System

Abstract

To provide a system that presents other possibilities and optimal treatment methods against doctors' diagnosis results.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, a presentation unit, and a proposal unit. The reception unit inputs a diagnosis result. The analysis unit analyzes the diagnosis result input by the reception unit. The presentation unit presents other possibilities based on the diagnosis result analyzed by the analysis unit. The proposal unit proposes a treatment method based on the other possibilities presented by the presentation unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques do not adequately present alternative possibilities or optimal treatment methods to a doctor's diagnosis, and there is room for improvement.

[0005] The system according to the embodiment aims to present alternative possibilities and optimal treatment methods in response to the doctor's diagnosis results. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an analyzing unit, a presenting unit, and a proposing unit. The receiving unit inputs a diagnosis result. The analyzing unit analyzes the diagnosis result input by the receiving unit. The presenting unit presents other possibilities based on the diagnosis result analyzed by the analyzing unit. The proposing unit proposes a treatment method based on the other possibilities presented by the presenting unit. Effect of the Invention

[0007] The system according to the embodiment can present alternative possibilities and optimal treatment methods to the doctor's diagnosis results. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The diagnostic support system according to the embodiment of the present invention is a system that checks for possibilities and oversights other than the diagnosis result given by the doctor himself and proposes the optimal treatment method. This diagnostic support system allows the patient to know other possibilities and treatment methods without getting a second opinion. It can also cover the lack of experience of on-call doctors and trainees who are not specialists. For example, a doctor inputs a diagnosis result. Next, the generating AI analyzes the diagnosis result and checks for other possibilities and oversights. The generating AI evaluates the validity of the diagnosis result based on past diagnostic data and medical literature and presents other possibilities. For example, if a doctor diagnoses pneumonia, the generating AI can present the possibility of other respiratory diseases. Furthermore, the generating AI proposes the optimal treatment method. The generating AI selects the optimal treatment method taking into account the patient's medical history and current symptoms. For example, if a patient is allergic to antibiotics, the generating AI can propose an alternative treatment method. This system allows the patient to know other possibilities and treatment methods without getting a second opinion. For example, even if a patient lives far away or does not have time to get a second opinion, the patient can use this system to know other possibilities and treatment methods. It can also make up for the lack of experience of doctors on call who are not specialists or trainees. For example, even if a doctor on call examines a patient outside of his / her specialty, he / she can use this system to learn about other possibilities and treatment methods. This enables appropriate medical care to be provided to patients. This allows the diagnosis support system to check for possibilities other than the diagnosis that the doctor himself / herself has made, or for oversights, and propose the optimal treatment method.

[0029] The diagnosis support system according to the embodiment includes a reception unit, an analysis unit, a presentation unit, and a suggestion unit. The reception unit receives a diagnosis result input by a doctor. The diagnosis result includes, for example, numerical data, text data, image data, and the like, but is not limited to such examples. The reception unit can acquire the diagnosis result from, for example, an electronic medical record system. The reception unit can also receive a diagnosis result manually input by a doctor. The analysis unit uses a generation AI to analyze the diagnosis result input by the reception unit. The analysis is performed, for example, using statistical analysis or a machine learning algorithm, but is not limited to such examples. For example, the analysis unit evaluates the validity of the diagnosis result by referring to past diagnosis data and medical literature based on the diagnosis result. The presentation unit presents other possibilities based on the diagnosis result analyzed by the analysis unit. The other possibilities include, for example, the possibility of other diseases and treatment options, but are not limited to such examples. For example, when a doctor diagnoses pneumonia, the presentation unit can present the possibility of other respiratory diseases. The proposal unit proposes an optimal treatment method based on the other possibilities presented by the presentation unit. The suggestions include, but are not limited to, treatment methods such as drug therapy, surgical therapy, and rehabilitation. For example, the suggestion unit selects an optimal treatment method in consideration of the patient's medical history and current symptoms. In this way, the diagnosis support system according to the embodiment can check whether there are any possibilities or oversights other than the diagnosis result given by the doctor himself, and suggest an optimal treatment method.

[0030] The reception unit is a unit in which a doctor inputs a diagnosis result. The diagnosis result includes, but is not limited to, for example, numerical data, text data, and image data. The reception unit can, for example, obtain the diagnosis result from an electronic medical record system. The reception unit can also receive a diagnosis result manually input by a doctor. Specifically, data is obtained from the electronic medical record system using a medical information exchange standard such as HL7 or FHIR. This ensures data compatibility between different systems and enables smooth data linkage. In the case of manual input, a doctor inputs a diagnosis result through a dedicated interface. This interface is designed with an emphasis on ease of use, and is equipped with sliders and drop-down menus for inputting numerical data and an auto-completion function for inputting text data. For inputting image data, a drag-and-drop function or a camera function can be used to quickly and accurately upload images. In addition, the reception unit also has a function of checking the consistency of the input data and notifying the doctor if there is an inconsistency. This ensures the accuracy of the data and facilitates subsequent analysis processing.

[0031] The analysis unit uses the generation AI to analyze the diagnosis results input by the reception unit. The analysis is performed, for example, using statistical analysis or machine learning algorithms, but is not limited to such examples. Specifically, the generation AI analyzes the text data using natural language processing technology and analyzes the image data using image recognition technology. For example, in the analysis of the text data, medical literature and past diagnostic data are referred to to evaluate the validity of the diagnostic result. The generation AI extracts keywords and phrases related to the diagnostic result, and evaluates the reliability of the diagnostic result based on them. In addition, in the analysis of the image data, deep learning technology is used to identify abnormalities in the image. For example, signs of pneumonia are detected from a chest X-ray image, and the results are presented to the doctor. Furthermore, the analysis unit uses statistical analysis to evaluate the statistical significance of the diagnostic result. For example, it statistically verifies whether a specific symptom or test result is associated with other diseases, and evaluates the validity of the diagnostic result based on the results. As a result, the analysis unit can comprehensively analyze various data input from the reception unit and increase the reliability of the diagnostic result.

[0032] The presentation unit presents other possibilities based on the diagnosis results analyzed by the analysis unit. The other possibilities include, but are not limited to, the possibility of other diseases and treatment options. Specifically, the presentation unit provides information to the doctor in a visually easy-to-understand format based on the results analyzed by the generation AI. For example, an overview of the diagnosis results is displayed in a dashboard format, and other disease possibilities and treatment options are shown in graphs and charts. The presentation unit also has a function of highlighting important information so that the doctor can make a quick decision. For example, if a patient is diagnosed with pneumonia, the possibility of other respiratory diseases is displayed in different colors, and data and literature on which each possibility is based are provided as links. In addition, the presentation unit provides detailed analysis results and access links to related medical literature in case the doctor needs additional information. This allows the doctor to quickly and accurately consider other possibilities and obtain information to make an optimal diagnosis.

[0033] The suggestion unit proposes an optimal treatment method based on other possibilities presented by the suggestion unit. The suggestions include, but are not limited to, treatment methods such as drug therapy, surgical therapy, and rehabilitation. Specifically, the suggestion unit selects the optimal treatment method based on the data analyzed by the generation AI, taking into consideration the patient's medical history and current symptoms. For example, if a patient is diagnosed with pneumonia, the suggestion unit refers to past treatment data and the latest medical guidelines to propose the optimal antibiotic selection and dosage. In addition, if surgery is required, the suggestion unit evaluates the risk and success rate of the surgery and proposes the optimal surgical method. Furthermore, the suggestion unit can also propose the need for rehabilitation and a specific rehabilitation plan. For example, as a post-surgery rehabilitation plan, it proposes specific exercise therapy and nutritional management methods to support the patient's early recovery. The suggestion unit provides these suggestions to the doctor in a visually easy-to-understand format, helping the doctor to quickly select the optimal treatment method. As a result, the suggestion unit can check whether there are any possibilities or oversights other than the diagnosis result given by the doctor, and propose the optimal treatment method.

[0034] The analysis unit may include an evaluation unit that evaluates the validity of the diagnostic result based on past diagnostic data or medical literature. The evaluation unit evaluates the validity of the diagnostic result based on, for example, past diagnostic data. Past diagnostic data includes, for example, electronic medical record data and medical records, but is not limited to such examples. The evaluation unit may also evaluate the validity of the diagnostic result based on medical literature. Medical literature includes, for example, academic papers and guidelines, but is not limited to such examples. By evaluating the validity of the diagnostic result, it is possible to prevent oversight. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit may perform the evaluation using an AI model that inputs past diagnostic data or medical literature and evaluates the validity of the diagnostic result.

[0035] The suggestion unit may include a consideration unit that considers the patient's medical history or current symptoms. The consideration unit, for example, proposes an optimal treatment method based on the patient's medical history. The medical history includes, for example, past diseases, surgical history, allergy information, etc., but is not limited to such examples. The consideration unit may also propose an optimal treatment method based on current symptoms. The current symptoms include, for example, the patient's chief complaint, physical findings, test results, etc., but are not limited to such examples. This makes it possible to propose an optimal treatment method according to the individual situation of the patient. Part or all of the above-mentioned processing in the consideration unit may be performed, for example, using AI, or may be performed without using AI. For example, the consideration unit may make a proposal using an AI model that inputs the patient's medical history and current symptoms and proposes an optimal treatment method.

[0036] The suggestion unit can suggest an alternative treatment method if the patient is allergic to antibiotics. The suggestion unit, for example, suggests an alternative treatment method if the patient is allergic to antibiotics. Alternative treatment methods include, but are not limited to, drug therapies other than antibiotics and non-drug therapies. This makes it possible to provide an appropriate treatment method even for patients with allergies. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can make suggestions using an AI model that inputs allergy information of the patient and suggests alternative treatment methods.

[0037] The presentation unit can present the possibility of other respiratory diseases when the doctor diagnoses pneumonia. For example, when the doctor diagnoses pneumonia, the presentation unit presents the possibility of other respiratory diseases. Examples of other respiratory diseases include, but are not limited to, bronchitis, pulmonary tuberculosis, and chronic obstructive pulmonary disease (COPD). This makes it possible to prevent oversight of diagnosis and to consider other possibilities. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can make a presentation using an AI model that presents the possibility of other respiratory diseases based on the diagnosis result input by the doctor.

[0038] The support unit can cover the lack of experience of on-call doctors or trainees who are not specialists. For example, the support unit provides a diagnostic support tool when a on-call doctor who is not a specialist makes a diagnosis. The diagnostic support tool includes, but is not limited to, for example, a diagnostic algorithm or a consultation system. The support unit can also provide an educational program or a supervision system when a trainee makes a diagnosis. This enables even a doctor with inexperience to make an appropriate diagnosis and propose treatment. A part or all of the above-mentioned processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit can provide support using an AI model that supports the diagnosis of on-call doctors or trainees who are not specialists.

[0039] The reception unit can analyze the doctor's past diagnostic history and select the optimal input method. For example, the reception unit preferentially suggests an input method (such as voice or text) that the doctor has frequently used in the past. The reception unit can also automatically display input items related to a specific diagnostic result from the doctor's past diagnostic history. Furthermore, the reception unit can analyze the doctor's past diagnostic history and suggest an optimal input order. This makes it possible to provide an optimal input method based on the doctor's past diagnostic history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the doctor's past diagnostic history data to the generation AI and cause the generation AI to select the optimal input method.

[0040] The reception unit can perform filtering based on the doctor's specialty and experience when inputting the diagnosis result. For example, the reception unit preferentially displays only the diagnosis result related to the doctor's specialty. The reception unit can also customize the input items of the diagnosis result based on the doctor's experience. Furthermore, the reception unit can filter the input items of the diagnosis result based on the doctor's specialty and experience and omit unnecessary items. This enables filtering based on the doctor's specialty and experience. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the doctor's specialty and experience data to the generation AI and cause the generation AI to perform filtering.

[0041] When inputting a diagnosis result, the reception unit can preferentially input a highly relevant diagnosis result in consideration of the geographical location information of the doctor. For example, when the doctor is examining a patient in a specific area, the reception unit preferentially inputs a diagnosis result related to the area. The reception unit can also preferentially input a diagnosis result related to a disease specific to the area based on the geographical location information of the doctor. Furthermore, the reception unit can also preferentially input a diagnosis result based on the medical resources of the area in consideration of the geographical location information of the doctor. This enables preferential input of a diagnosis result based on geographical location information. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the geographical location information data of the doctor to the generation AI and cause the generation AI to execute the preferential input of a highly relevant diagnosis result.

[0042] The reception unit can analyze the doctor's social media activity and input the related diagnosis result when inputting the diagnosis result. For example, the reception unit preferentially inputs the diagnosis result related to the disease of interest from the doctor's social media activity. The reception unit can also analyze the doctor's social media activity and preferentially input the diagnosis result related to the trend. Furthermore, the reception unit can also preferentially input the diagnosis result related to the specialty field based on the doctor's social media activity. This makes it possible to input the diagnosis result based on the social media activity. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the doctor's social media activity data to the generation AI and cause the generation AI to execute the input of the related diagnosis result.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the diagnostic result during the analysis. For example, the analysis unit performs a detailed analysis on an important diagnostic result. The analysis unit can also perform a concise analysis on a general diagnostic result. Furthermore, the analysis unit can also perform a quick analysis on a diagnostic result with high urgency. This makes it possible to adjust the level of detail of the analysis according to the importance of the diagnostic result. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the importance data of the diagnostic result to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the diagnosis result during analysis. For example, the analysis unit applies a dedicated analysis algorithm to respiratory diseases. The analysis unit can also apply a dedicated analysis algorithm to heart diseases. Furthermore, the analysis unit can also apply a dedicated analysis algorithm to digestive diseases. This makes it possible to apply an analysis algorithm depending on the category of the diagnosis result. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the diagnosis result to the generation AI and cause the generation AI to apply the analysis algorithm.

[0045] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the diagnostic results. For example, the analysis unit prioritizes analysis of diagnostic results with high urgency. The analysis unit can also prioritize analysis of diagnostic results submitted earlier. Furthermore, the analysis unit can also prioritize analysis of diagnostic results submitted more recently. This makes it possible to determine the priority of analysis based on the submission time. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission time data of the diagnostic results to the generation AI and cause the generation AI to determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the diagnostic results during analysis. For example, the analysis unit preferentially analyzes diagnostic results with high relevance. The analysis unit can also postpone diagnostic results with low relevance. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the diagnostic results. This makes it possible to adjust the order of analysis based on the relevance of the diagnostic results. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the diagnostic results to the generation AI and cause the generation AI to adjust the order of analysis.

[0047] The presentation unit can improve the accuracy of presentation by considering the interrelationships of diagnostic results at the time of presentation. For example, the presentation unit analyzes the interrelationships of diagnostic results and presents related information together. The presentation unit can also present important information preferentially by considering the interrelationships of diagnostic results. Furthermore, the presentation unit can present information in a visually easy-to-understand format based on the interrelationships of diagnostic results. This makes it possible to improve the accuracy of presentation based on the interrelationships of diagnostic results. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI or may be performed without using AI. For example, the presentation unit can input interrelationship data of diagnostic results to the generation AI and cause the generation AI to improve the accuracy of presentation.

[0048] The presentation unit can present the diagnostic results while taking into consideration the attribute information of the person who submitted the diagnostic results. For example, when the person who submitted the diagnostic results is a specialist, the presentation unit presents detailed information. Also, when the person who submitted the diagnostic results is a trainee, the presentation unit can present concise information. Furthermore, the presentation unit can present appropriate information based on the attribute information of the person who submitted the diagnostic results. This enables presentation based on the attribute information of the person who submitted the diagnostic results. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input attribute information data of the person who submitted the diagnostic results to the generation AI and cause the generation AI to perform the presentation.

[0049] The presentation unit can present the diagnostic results while taking into consideration the geographical distribution of the diagnostic results. For example, the presentation unit analyzes the geographical distribution of the diagnostic results and presents information specific to the region. The presentation unit can also present related information preferentially, taking into consideration the geographical distribution of the diagnostic results. Furthermore, the presentation unit can present information in a visually easy-to-understand format based on the geographical distribution of the diagnostic results. This makes it possible to present diagnostic results based on the geographical distribution. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input geographical distribution data of the diagnostic results to the generation AI and cause the generation AI to execute the presentation.

[0050] The presentation unit can improve the accuracy of presentation by referring to literature related to the diagnostic result at the time of presentation. The presentation unit, for example, refers to literature related to the diagnostic result and presents detailed information. The presentation unit can also present important information preferentially based on literature related to the diagnostic result. Furthermore, the presentation unit can also present information in a visually easy-to-understand format by referring to literature related to the diagnostic result. This makes it possible to present the diagnostic result based on the related literature. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input literature data related to the diagnostic result to the generation AI and cause the generation AI to improve the accuracy of presentation.

[0051] The suggestion unit can adjust the level of detail of the proposal based on the importance of the treatment method at the time of proposal. For example, the suggestion unit makes a detailed proposal for an important treatment method. The suggestion unit can also make a concise proposal for a general treatment method. Furthermore, the suggestion unit can also make a quick proposal for a treatment method with high urgency. This makes it possible to adjust the level of detail of the proposal according to the importance of the treatment method. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the importance data of the treatment method to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0052] The suggestion unit can apply different proposal algorithms depending on the category of the treatment method at the time of proposal. For example, the suggestion unit applies a dedicated proposal algorithm to respiratory diseases. The suggestion unit can also apply a dedicated proposal algorithm to heart diseases. Furthermore, the suggestion unit can also apply a dedicated proposal algorithm to digestive diseases. This makes it possible to apply a proposal algorithm according to the category of the treatment method. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input category data of the treatment method to the generation AI and cause the generation AI to apply the proposal algorithm.

[0053] At the time of proposal, the suggestion unit can determine the priority of the proposal based on the submission time of the treatment method. For example, the suggestion unit preferentially proposes a treatment method with high urgency. The suggestion unit can also preferentially propose a treatment method submitted earlier. Furthermore, the suggestion unit can also preferentially propose a treatment method submitted more recently. This makes it possible to determine the priority of the proposal based on the submission time. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the submission time data of the treatment method to the generation AI and cause the generation AI to execute the determination of the priority of the proposal.

[0054] The suggestion unit can adjust the order of suggestions based on the relevance of the treatment methods at the time of suggestion. For example, the suggestion unit preferentially suggests highly relevant treatment methods. The suggestion unit can also postpone less relevant treatment methods. Furthermore, the suggestion unit can dynamically adjust the order of suggestions based on the relevance of the treatment methods. This makes it possible to adjust the order of suggestions based on the relevance of the treatment methods. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input relevance data of the treatment methods to the generation AI and cause the generation AI to adjust the order of suggestions.

[0055] The evaluation unit can improve the accuracy of the evaluation by referring to past diagnostic data at the time of evaluation. The evaluation unit can, for example, evaluate the validity of the diagnostic result based on the past diagnostic data. The evaluation unit can also improve the accuracy of the diagnostic result by referring to the past diagnostic data. Furthermore, the evaluation unit can analyze the past diagnostic data and adjust the evaluation criteria of the diagnostic result. This makes it possible to improve the accuracy of the evaluation based on the past diagnostic data. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can input the past diagnostic data to the generation AI and cause the generation AI to improve the accuracy of the evaluation.

[0056] The evaluation unit can improve the accuracy of the evaluation by referring to the literature related to the diagnostic result at the time of evaluation. The evaluation unit, for example, refers to literature related to the diagnostic result and performs a detailed evaluation. The evaluation unit can also prioritize evaluation of important evaluation items based on literature related to the diagnostic result. Furthermore, the evaluation unit can also display the evaluation result in a visually easy-to-understand format by referring to literature related to the diagnostic result. This makes it possible to improve the accuracy of the evaluation based on the related literature. A part or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the literature data related to the diagnostic result to the generation AI and cause the generation AI to improve the accuracy of the evaluation.

[0057] The consideration unit can improve the accuracy of the consideration by referring to the patient's past medical history at the time of consideration. The consideration unit can consider the validity of the diagnosis result based on the patient's past medical history, for example. The consideration unit can also improve the accuracy of the diagnosis result by referring to the patient's past medical history. Furthermore, the consideration unit can analyze the patient's past medical history and adjust the consideration criteria for the diagnosis result. This makes it possible to improve the accuracy of the consideration based on the patient's past medical history. A part or all of the above-mentioned processing in the consideration unit may be performed using, for example, AI, or may be performed without using AI. For example, the consideration unit can input the patient's past medical history data to the generation AI and cause the generation AI to improve the accuracy of the consideration.

[0058] The consideration unit can improve the accuracy of the consideration by referring to the patient's related literature during the consideration. The consideration unit, for example, refers to literature related to the patient's medical history and performs detailed consideration. The consideration unit can also prioritize important consideration items based on literature related to the patient's medical history. Furthermore, the consideration unit can also display the consideration results in a visually easy-to-understand format by referring to literature related to the patient's medical history. This makes it possible to improve the accuracy of the consideration based on the related literature. A part or all of the above-mentioned processing in the consideration unit may be performed using, for example, AI, or may be performed without using AI. For example, the consideration unit can input the patient's related literature data to the generation AI and cause the generation AI to improve the accuracy of the consideration.

[0059] The support unit can improve the accuracy of the support by referring to the doctor's past diagnostic history at the time of support. The support unit supports the validity of the diagnosis result based on the doctor's past diagnostic history, for example. The support unit can also improve the accuracy of the diagnosis result by referring to the doctor's past diagnostic history. Furthermore, the support unit can analyze the doctor's past diagnostic history and adjust the support criteria for the diagnosis result. This makes it possible to improve the accuracy of the support based on the past diagnostic history. A part or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input the doctor's past diagnostic history data to the generation AI and cause the generation AI to improve the accuracy of the support.

[0060] The support unit can select the optimal support method in consideration of the geographical location information of the doctor when providing support. For example, when the doctor is examining a patient in a specific area, the support unit preferentially provides a support method related to the area. The support unit can also provide a support method related to a disease specific to the area based on the geographical location information of the doctor. Furthermore, the support unit can also provide a support method based on the medical resources of the area in consideration of the geographical location information of the doctor. This makes it possible to select the optimal support method based on the geographical location information. A part or all of the above-mentioned processing in the support unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the support unit can input the geographical location information data of the doctor to the generation AI and cause the generation AI to select the optimal support method.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The diagnostic support system can further collect lifestyle data of the patient and use the analysis unit to evaluate the validity of the diagnostic results. For example, data on the patient's diet, exercise, sleep patterns, etc. can be collected, and the validity of the diagnostic results can be evaluated based on this data. The system can also evaluate the risk of certain diseases and suggest preventive measures based on the lifestyle data. Furthermore, the system can monitor the effectiveness of treatment methods and adjust treatment plans as necessary based on the lifestyle data. This enables comprehensive diagnosis and treatment based on the patient's lifestyle.

[0063] The analysis unit can further analyze the genetic information of the patient and evaluate the validity of the diagnostic results. For example, the risk of a particular disease can be evaluated based on the genetic data of the patient. Also, based on the genetic information, personalized medicine can be provided and optimal treatment methods can be proposed. Furthermore, based on the genetic information, the effectiveness and side effects of drug therapy can be predicted and the treatment plan can be adjusted. This enables highly accurate diagnosis and treatment based on genetic information.

[0064] The proposal unit can further take into account the social background of the patient and propose the most suitable treatment method. For example, if the patient is in an economically difficult situation, it can propose a low-cost treatment method. Also, if the patient does not have family support, it can suggest local support services. Furthermore, it can adjust the treatment plan taking into account the social background of the patient. This makes it possible to provide comprehensive medical care that is suited to the patient's social background.

[0065] The presentation unit can further evaluate the reliability of the diagnostic result and preferentially present highly reliable information. For example, in order to evaluate the reliability of the diagnostic result, multiple data sources can be referenced and matching information can be preferentially presented. In addition, in order to evaluate the reliability of the diagnostic result, the quality and reliability of the data can be evaluated. Furthermore, the reliability of the diagnostic result can be evaluated and information with low reliability can be filtered. This makes it possible to provide diagnosis and treatment based on highly reliable information.

[0066] The support unit can further evaluate the expertise of the doctor and provide support based on the expertise. For example, in order to evaluate the expertise of the doctor, past diagnosis history and academic papers can be referred to and support based on the expertise can be provided. In addition, in order to evaluate the expertise of the doctor, training and educational programs related to the specialty can be provided. Furthermore, the support unit can evaluate the expertise of the doctor and support diagnosis and treatment based on the expertise. In this way, appropriate support can be provided according to the expertise of the doctor.

[0067] The analysis unit can further analyze the temporal changes in the diagnostic results and evaluate the validity of the diagnostic results. For example, in order to analyze the temporal changes in the diagnostic results, past diagnostic data can be referenced and temporal trends can be evaluated. In addition, in order to analyze the temporal changes in the diagnostic results, temporal patterns can be identified and abnormal changes can be detected. Furthermore, the temporal changes in the diagnostic results can be analyzed and the validity of the diagnostic results can be evaluated. This enables highly accurate diagnosis based on temporal changes.

[0068] The reception unit can further provide an optimal input method by taking into account the patient's language and cultural background. For example, if the patient speaks a different language, an input method corresponding to that language can be provided. Also, by taking into account the patient's cultural background, an input method that takes culture into consideration can be provided. Furthermore, by taking into account the patient's language and cultural background, the input method of the diagnosis result can be customized. In this way, an appropriate input method according to the patient's language and cultural background can be provided.

[0069] The analysis unit can further analyze the regional prevalence of the diagnostic result and evaluate the validity of the diagnostic result. For example, it can analyze diseases that are prevalent in a particular region and evaluate the validity of the diagnostic result for that region. In addition, in order to analyze regional prevalence, it can refer to regional medical data and identify prevalence patterns. It can also analyze regional prevalence and evaluate the validity of the diagnostic result. This enables highly accurate diagnosis based on regional prevalence.

[0070] The process flow of the first embodiment will be briefly described below.

[0071] Step 1: The reception unit receives the diagnosis results input by the doctor. The diagnosis results include numerical data, text data, image data, etc. The reception unit can obtain the diagnosis results from the electronic medical record system, and can also receive the diagnosis results manually input by the doctor. Step 2: The analysis unit uses the generation AI to analyze the diagnosis results entered by the reception unit. The analysis is performed using statistical analysis and machine learning algorithms, and refers to past diagnostic data and medical literature to evaluate the validity of the diagnosis results. Step 3: The presentation unit presents other possibilities based on the diagnosis results analyzed by the analysis unit. The other possibilities include other disease possibilities and treatment options. For example, if the doctor diagnoses pneumonia, the presentation unit presents the possibility of other respiratory diseases. Step 4: The suggestion unit proposes the optimal treatment method based on the other possibilities presented by the suggestion unit. The proposal includes treatment methods such as drug therapy, surgical therapy, and rehabilitation. The suggestion unit selects the optimal treatment method taking into account the patient's medical history and current symptoms.

[0072] (Example 2) The diagnostic support system according to the embodiment of the present invention is a system that checks for possibilities and oversights other than the diagnosis result given by the doctor himself and proposes the optimal treatment method. This diagnostic support system allows the patient to know other possibilities and treatment methods without getting a second opinion. It can also cover the lack of experience of on-call doctors and trainees who are not specialists. For example, a doctor inputs a diagnosis result. Next, the generating AI analyzes the diagnosis result and checks for other possibilities and oversights. The generating AI evaluates the validity of the diagnosis result based on past diagnostic data and medical literature and presents other possibilities. For example, if a doctor diagnoses pneumonia, the generating AI can present the possibility of other respiratory diseases. Furthermore, the generating AI proposes the optimal treatment method. The generating AI selects the optimal treatment method taking into account the patient's medical history and current symptoms. For example, if a patient is allergic to antibiotics, the generating AI can propose an alternative treatment method. This system allows the patient to know other possibilities and treatment methods without getting a second opinion. For example, even if a patient lives far away or does not have time to get a second opinion, the patient can use this system to know other possibilities and treatment methods. It can also make up for the lack of experience of doctors on call who are not specialists or trainees. For example, even if a doctor on call examines a patient outside of his / her specialty, he / she can use this system to learn about other possibilities and treatment methods. This enables appropriate medical care to be provided to patients. This allows the diagnosis support system to check for possibilities other than the diagnosis that the doctor himself / herself has made, or for oversights, and propose the optimal treatment method.

[0073] The diagnosis support system according to the embodiment includes a reception unit, an analysis unit, a presentation unit, and a suggestion unit. The reception unit receives a diagnosis result input by a doctor. The diagnosis result includes, for example, numerical data, text data, image data, and the like, but is not limited to such examples. The reception unit can acquire the diagnosis result from, for example, an electronic medical record system. The reception unit can also receive a diagnosis result manually input by a doctor. The analysis unit uses a generation AI to analyze the diagnosis result input by the reception unit. The analysis is performed, for example, using statistical analysis or a machine learning algorithm, but is not limited to such examples. For example, the analysis unit evaluates the validity of the diagnosis result by referring to past diagnosis data and medical literature based on the diagnosis result. The presentation unit presents other possibilities based on the diagnosis result analyzed by the analysis unit. The other possibilities include, for example, the possibility of other diseases and treatment options, but are not limited to such examples. For example, when a doctor diagnoses pneumonia, the presentation unit can present the possibility of other respiratory diseases. The proposal unit proposes an optimal treatment method based on the other possibilities presented by the presentation unit. The suggestions include, but are not limited to, treatment methods such as drug therapy, surgical therapy, and rehabilitation. For example, the suggestion unit selects an optimal treatment method in consideration of the patient's medical history and current symptoms. In this way, the diagnosis support system according to the embodiment can check whether there are any possibilities or oversights other than the diagnosis result given by the doctor himself, and suggest an optimal treatment method.

[0074] The reception unit is a unit in which a doctor inputs a diagnosis result. The diagnosis result includes, but is not limited to, for example, numerical data, text data, and image data. The reception unit can, for example, obtain the diagnosis result from an electronic medical record system. The reception unit can also receive a diagnosis result manually input by a doctor. Specifically, data is obtained from the electronic medical record system using a medical information exchange standard such as HL7 or FHIR. This ensures data compatibility between different systems and enables smooth data linkage. In the case of manual input, a doctor inputs a diagnosis result through a dedicated interface. This interface is designed with an emphasis on ease of use, and is equipped with sliders and drop-down menus for inputting numerical data and an auto-completion function for inputting text data. For inputting image data, a drag-and-drop function or a camera function can be used to quickly and accurately upload images. In addition, the reception unit also has a function of checking the consistency of the input data and notifying the doctor if there is an inconsistency. This ensures the accuracy of the data and facilitates subsequent analysis processing.

[0075] The analysis unit uses the generation AI to analyze the diagnosis results input by the reception unit. The analysis is performed, for example, using statistical analysis or machine learning algorithms, but is not limited to such examples. Specifically, the generation AI analyzes the text data using natural language processing technology and analyzes the image data using image recognition technology. For example, in the analysis of the text data, medical literature and past diagnostic data are referred to to evaluate the validity of the diagnostic result. The generation AI extracts keywords and phrases related to the diagnostic result, and evaluates the reliability of the diagnostic result based on them. In addition, in the analysis of the image data, deep learning technology is used to identify abnormalities in the image. For example, signs of pneumonia are detected from a chest X-ray image, and the results are presented to the doctor. Furthermore, the analysis unit uses statistical analysis to evaluate the statistical significance of the diagnostic result. For example, it statistically verifies whether a specific symptom or test result is associated with other diseases, and evaluates the validity of the diagnostic result based on the results. As a result, the analysis unit can comprehensively analyze various data input from the reception unit and increase the reliability of the diagnostic result.

[0076] The presentation unit presents other possibilities based on the diagnosis results analyzed by the analysis unit. The other possibilities include, but are not limited to, the possibility of other diseases and treatment options. Specifically, the presentation unit provides information to the doctor in a visually easy-to-understand format based on the results analyzed by the generation AI. For example, an overview of the diagnosis results is displayed in a dashboard format, and other disease possibilities and treatment options are shown in graphs and charts. The presentation unit also has a function of highlighting important information so that the doctor can make a quick decision. For example, if a patient is diagnosed with pneumonia, the possibility of other respiratory diseases is displayed in different colors, and data and literature on which each possibility is based are provided as links. In addition, the presentation unit provides detailed analysis results and access links to related medical literature in case the doctor needs additional information. This allows the doctor to quickly and accurately consider other possibilities and obtain information to make an optimal diagnosis.

[0077] The suggestion unit proposes an optimal treatment method based on other possibilities presented by the suggestion unit. The suggestions include, but are not limited to, treatment methods such as drug therapy, surgical therapy, and rehabilitation. Specifically, the suggestion unit selects the optimal treatment method based on the data analyzed by the generation AI, taking into consideration the patient's medical history and current symptoms. For example, if a patient is diagnosed with pneumonia, the suggestion unit refers to past treatment data and the latest medical guidelines to propose the optimal antibiotic selection and dosage. In addition, if surgery is required, the suggestion unit evaluates the risk and success rate of the surgery and proposes the optimal surgical method. Furthermore, the suggestion unit can also propose the need for rehabilitation and a specific rehabilitation plan. For example, as a post-surgery rehabilitation plan, it proposes specific exercise therapy and nutritional management methods to support the patient's early recovery. The suggestion unit provides these suggestions to the doctor in a visually easy-to-understand format, helping the doctor to quickly select the optimal treatment method. As a result, the suggestion unit can check whether there are any possibilities or oversights other than the diagnosis result given by the doctor, and propose the optimal treatment method.

[0078] The analysis unit may include an evaluation unit that evaluates the validity of the diagnostic result based on past diagnostic data or medical literature. The evaluation unit evaluates the validity of the diagnostic result based on, for example, past diagnostic data. Past diagnostic data includes, for example, electronic medical record data and medical records, but is not limited to such examples. The evaluation unit may also evaluate the validity of the diagnostic result based on medical literature. Medical literature includes, for example, academic papers and guidelines, but is not limited to such examples. By evaluating the validity of the diagnostic result, it is possible to prevent oversight. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit may perform the evaluation using an AI model that inputs past diagnostic data or medical literature and evaluates the validity of the diagnostic result.

[0079] The suggestion unit may include a consideration unit that considers the patient's medical history or current symptoms. The consideration unit, for example, proposes an optimal treatment method based on the patient's medical history. The medical history includes, for example, past diseases, surgical history, allergy information, etc., but is not limited to such examples. The consideration unit may also propose an optimal treatment method based on current symptoms. The current symptoms include, for example, the patient's chief complaint, physical findings, test results, etc., but are not limited to such examples. This makes it possible to propose an optimal treatment method according to the individual situation of the patient. Part or all of the above-mentioned processing in the consideration unit may be performed, for example, using AI, or may be performed without using AI. For example, the consideration unit may make a proposal using an AI model that inputs the patient's medical history and current symptoms and proposes an optimal treatment method.

[0080] The suggestion unit can suggest an alternative treatment method if the patient is allergic to antibiotics. The suggestion unit, for example, suggests an alternative treatment method if the patient is allergic to antibiotics. Alternative treatment methods include, but are not limited to, drug therapies other than antibiotics and non-drug therapies. This makes it possible to provide an appropriate treatment method even for patients with allergies. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can make suggestions using an AI model that inputs allergy information of the patient and suggests alternative treatment methods.

[0081] The presentation unit can present the possibility of other respiratory diseases when the doctor diagnoses pneumonia. For example, when the doctor diagnoses pneumonia, the presentation unit presents the possibility of other respiratory diseases. Examples of other respiratory diseases include, but are not limited to, bronchitis, pulmonary tuberculosis, and chronic obstructive pulmonary disease (COPD). This makes it possible to prevent oversight of diagnosis and to consider other possibilities. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can make a presentation using an AI model that presents the possibility of other respiratory diseases based on the diagnosis result input by the doctor.

[0082] The support unit can cover the lack of experience of on-call doctors or trainees who are not specialists. For example, the support unit provides a diagnostic support tool when a on-call doctor who is not a specialist makes a diagnosis. The diagnostic support tool includes, but is not limited to, for example, a diagnostic algorithm or a consultation system. The support unit can also provide an educational program or a supervision system when a trainee makes a diagnosis. This enables even a doctor with inexperience to make an appropriate diagnosis and propose treatment. A part or all of the above-mentioned processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit can provide support using an AI model that supports the diagnosis of on-call doctors or trainees who are not specialists.

[0083] The reception unit can estimate the doctor's emotion and adjust the input timing of the diagnosis result based on the estimated emotion of the doctor. For example, when the doctor is tired, the reception unit can simplify the input of the diagnosis result and allow the doctor to input it quickly. In addition, when the doctor is concentrating, the reception unit can provide detailed input options so that the doctor can input a highly accurate diagnosis result. Furthermore, when the doctor is stressed, the reception unit can provide an interface that allows the doctor to relax and allows the doctor to smoothly perform the input work. This makes it possible to adjust the input timing according to the doctor's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the doctor's facial expression data to the generation AI and cause the generation AI to execute the emotion estimation.

[0084] The reception unit can analyze the doctor's past diagnostic history and select the optimal input method. For example, the reception unit preferentially suggests an input method (such as voice or text) that the doctor has frequently used in the past. The reception unit can also automatically display input items related to a specific diagnostic result from the doctor's past diagnostic history. Furthermore, the reception unit can analyze the doctor's past diagnostic history and suggest an optimal input order. This makes it possible to provide an optimal input method based on the doctor's past diagnostic history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the doctor's past diagnostic history data to the generation AI and cause the generation AI to select the optimal input method.

[0085] The reception unit can perform filtering based on the doctor's specialty and experience when inputting the diagnosis result. For example, the reception unit preferentially displays only the diagnosis result related to the doctor's specialty. The reception unit can also customize the input items of the diagnosis result based on the doctor's experience. Furthermore, the reception unit can filter the input items of the diagnosis result based on the doctor's specialty and experience and omit unnecessary items. This enables filtering based on the doctor's specialty and experience. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the doctor's specialty and experience data to the generation AI and cause the generation AI to perform filtering.

[0086] The reception unit can estimate the doctor's emotion and determine the priority of the diagnosis results to be input based on the estimated doctor's emotion. For example, when the doctor is tired, the reception unit can preferentially input important diagnosis results. In addition, when the doctor is concentrating, the reception unit can also preferentially input detailed diagnosis results. Furthermore, when the doctor is stressed, the reception unit can also preferentially input simple diagnosis results. This makes it possible to determine the priority of diagnosis results according to the doctor's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input facial expression data of the doctor to the generation AI and cause the generation AI to execute emotion estimation.

[0087] When inputting a diagnosis result, the reception unit can preferentially input a highly relevant diagnosis result in consideration of the geographical location information of the doctor. For example, when the doctor is examining a patient in a specific area, the reception unit preferentially inputs a diagnosis result related to the area. The reception unit can also preferentially input a diagnosis result related to a disease specific to the area based on the geographical location information of the doctor. Furthermore, the reception unit can also preferentially input a diagnosis result based on the medical resources of the area in consideration of the geographical location information of the doctor. This enables preferential input of a diagnosis result based on geographical location information. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the geographical location information data of the doctor to the generation AI and cause the generation AI to execute the preferential input of a highly relevant diagnosis result.

[0088] The reception unit can analyze the doctor's social media activity and input the related diagnosis result when inputting the diagnosis result. For example, the reception unit preferentially inputs the diagnosis result related to the disease of interest from the doctor's social media activity. The reception unit can also analyze the doctor's social media activity and preferentially input the diagnosis result related to the trend. Furthermore, the reception unit can also preferentially input the diagnosis result related to the specialty field based on the doctor's social media activity. This makes it possible to input the diagnosis result based on the social media activity. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the doctor's social media activity data to the generation AI and cause the generation AI to execute the input of the related diagnosis result.

[0089] The analysis unit can estimate the doctor's emotion and adjust the expression method of the analysis based on the estimated emotion of the doctor. For example, the analysis unit provides a detailed analysis result when the doctor is relaxed. Furthermore, the analysis unit can also provide a concise analysis result when the doctor is in a hurry. Furthermore, the analysis unit can also provide a visually easy-to-understand analysis result when the doctor is stressed. This makes it possible to adjust the expression method of the analysis according to the doctor's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the doctor's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0090] The analysis unit can adjust the level of detail of the analysis based on the importance of the diagnostic result during the analysis. For example, the analysis unit performs a detailed analysis on an important diagnostic result. The analysis unit can also perform a concise analysis on a general diagnostic result. Furthermore, the analysis unit can also perform a quick analysis on a diagnostic result with high urgency. This makes it possible to adjust the level of detail of the analysis according to the importance of the diagnostic result. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the importance data of the diagnostic result to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0091] The analysis unit can apply different analysis algorithms depending on the category of the diagnosis result during analysis. For example, the analysis unit applies a dedicated analysis algorithm to respiratory diseases. The analysis unit can also apply a dedicated analysis algorithm to heart diseases. Furthermore, the analysis unit can also apply a dedicated analysis algorithm to digestive diseases. This makes it possible to apply an analysis algorithm depending on the category of the diagnosis result. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the diagnosis result to the generation AI and cause the generation AI to apply the analysis algorithm.

[0092] The analysis unit can estimate the doctor's emotion and adjust the length of the analysis based on the estimated doctor's emotion. For example, when the doctor is in a hurry, the analysis unit performs a short and to the point analysis. Also, when the doctor is relaxed, the analysis unit can perform a detailed analysis. Furthermore, when the doctor is excited, the analysis unit can perform a visually stimulating analysis. This makes it possible to adjust the length of the analysis according to the doctor's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the doctor's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0093] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the diagnostic results. For example, the analysis unit prioritizes analysis of diagnostic results with high urgency. The analysis unit can also prioritize analysis of diagnostic results submitted earlier. Furthermore, the analysis unit can also prioritize analysis of diagnostic results submitted more recently. This makes it possible to determine the priority of analysis based on the submission time. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission time data of the diagnostic results to the generation AI and cause the generation AI to determine the priority of analysis.

[0094] The analysis unit can adjust the order of analysis based on the relevance of the diagnostic results during analysis. For example, the analysis unit preferentially analyzes diagnostic results with high relevance. The analysis unit can also postpone diagnostic results with low relevance. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the diagnostic results. This makes it possible to adjust the order of analysis based on the relevance of the diagnostic results. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the diagnostic results to the generation AI and cause the generation AI to adjust the order of analysis.

[0095] The presentation unit can estimate the doctor's emotion and adjust the presentation criteria based on the estimated doctor's emotion. For example, when the doctor is relaxed, the presentation unit presents detailed information. When the doctor is in a hurry, the presentation unit can also present information that focuses on the main points. Furthermore, when the doctor is stressed, the presentation unit can present information that is visually easy to understand. This makes it possible to adjust the presentation criteria according to the doctor's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the presentation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the presentation unit can input the doctor's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0096] The presentation unit can improve the accuracy of presentation by considering the interrelationships of diagnostic results at the time of presentation. For example, the presentation unit analyzes the interrelationships of diagnostic results and presents related information together. The presentation unit can also present important information preferentially by considering the interrelationships of diagnostic results. Furthermore, the presentation unit can present information in a visually easy-to-understand format based on the interrelationships of diagnostic results. This makes it possible to improve the accuracy of presentation based on the interrelationships of diagnostic results. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI or may be performed without using AI. For example, the presentation unit can input interrelationship data of diagnostic results to the generation AI and cause the generation AI to improve the accuracy of presentation.

[0097] The presentation unit can present the diagnostic results while taking into consideration the attribute information of the person who submitted the diagnostic results. For example, when the person who submitted the diagnostic results is a specialist, the presentation unit presents detailed information. Also, when the person who submitted the diagnostic results is a trainee, the presentation unit can present concise information. Furthermore, the presentation unit can present appropriate information based on the attribute information of the person who submitted the diagnostic results. This enables presentation based on the attribute information of the person who submitted the diagnostic results. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input attribute information data of the person who submitted the diagnostic results to the generation AI and cause the generation AI to perform the presentation.

[0098] The presentation unit can estimate the doctor's emotion and adjust the order of displaying the presentation results based on the estimated doctor's emotion. For example, when the doctor is relaxed, the presentation unit displays detailed information first. Also, when the doctor is in a hurry, the presentation unit can display information with a focus on the main points first. Furthermore, when the doctor is stressed, the presentation unit can display information that is visually easy to understand first. This makes it possible to adjust the display order of the presentation results according to the doctor's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the presentation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the presentation unit can input facial expression data of the doctor to the generation AI and cause the generation AI to execute emotion estimation.

[0099] The presentation unit can present the diagnostic results while taking into consideration the geographical distribution of the diagnostic results. For example, the presentation unit analyzes the geographical distribution of the diagnostic results and presents information specific to the region. The presentation unit can also present related information preferentially, taking into consideration the geographical distribution of the diagnostic results. Furthermore, the presentation unit can present information in a visually easy-to-understand format based on the geographical distribution of the diagnostic results. This makes it possible to present diagnostic results based on the geographical distribution. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input geographical distribution data of the diagnostic results to the generation AI and cause the generation AI to execute the presentation.

[0100] The presentation unit can improve the accuracy of presentation by referring to literature related to the diagnostic result at the time of presentation. The presentation unit, for example, refers to literature related to the diagnostic result and presents detailed information. The presentation unit can also present important information preferentially based on literature related to the diagnostic result. Furthermore, the presentation unit can also present information in a visually easy-to-understand format by referring to literature related to the diagnostic result. This makes it possible to present the diagnostic result based on the related literature. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input literature data related to the diagnostic result to the generation AI and cause the generation AI to improve the accuracy of presentation.

[0101] The suggestion unit can estimate the doctor's emotion and adjust the way of expressing the suggestion based on the estimated emotion of the doctor. For example, when the doctor is relaxed, the suggestion unit makes a detailed suggestion. Furthermore, when the doctor is in a hurry, the suggestion unit can also make a concise suggestion. Furthermore, when the doctor is stressed, the suggestion unit can also make a visually easy-to-understand suggestion. This makes it possible to adjust the way of expressing the suggestion according to the doctor's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input facial expression data of the doctor to the generation AI and cause the generation AI to execute emotion estimation.

[0102] The suggestion unit can adjust the level of detail of the proposal based on the importance of the treatment method at the time of proposal. For example, the suggestion unit makes a detailed proposal for an important treatment method. The suggestion unit can also make a concise proposal for a general treatment method. Furthermore, the suggestion unit can also make a quick proposal for a treatment method with high urgency. This makes it possible to adjust the level of detail of the proposal according to the importance of the treatment method. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the importance data of the treatment method to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0103] The suggestion unit can apply different proposal algorithms depending on the category of the treatment method at the time of proposal. For example, the suggestion unit applies a dedicated proposal algorithm to respiratory diseases. The suggestion unit can also apply a dedicated proposal algorithm to heart diseases. Furthermore, the suggestion unit can also apply a dedicated proposal algorithm to digestive diseases. This makes it possible to apply a proposal algorithm according to the category of the treatment method. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input category data of the treatment method to the generation AI and cause the generation AI to apply the proposal algorithm.

[0104] The suggestion unit can estimate the doctor's emotion and adjust the length of the suggestion based on the estimated doctor's emotion. For example, when the doctor is in a hurry, the suggestion unit makes a short and to the point suggestion. Also, when the doctor is relaxed, the suggestion unit can make a longer suggestion including a detailed explanation. Furthermore, when the doctor is excited, the suggestion unit can make a suggestion with a visually stimulating effect. This makes it possible to adjust the length of the suggestion according to the doctor's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the doctor's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0105] At the time of proposal, the suggestion unit can determine the priority of the proposal based on the submission time of the treatment method. For example, the suggestion unit preferentially proposes a treatment method with high urgency. The suggestion unit can also preferentially propose a treatment method submitted earlier. Furthermore, the suggestion unit can also preferentially propose a treatment method submitted more recently. This makes it possible to determine the priority of the proposal based on the submission time. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the submission time data of the treatment method to the generation AI and cause the generation AI to execute the determination of the priority of the proposal.

[0106] The suggestion unit can adjust the order of suggestions based on the relevance of the treatment methods at the time of suggestion. For example, the suggestion unit preferentially suggests highly relevant treatment methods. The suggestion unit can also postpone less relevant treatment methods. Furthermore, the suggestion unit can dynamically adjust the order of suggestions based on the relevance of the treatment methods. This makes it possible to adjust the order of suggestions based on the relevance of the treatment methods. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input relevance data of the treatment methods to the generation AI and cause the generation AI to adjust the order of suggestions.

[0107] The evaluation unit can estimate the doctor's emotion and adjust the evaluation criteria based on the estimated doctor's emotion. For example, when the doctor is relaxed, the evaluation unit applies detailed evaluation criteria. In addition, when the doctor is in a hurry, the evaluation unit can also apply simple evaluation criteria. Furthermore, when the doctor is stressed, the evaluation unit can also apply visually easy-to-understand evaluation criteria. This makes it possible to adjust the evaluation criteria according to the doctor's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the evaluation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the evaluation unit can input the doctor's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0108] The evaluation unit can improve the accuracy of the evaluation by referring to past diagnostic data at the time of evaluation. The evaluation unit can, for example, evaluate the validity of the diagnostic result based on the past diagnostic data. The evaluation unit can also improve the accuracy of the diagnostic result by referring to the past diagnostic data. Furthermore, the evaluation unit can analyze the past diagnostic data and adjust the evaluation criteria of the diagnostic result. This makes it possible to improve the accuracy of the evaluation based on the past diagnostic data. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can input the past diagnostic data to the generation AI and cause the generation AI to improve the accuracy of the evaluation.

[0109] The evaluation unit can estimate the doctor's emotion and adjust the order of displaying the evaluation results based on the estimated doctor's emotion. For example, when the doctor is relaxed, the evaluation unit displays detailed evaluation results first. Also, when the doctor is in a hurry, the evaluation unit can display evaluation results with a focus on the main points first. Furthermore, when the doctor is stressed, the evaluation unit can display evaluation results that are visually easy to understand first. This makes it possible to adjust the display order of the evaluation results according to the doctor's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the evaluation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the evaluation unit can input facial expression data of the doctor to the generation AI and cause the generation AI to execute emotion estimation.

[0110] The evaluation unit can improve the accuracy of the evaluation by referring to the literature related to the diagnostic result at the time of evaluation. The evaluation unit, for example, refers to literature related to the diagnostic result and performs a detailed evaluation. The evaluation unit can also prioritize evaluation of important evaluation items based on literature related to the diagnostic result. Furthermore, the evaluation unit can also display the evaluation result in a visually easy-to-understand format by referring to literature related to the diagnostic result. This makes it possible to improve the accuracy of the evaluation based on the related literature. A part or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the literature data related to the diagnostic result to the generation AI and cause the generation AI to improve the accuracy of the evaluation.

[0111] The consideration unit can estimate the doctor's emotion and adjust the consideration criteria based on the estimated doctor's emotion. For example, the consideration unit applies detailed consideration criteria when the doctor is relaxed. Also, the consideration unit can apply concise consideration criteria when the doctor is in a hurry. Furthermore, the consideration unit can apply visually easy-to-understand consideration criteria when the doctor is stressed. This enables adjustment of the consideration criteria according to the doctor's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the consideration unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the consideration unit can input facial expression data of the doctor to the generation AI and cause the generation AI to execute emotion estimation.

[0112] The consideration unit can improve the accuracy of the consideration by referring to the patient's past medical history at the time of consideration. The consideration unit can consider the validity of the diagnosis result based on the patient's past medical history, for example. The consideration unit can also improve the accuracy of the diagnosis result by referring to the patient's past medical history. Furthermore, the consideration unit can analyze the patient's past medical history and adjust the consideration criteria for the diagnosis result. This makes it possible to improve the accuracy of the consideration based on the patient's past medical history. A part or all of the above-mentioned processing in the consideration unit may be performed using, for example, AI, or may be performed without using AI. For example, the consideration unit can input the patient's past medical history data to the generation AI and cause the generation AI to improve the accuracy of the consideration.

[0113] The consideration unit can estimate the doctor's emotion and adjust the order of displaying the consideration results based on the estimated doctor's emotion. For example, when the doctor is relaxed, the consideration unit displays the detailed consideration results first. Also, when the doctor is in a hurry, the consideration unit can display the consideration results with the main points first. Furthermore, when the doctor is stressed, the consideration unit can display the visually easy-to-understand consideration results first. This makes it possible to adjust the display order of the consideration results according to the doctor's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the consideration unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the consideration unit can input the doctor's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0114] The consideration unit can improve the accuracy of the consideration by referring to the patient's related literature during the consideration. The consideration unit, for example, refers to literature related to the patient's medical history and performs detailed consideration. The consideration unit can also prioritize important consideration items based on literature related to the patient's medical history. Furthermore, the consideration unit can also display the consideration results in a visually easy-to-understand format by referring to literature related to the patient's medical history. This makes it possible to improve the accuracy of the consideration based on the related literature. A part or all of the above-mentioned processing in the consideration unit may be performed using, for example, AI, or may be performed without using AI. For example, the consideration unit can input the patient's related literature data to the generation AI and cause the generation AI to improve the accuracy of the consideration.

[0115] The support unit can estimate the doctor's emotion and adjust the support method based on the estimated doctor's emotion. For example, when the doctor is relaxed, the support unit provides detailed support. Furthermore, when the doctor is in a hurry, the support unit can also provide brief support. Furthermore, when the doctor is stressed, the support unit can also provide visually easy-to-understand support. This makes it possible to adjust the support method according to the doctor's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the support unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the support unit can input the doctor's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0116] The support unit can improve the accuracy of the support by referring to the doctor's past diagnostic history at the time of support. The support unit supports the validity of the diagnosis result based on the doctor's past diagnostic history, for example. The support unit can also improve the accuracy of the diagnosis result by referring to the doctor's past diagnostic history. Furthermore, the support unit can analyze the doctor's past diagnostic history and adjust the support criteria for the diagnosis result. This makes it possible to improve the accuracy of the support based on the past diagnostic history. A part or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input the doctor's past diagnostic history data to the generation AI and cause the generation AI to improve the accuracy of the support.

[0117] The support unit can estimate the doctor's emotion and determine the priority of the support based on the estimated emotion of the doctor. For example, when the doctor is relaxed, the support unit can give priority to detailed support. Furthermore, when the doctor is in a hurry, the support unit can also give priority to simple support. Furthermore, when the doctor is stressed, the support unit can also give priority to visually easy-to-understand support. This makes it possible to determine the priority of the support according to the doctor's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the support unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the support unit can input facial expression data of the doctor to the generation AI and cause the generation AI to execute emotion estimation.

[0118] The support unit can select the optimal support method in consideration of the geographical location information of the doctor when providing support. For example, when the doctor is examining a patient in a specific area, the support unit preferentially provides a support method related to the area. The support unit can also provide a support method related to a disease specific to the area based on the geographical location information of the doctor. Furthermore, the support unit can also provide a support method based on the medical resources of the area in consideration of the geographical location information of the doctor. This makes it possible to select the optimal support method based on the geographical location information. A part or all of the above-mentioned processing in the support unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the support unit can input the geographical location information data of the doctor to the generation AI and cause the generation AI to select the optimal support method.

[0119] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0120] The diagnostic support system can further collect lifestyle data of the patient and use the analysis unit to evaluate the validity of the diagnostic results. For example, data on the patient's diet, exercise, sleep patterns, etc. can be collected, and the validity of the diagnostic results can be evaluated based on this data. The system can also evaluate the risk of certain diseases and suggest preventive measures based on the lifestyle data. Furthermore, the system can monitor the effectiveness of treatment methods and adjust treatment plans as necessary based on the lifestyle data. This enables comprehensive diagnosis and treatment based on the patient's lifestyle.

[0121] The analysis unit can further analyze the genetic information of the patient and evaluate the validity of the diagnostic results. For example, the risk of a particular disease can be evaluated based on the genetic data of the patient. Also, based on the genetic information, personalized medicine can be provided and optimal treatment methods can be proposed. Furthermore, based on the genetic information, the effectiveness and side effects of drug therapy can be predicted and the treatment plan can be adjusted. This enables highly accurate diagnosis and treatment based on genetic information.

[0122] The suggestion unit can further take into account the psychological state of the patient and suggest the most suitable treatment method. For example, if the patient is feeling stressed, it can suggest a treatment method to reduce stress. Also, if the patient is feeling anxious, it can suggest counseling or support to reduce anxiety. Furthermore, it can monitor the patient's psychological state and adjust the treatment plan. This makes it possible to provide personalized medical care according to the patient's psychological state.

[0123] The proposal unit can further take into account the social background of the patient and propose the most suitable treatment method. For example, if the patient is in an economically difficult situation, it can propose a low-cost treatment method. Also, if the patient does not have family support, it can suggest local support services. Furthermore, it can adjust the treatment plan taking into account the social background of the patient. This makes it possible to provide comprehensive medical care that is suited to the patient's social background.

[0124] The presentation unit can further evaluate the reliability of the diagnostic result and preferentially present highly reliable information. For example, in order to evaluate the reliability of the diagnostic result, multiple data sources can be referenced and matching information can be preferentially presented. In addition, in order to evaluate the reliability of the diagnostic result, the quality and reliability of the data can be evaluated. Furthermore, the reliability of the diagnostic result can be evaluated and information with low reliability can be filtered. This makes it possible to provide diagnosis and treatment based on highly reliable information.

[0125] The support unit can further evaluate the expertise of the doctor and provide support based on the expertise. For example, in order to evaluate the expertise of the doctor, past diagnosis history and academic papers can be referred to and support based on the expertise can be provided. In addition, in order to evaluate the expertise of the doctor, training and educational programs related to the specialty can be provided. Furthermore, the support unit can evaluate the expertise of the doctor and support diagnosis and treatment based on the expertise. In this way, appropriate support can be provided according to the expertise of the doctor.

[0126] The reception unit can further estimate the patient's emotions and adjust the input method of the diagnosis result based on the estimated patient's emotions. For example, if the patient is feeling anxious, a simple and easy-to-understand input method can be provided. If the patient is relaxed, a detailed input method can be provided. Furthermore, if the patient is feeling stressed, a relaxing interface can be provided to allow the input work to be performed smoothly. This makes it possible to adjust the input method according to the patient's emotions.

[0127] The analysis unit can further analyze the temporal changes in the diagnostic results and evaluate the validity of the diagnostic results. For example, in order to analyze the temporal changes in the diagnostic results, past diagnostic data can be referenced and temporal trends can be evaluated. In addition, in order to analyze the temporal changes in the diagnostic results, temporal patterns can be identified and abnormal changes can be detected. Furthermore, the temporal changes in the diagnostic results can be analyzed and the validity of the diagnostic results can be evaluated. This enables highly accurate diagnosis based on temporal changes.

[0128] The reception unit can further provide an optimal input method by taking into account the patient's language and cultural background. For example, if the patient speaks a different language, an input method corresponding to that language can be provided. Also, by taking into account the patient's cultural background, an input method that takes culture into consideration can be provided. Furthermore, by taking into account the patient's language and cultural background, the input method of the diagnosis result can be customized. In this way, an appropriate input method according to the patient's language and cultural background can be provided.

[0129] The analysis unit can further analyze the regional prevalence of the diagnostic result and evaluate the validity of the diagnostic result. For example, it can analyze diseases that are prevalent in a particular region and evaluate the validity of the diagnostic result for that region. In addition, in order to analyze regional prevalence, it can refer to regional medical data and identify prevalence patterns. It can also analyze regional prevalence and evaluate the validity of the diagnostic result. This enables highly accurate diagnosis based on regional prevalence.

[0130] The process flow of the second embodiment will be briefly described below.

[0131] Step 1: The reception unit receives the diagnosis results input by the doctor. The diagnosis results include numerical data, text data, image data, etc. The reception unit can obtain the diagnosis results from the electronic medical record system, and can also receive the diagnosis results manually input by the doctor. Step 2: The analysis unit uses the generation AI to analyze the diagnosis results entered by the reception unit. The analysis is performed using statistical analysis and machine learning algorithms, and refers to past diagnostic data and medical literature to evaluate the validity of the diagnosis results. Step 3: The presentation unit presents other possibilities based on the diagnosis results analyzed by the analysis unit. The other possibilities include other disease possibilities and treatment options. For example, if the doctor diagnoses pneumonia, the presentation unit presents the possibility of other respiratory diseases. Step 4: The suggestion unit proposes the optimal treatment method based on the other possibilities presented by the suggestion unit. The proposal includes treatment methods such as drug therapy, surgical therapy, and rehabilitation. The suggestion unit selects the optimal treatment method taking into account the patient's medical history and current symptoms.

[0132] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0133] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0134] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0135] Each of the multiple elements including the above-mentioned reception unit, analysis unit, presentation unit, and suggestion unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and a doctor can input a diagnosis result. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the diagnosis result using a generation AI. The presentation unit is realized, for example, by the control unit 46A of the smart device 14, and presents other possibilities based on the analyzed diagnosis result. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal treatment method. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0136] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0137] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0138] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0143] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0144] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0146] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0147] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0148] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0150] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements including the above-mentioned reception unit, analysis unit, presentation unit, and suggestion unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and a doctor can input a diagnosis result. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the diagnosis result using a generation AI. The presentation unit is realized, for example, by the control unit 46A of the smart glasses 214, and presents other possibilities based on the analyzed diagnosis result. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal treatment method. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0152] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0153] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0154] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0155] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0156] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0157] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0158] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0159] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0160] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0161] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0162] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0163] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0164] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0165] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0166] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0167] Each of the multiple elements including the above-mentioned reception unit, analysis unit, presentation unit, and suggestion unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314, and a doctor can input a diagnosis result. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the diagnosis result using a generation AI. The presentation unit is realized, for example, by the control unit 46A of the headset type terminal 314, and presents other possibilities based on the analyzed diagnosis result. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal treatment method. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0168] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0169] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0170] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0171] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0172] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0173] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0174] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0175] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0176] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0177] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0178] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0179] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0180] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0181] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0182] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0183] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0184] Each of the multiple elements including the above-mentioned reception unit, analysis unit, presentation unit, and suggestion unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and a doctor can input a diagnosis result. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the diagnosis result using a generation AI. The presentation unit is realized, for example, by the control unit 46A of the robot 414, and presents other possibilities based on the analyzed diagnosis result. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal treatment method. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0185] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0186] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0187] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0188] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0189] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0190] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0191] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0192] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0193] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0194] In addition, 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 upon request from the data processing device 12.

[0195] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0196] As the hardware resource for executing the specific process, the following various processors can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application specific integrated circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0197] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0198] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0199] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0200] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0201] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0202] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0203] (Appendix 1) a reception unit for inputting diagnosis results; an analysis unit that analyzes the diagnosis result input by the reception unit; a presentation unit that presents other possibilities based on the diagnostic results analyzed by the analysis unit; A suggestion unit that suggests a treatment method based on the other possibilities suggested by the suggestion unit. A system characterized by: (Appendix 2) The analysis unit is Equipped with an evaluation section that evaluates the validity of diagnostic results based on past diagnostic data or medical literature 2. The system of claim 1, (Appendix 3) The suggestion unit, Equipped with a consideration section that takes into account the patient's medical history or current symptoms 2. The system of claim 1, (Appendix 4) The suggestion unit, Suggest alternative treatments if the patient is allergic to antibiotics 2. The system of claim 1, (Appendix 5) The presentation unit is If a doctor diagnoses pneumonia, they will raise the possibility of other respiratory illnesses. 2. The system of claim 1, (Appendix 6) The support unit includes: Covering the lack of experience of doctors on call or trainees who are not specialists 2. The system of claim 1, (Appendix 7) The reception unit is Estimating the doctor's emotions and adjusting the timing of inputting the diagnosis result based on the estimated doctor's emotions 2. The system of claim 1, (Appendix 8) The reception unit is Analyze the doctor's past diagnosis history and select the optimal input method 2. The system of claim 1, (Appendix 9) The reception unit is Filtering diagnostic results based on doctors' specialties and experience 2. The system of claim 1, (Appendix 10) The reception unit is Estimate the doctor's emotions and determine the priority of the diagnosis results to be input based on the estimated doctor's emotions 2. The system of claim 1, (Appendix 11) The reception unit is When entering diagnostic results, the geographic location information of the doctor is taken into consideration to prioritize the most relevant diagnostic results. 2. The system of claim 1, (Appendix 12) The reception unit is When entering diagnostic results, analyze the doctor's social media activity and enter relevant diagnostic results 2. The system of claim 1, (Appendix 13) The analysis unit is Estimate the doctor's emotions and adjust the way the analysis is presented based on the estimated emotions of the doctor 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, adjust the level of analysis detail based on the importance of diagnostic results 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, different analysis algorithms are applied depending on the category of the diagnosis result. 2. The system of claim 1, (Appendix 16) The analysis unit is Inferring physician sentiment and adjusting the length of the analysis based on the inferred physician sentiment 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, analysis prioritization will be determined based on when diagnostic results were submitted. 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, adjust the order of analysis based on the relevance of diagnostic results 2. The system of claim 1, (Appendix 19) The presentation unit is Estimate the doctor's emotions and adjust the presentation criteria based on the estimated doctor's emotions 2. The system of claim 1, (Appendix 20) The presentation unit is When presenting, improve the accuracy of the presentation by taking into account the correlation of diagnostic results 2. The system of claim 1, (Appendix 21) The presentation unit is When presenting diagnostic results, the attribute information of the person submitting the results is taken into consideration. 2. The system of claim 1, (Appendix 22) The presentation unit is Estimating the doctor's sentiment and adjusting the order in which the results of the suggestions are displayed based on the estimated doctor's sentiment 2. The system of claim 1, (Appendix 23) The presentation unit is When presenting, the geographical distribution of diagnostic results is taken into account. 2. The system of claim 1, (Appendix 24) The presentation unit is When presenting, refer to related literature on diagnostic results to improve the accuracy of the presentation 2. The system of claim 1, (Appendix 25) The suggestion unit, Estimate the doctor's emotions and adjust the way the suggestions are presented based on the estimated emotions of the doctor 2. The system of claim 1, (Appendix 26) The suggestion unit, When making a recommendation, adjust the level of detail of the recommendation based on the importance of the treatment method 2. The system of claim 1, (Appendix 27) The suggestion unit, When making suggestions, different suggestion algorithms are applied depending on the category of treatment method. 2. The system of claim 1, (Appendix 28) The suggestion unit, Estimate the doctor's sentiment and adjust the length of the suggestion based on the estimated doctor's sentiment 2. The system of claim 1, (Appendix 29) The suggestion unit, At the time of submission, proposals will be prioritized based on the timing of the treatment submission. 2. The system of claim 1, (Appendix 30) The suggestion unit, When making suggestions, adjust the order of suggestions based on the relevance of the treatment modality 2. The system of claim 1, (Appendix 31) The evaluation unit is Estimate the doctor's emotions and adjust the evaluation criteria based on the estimated doctor's emotions. 3. The system of claim 2, (Appendix 32) The evaluation unit is During evaluation, past diagnostic data is referenced to improve the accuracy of the evaluation. 3. The system of claim 2, (Appendix 33) The evaluation unit is Estimate the doctor's sentiment and adjust the order in which the evaluation results are displayed based on the estimated doctor's sentiment. 3. The system of claim 2, (Appendix 34) The evaluation unit is During evaluation, refer to relevant literature on diagnostic results to improve the accuracy of the evaluation. 3. The system of claim 2, (Appendix 35) The consideration unit is Estimating the doctor's feelings and adjusting the standard of consideration based on the estimated doctor's feelings 4. The system of claim 3. (Appendix 36) The consideration unit is When considering a patient, refer to their past medical history to improve the accuracy of the consideration. 4. The system of claim 3. (Appendix 37) The consideration unit is Estimating the doctor's feelings and adjusting the order in which the results of the consideration are displayed based on the estimated doctor's feelings 4. The system of claim 3. (Appendix 38) The consideration unit is When considering, refer to the patient's relevant literature to improve the accuracy of the consideration 4. The system of claim 3. (Appendix 39) The support unit includes: Estimate the doctor's emotions and adjust the support method based on the estimated doctor's emotions 7. The system of claim 6, (Appendix 40) The support unit includes: When providing support, the accuracy of the support can be improved by referring to the doctor's past diagnostic history. 7. The system of claim 6, (Appendix 41) The support unit includes: Estimating the doctor's emotions and determining the priority of support based on the estimated doctor's emotions 7. The system of claim 6, (Appendix 42) The support unit includes: When providing support, the optimal support method is selected taking into account the doctor's geographic location information. 7. The system of claim 6, [Explanation of symbols]

[0204] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for inputting diagnosis results; an analysis unit that analyzes the diagnosis result input by the reception unit; a presentation unit that presents other possibilities based on the diagnostic results analyzed by the analysis unit; A suggestion unit that suggests a treatment method based on the other possibilities suggested by the suggestion unit. A system characterized by:

2. The analysis unit is Equipped with an evaluation section that evaluates the validity of diagnostic results based on past diagnostic data or medical literature 2. The system of claim 1 .

3. The suggestion unit, Equipped with a consideration section that takes into account the patient's medical history or current symptoms 2. The system of claim 1 .

4. The suggestion unit, Suggest alternative treatments if the patient is allergic to antibiotics 2. The system of claim 1 .

5. The presentation unit is If a doctor diagnoses pneumonia, they will raise the possibility of other respiratory illnesses.

2. The system of claim 1 .

6. The reception unit is Estimating the doctor's emotions and adjusting the timing of inputting the diagnosis result based on the estimated doctor's emotions 2. The system of claim 1 .

7. The reception unit is Analyze the doctor's past diagnosis history and select the optimal input method 2. The system of claim 1 .

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