System

Abstract

An object of the system according to the embodiment is to provide information about failure cases and allow doctors and patients to find an appropriate countermeasure.SOLUTION: A system according to an embodiment includes a medical history upload unit, a database construction unit, a similar case search unit, and an information providing unit. The medical history uploading unit uploads the medical history. The database construction unit analyzes the medical history uploaded by the medical history upload unit to construct a database. The similar case search unit searches for a similar case from the database constructed by the database construction unit. The information providing unit provides a treatment method, an explanation method, and a prognosis process based on the similar case searched for by the similar case search 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 an instruction sentence related to a description of 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has a lack of information on failed cases, making it difficult for doctors and patients to find appropriate countermeasures.

[0005] The system according to the embodiment aims to provide information on failed cases and help doctors and patients find appropriate countermeasures. [Means for solving the problem]

[0006] The system according to the embodiment includes a medical history uploading unit, a database construction unit, a similar case search unit, and an information providing unit. The medical history uploading unit uploads medical histories. The database construction unit analyzes the medical histories uploaded by the medical history uploading unit and constructs a database. The similar case search unit searches for similar cases from the database constructed by the database construction unit. The information providing unit provides treatments, explanations, and prognoses based on the similar cases searched for by the similar case search unit. [Effects of the Invention]

[0007] The system according to the embodiment provides information about failure cases, allowing doctors and patients to find appropriate countermeasures. [Brief explanation 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. [Figure 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. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 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. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 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 INVENTION

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

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single 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 coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, 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 symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and 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. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "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, RAM 30, and 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 (for example, 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., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with 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 CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

[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 user's emotion using the emotion identification model 59 and perform identification processing using the user's 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 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 device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The failure case information provision system according to an embodiment of the present invention is a system in which a patient's medical history is uploaded, a generation AI searches for similar cases, and provides treatment methods, explanations, and prognosis. This allows the failure case information provision system to aim for a world in which doctors and patients can access information about failure cases and seek advice.

[0029] The failed case information provision system according to the embodiment includes a medical history uploading unit, a database construction unit, a similar case search unit, and an information provision unit. The medical history uploading unit uploads medical histories. For example, doctors and patients input information about diagnosis results, treatment details, and follow-up observations into the system. The medical history uploading unit can also automatically extract and tag important points in the medical history using a generation AI. The database construction unit analyzes the medical histories uploaded by the medical history uploading unit to construct a database. For example, the generation AI can analyze the medical history using text mining technology and register it in the database. The generation AI can also compare it with past medical history data to detect abnormal values ​​and abnormal patterns in real time. The similar case search unit searches for similar cases from the database constructed by the database construction unit. For example, the generation AI can prioritize the most relevant cases by considering the chronological progression of the case. The generation AI can also consider the geographical information of the case and provide region-specific treatment methods and prognoses. The information provision unit provides treatment methods, explanations, and prognoses based on the similar cases searched by the similar case search unit. For example, generative AI can provide doctors with detailed analysis results of cases and present treatment options from multiple angles. Generative AI can also generate infographics to explain the progress of cases to patients in an easy-to-understand manner. In this way, the failure case information provision system can aim for a world in which doctors and patients can access information about failure cases and consult with them.

[0030] In the medical history uploading section, the generation AI can automatically extract and tag important points in the medical history when the medical history is uploaded. For example, when the medical history is uploaded, the generation AI analyzes information on diagnosis results, treatment details, and follow-up observations, and automatically extracts important points. For example, information on specific symptoms and treatments is tagged and registered in the database. This automatically extracts and tags important points in the medical history, improving the accuracy of the database.

[0031] When uploading a medical history, the generation AI compares it with past medical history data, enabling it to detect abnormal values ​​and patterns in real time. When uploading a medical history, the generation AI compares it with past medical history data, enabling it to detect abnormal values ​​and patterns in real time. For example, when uploading a medical history, the generation AI analyzes blood test results and diagnostic imaging data to identify abnormal values ​​and patterns. This allows for early detection of abnormal values ​​and patterns, enabling early response.

[0032] The medical history uploading unit can be added with a function that allows voice input and image data to be uploaded simultaneously when uploading a medical history, thereby building a multimodal database.The medical history uploading unit can be added with a function that allows voice input and image data to be uploaded simultaneously when uploading a medical history, thereby building a multimodal database.For example, a patient's voice memo and diagnostic imaging data can be uploaded together.This allows voice input and image data to be uploaded simultaneously, thereby building a database that contains a wider variety of information.

[0033] The medical history uploading unit can automatically translate inputs in different languages ​​when uploading medical histories and build an international database. The medical history uploading unit can automatically translate inputs in different languages ​​when uploading medical histories and build an international database. For example, medical history data in English, French, etc. can be automatically translated and registered in the database. This allows an international database to be built by automatically translating inputs in different languages.

[0034] When searching for similar cases, the generation AI takes into account the chronological progression of the case, and prioritizes displaying the most relevant cases. For example, when searching for similar cases, the generation AI takes into account the chronological progression of the case, and prioritizes displaying the most relevant cases. For example, search results are displayed based on the time elapsed since the start of treatment and the degree of progression of symptoms. This allows for the provision of more relevant information by taking into account the chronological progression of the case.

[0035] When searching for similar cases, the generation AI also takes into account the geographical information of the case, making it possible to provide region-specific treatment methods and prognosis. For example, when searching for similar cases, the generation AI also takes into account the geographical information of the case, making it possible to provide region-specific treatment methods and prognosis. For example, common treatment methods and prognosis in a specific region are reflected in the search results. By taking geographical information into account, it is possible to provide region-specific treatment methods and prognosis.

[0036] The similar case search unit can also search for cases in different medical fields and provide cross-disciplinary information. For example, when searching for similar cases, the generation AI can search for cases in different medical fields and provide cross-disciplinary information. For example, internal medicine and surgery cases can be integrated and reflected in the search results. This makes it possible to provide information from a broader perspective by integrating information from different medical fields.

[0037] The similar case search unit can also analyze visual data of cases (for example, X-ray images or MRI images) and provide visual information. For example, when searching for similar cases, the similar case search unit uses the generation AI to analyze visual data of cases (for example, X-ray images or MRI images) and provide visual information. For example, it displays search results based on diagnostic imaging data. This makes it possible to provide information that is visually easy to understand by analyzing visual data.

[0038] When providing information to doctors, the information provision unit uses the generating AI to provide detailed analysis results of the case, allowing for a multifaceted presentation of treatment options. For example, when providing information to doctors, the information provision unit uses the generating AI to provide detailed analysis results of the case, allowing for a multifaceted presentation of treatment options. For example, the success rate and risks of specific treatments may be analyzed, and multiple treatment options may be proposed. This allows for a multifaceted presentation of treatment options by providing detailed analysis results of the case.

[0039] The information provision unit can provide relevant information by having the generating AI automatically search for the latest research papers and academic presentations when providing information to doctors. For example, when providing information to doctors, the information provision unit can provide relevant information by having the generating AI automatically search for the latest research papers and academic presentations. For example, the latest research results and academic presentations on a specific treatment method are reflected in the search results. This makes it possible to provide relevant information by automatically searching for the latest research papers and academic presentations.

[0040] When providing information to doctors, the information provision unit integrates data from medical institutions with different generation AIs, making it possible to provide information from a wide range of perspectives.For example, when providing information to doctors, the information provision unit integrates data from medical institutions with different generation AIs, making it possible to provide information from a wide range of perspectives.For example, treatment data from multiple hospitals can be integrated and analyzed to propose the optimal treatment method.In this way, by integrating data from different medical institutions, it becomes possible to provide information from a wide range of perspectives.

[0041] When providing information to doctors, the information provision unit uses the generation AI to generate a 3D model of the case, making it possible to provide information that is visually easy to understand. For example, when providing information to doctors, the information provision unit uses the generation AI to generate a 3D model of the case, making it possible to provide information that is visually easy to understand. For example, a surgical simulation or anatomical structure is displayed in 3D model. In this way, by generating a 3D model of the case, it is possible to provide information that is visually easy to understand.

[0042] The information provision unit can cause the generation AI to generate infographics that explain the progress of a case in an easy-to-understand manner when providing information to patients. For example, when providing information to patients, the information provision unit causes the generation AI to generate infographics that explain the progress of a case in an easy-to-understand manner. For example, the information provision unit visually displays treatment steps and prognosis progress. In this way, generating infographics that explain the progress of a case in an easy-to-understand manner makes it easier for patients to understand the information.

[0043] The information provision unit allows the generation AI to provide lifestyle advice related to the case when providing information to the patient. For example, when providing information to the patient, the information provision unit allows the generation AI to provide lifestyle advice related to the case. For example, specific advice on diet, exercise, and stress management is displayed. In this way, providing lifestyle advice related to the case improves the patient's quality of life.

[0044] When providing information to patients, the information provision unit uses the generation AI to provide video content related to the case, allowing the provision of information that is visually easy to understand. For example, when providing information to patients, the information provision unit uses the generation AI to provide video content related to the case, allowing the provision of information that is visually easy to understand. For example, treatment procedures and prognosis progress may be explained through video. In this way, providing video content related to the case makes it easier for patients to visually understand the information.

[0045] When providing information to patients, the information provision department can have the generating AI introduce community forums related to the case, promoting interaction with other patients. For example, when providing information to patients, the information provision department can have the generating AI introduce community forums related to the case, promoting interaction with other patients. For example, by introducing an online forum where patients with the same case gather, this can promote interaction between patients by introducing community forums related to the case.

[0046] When providing the consultation function, the generation AI analyzes past consultation history and can provide the most effective advice. When providing the consultation function, the consultation function provision unit, for example, analyzes past consultation history and can provide the most effective advice. For example, the optimal advice is proposed based on past success stories and failure stories. In this way, by analyzing past consultation history, the most effective advice can be provided.

[0047] The consultation function providing unit can provide a chatbot in which the generation AI answers questions from doctors and patients in real time when the consultation function is provided. The consultation function providing unit, for example, provides a chatbot in which the generation AI answers questions from doctors and patients in real time when the consultation function is provided. For example, it provides immediate answers to questions about medical care. This allows for a quick response by providing a chatbot that answers questions in real time.

[0048] The consultation function providing unit can support collaboration between the generation AI and different experts (for example, psychological counselors and nutritionists) when providing the consultation function. The consultation function providing unit can support collaboration between the generation AI and different experts (for example, psychological counselors and nutritionists) when providing the consultation function. For example, a doctor can share information with a psychological counselor or nutritionist and provide comprehensive advice. This makes it possible to provide comprehensive advice by supporting collaboration between different experts.

[0049] The consultation function providing unit can have the generation AI automatically summarize the consultation content when the consultation function is provided and convert it into a format that can be shared with other users. For example, when the consultation function is provided, the consultation function providing unit can have the generation AI automatically summarize the consultation content and convert it into a format that can be shared with other users. For example, the consultation content can be summarized so that other users can view it. In this way, by automatically summarizing the consultation content and converting it into a format that can be shared with other users, sharing of information becomes easier.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] When uploading a medical history, the medical history uploading unit uses the AI ​​to analyze the patient's living environment and build a database that takes environmental factors into account. For example, it can collect information about the patient's living and working environment and identify factors that cause allergies or stress. By building a database that takes the patient's living environment into account, it becomes possible to provide more appropriate treatments and preventative measures.

[0052] When uploading medical history, the medical history uploading unit uses the AI ​​to analyze the patient's diet and exercise habits and build a database that takes lifestyle habits into account. For example, it can record the patient's diet and exercise frequency and evaluate the risk of lifestyle-related diseases. By building a database that takes into account the patient's lifestyle habits, it becomes possible to provide information useful for preventive medicine and health management.

[0053] The medical history uploading unit can also use the generation AI to analyze the patient's genetic information when uploading the medical history and build a database that takes genetic factors into account. For example, it can evaluate genetic risk based on family history and genetic test results. This allows for the creation of a database that takes into account the patient's genetic information, making it possible to provide personalized medicine and preventative measures.

[0054] The similar case search function can also search for cases in different medical fields and provide cross-disciplinary information. For example, internal medicine and surgery cases can be integrated and reflected in the search results. This allows information from a broader perspective to be provided by integrating information from different medical fields.

[0055] The similar case search unit can also analyze visual data of cases (e.g., X-ray images and MRI images) and provide visual information. For example, it displays search results based on diagnostic imaging data. This allows for the analysis of visual data to provide information that is easy to understand visually.

[0056] When providing information to doctors, the information provision section uses the generation AI to automatically search for the latest research papers and academic presentations and provide relevant information. For example, the latest research results and academic presentations on a specific treatment method are reflected in the search results. This allows the system to provide relevant information by automatically searching for the latest research papers and academic presentations.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The medical history uploading unit uploads the medical history. For example, doctors and patients enter information about diagnosis results, treatment details, and follow-up observations into the system. The medical history uploading unit can also use the generation AI to automatically extract and tag important points in the medical history. Step 2: The database construction unit analyzes the medical history uploaded by the medical history upload unit and builds a database. For example, the generation AI can analyze the medical history using text mining technology and register it in the database. The generation AI can also compare it with past medical history data to detect abnormal values ​​and patterns in real time. Step 3: The similar case search unit searches for similar cases from the database constructed by the database construction unit. For example, the generation AI takes into account the chronological progression of the case and prioritizes displaying the most relevant cases. The generation AI can also take into account the geographical information of the case and provide region-specific treatments and prognoses. Step 4: The information provision unit provides treatment methods, explanations, and prognosis based on similar cases found by the similar case search unit. For example, the generation AI can provide detailed case analysis results to doctors and present treatment options from multiple angles. The generation AI can also generate infographics to explain the progress of the case to patients in an easy-to-understand manner.

[0059] (Example 2) The failure case information provision system according to an embodiment of the present invention is a system in which a patient's medical history is uploaded, a generation AI searches for similar cases, and provides treatment methods, explanations, and prognosis. This allows the failure case information provision system to aim for a world in which doctors and patients can access information about failure cases and seek advice.

[0060] The failed case information provision system according to the embodiment includes a medical history uploading unit, a database construction unit, a similar case search unit, and an information provision unit. The medical history uploading unit uploads medical histories. For example, doctors and patients input information about diagnosis results, treatment details, and follow-up observations into the system. The medical history uploading unit can also automatically extract and tag important points in the medical history using a generation AI. The database construction unit analyzes the medical histories uploaded by the medical history uploading unit to construct a database. For example, the generation AI can analyze the medical history using text mining technology and register it in the database. The generation AI can also compare it with past medical history data to detect abnormal values ​​and abnormal patterns in real time. The similar case search unit searches for similar cases from the database constructed by the database construction unit. For example, the generation AI can prioritize the most relevant cases by considering the chronological progression of the case. The generation AI can also consider the geographical information of the case and provide region-specific treatment methods and prognoses. The information provision unit provides treatment methods, explanations, and prognoses based on the similar cases searched by the similar case search unit. For example, generative AI can provide doctors with detailed analysis results of cases and present treatment options from multiple angles. Generative AI can also generate infographics to explain the progress of cases to patients in an easy-to-understand manner. In this way, the failure case information provision system can aim for a world in which doctors and patients can access information about failure cases and consult with them.

[0061] In the medical history uploading section, the generation AI can automatically extract and tag important points in the medical history when the medical history is uploaded. For example, when the medical history is uploaded, the generation AI analyzes information on diagnosis results, treatment details, and follow-up observations, and automatically extracts important points. For example, information on specific symptoms and treatments is tagged and registered in the database. This automatically extracts and tags important points in the medical history, improving the accuracy of the database.

[0062] When uploading a medical history, the generation AI compares it with past medical history data, enabling it to detect abnormal values ​​and patterns in real time. When uploading a medical history, the generation AI compares it with past medical history data, enabling it to detect abnormal values ​​and patterns in real time. For example, when uploading a medical history, the generation AI analyzes blood test results and diagnostic imaging data to identify abnormal values ​​and patterns. This allows for early detection of abnormal values ​​and patterns, enabling early response.

[0063] The medical history uploading unit uses a generating AI to analyze the emotional elements of the patient when uploading the medical history, and can build a database that takes into account their psychological state. For example, when uploading a medical history, the medical history uploading unit uses a generating AI to analyze the emotional elements of the patient and build a database that takes into account their psychological state. For example, cases that require psychological support can be identified based on the patient's emotional score. This makes it possible to build a database that takes into account the patient's psychological state, and provide more appropriate information.

[0064] The medical history uploading unit can be added with a function that allows voice input and image data to be uploaded simultaneously when uploading a medical history, thereby building a multimodal database.The medical history uploading unit can be added with a function that allows voice input and image data to be uploaded simultaneously when uploading a medical history, thereby building a multimodal database.For example, a patient's voice memo and diagnostic imaging data can be uploaded together.This allows voice input and image data to be uploaded simultaneously, thereby building a database that contains a wider variety of information.

[0065] The medical history uploading unit can automatically translate inputs in different languages ​​when uploading medical histories and build an international database. The medical history uploading unit can automatically translate inputs in different languages ​​when uploading medical histories and build an international database. For example, medical history data in English, French, etc. can be automatically translated and registered in the database. This allows an international database to be built by automatically translating inputs in different languages.

[0066] The medical history uploading unit uses a generation AI to analyze the patient's emotions in real time when uploading the medical history and provide positive feedback. For example, the medical history uploading unit uses a generation AI to analyze the patient's emotions in real time when uploading the medical history and provide positive feedback. For example, it can display an encouraging message based on the patient's emotion score. This makes it possible to analyze the patient's emotions in real time and provide positive feedback, thereby providing psychological support to the patient.

[0067] When searching for similar cases, the generation AI takes into account the chronological progression of the case, and prioritizes displaying the most relevant cases. For example, when searching for similar cases, the generation AI takes into account the chronological progression of the case, and prioritizes displaying the most relevant cases. For example, search results are displayed based on the time elapsed since the start of treatment and the degree of progression of symptoms. This allows for the provision of more relevant information by taking into account the chronological progression of the case.

[0068] When searching for similar cases, the generation AI also takes into account the geographical information of the case, making it possible to provide region-specific treatment methods and prognosis. For example, when searching for similar cases, the generation AI also takes into account the geographical information of the case, making it possible to provide region-specific treatment methods and prognosis. For example, common treatment methods and prognosis in a specific region are reflected in the search results. By taking geographical information into account, it is possible to provide region-specific treatment methods and prognosis.

[0069] The similar case search unit uses the emotion estimation function to analyze the user's emotional response to the search results of similar cases and can provide information that is likely to resonate emotionally with the user preferentially. The similar case search unit, for example, analyzes the user's emotional response to the search results of similar cases and can provide information that is likely to resonate emotionally with the user preferentially. For example, information with a high number of positive emotional responses is preferentially displayed. In this way, by analyzing the user's emotional response, it is possible to provide information that is likely to resonate emotionally with the user.

[0070] The similar case search unit can also search for cases in different medical fields and provide cross-disciplinary information. For example, when searching for similar cases, the generation AI can search for cases in different medical fields and provide cross-disciplinary information. For example, internal medicine and surgery cases can be integrated and reflected in the search results. This makes it possible to provide information from a broader perspective by integrating information from different medical fields.

[0071] The similar case search unit can also analyze visual data of cases (for example, X-ray images or MRI images) and provide visual information. For example, when searching for similar cases, the similar case search unit uses the generation AI to analyze visual data of cases (for example, X-ray images or MRI images) and provide visual information. For example, it displays search results based on diagnostic imaging data. This makes it possible to provide information that is visually easy to understand by analyzing visual data.

[0072] The similar case search unit uses the emotion estimation function to monitor the user's emotional response to the search results of similar cases in real time, and can continuously provide optimal information. The similar case search unit, for example, monitors the user's emotional response to the search results of similar cases in real time, and can continuously provide optimal information. For example, information with a high number of positive emotional responses is preferentially displayed, thereby increasing user satisfaction. In this way, by monitoring the user's emotional response in real time, optimal information can be continuously provided.

[0073] When providing information to doctors, the information provision unit uses the generating AI to provide detailed analysis results of the case, allowing for a multifaceted presentation of treatment options. For example, when providing information to doctors, the information provision unit uses the generating AI to provide detailed analysis results of the case, allowing for a multifaceted presentation of treatment options. For example, the success rate and risks of specific treatments may be analyzed, and multiple treatment options may be proposed. This allows for a multifaceted presentation of treatment options by providing detailed analysis results of the case.

[0074] The information provision unit can provide relevant information by having the generating AI automatically search for the latest research papers and academic presentations when providing information to doctors. For example, when providing information to doctors, the information provision unit can provide relevant information by having the generating AI automatically search for the latest research papers and academic presentations. For example, the latest research results and academic presentations on a specific treatment method are reflected in the search results. This makes it possible to provide relevant information by automatically searching for the latest research papers and academic presentations.

[0075] The information provision unit can use the emotion estimation function to analyze the stress and anxiety that doctors are experiencing and provide psychological support information. For example, when providing information to doctors, the information provision unit uses the generation AI to analyze the doctor's emotions and provide psychological support information to reduce stress and anxiety. For example, it can display advice on relaxation techniques and stress management. This allows the system to analyze doctors' stress and anxiety and provide psychological support information to support their mental health.

[0076] When providing information to doctors, the information provision unit integrates data from medical institutions with different generation AIs, making it possible to provide information from a wide range of perspectives.For example, when providing information to doctors, the information provision unit integrates data from medical institutions with different generation AIs, making it possible to provide information from a wide range of perspectives.For example, treatment data from multiple hospitals can be integrated and analyzed to propose the optimal treatment method.In this way, by integrating data from different medical institutions, it becomes possible to provide information from a wide range of perspectives.

[0077] When providing information to doctors, the information provision unit uses the generation AI to generate a 3D model of the case, making it possible to provide information that is visually easy to understand. For example, when providing information to doctors, the information provision unit uses the generation AI to generate a 3D model of the case, making it possible to provide information that is visually easy to understand. For example, a surgical simulation or anatomical structure is displayed in 3D model. In this way, by generating a 3D model of the case, it is possible to provide information that is visually easy to understand.

[0078] The information provision unit can use the emotion estimation function to identify the information that doctors are most interested in and provide that information preferentially. For example, when providing information to doctors, the information provision unit uses the generation AI to analyze the doctor's emotions, identify the information that doctors are most interested in, and provide it preferentially. For example, information about specific treatments or cases is displayed preferentially. This allows the information that doctors are most interested in to be identified and provided preferentially, making it possible to provide information that meets the doctor's needs.

[0079] The information provision unit can cause the generation AI to generate infographics that explain the progress of a case in an easy-to-understand manner when providing information to patients. For example, when providing information to patients, the information provision unit causes the generation AI to generate infographics that explain the progress of a case in an easy-to-understand manner. For example, the information provision unit visually displays treatment steps and prognosis progress. In this way, generating infographics that explain the progress of a case in an easy-to-understand manner makes it easier for patients to understand the information.

[0080] The information provision unit allows the generation AI to provide lifestyle advice related to the case when providing information to the patient. For example, when providing information to the patient, the information provision unit allows the generation AI to provide lifestyle advice related to the case. For example, specific advice on diet, exercise, and stress management is displayed. In this way, providing lifestyle advice related to the case improves the patient's quality of life.

[0081] The information provision unit can use the emotion estimation function to analyze the patient's psychological state and provide information containing positive messages. For example, when providing information to a patient, the information provision unit uses the generation AI to analyze the patient's emotions and provide information containing positive messages. For example, encouraging messages and success stories are displayed. This makes it possible to analyze the patient's psychological state and provide positive messages to provide psychological support to the patient.

[0082] When providing information to patients, the information provision unit uses the generation AI to provide video content related to the case, allowing the provision of information that is visually easy to understand. For example, when providing information to patients, the information provision unit uses the generation AI to provide video content related to the case, allowing the provision of information that is visually easy to understand. For example, treatment procedures and prognosis progress may be explained through video. In this way, providing video content related to the case makes it easier for patients to visually understand the information.

[0083] When providing information to patients, the information provision department can have the generating AI introduce community forums related to the case, promoting interaction with other patients. For example, when providing information to patients, the information provision department can have the generating AI introduce community forums related to the case, promoting interaction with other patients. For example, by introducing an online forum where patients with the same case gather, this can promote interaction between patients by introducing community forums related to the case.

[0084] The information provision unit can use the emotion estimation function to identify information that patients find most reassuring and provide that information preferentially. For example, when providing information to patients, the information provision unit uses the generation AI to analyze the patient's emotions, identify the most reassuring information, and provide it preferentially. For example, information that gives a sense of security can be displayed based on the patient's emotion score. This allows the information that patients find most reassuring to be identified and provided preferentially, making it possible to provide psychological support to patients.

[0085] When providing the consultation function, the generation AI analyzes past consultation history and can provide the most effective advice. When providing the consultation function, the consultation function provision unit, for example, analyzes past consultation history and can provide the most effective advice. For example, the optimal advice is proposed based on past success stories and failure stories. In this way, by analyzing past consultation history, the most effective advice can be provided.

[0086] The consultation function providing unit can provide a chatbot in which the generation AI answers questions from doctors and patients in real time when the consultation function is provided. The consultation function providing unit, for example, provides a chatbot in which the generation AI answers questions from doctors and patients in real time when the consultation function is provided. For example, it provides immediate answers to questions about medical care. This allows for a quick response by providing a chatbot that answers questions in real time.

[0087] The consultation function providing unit can use the emotion estimation function to analyze the user's emotional reaction to the consultation content and provide advice that is likely to resonate emotionally. For example, when providing the consultation function, the consultation function providing unit uses a generation AI to analyze the user's emotions and provide advice that is likely to resonate emotionally. For example, advice that shows empathy is displayed based on the user's emotion score. In this way, by analyzing the user's emotional reaction, it is possible to provide advice that is likely to resonate emotionally.

[0088] The consultation function providing unit can support collaboration between the generation AI and different experts (for example, psychological counselors and nutritionists) when providing the consultation function. The consultation function providing unit can support collaboration between the generation AI and different experts (for example, psychological counselors and nutritionists) when providing the consultation function. For example, a doctor can share information with a psychological counselor or nutritionist and provide comprehensive advice. This makes it possible to provide comprehensive advice by supporting collaboration between different experts.

[0089] The consultation function providing unit can have the generation AI automatically summarize the consultation content when the consultation function is provided and convert it into a format that can be shared with other users. For example, when the consultation function is provided, the consultation function providing unit can have the generation AI automatically summarize the consultation content and convert it into a format that can be shared with other users. For example, the consultation content can be summarized so that other users can view it. In this way, by automatically summarizing the consultation content and converting it into a format that can be shared with other users, sharing of information becomes easier.

[0090] The consultation function providing unit uses the emotion estimation function to monitor the user's emotional response to the consultation content in real time and continuously provide optimal advice. For example, when providing the consultation function, the consultation function providing unit has the generation AI monitor the user's emotions in real time and continuously provide optimal advice. For example, optimal advice is displayed based on the user's emotion score. In this way, by monitoring the user's emotional response in real time, optimal advice can be continuously provided.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] When uploading a medical history, the medical history uploading unit uses the AI ​​to analyze the patient's living environment and build a database that takes environmental factors into account. For example, it can collect information about the patient's living and working environment and identify factors that cause allergies or stress. By building a database that takes the patient's living environment into account, it becomes possible to provide more appropriate treatments and preventative measures.

[0093] When uploading medical history, the medical history uploading unit uses the AI ​​to analyze the patient's diet and exercise habits and build a database that takes lifestyle habits into account. For example, it can record the patient's diet and exercise frequency and evaluate the risk of lifestyle-related diseases. By building a database that takes into account the patient's lifestyle habits, it becomes possible to provide information useful for preventive medicine and health management.

[0094] The medical history uploading unit can also use the generation AI to analyze the patient's genetic information when uploading the medical history and build a database that takes genetic factors into account. For example, it can evaluate genetic risk based on family history and genetic test results. This allows for the creation of a database that takes into account the patient's genetic information, making it possible to provide personalized medicine and preventative measures.

[0095] The medical history uploading section uses a generation AI to analyze the patient's emotional factors when uploading the medical history, and can build a database that takes into account their psychological state. For example, it can identify cases that require psychological support based on the patient's emotional score. This allows for the creation of a database that takes into account the patient's psychological state, making it possible to provide more appropriate information.

[0096] When uploading a medical history, the AI ​​analyzes the patient's emotions in real time and can provide positive feedback. For example, it can display an encouraging message based on the patient's emotion score. This allows for psychological support for patients by analyzing their emotions in real time and providing positive feedback.

[0097] The similar case search function can also search for cases in different medical fields and provide cross-disciplinary information. For example, internal medicine and surgery cases can be integrated and reflected in the search results. This allows information from a broader perspective to be provided by integrating information from different medical fields.

[0098] The similar case search unit can also analyze visual data of cases (e.g., X-ray images and MRI images) and provide visual information. For example, it displays search results based on diagnostic imaging data. This allows for the analysis of visual data to provide information that is easy to understand visually.

[0099] The similar case search unit uses the emotion estimation function to analyze the user's emotional response to the search results of similar cases and can provide information that is likely to resonate with the user emotionally. For example, information that has a high number of positive emotional responses can be displayed preferentially. In this way, by analyzing the user's emotional response, it is possible to provide information that is likely to resonate with the user emotionally.

[0100] When providing information to doctors, the information provision section uses the generation AI to automatically search for the latest research papers and academic presentations and provide relevant information. For example, the latest research results and academic presentations on a specific treatment method are reflected in the search results. This allows the system to provide relevant information by automatically searching for the latest research papers and academic presentations.

[0101] The information provider can use the emotion estimation function to analyze the stress and anxiety experienced by doctors and provide psychological support information. For example, it can display relaxation techniques and stress management advice. This allows the system to analyze doctors' stress and anxiety and provide psychological support information to support their mental health.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The medical history uploading unit uploads the medical history. For example, doctors and patients enter information about diagnosis results, treatment details, and follow-up observations into the system. The medical history uploading unit can also use the generation AI to automatically extract and tag important points in the medical history. Step 2: The database construction unit analyzes the medical history uploaded by the medical history upload unit and builds a database. For example, the generation AI can analyze the medical history using text mining technology and register it in the database. The generation AI can also compare it with past medical history data to detect abnormal values ​​and patterns in real time. Step 3: The similar case search unit searches for similar cases from the database constructed by the database construction unit. For example, the generation AI takes into account the chronological progression of the case and prioritizes displaying the most relevant cases. The generation AI can also take into account the geographical information of the case and provide region-specific treatments and prognoses. Step 4: The information provision unit provides treatment methods, explanations, and prognosis based on similar cases found by the similar case search unit. For example, the generation AI can provide detailed case analysis results to doctors and present treatment options from multiple angles. The generation AI can also generate infographics to explain the progress of the case to patients in an easy-to-understand manner.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] Furthermore, the processing by the data processing system 10 described above 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 necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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, RAM 30, and 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.

[0110] 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.

[0111] 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 audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] 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 carried out in a secure state.

[0114] Fig. 4 shows an example of the 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.

[0115] 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.

[0116] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0120] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, 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, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] 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, RAM 30, and 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.

[0125] 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.

[0126] 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 audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] 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 carried out in a secure state.

[0129] 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.

[0130] 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.

[0131] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0133] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] 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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0135] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] 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 also 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. Furthermore, 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, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] 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, RAM 30, and 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.

[0140] 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. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0141] 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 audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] 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 carried out in a secure state.

[0144] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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.

[0145] 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.

[0146] 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.

[0147] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0148] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0149] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] 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 voice indicating a user input regarding 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.

[0151] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, 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.

[0153] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] FIG. 9 illustrates 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. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0155] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically 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.

[0156] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0157] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of 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, "relieved," "calm," and "reassuring," have similar emotion values.

[0160] 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.

[0161] 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 USB (Universal Serial Bus) 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.

[0162] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0163] 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.

[0164] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0165] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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.

[0166] As an example of a system configured with a single 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 system that uses a processor that realizes the functions of an 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.

[0167] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0168] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0169] The above-described description and illustrations are a detailed explanation 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, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects 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 replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0171] 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 medical history uploading section for uploading medical history; a database construction unit that analyzes the medical history uploaded by the medical history upload unit and constructs a database; a similar case search unit that searches for similar cases from the database constructed by the database construction unit; an information providing unit that provides treatment methods, explanations, and prognosis based on the similar cases searched for by the similar case search unit; A system characterized by:

2. The medical history upload unit When the medical history is uploaded, the generation AI automatically extracts and tags the key points of the medical history. The system of claim 1 .

3. The similar case search unit When searching for similar cases, the generation AI takes into account the chronological progression of the cases and prioritizes displaying the most relevant cases. The system of claim 1 .

4. The information providing unit When providing information to doctors, the generative AI provides detailed analysis of the case and presents a variety of treatment options. The system of claim 1 .

5. The information providing unit When providing information to patients, generative AI generates infographics to clearly explain the progress of the case. The system of claim 1 .

6. The medical history upload unit When the medical history is uploaded, the generative AI analyzes the patient's emotional factors and builds the database taking into account their psychological state. The system of claim 1 .

7. The similar case search unit Using the emotion estimation function, the emotional response of the user to the search results of similar cases is analyzed, and information that is likely to resonate emotionally is provided preferentially. The system of claim 1 .

8. The information providing unit Using emotion estimation functionality, the system analyzes the stress and anxiety experienced by doctors and provides psychological support information. The system of claim 1 .

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