delivery method at the time of delivery, taking into account the geographical location information of healthcare providers. For example, the service provider can provide region-specific medical information based on the geographical location information of healthcare providers. The service provider can also provide information on nearby medical institutions, taking into account the geographical location information of healthcare providers. The service provider can also provide information on local medical resources, taking into account the geographical location information of healthcare providers. This allows for the provision of region-specific medical information by considering the geographical location information of healthcare providers. Some or all of the above-described processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide region-specific medical information based on the geographical location information of healthcare providers.

[0084] The service provider can analyze the social media activities of healthcare professionals and propose means of delivery at the time of delivery. For example, the service provider can analyze the social media activities of healthcare professionals and provide relevant medical information. For example, the service provider can also provide information on medical topics of interest based on the social media activities of healthcare professionals. For example, the service provider can propose means of delivery based on the social media activities of healthcare professionals. This allows for the provision of information on medical topics of interest by analyzing the social media activities of healthcare professionals. 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 analyze the social media activities of healthcare professionals and provide relevant medical information.

[0085] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0086] The acquisition unit can acquire the patient's genetic information and provide it to the analysis unit. For example, the acquisition unit can acquire the patient's DNA sequencing data and provide it to the analysis unit. The acquisition unit can also acquire information to assess genetic risk based on the patient's family history, for example. The acquisition unit can also acquire information on the patient's gene mutations and provide it to the analysis unit. This enables more accurate diagnosis by estimating the cause of the disease based on the genetic information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI. For example, the acquisition unit acquires the patient's genetic information and provides it to the analysis unit.

[0087] The analysis unit can analyze a patient's lifestyle data and estimate the cause of their illness. For example, the analysis unit can analyze a patient's dietary records to assess the risk of nutritional deficiencies or excesses. The analysis unit can also analyze a patient's exercise habits to assess the effects of insufficient or excessive exercise. The analysis unit can also analyze a patient's sleep patterns to assess the risk of sleep deprivation or excessive sleep. This allows for a more comprehensive diagnosis by estimating the cause of illness based on lifestyle data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit analyzes a patient's lifestyle data to estimate the cause of their illness.

[0088] The information provider can estimate the patient's emotions and adjust the format of the information provided based on the estimated emotions. For example, if the patient is feeling anxious, the information provider can provide information in reassuring and gentle language. If the patient is relaxed, the information provider can also provide detailed information. If the patient is tense, the information provider can also provide information in a concise and easy-to-understand format. By adjusting the format of the information according to the patient's emotions, it becomes possible to provide information that is easy for the patient to understand. 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 provider may be performed using AI, for example, or not using AI. For example, the information provider estimates the patient's emotions and adjusts the format of the information provided based on the estimated emotions.

[0089] The acquisition unit can acquire environmental data of patients and provide it to the analysis unit. For example, the acquisition unit can acquire data about the patient's living environment and provide it to the analysis unit. The acquisition unit can also acquire data about the patient's work environment. The acquisition unit can also acquire data about the air quality and water quality around the patient and provide it to the analysis unit. This makes it possible to evaluate health risks due to environmental factors by estimating the cause of disease based on the environmental data. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI. For example, the acquisition unit acquires environmental data of patients and provides it to the analysis unit.

[0090] The analysis unit can estimate the patient's emotions and determine the priority of analysis based on the estimated emotions. For example, if the patient is feeling anxious, the analysis unit will prioritize analyzing information that provides reassurance. If the patient is relaxed, the analysis unit may also prioritize analyzing detailed information. If the patient is tense, the analysis unit may also prioritize analyzing concise information. By determining the priority of analysis according to the patient's emotions, the system can provide the patient with the most optimal analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit estimates the patient's emotions and determines the priority of analysis based on the estimated emotions.

[0091] The information provider can customize the information it offers based on the medical professional's area of ​​expertise. For example, the provider will prioritize providing urological information to a urologist. Similarly, it can prioritize providing ophthalmological information to an ophthalmologist. The provider can also provide relevant, up-to-date research papers and statistical data according to the medical professional's area of ​​expertise. This allows for more appropriate information to be provided by customizing the information based on the medical professional's area of ​​expertise. Some or all of the processing described above in the information provider may be performed using AI, for example, or without AI. For example, the provider customizes the information it offers based on the medical professional's area of ​​expertise.

[0092] The acquisition unit can estimate the patient's emotions and adjust the level of detail of the information acquired based on the estimated emotions. For example, if the patient is feeling anxious, the acquisition unit can acquire concise and to-the-point information. If the patient is relaxed, for example, the acquisition unit can acquire detailed information. If the patient is tense, for example, the acquisition unit can acquire information in stages to reduce the burden. By adjusting the level of detail of the information according to the patient's emotions, it becomes possible to acquire information that is optimal for the patient. 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 acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit estimates the patient's emotions and adjusts the level of detail of the information acquired based on the estimated emotions.

[0093] The analysis unit can perform analyses while considering the patient's socioeconomic background. For example, the analysis unit can assess the risk of disease by considering the patient's income and education level. The analysis unit can also estimate the cause of disease by considering, for example, the patient's occupation and living environment. The analysis unit can also assess the risk of disease progression by considering, for example, the patient's social support network. This allows for a more comprehensive estimation of the cause of disease by considering socioeconomic background. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit performs analyses while considering the patient's socioeconomic background.

[0094] The information provider can estimate the patient's emotions and adjust the timing of information delivery based on the estimated emotions. For example, if the patient is feeling anxious, the information provider may delay providing information until the patient is relaxed. If the patient is relaxed, the information provider may also provide information quickly. If the patient is tense, the information provider may provide information gradually to reduce the burden. By adjusting the timing of information delivery according to the patient's emotions, it becomes possible to provide information that is optimal for the patient. 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 provider may be performed using AI or not using AI. For example, the information provider estimates the patient's emotions and adjusts the timing of information delivery based on the estimated emotions.

[0095] The acquisition unit can acquire vital sign data from a patient and provide it to the analysis unit. For example, the acquisition unit can acquire data such as the patient's heart rate, blood pressure, and body temperature and provide it to the analysis unit. The acquisition unit can also acquire data such as the patient's respiratory rate and oxygen saturation. The acquisition unit can also acquire data such as the patient's blood glucose level and cholesterol level and provide it to the analysis unit. This makes it possible to estimate the cause of the disease based on the vital sign data, thereby enabling a more accurate diagnosis. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI. For example, the acquisition unit acquires vital sign data from a patient and provides it to the analysis unit.

[0096] The following briefly describes the processing flow for example form 2.

[0097] Step 1: The acquisition unit automatically acquires basic patient information, statistical information, and relevant information such as research papers. For example, it acquires the patient's medical history from the electronic medical record, test results from the hospital's laboratory system, and image data from the medical imaging system. All of this information is acquired automatically and provided to the analysis unit. Step 2: The analysis unit analyzes the information acquired by the acquisition unit and estimates the cause of the disease. For example, it estimates the cause of the disease using symptom pattern analysis, statistical estimation, and machine learning algorithms. This allows for highly accurate estimation of the cause of the disease based on the acquired information. Step 3: The provisioning unit provides healthcare professionals with the disease cause estimated by the analysis unit. For example, the estimated disease cause is provided to healthcare professionals via email, a dashboard display, or a mobile app. This allows healthcare professionals to quickly and accurately understand the cause of the disease.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] Each of the multiple elements described above, including the acquisition unit, analysis unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit acquires the patient's medical history and test results using the camera 42 and communication I / F 44 of the smart device 14 and provides them to the analysis unit via the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and estimates the cause of the disease based on the acquired information. The provision unit is implemented, for example, by the control unit 46A of the smart device 14 and provides the estimated cause of the disease to healthcare providers via email, dashboard display, or mobile app. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0102] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements described above, including the acquisition unit, analysis unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit acquires the patient's medical history and test results using the camera 42 and communication I / F 44 of the smart glasses 214 and provides them to the analysis unit via the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and estimates the cause of the disease based on the acquired information. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides the estimated cause of the disease to healthcare providers via email, dashboard display, or mobile app. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0118] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0119] 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.

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

[0121] The 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.

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

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

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

[0125] Figure 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.

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

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

[0128] In the 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.

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

[0130] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0132] The data processing system 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.

[0133] Each of the multiple elements described above, including the acquisition unit, analysis unit, and provision unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit acquires the patient's medical history and test results using the camera 42 and communication I / F 44 of the headset terminal 314 and provides them to the analysis unit via the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and estimates the cause of the disease based on the acquired information. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314 and provides the estimated cause of the disease to healthcare providers via email, dashboard display, or mobile app. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0134] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0135] 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.

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

[0137] The 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.

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

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS 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).

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

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the acquisition unit, analysis unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit acquires the patient's medical history and test results using the camera 42 and communication I / F 44 of the robot 414 and provides them to the analysis unit via the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and estimates the cause of the disease based on the acquired information. The provision unit is implemented, for example, by the control unit 46A of the robot 414 and provides the estimated cause of the disease to healthcare providers via email, dashboard display, or mobile app. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] (Note 1) An acquisition unit that automatically retrieves basic patient information, statistical information, and related information such as research papers, An analysis unit analyzes the information acquired by the acquisition unit and estimates the cause of the disease, The system comprises a provisioning unit that provides medical professionals with the cause of the disease estimated by the analysis unit. A system characterized by the following features. (Note 2) The acquisition unit is, Obtain patient's medical history, test results, imaging data, etc. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the information obtained, the cause of the disease is estimated. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Provide healthcare professionals with the estimated cause of the illness. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Analyze statistical information and research papers related to urology and ophthalmology to estimate the causes of diseases. The system described in Appendix 1, characterized by the features described herein. (Note 6) The acquisition unit is, The system estimates the patient's emotions and adjusts the timing of information acquisition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, Analyze the patient's past medical history and select the most appropriate method for obtaining the information. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, When acquiring information, filtering is performed based on the patient's current living situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, The system estimates the patient's emotions and prioritizes the information to be acquired based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, When acquiring information, the system prioritizes the acquisition of highly relevant information, taking into account the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, When acquiring information, the patient's social media activity is analyzed to obtain relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the patient's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the severity of the disease. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the disease category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the patient's emotions and adjusts the length of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on the onset time of the disease. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the association with the disease. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, We estimate the patient's emotions and adjust the delivery method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing the service, the optimal method of delivery is selected by referring to the healthcare provider's past medical history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing the service, the method of delivery will be customized based on the healthcare provider's current clinical situation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, The system estimates the patient's emotions and determines the priority of services based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing information, the optimal delivery method will be selected, taking into account the geographical location of the healthcare provider. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing information, we analyze the social media activity of healthcare professionals and propose methods for providing the information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0170] 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. An acquisition unit that automatically retrieves basic patient information, statistical information, and related information such as research papers, An analysis unit analyzes the information acquired by the acquisition unit and estimates the cause of the disease, The system comprises a provisioning unit that provides medical professionals with the cause of the disease estimated by the analysis unit. A system characterized by the following features.

2. The acquisition unit is, Obtain patient's medical history, test results, imaging data, etc. The system according to feature 1.

3. The aforementioned analysis unit, Based on the information obtained, the cause of the disease is estimated. The system according to feature 1.

4. The aforementioned supply unit is, Provide healthcare professionals with the estimated cause of the illness. The system according to feature 1.

5. The aforementioned analysis unit, Analyze statistical information and research papers related to urology and ophthalmology to estimate the causes of diseases. The system according to feature 1.

6. The acquisition unit is, The system estimates the patient's emotions and adjusts the timing of information acquisition based on the estimated emotions. The system according to feature 1.

7. The acquisition unit is, Analyze the patient's past medical history and select the most appropriate method for obtaining the information. The system according to feature 1.

8. The acquisition unit is, When acquiring information, filtering is performed based on the patient's current living situation and areas of interest. The system according to feature 1.

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