System

Abstract

An object of the system according to the embodiment is to efficiently collect and share information on unknown symptoms and rare cases.SOLUTION: A system includes a collection unit, an analysis unit, a cleansing unit, and a provision unit. The collection unit collects information on symptoms and cases of a patient. The analysis unit analyzes the information collected by the collection unit, and collects and shares necessary information. The cleansing unit cleanses the information analyzed by the analysis unit and excludes erroneous information. The providing unit provides the information cleansed by the cleansing unit to the healthcare professional.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 been inadequate in collecting and sharing information on unknown symptoms and rare cases, making it difficult for medical professionals to access appropriate information.

[0005] The system according to the embodiment aims to efficiently collect and share information on unknown symptoms and rare cases. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a cleansing unit, and a providing unit. The collection unit collects information related to a patient's symptoms and cases. The analysis unit analyzes the information collected by the collection unit, collects necessary information, and shares it. The cleansing unit cleanses the information analyzed by the analysis unit and eliminates erroneous information. The providing unit provides the information cleansed by the cleansing unit to a medical professional. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect and share information on unknown symptoms and rare cases. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. 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) A medical information provision system according to an embodiment of the present invention efficiently collects, analyzes, cleanses, and provides information about patient symptoms and cases. The medical information provision system collects information about patient symptoms and cases, which is then analyzed by a generation AI, which then collects and shares the necessary information. Furthermore, data cleansing ensures the accuracy of the collected information and reduces the risk of incorrect information. For example, the medical information provision system collects detailed data, such as the patient's medical history, current symptoms, and past medical records. Next, the generation AI analyzes the collected information and identifies the most appropriate treatment for the patient's symptoms based on past case data and the latest medical literature. Furthermore, data cleansing eliminates duplicate data and inaccurate information, providing only reliable information. This allows medical professionals to instantly access the information they need and improves patients' quality of medical care (QoM). This allows the medical information provision system to efficiently collect, analyze, cleanse, and provide information about patient symptoms and cases. For example, patients with unknown symptoms or rare cases can receive appropriate treatment promptly, reducing the risk of patient confusion, unnecessary tests, and incorrect treatment. This allows healthcare professionals to instantly access the information they need, improving patient quality of life.

[0029] A medical information provision system according to an embodiment includes a collection unit, an analysis unit, a cleansing unit, and a provision unit. The collection unit collects information about a patient's symptoms and cases. Information about a patient's symptoms and cases includes, but is not limited to, medical history, medical records, and test results. The collection unit can collect information, for example, by retrieving it from a database or by direct input from the patient. The collection unit can also collect detailed data such as a patient's medical history, current symptoms, and past medical records. For example, the collection unit collects information such as symptoms reported by the patient and findings observed by a doctor. The analysis unit uses a generation AI to analyze the information collected by the collection unit. The analysis can be performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to, examples. For example, the generation AI can analyze past case data and the latest medical papers to identify the treatment most appropriate for the patient's symptoms. The analysis unit can also use the generation AI to efficiently collect and share necessary information based on the collected information. The cleansing unit cleanses the information collected by the collection unit and analyzed by the analysis unit. Cleansing may, for example, involve deleting duplicate data or correcting erroneous data, but is not limited to such examples. For example, the cleansing unit removes duplicate data and inaccurate information and provides only highly reliable information. The providing unit provides the information cleansed by the cleansing unit to medical professionals. The provision may, for example, involve sending the information via email or displaying it on a dedicated portal, but is not limited to such examples. For example, the providing unit provides the cleansed information to medical professionals, allowing the medical professionals to perform diagnosis and treatment based on accurate information. As a result, the medical information providing system according to the embodiment can efficiently collect, analyze, cleanse, and provide information about patient symptoms and cases. This allows medical professionals to quickly access appropriate information, improving patients' QoM.

[0030] The collection unit can collect detailed data on the patient's medical history, current symptoms, and past medical records. Examples of detailed data include, but are not limited to, medical history, current symptoms, and past medical records. For example, the collection unit acquires the patient's medical history from an electronic medical record. The collection unit can also acquire the patient's current symptoms from a doctor's examination results. The collection unit can also acquire the patient's past medical records from a hospital database. By collecting detailed patient data in this way, more accurate information can be provided. Some or all of the above-described processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the patient's medical history, current symptoms, and past medical records into the generation AI and cause the generation AI to collect detailed data.

[0031] The analysis unit can use the generative AI to analyze past case data and the latest medical papers and identify the treatment most appropriate for the patient's symptoms. The generative AI can be realized using, for example, natural language processing technology or a deep learning model, but is not limited to these examples. The analysis unit can, for example, use the generative AI to analyze past case data. The analysis unit can also use the generative AI to analyze the latest medical papers. The analysis unit can also use the generative AI to identify the treatment most appropriate for the patient's symptoms. For example, the generative AI can identify the treatment most appropriate for the patient's symptoms based on past case data and the latest medical papers. In this way, the generative AI can quickly identify the treatment most appropriate for the patient's symptoms. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input past case data and the latest medical papers into the generative AI and have the generative AI identify the treatment most appropriate for the patient's symptoms.

[0032] The cleansing unit can eliminate duplicate data and inaccurate information and provide only accurate information. Duplicate data includes, for example, multiple medical records for the same patient, but is not limited to such examples. Inaccurate information includes, for example, erroneous diagnosis results and input errors, but is not limited to such examples. The cleansing unit, for example, detects and eliminates duplicate data. The cleansing unit can also detect and correct inaccurate information. The cleansing unit can also provide only reliable information. For example, the cleansing unit can eliminate duplicate data and inaccurate information and provide only reliable information. By providing only reliable information, the risk of incorrect information can be reduced. Some or all of the above-mentioned processing in the cleansing unit can be performed, for example, using AI or without AI. For example, the cleansing unit can input duplicate data and inaccurate information into the generation AI and have the generation AI perform cleansing.

[0033] The providing unit can provide the cleansed information to a healthcare professional. Examples of cleansed information include, but are not limited to, data with errors corrected and data with duplicates removed. For example, the providing unit can send the cleansed information by email. The providing unit can also display the cleansed information on a dedicated portal. The providing unit can provide the cleansed information to a healthcare professional, allowing the healthcare professional to make a diagnosis or treatment based on accurate information. For example, the providing unit can provide the cleansed information to a healthcare professional, allowing the healthcare professional to make a diagnosis or treatment based on accurate information. Providing the cleansed information allows the healthcare professional to make a diagnosis or treatment based on accurate information. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the cleansed information to a generation AI and cause the generation AI to provide the information.

[0034] The collection unit can analyze the patient's past medical records and select the optimal information collection method. For example, if the collection unit determines that voice input is effective based on the patient's past medical records, it can prioritize voice input. Furthermore, if the collection unit determines that text input is effective based on the patient's past medical records, it can prioritize text input. Furthermore, if the collection unit determines that image input is effective based on the patient's past medical records, it can prioritize image input. For example, the collection unit analyzes the patient's past medical records and selects the optimal information collection method. This enables efficient information collection by selecting the optimal information collection method based on the patient's past medical records. Some or all of the above-described processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the patient's past medical records into the generation AI and cause the generation AI to select the optimal information collection method.

[0035] When collecting information, the collection unit can filter the information based on the patient's current living situation and areas of interest. For example, if the patient is currently working, the collection unit can prioritize collecting information related to work. Also, if the patient is currently on vacation, the collection unit can prioritize collecting information related to relaxation. Also, if the patient is interested in a particular illness, the collection unit can prioritize collecting information related to that illness. For example, the collection unit filters information based on the patient's current living situation and areas of interest. In this way, by filtering information based on the patient's living situation and areas of interest, more relevant information can be collected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the patient's living situation and areas of interest into the generation AI and have the generation AI perform information filtering.

[0036] When collecting information, the collection unit can select the optimal collection means depending on the patient's input method. For example, if the patient selects voice input, the collection unit can collect information using voice recognition technology. Furthermore, if the patient selects text input, the collection unit can also collect information using text analysis technology. Furthermore, if the patient selects image input, the collection unit can also collect information using image analysis technology. For example, the collection unit selects the optimal collection means depending on the patient's input method. This enables efficient information collection by selecting the optimal collection means depending on the patient's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the patient's input data into a generation AI and have the generation AI select the optimal collection means.

[0037] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the patient's geographical location information. For example, if the patient lives in a specific area, the collection unit can prioritize collecting medical information related to that area. Furthermore, if the patient is traveling, the collection unit can prioritize collecting medical information for the patient's travel destination. Furthermore, if the patient is planning to move, the collection unit can prioritize collecting medical information for the patient's new residence. For example, the collection unit prioritizes collecting highly relevant information by taking into account the patient's geographical location information. In this way, more relevant information can be collected by taking into account the patient's geographical location information. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input the patient's geographical location information into the generation AI and cause the generation AI to collect highly relevant information.

[0038] When collecting information, the collection unit can analyze the patient's social media activities and collect relevant information. For example, the collection unit can collect relevant medical information based on health information shared by the patient on social media. The collection unit can also analyze the patient's social media posts and collect relevant medical information. The collection unit can also collect relevant medical information by referring to the activities of the patient's friends on social media. For example, the collection unit can analyze the patient's social media activities and collect relevant information. In this way, more relevant information can be collected by analyzing the patient's social media activities. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input the patient's social media data into the generation AI and cause the generation AI to collect relevant information.

[0039] The collection unit can customize the collection method by reflecting the patient's past feedback when collecting information. For example, the collection unit adjusts the information collection method based on feedback provided by the patient in the past. The collection unit can also preferentially use information collection methods that the patient has previously preferred. The collection unit can also avoid information collection methods that the patient has previously dissatisfied with. For example, the collection unit customizes the collection method by reflecting the patient's past feedback. This enables more appropriate information collection by reflecting the patient's past feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the patient's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the case. For example, the analysis unit performs a detailed analysis for cases of high importance. The analysis unit can also perform a simplified analysis for cases of low importance. The analysis unit can also perform an analysis with an appropriate level of detail for cases of medium importance. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the case. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the case. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input case importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the case category. For example, the analysis unit can apply an analysis algorithm specialized for infectious diseases to cases of infectious diseases. Furthermore, the analysis unit can apply an analysis algorithm specialized for cancer to cases of cancer. Furthermore, the analysis unit can apply an analysis algorithm specialized for heart disease to cases of heart disease. For example, the analysis unit applies different analysis algorithms depending on the case category. In this way, applying different analysis algorithms depending on the case category enables more accurate analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input case category data into the generation AI and cause the generation AI to apply different analysis algorithms.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the patient's past analysis results. The analysis unit, for example, corrects the current analysis result based on the patient's past analysis results. The analysis unit can also adjust the analysis algorithm based on the patient's past analysis results. The analysis unit can also improve the reliability of the analysis based on the patient's past analysis results. For example, the analysis unit improves the accuracy of the analysis by referring to the patient's past analysis results. In this way, the accuracy of the analysis is improved by referring to the patient's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the patient's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0043] During analysis, the analysis unit can determine the priority of analysis based on the time of case submission. For example, the analysis unit prioritizes analysis of recently submitted cases. The analysis unit can also postpone analysis of cases submitted recently. The analysis unit can also perform analysis with a moderate priority for cases submitted recently. For example, the analysis unit determines the priority of analysis based on the time of case submission. This enables efficient analysis by determining the priority of analysis based on the time of case submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input case submission time data into the generation AI and have the generation AI determine the analysis priority.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of cases. For example, the analysis unit prioritizes analysis of highly relevant cases. The analysis unit can also postpone analysis of low-relevant cases. The analysis unit can also analyze moderately relevant cases in an appropriate order. For example, the analysis unit adjusts the order of analysis based on the relevance of cases. This enables efficient analysis by adjusting the order of analysis based on the relevance of cases. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input case relevance data into the generation AI and have the generation AI adjust the order of analysis.

[0045] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the patient's level of expertise. For example, if the patient has technical expertise, the analysis unit uses a lot of technical terminology. Furthermore, if the patient does not have technical expertise, the analysis unit can also avoid technical terminology. Furthermore, the analysis unit can use technical terminology appropriately according to the patient's level of expertise. For example, the analysis unit adjusts the use of technical terminology in the analysis according to the patient's level of expertise. This allows for adjustment of the use of technical terminology according to the patient's level of expertise, thereby providing analysis results that are easier to understand. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the patient's level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology.

[0046] The cleansing unit can improve the accuracy of cleansing by taking into account the interrelationships between information during cleansing. For example, the cleansing unit analyzes the interrelationships between information and eliminates duplicate data. The cleansing unit can also analyze the interrelationships between information and correct inaccurate information. The cleansing unit can also analyze the interrelationships between information and prioritize highly reliable information. For example, the cleansing unit improves the accuracy of cleansing by taking into account the interrelationships between information. In this way, the accuracy of cleansing is improved by taking the interrelationships between information into account. Some or all of the above-mentioned processing in the cleansing unit may be performed using AI, for example, or may be performed without using AI. For example, the cleansing unit can input interrelationship data between information to the generation AI and cause the generation AI to improve the accuracy of cleansing.

[0047] The cleansing unit can perform cleansing while taking into account attribute information of the information submitter. For example, if the submitter is an expert, the cleansing unit will prioritize cleansing of the information. Furthermore, if the submitter is an ordinary person, the cleansing unit can also carefully cleanse the information. Furthermore, the cleansing unit can adjust the cleansing criteria based on the attribute information of the submitter. For example, the cleansing unit performs cleansing while taking into account attribute information of the information submitter. In this way, by taking into account the attribute information of the information submitter, more reliable information can be provided. Some or all of the above-described processing in the cleansing unit may be performed using, for example, AI, or may be performed without using AI. For example, the cleansing unit can input attribute information data of the submitter to a generation AI and have the generation AI perform cleansing.

[0048] During cleansing, the cleansing unit can weight the cleansing based on the frequency of information submission. For example, the cleansing unit can perform strict cleansing on information that is submitted frequently. The cleansing unit can also perform flexible cleansing on information that is submitted infrequently. The cleansing unit can also perform moderate cleansing on information that is submitted at a medium frequency. For example, the cleansing unit weights the cleansing based on the frequency of information submission. This enables efficient cleansing by weighting the cleansing based on the frequency of information submission. Some or all of the above-described processing in the cleansing unit may be performed using, for example, AI, or may be performed without using AI. For example, the cleansing unit can input information submission frequency data to the generation AI and have the generation AI perform the cleansing weighting.

[0049] The cleansing unit can perform cleansing taking into account the geographical distribution of information. For example, the cleansing unit prioritizes cleansing of information related to a specific region. The cleansing unit can also integrate and cleanse geographically dispersed information. The cleansing unit can also adjust the cleansing criteria based on the geographical distribution. For example, the cleansing unit performs cleansing taking into account the geographical distribution of information. This makes it possible to provide more relevant information by taking the geographical distribution of information into consideration. Some or all of the above-described processing in the cleansing unit may be performed using, for example, AI, or may be performed without using AI. For example, the cleansing unit can input geographical distribution data of information to a generation AI and have the generation AI perform cleansing.

[0050] During cleansing, the cleansing unit can improve the accuracy of the cleansing by referring to related literature of the information. For example, the cleansing unit can refer to related literature to confirm the reliability of the information. The cleansing unit can also refer to related literature to complement the accuracy of the information. The cleansing unit can also refer to related literature to eliminate duplication of information. For example, the cleansing unit can improve the accuracy of the cleansing by referring to related literature of the information. In this way, by referring to related literature of the information, the accuracy of the cleansing is improved. Some or all of the above-mentioned processing in the cleansing unit may be performed using, for example, AI, or may be performed without using AI. For example, the cleansing unit can input related literature data to the generation AI and cause the generation AI to improve the accuracy of the cleansing.

[0051] The cleansing unit can perform cleansing taking into account the market value of the information during cleansing. For example, the cleansing unit performs strict cleansing on information with high market value. The cleansing unit can also perform flexible cleansing on information with low market value. The cleansing unit can also perform moderate cleansing on information with medium market value. For example, the cleansing unit performs cleansing taking into account the market value of the information. In this way, more important information can be provided by taking the market value of the information into account. Some or all of the above-mentioned processing in the cleansing unit may be performed using, for example, AI, or may be performed without using AI. For example, the cleansing unit can input market value data of the information to the generation AI and have the generation AI perform cleansing.

[0052] When providing information, the providing unit can select the optimal delivery method by referring to the medical professional's past usage history. For example, the providing unit can preferentially use a delivery method that the medical professional has previously preferred. The providing unit can also suggest the optimal delivery method based on the medical professional's past usage history. The providing unit can also adjust the delivery method by referring to the medical professional's past feedback. For example, when providing information, the providing unit selects the optimal delivery method by referring to the medical professional's past usage history. This makes it possible to provide more appropriate information by referring to the medical professional's past usage history. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the medical professional's past usage history data into the generation AI and cause the generation AI to select the optimal delivery method.

[0053] The providing unit can customize the content to be provided according to the current task of the medical worker when providing information. For example, when the medical worker is in a consultation, the providing unit can prioritize providing information related to the consultation. Furthermore, when the medical worker is conducting research, the providing unit can also prioritize providing information related to the research. Furthermore, when the medical worker is taking a break, the providing unit can provide information related to relaxation. For example, the providing unit customizes the content to be provided according to the current task of the medical worker when providing information. This enables more appropriate information to be provided by customizing the content to be provided according to the current task of the medical worker. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input current task data of the medical worker into the generating AI and cause the generating AI to customize the content to be provided.

[0054] The providing unit can improve the method of providing information by reflecting feedback from the medical professional when providing information. The providing unit, for example, adjusts the method of providing information based on the feedback from the medical professional. The providing unit can also improve the content of the information provided based on the feedback from the medical professional. The providing unit can also adjust the timing of providing information based on the feedback from the medical professional. For example, the providing unit improves the method of providing information by reflecting feedback from the medical professional when providing information. This makes it possible to provide more appropriate information by reflecting feedback from the medical professional. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input feedback data from the medical professional into the generating AI and cause the generating AI to improve the method of providing information.

[0055] The providing unit can select the optimal delivery method by taking into account the medical professional's device information when providing information. For example, if the medical professional is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the medical professional is using a tablet, the providing unit can also provide a display method optimized for a large screen. Furthermore, if the medical professional is using a desktop, the providing unit can also provide detailed information. For example, the providing unit selects the optimal delivery method by taking into account the medical professional's device information when providing information. This enables more appropriate information to be provided by taking into account the medical professional's device information. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the medical professional's device information data into the generating AI and cause the generating AI to select the optimal delivery method.

[0056] The providing unit can provide the information in multiple languages ​​according to the language setting of the medical professional when providing the information. The providing unit can automatically set the language of the information provided based on, for example, the language setting of the medical professional's device. The providing unit can also provide a language switching function when the medical professional uses multiple languages. The providing unit can also provide information in a specific language when the medical professional selects that language. For example, the providing unit can provide the information in multiple languages ​​according to the language setting of the medical professional when providing the information. This enables more appropriate information to be provided by providing the information in multiple languages ​​according to the language setting of the medical professional. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the language setting data of the medical professional to a generation AI and have the generation AI perform multilingual support.

[0057] The providing unit can provide relevant information by analyzing the social media activity of the medical professional when providing information. For example, the providing unit can provide relevant medical information based on information shared by the medical professional on social media. The providing unit can also analyze the content of the medical professional's social media posts and provide relevant medical information. The providing unit can also provide relevant medical information by referring to the activities of the medical professional's friends on social media. For example, the providing unit can analyze the social media activity of the medical professional when providing information and provide relevant information. In this way, by analyzing the social media activity of the medical professional, more relevant information can be provided. Some or all of the above-described processing by the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the social media data of the medical professional into the generation AI and cause the generation AI to provide relevant information.

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

[0059] The collection unit collects lifestyle data on patients, and the analysis unit can predict the patient's health risks based on this data. For example, the collection unit collects data on the patient's diet, exercise habits, sleep patterns, etc. The analysis unit analyzes this data and predicts what health risks the patient may face in the future. Furthermore, the provision unit can provide the patient with preventive measures and lifestyle improvement suggestions based on the predicted health risks. This allows the patient to understand their health condition and take measures early.

[0060] The collection unit collects the patient's genetic information, and the analysis unit can evaluate the patient's genetic risk based on this information. For example, the collection unit collects the patient's DNA sample and analyzes the genetic information. The analysis unit can also evaluate the patient's risk of developing a specific genetic disease or disorder based on the genetic information. Furthermore, the provision unit can suggest appropriate preventive measures and treatments to the patient based on the genetic risk. This allows the patient to understand their genetic risk and take appropriate measures.

[0061] The collection unit can prioritize collection of highly relevant information by taking into account the geographical location information of the patient. For example, if the patient lives in a specific area, medical information related to that area can be collected with priority. Also, if the patient is traveling, medical information for the travel destination can be collected with priority. Furthermore, if the patient is planning to move, medical information for the new residence can be collected with priority. In this way, more relevant information can be collected by taking into account the geographical location information of the patient.

[0062] The collection unit can analyze the patient's social media activities and collect relevant information. For example, it can collect relevant medical information based on health information shared by the patient on social media. It can also analyze the patient's social media posts to collect relevant medical information. It can also collect relevant medical information by referring to the activities of the patient's friends on social media. This allows more relevant information to be collected by analyzing the patient's social media activities.

[0063] The analysis unit can adjust the level of detail of the analysis based on the importance of the case. For example, a detailed analysis can be performed for cases of high importance. A simplified analysis can also be performed for cases of low importance. Furthermore, an analysis with an appropriate level of detail can also be performed for cases of medium importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the case.

[0064] The cleansing unit can improve the accuracy of cleansing by taking into account the interrelationships between information. For example, the cleansing unit can analyze the interrelationships between information and eliminate duplicate data. It can also analyze the interrelationships between information and correct inaccurate information. It can also analyze the interrelationships between information and prioritize highly reliable information. In this way, the accuracy of cleansing is improved by taking into account the interrelationships between information.

[0065] When providing information, the providing unit can select the optimal delivery method by referring to the medical worker's past usage history. For example, it can prioritize the delivery method that the medical worker has previously preferred. It can also suggest the optimal delivery method based on the medical worker's past usage history. Furthermore, it can also adjust the delivery method by referring to the medical worker's past feedback. In this way, by referring to the medical worker's past usage history, it becomes possible to provide more appropriate information.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The collection department collects information about the patient's symptoms and case. This information includes medical history, medical records, and test results. The collection department can collect information from a database or through direct input from the patient. It also collects detailed data such as the symptoms reported by the patient and the findings observed by the doctor. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis is performed using generative AI and can use statistical analysis and machine learning algorithms. The analysis unit analyzes past case data and the latest medical papers to identify the most appropriate treatment for the patient's symptoms. The analysis unit also efficiently collects and shares necessary information based on the collected information. Step 3: The cleansing unit cleanses the information collected by the collection unit and analyzed by the analysis unit. Cleansing involves removing duplicate data and correcting incorrect data. The cleansing unit eliminates duplicate data and inaccurate information and provides only reliable information. Step 4: The provision unit provides the information cleansed by the cleansing unit to medical professionals. Provision is done by sending it by email or displaying it on a dedicated portal. The provision unit provides the cleansed information to medical professionals, allowing them to make diagnoses and provide treatment based on accurate information.

[0068] (Example 2) A medical information provision system according to an embodiment of the present invention efficiently collects, analyzes, cleanses, and provides information about patient symptoms and cases. The medical information provision system collects information about patient symptoms and cases, which is then analyzed by a generation AI, which then collects and shares the necessary information. Furthermore, data cleansing ensures the accuracy of the collected information and reduces the risk of incorrect information. For example, the medical information provision system collects detailed data, such as the patient's medical history, current symptoms, and past medical records. Next, the generation AI analyzes the collected information and identifies the most appropriate treatment for the patient's symptoms based on past case data and the latest medical literature. Furthermore, data cleansing eliminates duplicate data and inaccurate information, providing only reliable information. This allows medical professionals to instantly access the information they need and improves patients' quality of medical care (QoM). This allows the medical information provision system to efficiently collect, analyze, cleanse, and provide information about patient symptoms and cases. For example, patients with unknown symptoms or rare cases can receive appropriate treatment promptly, reducing the risk of patient confusion, unnecessary tests, and incorrect treatment. This allows healthcare professionals to instantly access the information they need, improving patient quality of life.

[0069] A medical information provision system according to an embodiment includes a collection unit, an analysis unit, a cleansing unit, and a provision unit. The collection unit collects information about a patient's symptoms and cases. Information about a patient's symptoms and cases includes, but is not limited to, medical history, medical records, and test results. The collection unit can collect information, for example, by retrieving it from a database or by direct input from the patient. The collection unit can also collect detailed data such as a patient's medical history, current symptoms, and past medical records. For example, the collection unit collects information such as symptoms reported by the patient and findings observed by a doctor. The analysis unit uses a generation AI to analyze the information collected by the collection unit. The analysis can be performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to, examples. For example, the generation AI can analyze past case data and the latest medical papers to identify the treatment most appropriate for the patient's symptoms. The analysis unit can also use the generation AI to efficiently collect and share necessary information based on the collected information. The cleansing unit cleanses the information collected by the collection unit and analyzed by the analysis unit. Cleansing may, for example, involve deleting duplicate data or correcting erroneous data, but is not limited to such examples. For example, the cleansing unit removes duplicate data and inaccurate information and provides only highly reliable information. The providing unit provides the information cleansed by the cleansing unit to medical professionals. The provision may, for example, involve sending the information via email or displaying it on a dedicated portal, but is not limited to such examples. For example, the providing unit provides the cleansed information to medical professionals, allowing the medical professionals to perform diagnosis and treatment based on accurate information. As a result, the medical information providing system according to the embodiment can efficiently collect, analyze, cleanse, and provide information about patient symptoms and cases. This allows medical professionals to quickly access appropriate information, improving patients' QoM.

[0070] The collection unit can collect detailed data on the patient's medical history, current symptoms, and past medical records. Examples of detailed data include, but are not limited to, medical history, current symptoms, and past medical records. For example, the collection unit acquires the patient's medical history from an electronic medical record. The collection unit can also acquire the patient's current symptoms from a doctor's examination results. The collection unit can also acquire the patient's past medical records from a hospital database. By collecting detailed patient data in this way, more accurate information can be provided. Some or all of the above-described processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the patient's medical history, current symptoms, and past medical records into the generation AI and cause the generation AI to collect detailed data.

[0071] The analysis unit can use the generative AI to analyze past case data and the latest medical papers and identify the treatment most appropriate for the patient's symptoms. The generative AI can be realized using, for example, natural language processing technology or a deep learning model, but is not limited to these examples. The analysis unit can, for example, use the generative AI to analyze past case data. The analysis unit can also use the generative AI to analyze the latest medical papers. The analysis unit can also use the generative AI to identify the treatment most appropriate for the patient's symptoms. For example, the generative AI can identify the treatment most appropriate for the patient's symptoms based on past case data and the latest medical papers. In this way, the generative AI can quickly identify the treatment most appropriate for the patient's symptoms. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input past case data and the latest medical papers into the generative AI and have the generative AI identify the treatment most appropriate for the patient's symptoms.

[0072] The cleansing unit can eliminate duplicate data and inaccurate information and provide only accurate information. Duplicate data includes, for example, multiple medical records for the same patient, but is not limited to such examples. Inaccurate information includes, for example, erroneous diagnosis results and input errors, but is not limited to such examples. The cleansing unit, for example, detects and eliminates duplicate data. The cleansing unit can also detect and correct inaccurate information. The cleansing unit can also provide only reliable information. For example, the cleansing unit can eliminate duplicate data and inaccurate information and provide only reliable information. By providing only reliable information, the risk of incorrect information can be reduced. Some or all of the above-mentioned processing in the cleansing unit can be performed, for example, using AI or without AI. For example, the cleansing unit can input duplicate data and inaccurate information into the generation AI and have the generation AI perform cleansing.

[0073] The providing unit can provide the cleansed information to a healthcare professional. Examples of cleansed information include, but are not limited to, data with errors corrected and data with duplicates removed. For example, the providing unit can send the cleansed information by email. The providing unit can also display the cleansed information on a dedicated portal. The providing unit can provide the cleansed information to a healthcare professional, allowing the healthcare professional to make a diagnosis or treatment based on accurate information. For example, the providing unit can provide the cleansed information to a healthcare professional, allowing the healthcare professional to make a diagnosis or treatment based on accurate information. Providing the cleansed information allows the healthcare professional to make a diagnosis or treatment based on accurate information. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the cleansed information to a generation AI and cause the generation AI to provide the information.

[0074] The collection unit can estimate the patient's emotions and adjust the timing of information collection based on the estimated patient's emotions. For example, if the patient is feeling anxious, the collection unit delays information collection until the patient is relaxed. Furthermore, if the patient is relaxed, the collection unit can immediately start information collection. Furthermore, if the patient is in a hurry, the collection unit can quickly collect information. For example, the collection unit analyzes the patient's emotions and adjusts the timing of information collection based on the analyzed patient's emotions. This enables more appropriate information collection by adjusting the timing of information collection according to the patient's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input patient emotion data into the generation AI and have the generation AI adjust the timing of information collection.

[0075] The collection unit can analyze the patient's past medical records and select the optimal information collection method. For example, if the collection unit determines that voice input is effective based on the patient's past medical records, it can prioritize voice input. Furthermore, if the collection unit determines that text input is effective based on the patient's past medical records, it can prioritize text input. Furthermore, if the collection unit determines that image input is effective based on the patient's past medical records, it can prioritize image input. For example, the collection unit analyzes the patient's past medical records and selects the optimal information collection method. This enables efficient information collection by selecting the optimal information collection method based on the patient's past medical records. Some or all of the above-described processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the patient's past medical records into the generation AI and cause the generation AI to select the optimal information collection method.

[0076] When collecting information, the collection unit can filter the information based on the patient's current living situation and areas of interest. For example, if the patient is currently working, the collection unit can prioritize collecting information related to work. Also, if the patient is currently on vacation, the collection unit can prioritize collecting information related to relaxation. Also, if the patient is interested in a particular illness, the collection unit can prioritize collecting information related to that illness. For example, the collection unit filters information based on the patient's current living situation and areas of interest. In this way, by filtering information based on the patient's living situation and areas of interest, more relevant information can be collected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the patient's living situation and areas of interest into the generation AI and have the generation AI perform information filtering.

[0077] When collecting information, the collection unit can select the optimal collection means depending on the patient's input method. For example, if the patient selects voice input, the collection unit can collect information using voice recognition technology. Furthermore, if the patient selects text input, the collection unit can also collect information using text analysis technology. Furthermore, if the patient selects image input, the collection unit can also collect information using image analysis technology. For example, the collection unit selects the optimal collection means depending on the patient's input method. This enables efficient information collection by selecting the optimal collection means depending on the patient's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the patient's input data into a generation AI and have the generation AI select the optimal collection means.

[0078] The collection unit can estimate the patient's emotions and prioritize the information to be collected based on the estimated patient's emotions. For example, if the patient is feeling anxious, the collection unit can prioritize collecting information that provides a sense of security. Furthermore, if the patient is relaxed, the collection unit can prioritize collecting detailed information. Furthermore, if the patient is in a hurry, the collection unit can prioritize collecting important information. For example, the collection unit can analyze the patient's emotions and prioritize the information to be collected based on the analyzed patient's emotions. This enables more appropriate information collection by prioritizing information according to the patient's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the patient's emotion data into the generation AI and have the generation AI determine the priority of information.

[0079] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the patient's geographical location information. For example, if the patient lives in a specific area, the collection unit can prioritize collecting medical information related to that area. Furthermore, if the patient is traveling, the collection unit can prioritize collecting medical information for the patient's travel destination. Furthermore, if the patient is planning to move, the collection unit can prioritize collecting medical information for the patient's new residence. For example, the collection unit prioritizes collecting highly relevant information by taking into account the patient's geographical location information. In this way, more relevant information can be collected by taking into account the patient's geographical location information. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input the patient's geographical location information into the generation AI and cause the generation AI to collect highly relevant information.

[0080] When collecting information, the collection unit can analyze the patient's social media activities and collect relevant information. For example, the collection unit can collect relevant medical information based on health information shared by the patient on social media. The collection unit can also analyze the patient's social media posts and collect relevant medical information. The collection unit can also collect relevant medical information by referring to the activities of the patient's friends on social media. For example, the collection unit can analyze the patient's social media activities and collect relevant information. In this way, more relevant information can be collected by analyzing the patient's social media activities. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input the patient's social media data into the generation AI and cause the generation AI to collect relevant information.

[0081] The collection unit can customize the collection method by reflecting the patient's past feedback when collecting information. For example, the collection unit adjusts the information collection method based on feedback provided by the patient in the past. The collection unit can also preferentially use information collection methods that the patient has previously preferred. The collection unit can also avoid information collection methods that the patient has previously dissatisfied with. For example, the collection unit customizes the collection method by reflecting the patient's past feedback. This enables more appropriate information collection by reflecting the patient's past feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the patient's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0082] The analysis unit can estimate the patient's emotions and adjust the presentation method of the analysis based on the estimated patient's emotions. For example, if the patient is feeling anxious, the analysis unit uses a simple and easy-to-understand presentation method. Furthermore, if the patient is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the patient is in a hurry, the analysis unit can provide analysis results that focus on the main points. For example, the analysis unit analyzes the patient's emotions and adjusts the presentation method of the analysis based on the analyzed patient's emotions. This allows for more appropriate analysis results to be provided by adjusting the presentation method of the analysis according to the patient's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the patient's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0083] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the case. For example, the analysis unit performs a detailed analysis for cases of high importance. The analysis unit can also perform a simplified analysis for cases of low importance. The analysis unit can also perform an analysis with an appropriate level of detail for cases of medium importance. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the case. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the case. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input case importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0084] During analysis, the analysis unit can apply different analysis algorithms depending on the case category. For example, the analysis unit can apply an analysis algorithm specialized for infectious diseases to cases of infectious diseases. Furthermore, the analysis unit can apply an analysis algorithm specialized for cancer to cases of cancer. Furthermore, the analysis unit can apply an analysis algorithm specialized for heart disease to cases of heart disease. For example, the analysis unit applies different analysis algorithms depending on the case category. In this way, applying different analysis algorithms depending on the case category enables more accurate analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input case category data into the generation AI and cause the generation AI to apply different analysis algorithms.

[0085] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the patient's past analysis results. The analysis unit, for example, corrects the current analysis result based on the patient's past analysis results. The analysis unit can also adjust the analysis algorithm based on the patient's past analysis results. The analysis unit can also improve the reliability of the analysis based on the patient's past analysis results. For example, the analysis unit improves the accuracy of the analysis by referring to the patient's past analysis results. In this way, the accuracy of the analysis is improved by referring to the patient's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the patient's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0086] The analysis unit can estimate the patient's emotions and adjust the length of the analysis based on the estimated patient's emotions. For example, if the patient is feeling anxious, the analysis unit can provide a short and concise analysis result. If the patient is relaxed, the analysis unit can also provide a detailed analysis result. If the patient is in a hurry, the analysis unit can also provide a quick analysis result. For example, the analysis unit can analyze the patient's emotions and adjust the length of the analysis based on the analyzed patient's emotions. This allows for more appropriate analysis results to be provided by adjusting the length of the analysis according to the patient's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input patient emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0087] During analysis, the analysis unit can determine the priority of analysis based on the time of case submission. For example, the analysis unit prioritizes analysis of recently submitted cases. The analysis unit can also postpone analysis of cases submitted recently. The analysis unit can also perform analysis with a moderate priority for cases submitted recently. For example, the analysis unit determines the priority of analysis based on the time of case submission. This enables efficient analysis by determining the priority of analysis based on the time of case submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input case submission time data into the generation AI and have the generation AI determine the analysis priority.

[0088] During analysis, the analysis unit can adjust the order of analysis based on the relevance of cases. For example, the analysis unit prioritizes analysis of highly relevant cases. The analysis unit can also postpone analysis of low-relevant cases. The analysis unit can also analyze moderately relevant cases in an appropriate order. For example, the analysis unit adjusts the order of analysis based on the relevance of cases. This enables efficient analysis by adjusting the order of analysis based on the relevance of cases. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input case relevance data into the generation AI and have the generation AI adjust the order of analysis.

[0089] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the patient's level of expertise. For example, if the patient has technical expertise, the analysis unit uses a lot of technical terminology. Furthermore, if the patient does not have technical expertise, the analysis unit can also avoid technical terminology. Furthermore, the analysis unit can use technical terminology appropriately according to the patient's level of expertise. For example, the analysis unit adjusts the use of technical terminology in the analysis according to the patient's level of expertise. This allows for adjustment of the use of technical terminology according to the patient's level of expertise, thereby providing analysis results that are easier to understand. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the patient's level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology.

[0090] The cleansing unit can estimate the patient's emotions and adjust the cleansing criteria based on the estimated patient's emotions. For example, if the patient is feeling anxious, the cleansing unit can apply strict cleansing criteria. Alternatively, if the patient is relaxed, the cleansing unit can apply flexible cleansing criteria. Alternatively, if the patient is in a hurry, the cleansing unit can quickly perform cleansing. For example, the cleansing unit can analyze the patient's emotions and adjust the cleansing criteria based on the analyzed patient's emotions. This allows the cleansing criteria to be adjusted according to the patient's emotions, thereby providing more appropriate information. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the cleansing unit can be performed using, for example, an AI. For example, the cleansing unit can input the patient's emotion data into the generation AI and have the generation AI adjust the cleansing criteria.

[0091] The cleansing unit can improve the accuracy of cleansing by taking into account the interrelationships between information during cleansing. For example, the cleansing unit analyzes the interrelationships between information and eliminates duplicate data. The cleansing unit can also analyze the interrelationships between information and correct inaccurate information. The cleansing unit can also analyze the interrelationships between information and prioritize highly reliable information. For example, the cleansing unit improves the accuracy of cleansing by taking into account the interrelationships between information. In this way, the accuracy of cleansing is improved by taking the interrelationships between information into account. Some or all of the above-mentioned processing in the cleansing unit may be performed using AI, for example, or may be performed without using AI. For example, the cleansing unit can input interrelationship data between information to the generation AI and cause the generation AI to improve the accuracy of cleansing.

[0092] The cleansing unit can perform cleansing while taking into account attribute information of the information submitter. For example, if the submitter is an expert, the cleansing unit will prioritize cleansing of the information. Furthermore, if the submitter is an ordinary person, the cleansing unit can also carefully cleanse the information. Furthermore, the cleansing unit can adjust the cleansing criteria based on the attribute information of the submitter. For example, the cleansing unit performs cleansing while taking into account attribute information of the information submitter. In this way, by taking into account the attribute information of the information submitter, more reliable information can be provided. Some or all of the above-described processing in the cleansing unit may be performed using, for example, AI, or may be performed without using AI. For example, the cleansing unit can input attribute information data of the submitter to a generation AI and have the generation AI perform cleansing.

[0093] During cleansing, the cleansing unit can weight the cleansing based on the frequency of information submission. For example, the cleansing unit can perform strict cleansing on information that is submitted frequently. The cleansing unit can also perform flexible cleansing on information that is submitted infrequently. The cleansing unit can also perform moderate cleansing on information that is submitted at a medium frequency. For example, the cleansing unit weights the cleansing based on the frequency of information submission. This enables efficient cleansing by weighting the cleansing based on the frequency of information submission. Some or all of the above-described processing in the cleansing unit may be performed using, for example, AI, or may be performed without using AI. For example, the cleansing unit can input information submission frequency data to the generation AI and have the generation AI perform the cleansing weighting.

[0094] The cleansing unit can estimate the patient's emotions and adjust the order in which the cleansing results are displayed based on the estimated patient's emotions. For example, if the patient is feeling anxious, the cleansing unit can prioritize displaying information that provides a sense of security. Furthermore, if the patient is relaxed, the cleansing unit can prioritize displaying detailed information. Furthermore, if the patient is in a hurry, the cleansing unit can prioritize displaying important information. For example, the cleansing unit can analyze the patient's emotions and adjust the order in which the cleansing results are displayed based on the analyzed patient's emotions. This allows for more appropriate information to be provided by adjusting the order in which the cleansing results are displayed according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the cleansing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the cleansing unit can input patient emotion data into the generation AI and have the generation AI adjust the display order of the cleansing results.

[0095] The cleansing unit can perform cleansing taking into account the geographical distribution of information. For example, the cleansing unit prioritizes cleansing of information related to a specific region. The cleansing unit can also integrate and cleanse geographically dispersed information. The cleansing unit can also adjust the cleansing criteria based on the geographical distribution. For example, the cleansing unit performs cleansing taking into account the geographical distribution of information. This makes it possible to provide more relevant information by taking the geographical distribution of information into consideration. Some or all of the above-described processing in the cleansing unit may be performed using, for example, AI, or may be performed without using AI. For example, the cleansing unit can input geographical distribution data of information to a generation AI and have the generation AI perform cleansing.

[0096] During cleansing, the cleansing unit can improve the accuracy of the cleansing by referring to related literature of the information. For example, the cleansing unit can refer to related literature to confirm the reliability of the information. The cleansing unit can also refer to related literature to complement the accuracy of the information. The cleansing unit can also refer to related literature to eliminate duplication of information. For example, the cleansing unit can improve the accuracy of the cleansing by referring to related literature of the information. In this way, by referring to related literature of the information, the accuracy of the cleansing is improved. Some or all of the above-mentioned processing in the cleansing unit may be performed using, for example, AI, or may be performed without using AI. For example, the cleansing unit can input related literature data to the generation AI and cause the generation AI to improve the accuracy of the cleansing.

[0097] The cleansing unit can perform cleansing taking into account the market value of the information during cleansing. For example, the cleansing unit performs strict cleansing on information with high market value. The cleansing unit can also perform flexible cleansing on information with low market value. The cleansing unit can also perform moderate cleansing on information with medium market value. For example, the cleansing unit performs cleansing taking into account the market value of the information. In this way, more important information can be provided by taking the market value of the information into account. Some or all of the above-mentioned processing in the cleansing unit may be performed using, for example, AI, or may be performed without using AI. For example, the cleansing unit can input market value data of the information to the generation AI and have the generation AI perform cleansing.

[0098] The providing unit can estimate the patient's emotions and adjust the method of providing information based on the estimated patient's emotions. For example, if the patient is feeling anxious, the providing unit can provide information in a manner that provides a sense of security. Furthermore, if the patient is relaxed, the providing unit can provide detailed information. Furthermore, if the patient is in a hurry, the providing unit can provide information that focuses on the main points. For example, the providing unit can analyze the patient's emotions and adjust the method of providing information based on the analyzed patient's emotions. This allows for adjusting the method of providing information according to the patient's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the providing unit can input patient emotion data into the generation AI and cause the generation AI to adjust the method of providing information.

[0099] When providing information, the providing unit can select the optimal delivery method by referring to the medical professional's past usage history. For example, the providing unit can preferentially use a delivery method that the medical professional has previously preferred. The providing unit can also suggest the optimal delivery method based on the medical professional's past usage history. The providing unit can also adjust the delivery method by referring to the medical professional's past feedback. For example, when providing information, the providing unit selects the optimal delivery method by referring to the medical professional's past usage history. This makes it possible to provide more appropriate information by referring to the medical professional's past usage history. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the medical professional's past usage history data into the generation AI and cause the generation AI to select the optimal delivery method.

[0100] The providing unit can customize the content to be provided according to the current task of the medical worker when providing information. For example, when the medical worker is in a consultation, the providing unit can prioritize providing information related to the consultation. Furthermore, when the medical worker is conducting research, the providing unit can also prioritize providing information related to the research. Furthermore, when the medical worker is taking a break, the providing unit can provide information related to relaxation. For example, the providing unit customizes the content to be provided according to the current task of the medical worker when providing information. This enables more appropriate information to be provided by customizing the content to be provided according to the current task of the medical worker. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input current task data of the medical worker into the generating AI and cause the generating AI to customize the content to be provided.

[0101] The providing unit can improve the method of providing information by reflecting feedback from the medical professional when providing information. The providing unit, for example, adjusts the method of providing information based on the feedback from the medical professional. The providing unit can also improve the content of the information provided based on the feedback from the medical professional. The providing unit can also adjust the timing of providing information based on the feedback from the medical professional. For example, the providing unit improves the method of providing information by reflecting feedback from the medical professional when providing information. This makes it possible to provide more appropriate information by reflecting feedback from the medical professional. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input feedback data from the medical professional into the generating AI and cause the generating AI to improve the method of providing information.

[0102] The providing unit can estimate the patient's emotions and determine the priority of information provision based on the estimated patient's emotions. For example, if the patient is feeling anxious, the providing unit can prioritize providing information that provides a sense of security. Furthermore, if the patient is relaxed, the providing unit can prioritize providing detailed information. Furthermore, if the patient is in a hurry, the providing unit can prioritize providing important information. For example, the providing unit can analyze the patient's emotions and determine the priority of information provision based on the analyzed patient's emotions. This enables more appropriate information provision by determining the priority of information provision according to the patient's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI. For example, the providing unit can input patient emotion data into the generation AI and have the generation AI determine the priority of information provision.

[0103] The providing unit can select the optimal delivery method by taking into account the medical professional's device information when providing information. For example, if the medical professional is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the medical professional is using a tablet, the providing unit can also provide a display method optimized for a large screen. Furthermore, if the medical professional is using a desktop, the providing unit can also provide detailed information. For example, the providing unit selects the optimal delivery method by taking into account the medical professional's device information when providing information. This enables more appropriate information to be provided by taking into account the medical professional's device information. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the medical professional's device information data into the generating AI and cause the generating AI to select the optimal delivery method.

[0104] The providing unit can provide the information in multiple languages ​​according to the language setting of the medical professional when providing the information. The providing unit can automatically set the language of the information provided based on, for example, the language setting of the medical professional's device. The providing unit can also provide a language switching function when the medical professional uses multiple languages. The providing unit can also provide information in a specific language when the medical professional selects that language. For example, the providing unit can provide the information in multiple languages ​​according to the language setting of the medical professional when providing the information. This enables more appropriate information to be provided by providing the information in multiple languages ​​according to the language setting of the medical professional. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the language setting data of the medical professional to a generation AI and have the generation AI perform multilingual support.

[0105] The providing unit can provide relevant information by analyzing the social media activity of the medical professional when providing information. For example, the providing unit can provide relevant medical information based on information shared by the medical professional on social media. The providing unit can also analyze the content of the medical professional's social media posts and provide relevant medical information. The providing unit can also provide relevant medical information by referring to the activities of the medical professional's friends on social media. For example, the providing unit can analyze the social media activity of the medical professional when providing information and provide relevant information. In this way, by analyzing the social media activity of the medical professional, more relevant information can be provided. Some or all of the above-described processing by the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the social media data of the medical professional into the generation AI and cause the generation AI to provide relevant information. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, cleansing unit, and providing unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects information on a patient's symptoms and case using the camera 42 and microphone 38B of the smart device 14 and transmits the information to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information using a generative AI. The cleansing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and deletes duplicate data and corrects erroneous data. The providing unit provides the cleansed information to a medical professional via, for example, the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, cleansing unit, and providing unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects information about a patient's symptoms and case using the camera 42 and microphone 238 of the smart glasses 214 and transmits the information to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information using a generative AI. The cleansing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and deletes duplicate data and corrects erroneous data. The providing unit provides the cleansed information to a medical professional via, for example, the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, cleansing unit, and providing unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects information on a patient's symptoms and case using the camera 42 and microphone 238 of the headset-type terminal 314 and transmits the information to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information using a generation AI. The cleansing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and deletes duplicate data and corrects erroneous data. The providing unit provides the cleansed information to a medical professional via, for example, the display 343 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, cleansing unit, and providing unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects information on a patient's symptoms and case using the camera 42 and microphone 238 of the robot 414 and transmits the information to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information using a generative AI. The cleansing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and deletes duplicate data and corrects erroneous data. The providing unit provides the cleansed information to a medical professional via, for example, the speaker 240 of the robot 414.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The collection unit collects lifestyle data on patients, and the analysis unit can predict the patient's health risks based on this data. For example, the collection unit collects data on the patient's diet, exercise habits, sleep patterns, etc. The analysis unit analyzes this data and predicts what health risks the patient may face in the future. Furthermore, the provision unit can provide the patient with preventive measures and lifestyle improvement suggestions based on the predicted health risks. This allows the patient to understand their health condition and take measures early.

[0108] The collection unit collects the patient's genetic information, and the analysis unit can evaluate the patient's genetic risk based on this information. For example, the collection unit collects the patient's DNA sample and analyzes the genetic information. The analysis unit can also evaluate the patient's risk of developing a specific genetic disease or disorder based on the genetic information. Furthermore, the provision unit can suggest appropriate preventive measures and treatments to the patient based on the genetic risk. This allows the patient to understand their genetic risk and take appropriate measures.

[0109] The analysis unit can estimate the patient's emotions and adjust the way the analysis results are presented based on the estimated patient emotions. For example, if the patient is feeling anxious, the analysis unit will use a simple, easy-to-understand presentation. If the patient is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the patient is in a hurry, the analysis unit can provide analysis results that focus on the main points. This makes it possible to provide more appropriate information by adjusting the way the analysis results are presented according to the patient's emotions.

[0110] The cleansing unit can estimate the patient's emotions and adjust the cleansing criteria based on the estimated patient's emotions. For example, if the patient feels anxious, strict cleansing criteria can be applied. If the patient feels relaxed, flexible cleansing criteria can be applied. Furthermore, if the patient is in a hurry, cleansing can be performed quickly. This allows the system to provide more appropriate information by adjusting the cleansing criteria according to the patient's emotions.

[0111] The providing unit can estimate the patient's emotions and adjust the method of providing information based on the estimated patient's emotions. For example, if the patient is feeling anxious, information can be provided in a way that gives the patient a sense of security. If the patient is relaxed, detailed information can be provided. Furthermore, if the patient is in a hurry, information that focuses on the main points can be provided. In this way, by adjusting the method of providing information according to the patient's emotions, more appropriate information can be provided.

[0112] The collection unit can prioritize collection of highly relevant information by taking into account the geographical location information of the patient. For example, if the patient lives in a specific area, medical information related to that area can be collected with priority. Also, if the patient is traveling, medical information for the travel destination can be collected with priority. Furthermore, if the patient is planning to move, medical information for the new residence can be collected with priority. In this way, more relevant information can be collected by taking into account the geographical location information of the patient.

[0113] The collection unit can analyze the patient's social media activities and collect relevant information. For example, it can collect relevant medical information based on health information shared by the patient on social media. It can also analyze the patient's social media posts to collect relevant medical information. It can also collect relevant medical information by referring to the activities of the patient's friends on social media. This allows more relevant information to be collected by analyzing the patient's social media activities.

[0114] The analysis unit can adjust the level of detail of the analysis based on the importance of the case. For example, a detailed analysis can be performed for cases of high importance. A simplified analysis can also be performed for cases of low importance. Furthermore, an analysis with an appropriate level of detail can also be performed for cases of medium importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the case.

[0115] The cleansing unit can improve the accuracy of cleansing by taking into account the interrelationships between information. For example, the cleansing unit can analyze the interrelationships between information and eliminate duplicate data. It can also analyze the interrelationships between information and correct inaccurate information. It can also analyze the interrelationships between information and prioritize highly reliable information. In this way, the accuracy of cleansing is improved by taking into account the interrelationships between information.

[0116] When providing information, the providing unit can select the optimal delivery method by referring to the medical worker's past usage history. For example, it can prioritize the delivery method that the medical worker has previously preferred. It can also suggest the optimal delivery method based on the medical worker's past usage history. Furthermore, it can also adjust the delivery method by referring to the medical worker's past feedback. In this way, by referring to the medical worker's past usage history, it becomes possible to provide more appropriate information.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The collection department collects information about the patient's symptoms and case. This information includes medical history, medical records, and test results. The collection department can collect information from a database or through direct input from the patient. It also collects detailed data such as the symptoms reported by the patient and the findings observed by the doctor. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis is performed using generative AI and can use statistical analysis and machine learning algorithms. The analysis unit analyzes past case data and the latest medical papers to identify the most appropriate treatment for the patient's symptoms. The analysis unit also efficiently collects and shares necessary information based on the collected information. Step 3: The cleansing unit cleanses the information collected by the collection unit and analyzed by the analysis unit. Cleansing involves removing duplicate data and correcting incorrect data. The cleansing unit eliminates duplicate data and inaccurate information and provides only reliable information. Step 4: The provision unit provides the information cleansed by the cleansing unit to medical professionals. Provision is done by sending it by email or displaying it on a dedicated portal. The provision unit provides the cleansed information to medical professionals, allowing them to make diagnoses and provide treatment based on accurate information.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] 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 AI 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] 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 AI 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] 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.

[0168] 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.

[0169] 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 AI 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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 system comprising: a collection unit that collects information on patient symptoms and cases; an analysis unit that analyzes the information collected by the collection unit, collects and shares necessary information; a cleansing unit that cleanses the information analyzed by the analysis unit and eliminates erroneous information; and a provision unit that provides the information cleansed by the cleansing unit to medical professionals.

2. 2. The system of claim 1, wherein the collection unit collects detailed data on a patient's medical history, current symptoms, and past medical records.

3. The analysis unit Generative AI analyzes past case data and the latest medical literature to identify the most appropriate treatment for a patient's symptoms. The system of claim 1 .

4. The system of claim 1 , wherein the cleansing unit eliminates duplicate data and inaccurate information and provides only accurate information.

5. The providing unit Providing cleansed information to healthcare professionals The system of claim 1 .

6. The system according to claim 1 , wherein the collection unit analyzes the patient's emotions and adjusts the timing of information collection based on the analyzed patient's emotions.

7. The collecting unit Analyze patients' past medical records and select the most appropriate method of collecting information The system of claim 1 .

8. The collecting unit When collecting information, filter it based on the patient's current life situation and areas of interest. The system of claim 1 .

9. The collecting unit When collecting information, select the most appropriate collection method depending on the patient's input method. The system of claim 1 .

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