System

Abstract

An object of a system according to an embodiment is to analyze clinical data and information of an electronic medical chart in real time and provide the information to a medical worker.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects clinical data or electronic medical record information. The analysis unit analyzes the information collected by the collection unit in real time. The providing unit provides the result analyzed by the analyzing unit to a medical worker.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 technologies do not adequately analyze clinical data and electronic medical record information in real time and provide it to medical professionals, so there is room for improvement.

[0005] The system according to the embodiment aims to analyze clinical data and electronic medical record information in real time and provide the results to medical professionals. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects clinical data or information from electronic medical records. The analysis unit analyzes the information collected by the collection unit in real time. The provision unit provides the results of the analysis by the analysis unit to a medical professional. [Effects of the Invention]

[0007] The system according to the embodiment can analyze clinical data and electronic medical record information in real time and provide the results to medical professionals. [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 data collection and advanced treatment platform according to an embodiment of the present invention is a system that collects clinical data and electronic medical record information, analyzes it in real time using a generative AI, and provides it to medical professionals. The medical data collection and advanced treatment platform collects clinical data and electronic medical record information, analyzes it using a generative AI, and provides it to medical professionals, enabling rapid and accurate diagnosis and treatment planning. For example, the medical data collection and advanced treatment platform collects patient medical records and test results, and the generative AI analyzes them to detect abnormalities. The medical data collection and advanced treatment platform can also communicate the analysis results to medical professionals via audio, enabling medical professionals to quickly and accurately diagnose and plan treatment. For example, even in emergencies, the medical data collection and advanced treatment platform can quickly and accurately grasp a patient's condition and provide appropriate treatment. Furthermore, even in routine medical care, the medical data collection and advanced treatment platform can efficiently manage patient information and improve the accuracy of diagnosis and treatment. This allows medical professionals to grasp patient information in real time and use the analysis results from the generative AI to quickly and accurately diagnose and plan treatment. For example, even in emergencies, the medical data collection and advanced treatment platform can quickly and accurately grasp a patient's condition and provide appropriate treatment. In addition, in daily medical practice, patient information can be managed efficiently, improving the accuracy of diagnosis and treatment.

[0029] A medical data collection and advanced treatment platform according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects clinical data or information from electronic medical records. Clinical data includes, but is not limited to, a patient's vital signs, test results, and treatment history. For example, the collection unit acquires a patient's medical records from electronic medical records. The collection unit can also acquire test results directly from medical devices. The collection unit can also acquire image data from a medical imaging system. For example, the collection unit acquires a patient's vital signs from a monitoring system and collects them in real time. The analysis unit uses a generation AI to analyze the information collected by the collection unit in real time. The analysis is performed based on, for example, but is not limited to, the data update frequency and the type of analysis algorithm. For example, the generation AI analyzes medical records using a text generation AI (e.g., LLM) to detect abnormalities. The analysis unit can also analyze image data using a multimodal generation AI to detect abnormalities. The analysis unit can also analyze test results using the generation AI to assist in diagnosis. For example, the generation AI has learned a large amount of medical data and has advanced analytical capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses pattern recognition technology to detect abnormalities and assist in diagnosis. The providing unit provides the results analyzed by the analysis unit to medical professionals. The results may be provided in the form of, for example, a mobile app, a web portal, a voice assistant, or the like, but are not limited to these examples. For example, the providing unit communicates the analysis results to medical professionals by voice using voice synthesis technology. The providing unit can also display the analysis results to medical professionals through a mobile app. The providing unit can also provide the analysis results to medical professionals through a web portal. For example, the providing unit communicates the analysis results to medical professionals by voice using a voice assistant. As a result, the medical data collection and advanced treatment platform according to the embodiment collects clinical data and electronic medical record information, analyzes them in real time, and provides them to medical professionals, enabling rapid and accurate diagnoses and treatment plans.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 may input the results of the analysis performed by the analyzing unit and provide the analysis results to a medical professional using an AI model.

[0030] The collection unit can collect information such as a patient's medical records, test results, and image data. For example, the collection unit acquires the patient's medical records from an electronic medical record. The medical records include, but are not limited to, diagnosis results, treatment plans, and prescription information. The collection unit can also acquire test results directly from medical devices. Test results include, but are not limited to, blood tests, urine tests, and imaging tests. The collection unit can also acquire image data from a medical imaging system. Image data include, but are not limited to, X-ray images, MRI images, and CT scan images. This allows comprehensive medical data to be obtained by collecting information such as the patient's medical records, test results, and image data. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the medical records acquired from the electronic medical record into a generation AI and have the generation AI analyze the medical records.

[0031] The analysis unit can analyze the collected information and detect abnormalities or assist in diagnosis. The analysis unit, for example, analyzes the collected information and detects abnormalities. For example, the analysis unit detects abnormalities based on a threshold setting for abnormal values. The analysis unit can also detect abnormalities using a pattern recognition algorithm. The analysis unit can also analyze the collected information and assist in diagnosis. For example, the analysis unit can assist in diagnosis using a diagnostic algorithm. The analysis unit can also assist in diagnosis using a reference database. In this way, by analyzing the collected information and detecting abnormalities or assisting in diagnosis, the diagnostic accuracy of medical professionals is improved. 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 collected information to a generation AI and cause the generation AI to detect abnormalities or assist in diagnosis.

[0032] The providing unit can provide the analysis results to the medical professional by voice. For example, the providing unit communicates the analysis results to the medical professional by voice using voice synthesis technology. For example, the providing unit communicates the analysis results to the medical professional by voice using a voice assistant. The providing unit can also display the analysis results to the medical professional through a mobile app. For example, the providing unit displays the analysis results to the medical professional through a mobile app. The providing unit can also provide the analysis results to the medical professional through a web portal. For example, the providing unit provides the analysis results to the medical professional through a web portal. This allows the medical professional to be informed of the analysis results by voice, thereby providing information quickly and intuitively. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the analysis results to a generation AI and have the generation AI provide the analysis results by voice.

[0033] The analysis unit can analyze the image data and detect abnormalities. The analysis unit, for example, analyzes the image data and detects abnormalities. For example, the analysis unit can analyze the image data using image recognition technology and detect abnormalities. The analysis unit can also analyze the image data using a deep learning algorithm and detect abnormalities. The analysis unit can also analyze the image data using a pattern recognition algorithm and detect abnormalities. By analyzing the image data and detecting abnormalities, abnormalities can be discovered early and appropriate measures can be taken. 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 the image data to a generation AI and have the generation AI detect abnormalities.

[0034] The analysis unit can analyze medical records and provide diagnostic assistance. The analysis unit, for example, analyzes medical records and provides diagnostic assistance. For example, the analysis unit can analyze medical records using text mining technology and provide diagnostic assistance. The analysis unit can also analyze medical records using natural language processing technology and provide diagnostic assistance. The analysis unit can also analyze medical records using a diagnostic algorithm and provide diagnostic assistance. In this way, analyzing medical records and providing diagnostic assistance improves the diagnostic accuracy of medical professionals. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input medical records into a generation AI and have the generation AI perform diagnostic assistance.

[0035] The collection unit can analyze the patient's past medical records and select an appropriate data collection method. The collection unit, for example, analyzes the patient's past medical records and selects the most effective data collection method. For example, the collection unit prioritizes collecting specific test results based on the patient's past medical records. The collection unit can also analyze the patient's past medical records and adjust the frequency of data collection. In this way, the optimal data collection method can be selected by analyzing the patient's past medical records. 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 past medical records into the generation AI and have the generation AI select a data collection method.

[0036] The collection unit can perform filtering based on the patient's current health condition or treatment plan when collecting data. The collection unit, for example, collects only necessary data based on the patient's current health condition. For example, the collection unit prioritizes collection of specific data based on the patient's treatment plan. The collection unit can also adjust the scope of data collection taking into account the patient's health condition and treatment plan. This allows data to be filtered based on the patient's current health condition and treatment plan, thereby collecting only necessary data. 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 data on the patient's health condition and treatment plan to the generation AI and have the generation AI perform data filtering.

[0037] When collecting data, the collection unit can select an appropriate collection means depending on the patient's input method. For example, if the patient uses voice input, the collection unit prioritizes collecting voice data. For example, if the patient uses text input, the collection unit prioritizes collecting text data. Furthermore, if the patient provides image data, the collection unit can also prioritize collecting image data. This allows efficient data collection by selecting the optimal collection means depending on the patient's input method. 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 input data into the generation AI and have the generation AI select the collection means.

[0038] The collection unit can collect a patient's lifestyle data and integrate it with medical records to provide comprehensive health information. The collection unit, for example, collects a patient's dietary and exercise data and integrates it with medical records. For example, the collection unit can collect a patient's sleep patterns and integrate them with medical records. The collection unit can also collect a patient's stress level and integrate them with medical records. In this way, comprehensive health information can be provided by collecting a patient's lifestyle data and integrating it with medical records. 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 lifestyle data into a generation AI and have the generation AI integrate it with medical records.

[0039] When collecting data, the collection unit can prioritize collecting highly relevant data based on the patient's geographical location information. For example, if the patient is in a specific area, the collection unit prioritizes collecting data related to that area. For example, if the patient is traveling, the collection unit prioritizes collecting data related to the patient's destination. Furthermore, if the patient is in a specific medical facility, the collection unit can also prioritize collecting data related to the facility. This allows for more appropriate data to be collected by prioritizing the collection of highly relevant data in consideration of the patient's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the patient's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0040] The collection unit can analyze the patient's social media activities and collect related health information when collecting data. The collection unit, for example, collects health-related information from the patient's social media posts. For example, the collection unit analyzes the patient's social media activities and infers the patient's health condition. The collection unit can also collect related health information by referring to the activities of the patient's friends on social media. In this way, related health information can be collected by analyzing the patient's social media activities. 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 social media data into the generation AI and cause the generation AI to collect health information.

[0041] The collection unit can customize the collection method based on the patient's past feedback when collecting data. The collection unit, for example, adjusts the data collection method based on the patient's past feedback. For example, the collection unit changes the type of data to be collected by reflecting the patient's past feedback. The collection unit can also adjust the frequency of data collection by referring to the patient's past feedback. In this way, the collection method can be customized by reflecting the patient's past feedback. 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 past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0042] The collection unit can collect the patient's family history or genetic information and integrate it with medical records to perform risk assessment. The collection unit, for example, collects the patient's family history and integrates it with medical records to perform risk assessment. For example, the collection unit collects the patient's genetic information and integrates it with medical records to perform risk assessment. The collection unit can also integrate the patient's family history and genetic information to perform comprehensive risk assessment. In this way, comprehensive risk assessment can be performed by collecting the patient's family history and genetic information and integrating it with medical records. 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 family history and genetic information into the generation AI and have the generation AI perform risk assessment.

[0043] During analysis, the analysis unit can optimize the level of detail of the analysis based on the importance of the data. The analysis unit adjusts the level of detail of the analysis based on, for example, the importance of the data. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a basic analysis on general data. The analysis unit can also perform a quick analysis on data with high urgency. In this way, by adjusting the level of detail of the analysis based on the importance of the data, analysis can be performed efficiently. 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 importance of the data to the generation AI and cause the generation AI to optimize the level of detail of the analysis.

[0044] During analysis, the analysis unit can apply an appropriate analysis algorithm depending on the data category. For example, the analysis unit applies an image analysis algorithm to image data. For example, the analysis unit applies a natural language processing algorithm to text data. The analysis unit can also apply a statistical analysis algorithm to numerical data. This allows for more accurate analysis by applying different analysis algorithms depending on the data category. 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 data category to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0045] During analysis, the analysis unit can improve the accuracy of the analysis based on the patient's past analysis results. The analysis unit, for example, improves the accuracy of the current analysis based on the patient's past analysis results. For example, the analysis unit adjusts the analysis algorithm by referring to the patient's past analysis results. The analysis unit can also improve the accuracy of abnormality detection by using the patient's past analysis results. In this way, the accuracy of the current analysis can be 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 results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0046] During analysis, the analysis unit can detect abnormalities or assist in diagnosis based on the patient's lifestyle data. The analysis unit detects abnormalities, for example, by taking into account the patient's diet and exercise data. For example, the analysis unit can assist in diagnosis by taking into account the patient's sleep patterns. The analysis unit can also assist in abnormality detection or diagnosis by taking into account the patient's stress level. This allows for more accurate abnormality detection and diagnosis assistance by taking into account the patient's lifestyle data. Some or all of the above-described 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 lifestyle data into the generation AI and cause the generation AI to detect abnormalities or assist in diagnosis.

[0047] During analysis, the analysis unit can set analysis priorities based on the time of data submission. The analysis unit, for example, prioritizes analysis of data with high urgency. For example, the analysis unit prioritizes analysis of data submitted earlier. The analysis unit can also prioritize analysis of data submitted more recently. This allows analysis to be performed efficiently by determining the analysis priorities based on the time of data submission. Some or all of the above-described 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 time of data submission to the generation AI and have the generation AI set the analysis priorities.

[0048] During analysis, the analysis unit can optimize the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. For example, the analysis unit postpones analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described 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 relevance of the data to the generation AI and cause the generation AI to optimize the order of analysis.

[0049] During analysis, the analysis unit can optimize the use of technical terms in the analysis according to the level of expertise of the medical professional. For example, the analysis unit uses detailed technical terms for medical professionals with high expertise. For example, the analysis unit uses concise terms for medical professionals with low expertise. The analysis unit can also adjust the way in which the analysis results are expressed according to the level of expertise of the medical professional. This makes it possible to provide analysis results that are easier to understand by adjusting the use of technical terms in the analysis according to the level of expertise of the medical professional. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the level of expertise of the medical professional into the generation AI and cause the generation AI to optimize the use of technical terms.

[0050] During analysis, the analysis unit can perform risk assessment based on the patient's genetic information. The analysis unit, for example, assesses the risk of a particular disease based on the patient's genetic information. For example, the analysis unit adjusts a treatment plan taking into account the patient's genetic information. The analysis unit can also suggest preventive measures using the patient's genetic information. This allows for more accurate risk assessment by taking into account the patient's genetic information. Some or all of the above-described 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 genetic information into the generation AI and have the generation AI perform a risk assessment.

[0051] The providing unit can optimize the level of detail of the provided information based on the importance of the analysis result when providing the information. For example, the providing unit provides detailed information for important analysis results. For example, the providing unit provides basic information for general analysis results. The providing unit can also quickly provide the gist of analysis results that are highly urgent. This allows information to be provided efficiently by adjusting the level of detail of the provided information based on the importance of the analysis results. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the importance of the analysis results to the generating AI and cause the generating AI to optimize the level of detail of the provided information.

[0052] The providing unit can apply an appropriate providing method depending on the category of the analysis results when providing the results. For example, the providing unit provides visual graphs or charts for image analysis results. For example, the providing unit provides summarized text information for text analysis results. The providing unit can also provide a detailed report including statistical data for numerical analysis results. This makes it possible to provide more appropriate information by applying different providing methods depending on the category of the analysis results. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the category of the analysis results into the generation AI and cause the generation AI to apply an appropriate providing method.

[0053] The providing unit can improve the accuracy of the information provided based on the medical professional's past feedback at the time of providing the information. The providing unit improves the accuracy of the information to be provided, for example, based on the medical professional's past feedback. For example, the providing unit adjusts the providing method by reflecting the medical professional's past feedback. The providing unit can also adjust the range of information to be provided by referring to the medical professional's past feedback. In this way, the accuracy of the information provided can be improved by referring to the medical professional's past feedback. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the medical professional's past feedback into the generating AI and cause the generating AI to improve the accuracy of the information provided.

[0054] The providing unit can provide comprehensive health information based on the patient's lifestyle data at the time of providing the information. The providing unit provides comprehensive health information, for example, by taking into account the patient's diet and exercise data. For example, the providing unit provides comprehensive health information by taking into account the patient's sleep patterns. The providing unit can also provide comprehensive health information by taking into account the patient's stress level. In this way, comprehensive health information can be provided by taking into account the patient's lifestyle data. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the patient's lifestyle data into the generating AI and cause the generating AI to provide comprehensive health information.

[0055] When providing the information, the providing unit can prioritize providing highly relevant information based on the patient's geographical location information. For example, if the patient is in a specific area, the providing unit prioritizes providing information related to that area. For example, if the patient is traveling, the providing unit prioritizes providing information related to the patient's destination. Furthermore, if the patient is in a specific medical facility, the providing unit can also prioritize providing information related to that facility. This allows for more appropriate information to be provided by prioritizing highly relevant information in consideration of the patient's geographical location information. Some or all of the above-described processing by the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the patient's geographical location information to the generating AI and cause the generating AI to provide highly relevant information.

[0056] The providing unit can analyze the patient's social media activity and provide related health information at the time of providing the data. The providing unit provides health-related information, for example, from the patient's social media posts. For example, the providing unit analyzes the patient's social media activity and infers the patient's health condition. The providing unit can also provide related health information by referring to the activity of the patient's friends on social media. In this way, related health information can be provided by analyzing the patient's social media activity. 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 patient's social media data into the generating AI and cause the generating AI to provide health information.

[0057] The providing unit can customize the method of providing information based on the patient's past feedback when providing the information. The providing unit, for example, adjusts the method of providing information based on the patient's past feedback. For example, the providing unit changes the type of information to be provided by reflecting the patient's past feedback. The providing unit can also adjust the frequency of information to be provided by referring to the patient's past feedback. In this way, the method of providing information can be customized by reflecting the patient's past feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the patient's past feedback data into the generating AI and cause the generating AI to customize the method of providing information.

[0058] At the time of provision, the providing unit can perform risk assessment based on the patient's family history and genetic information. The providing unit, for example, performs risk assessment taking into account the patient's family history. For example, the providing unit performs risk assessment taking into account the patient's genetic information. The providing unit can also perform comprehensive risk assessment by integrating the patient's family history and genetic information. This allows for more accurate risk assessment by taking into account the patient's family history and genetic information. Some or all of the above-described processing in the providing unit may be performed using, or without, AI, for example. For example, the providing unit can input the patient's family history and genetic information into the generating AI and cause the generating AI to perform risk assessment.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The collection unit can collect a patient's lifestyle data and integrate it with medical records to provide comprehensive health information. For example, the collection unit can collect a patient's dietary and exercise data and integrate it with medical records. The collection unit can also collect a patient's sleep patterns and integrate it with medical records. The collection unit can also collect a patient's stress level and integrate it with medical records. In this way, comprehensive health information can be provided by collecting a patient's lifestyle data and integrating it with medical records. 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 lifestyle data into a generation AI and have the generation AI integrate it with medical records.

[0061] The providing unit can optimize the level of detail of the provided information based on the importance of the analysis results when providing the information. For example, the providing unit provides detailed information for important analysis results. The providing unit can also provide basic information for general analysis results. Furthermore, the providing unit can quickly provide the gist of analysis results that are highly urgent. This allows information to be provided efficiently by adjusting the level of detail of the provided information based on the importance of the analysis results. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the importance of the analysis results to the generating AI and cause the generating AI to optimize the level of detail of the provided information.

[0062] When collecting data, the collection unit can prioritize collecting highly relevant data based on the patient's geographical location information. For example, if the patient is in a specific area, the collection unit can prioritize collecting data related to that area. Also, if the patient is traveling, the collection unit can prioritize collecting data related to the patient's destination. Furthermore, if the patient is in a specific medical facility, the collection unit can prioritize collecting data related to that facility. This allows for more appropriate data to be collected by prioritizing the collection of highly relevant data in consideration of the patient's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the patient's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0063] During analysis, the analysis unit can optimize the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a basic analysis on general data. Furthermore, the analysis unit can also perform a quick analysis on data with high urgency. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. 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 importance of the data to the generation AI and cause the generation AI to optimize the level of detail of the analysis.

[0064] The collection unit can analyze the patient's social media activities and collect related health information when collecting data. For example, the collection unit can collect health-related information from the patient's social media posts. The collection unit can also analyze the patient's social media activities to infer their health status. Furthermore, the collection unit can also collect related health information by referring to the activities of the patient's friends on social media. In this way, related health information can be collected by analyzing the patient's social media activities. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using 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 health information.

[0065] The providing unit can customize the providing method based on the patient's past feedback when providing information. For example, the providing unit adjusts the method of providing information based on the patient's past feedback. The providing unit can also change the type of information to be provided by reflecting the patient's past feedback. Furthermore, the providing unit can also adjust the frequency of providing information by referring to the patient's past feedback. In this way, the providing method can be customized by reflecting the patient's past feedback. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the patient's past feedback data into the generating AI and cause the generating AI to customize the providing method.

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

[0067] Step 1: The collection unit collects clinical data or information from electronic medical records. Clinical data includes patient vital signs, test results, treatment history, etc. The collection unit can obtain patient medical records from electronic medical records and test results directly from medical devices. It also obtains image data from medical imaging systems and collects patient vital signs in real time from monitoring systems. Step 2: The analysis unit uses the generation AI to analyze the information collected by the collection unit in real time. The analysis is performed based on the data update frequency and the type of analysis algorithm. The generation AI uses text generation AI (e.g., LLM) to analyze medical records and detect abnormalities. It can also use multimodal generation AI to analyze image data and detect abnormalities. Furthermore, the generation AI is used to analyze test results and assist in diagnosis. Step 3: The providing unit provides the results analyzed by the analyzing unit to the healthcare professional. The results may be provided in the form of a mobile app, a web portal, a voice assistant, or the like. For example, the providing unit may communicate the analysis results to the healthcare professional by voice using voice synthesis technology. The analysis results may also be displayed to the healthcare professional through a mobile app or a web portal. Furthermore, the providing unit may communicate the analysis results to the healthcare professional by voice using a voice assistant.

[0068] (Example 2) A medical data collection and advanced treatment platform according to an embodiment of the present invention is a system that collects clinical data and electronic medical record information, analyzes it in real time using a generative AI, and provides it to medical professionals. The medical data collection and advanced treatment platform collects clinical data and electronic medical record information, analyzes it using a generative AI, and provides it to medical professionals, enabling rapid and accurate diagnosis and treatment planning. For example, the medical data collection and advanced treatment platform collects patient medical records and test results, and the generative AI analyzes them to detect abnormalities. The medical data collection and advanced treatment platform can also communicate the analysis results to medical professionals via audio, enabling medical professionals to quickly and accurately diagnose and plan treatment. For example, even in emergencies, the medical data collection and advanced treatment platform can quickly and accurately grasp a patient's condition and provide appropriate treatment. Furthermore, even in routine medical care, the medical data collection and advanced treatment platform can efficiently manage patient information and improve the accuracy of diagnosis and treatment. This allows medical professionals to grasp patient information in real time and use the analysis results from the generative AI to quickly and accurately diagnose and plan treatment. For example, even in emergencies, the medical data collection and advanced treatment platform can quickly and accurately grasp a patient's condition and provide appropriate treatment. In addition, in daily medical practice, patient information can be managed efficiently, improving the accuracy of diagnosis and treatment.

[0069] A medical data collection and advanced treatment platform according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects clinical data or information from electronic medical records. Clinical data includes, but is not limited to, a patient's vital signs, test results, and treatment history. For example, the collection unit acquires a patient's medical records from electronic medical records. The collection unit can also acquire test results directly from medical devices. The collection unit can also acquire image data from a medical imaging system. For example, the collection unit acquires a patient's vital signs from a monitoring system and collects them in real time. The analysis unit uses a generation AI to analyze the information collected by the collection unit in real time. The analysis is performed based on, for example, but is not limited to, the data update frequency and the type of analysis algorithm. For example, the generation AI analyzes medical records using a text generation AI (e.g., LLM) to detect abnormalities. The analysis unit can also analyze image data using a multimodal generation AI to detect abnormalities. The analysis unit can also analyze test results using the generation AI to assist in diagnosis. For example, the generation AI has learned a large amount of medical data and has advanced analytical capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses pattern recognition technology to detect abnormalities and assist in diagnosis. The providing unit provides the results analyzed by the analysis unit to medical professionals. The results may be provided in the form of, for example, a mobile app, a web portal, a voice assistant, or the like, but are not limited to these examples. For example, the providing unit communicates the analysis results to medical professionals by voice using voice synthesis technology. The providing unit can also display the analysis results to medical professionals through a mobile app. The providing unit can also provide the analysis results to medical professionals through a web portal. For example, the providing unit communicates the analysis results to medical professionals by voice using a voice assistant. As a result, the medical data collection and advanced treatment platform according to the embodiment collects clinical data and electronic medical record information, analyzes them in real time, and provides them to medical professionals, enabling rapid and accurate diagnoses and treatment plans.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 may input the results of the analysis performed by the analyzing unit and provide the analysis results to a medical professional using an AI model.

[0070] The collection unit can collect information such as a patient's medical records, test results, and image data. For example, the collection unit acquires the patient's medical records from an electronic medical record. The medical records include, but are not limited to, diagnosis results, treatment plans, and prescription information. The collection unit can also acquire test results directly from medical devices. Test results include, but are not limited to, blood tests, urine tests, and imaging tests. The collection unit can also acquire image data from a medical imaging system. Image data include, but are not limited to, X-ray images, MRI images, and CT scan images. This allows comprehensive medical data to be obtained by collecting information such as the patient's medical records, test results, and image data. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the medical records acquired from the electronic medical record into a generation AI and have the generation AI analyze the medical records.

[0071] The analysis unit can analyze the collected information and detect abnormalities or assist in diagnosis. The analysis unit, for example, analyzes the collected information and detects abnormalities. For example, the analysis unit detects abnormalities based on a threshold setting for abnormal values. The analysis unit can also detect abnormalities using a pattern recognition algorithm. The analysis unit can also analyze the collected information and assist in diagnosis. For example, the analysis unit can assist in diagnosis using a diagnostic algorithm. The analysis unit can also assist in diagnosis using a reference database. In this way, by analyzing the collected information and detecting abnormalities or assisting in diagnosis, the diagnostic accuracy of medical professionals is improved. 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 collected information to a generation AI and cause the generation AI to detect abnormalities or assist in diagnosis.

[0072] The providing unit can provide the analysis results to the medical professional by voice. For example, the providing unit communicates the analysis results to the medical professional by voice using voice synthesis technology. For example, the providing unit communicates the analysis results to the medical professional by voice using a voice assistant. The providing unit can also display the analysis results to the medical professional through a mobile app. For example, the providing unit displays the analysis results to the medical professional through a mobile app. The providing unit can also provide the analysis results to the medical professional through a web portal. For example, the providing unit provides the analysis results to the medical professional through a web portal. This allows the medical professional to be informed of the analysis results by voice, thereby providing information quickly and intuitively. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the analysis results to a generation AI and have the generation AI provide the analysis results by voice.

[0073] The analysis unit can analyze the image data and detect abnormalities. The analysis unit, for example, analyzes the image data and detects abnormalities. For example, the analysis unit can analyze the image data using image recognition technology and detect abnormalities. The analysis unit can also analyze the image data using a deep learning algorithm and detect abnormalities. The analysis unit can also analyze the image data using a pattern recognition algorithm and detect abnormalities. By analyzing the image data and detecting abnormalities, abnormalities can be discovered early and appropriate measures can be taken. 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 the image data to a generation AI and have the generation AI detect abnormalities.

[0074] The analysis unit can analyze medical records and provide diagnostic assistance. The analysis unit, for example, analyzes medical records and provides diagnostic assistance. For example, the analysis unit can analyze medical records using text mining technology and provide diagnostic assistance. The analysis unit can also analyze medical records using natural language processing technology and provide diagnostic assistance. The analysis unit can also analyze medical records using a diagnostic algorithm and provide diagnostic assistance. In this way, analyzing medical records and providing diagnostic assistance improves the diagnostic accuracy of medical professionals. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input medical records into a generation AI and have the generation AI perform diagnostic assistance.

[0075] The collection unit can estimate the user's emotions and optimize the timing of data collection based on the estimated user emotions. For example, the collection unit estimates the user's emotions and adjusts the timing of data collection based on the estimated user emotions. For example, if the patient is relaxed, the collection unit can immediately collect data and acquire detailed information. If the patient is nervous, the collection unit can delay data collection and wait until the patient calms down. If the patient is in a hurry, the collection unit can quickly collect data and acquire the minimum necessary information. This allows data to be collected at a more appropriate time by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input user emotion data into the generation AI and cause the generation AI to optimize the timing of data collection.

[0076] The collection unit can analyze the patient's past medical records and select an appropriate data collection method. The collection unit, for example, analyzes the patient's past medical records and selects the most effective data collection method. For example, the collection unit prioritizes collecting specific test results based on the patient's past medical records. The collection unit can also analyze the patient's past medical records and adjust the frequency of data collection. In this way, the optimal data collection method can be selected by analyzing the patient's past medical records. 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 past medical records into the generation AI and have the generation AI select a data collection method.

[0077] The collection unit can perform filtering based on the patient's current health condition or treatment plan when collecting data. The collection unit, for example, collects only necessary data based on the patient's current health condition. For example, the collection unit prioritizes collection of specific data based on the patient's treatment plan. The collection unit can also adjust the scope of data collection taking into account the patient's health condition and treatment plan. This allows data to be filtered based on the patient's current health condition and treatment plan, thereby collecting only necessary data. 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 data on the patient's health condition and treatment plan to the generation AI and have the generation AI perform data filtering.

[0078] When collecting data, the collection unit can select an appropriate collection means depending on the patient's input method. For example, if the patient uses voice input, the collection unit prioritizes collecting voice data. For example, if the patient uses text input, the collection unit prioritizes collecting text data. Furthermore, if the patient provides image data, the collection unit can also prioritize collecting image data. This allows efficient data collection by selecting the optimal collection means depending on the patient's input method. 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 input data into the generation AI and have the generation AI select the collection means.

[0079] The collection unit can collect a patient's lifestyle data and integrate it with medical records to provide comprehensive health information. The collection unit, for example, collects a patient's dietary and exercise data and integrates it with medical records. For example, the collection unit can collect a patient's sleep patterns and integrate them with medical records. The collection unit can also collect a patient's stress level and integrate them with medical records. In this way, comprehensive health information can be provided by collecting a patient's lifestyle data and integrating it with medical records. 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 lifestyle data into a generation AI and have the generation AI integrate it with medical records.

[0080] The collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of the data to be collected based on the estimated user emotions. For example, if the patient is relaxed, the collection unit can prioritize collecting detailed data. Also, if the patient is nervous, the collection unit can prioritize collecting basic data. Also, if the patient is in a hurry, the collection unit can prioritize collecting the most important data. Thus, by determining the priority of the data to be collected based on the user's emotions, more important data can be collected preferentially. The emotion estimation is realized 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, an AI, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI set the data priority.

[0081] When collecting data, the collection unit can prioritize collecting highly relevant data based on the patient's geographical location information. For example, if the patient is in a specific area, the collection unit prioritizes collecting data related to that area. For example, if the patient is traveling, the collection unit prioritizes collecting data related to the patient's destination. Furthermore, if the patient is in a specific medical facility, the collection unit can also prioritize collecting data related to the facility. This allows for more appropriate data to be collected by prioritizing the collection of highly relevant data in consideration of the patient's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the patient's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0082] The collection unit can analyze the patient's social media activities and collect related health information when collecting data. The collection unit, for example, collects health-related information from the patient's social media posts. For example, the collection unit analyzes the patient's social media activities and infers the patient's health condition. The collection unit can also collect related health information by referring to the activities of the patient's friends on social media. In this way, related health information can be collected by analyzing the patient's social media activities. 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 social media data into the generation AI and cause the generation AI to collect health information.

[0083] The collection unit can customize the collection method based on the patient's past feedback when collecting data. The collection unit, for example, adjusts the data collection method based on the patient's past feedback. For example, the collection unit changes the type of data to be collected by reflecting the patient's past feedback. The collection unit can also adjust the frequency of data collection by referring to the patient's past feedback. In this way, the collection method can be customized by reflecting the patient's past feedback. 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 past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0084] The collection unit can collect the patient's family history or genetic information and integrate it with medical records to perform risk assessment. The collection unit, for example, collects the patient's family history and integrates it with medical records to perform risk assessment. For example, the collection unit collects the patient's genetic information and integrates it with medical records to perform risk assessment. The collection unit can also integrate the patient's family history and genetic information to perform comprehensive risk assessment. In this way, comprehensive risk assessment can be performed by collecting the patient's family history and genetic information and integrating it with medical records. 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 family history and genetic information into the generation AI and have the generation AI perform risk assessment.

[0085] The analysis unit can estimate the user's emotion and optimize the analysis presentation method based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the analysis presentation method based on the estimated user's emotion. For example, the analysis unit can provide detailed analysis results when the patient is relaxed. The analysis unit can also provide concise analysis results when the patient is nervous. The analysis unit can also provide analysis results that focus on the main points when the patient is in a hurry. This allows for more appropriate analysis results to be provided by adjusting the analysis presentation method based on the user's emotion. 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 these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and cause the generation AI to optimize the analysis presentation method.

[0086] During analysis, the analysis unit can optimize the level of detail of the analysis based on the importance of the data. The analysis unit adjusts the level of detail of the analysis based on, for example, the importance of the data. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a basic analysis on general data. The analysis unit can also perform a quick analysis on data with high urgency. In this way, by adjusting the level of detail of the analysis based on the importance of the data, analysis can be performed efficiently. 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 importance of the data to the generation AI and cause the generation AI to optimize the level of detail of the analysis.

[0087] During analysis, the analysis unit can apply an appropriate analysis algorithm depending on the data category. For example, the analysis unit applies an image analysis algorithm to image data. For example, the analysis unit applies a natural language processing algorithm to text data. The analysis unit can also apply a statistical analysis algorithm to numerical data. This allows for more accurate analysis by applying different analysis algorithms depending on the data category. 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 data category to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0088] During analysis, the analysis unit can improve the accuracy of the analysis based on the patient's past analysis results. The analysis unit, for example, improves the accuracy of the current analysis based on the patient's past analysis results. For example, the analysis unit adjusts the analysis algorithm by referring to the patient's past analysis results. The analysis unit can also improve the accuracy of abnormality detection by using the patient's past analysis results. In this way, the accuracy of the current analysis can be 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 results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0089] During analysis, the analysis unit can detect abnormalities or assist in diagnosis based on the patient's lifestyle data. The analysis unit detects abnormalities, for example, by taking into account the patient's diet and exercise data. For example, the analysis unit can assist in diagnosis by taking into account the patient's sleep patterns. The analysis unit can also assist in abnormality detection or diagnosis by taking into account the patient's stress level. This allows for more accurate abnormality detection and diagnosis assistance by taking into account the patient's lifestyle data. Some or all of the above-described 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 lifestyle data into the generation AI and cause the generation AI to detect abnormalities or assist in diagnosis.

[0090] The analysis unit can estimate the user's emotion and optimize the length of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the length of the analysis based on the estimated user's emotion. For example, the analysis unit can perform a detailed analysis when the patient is relaxed. The analysis unit can also perform a concise analysis when the patient is nervous. The analysis unit can also perform a concise analysis when the patient is in a hurry. This allows for adjusting the length of the analysis based on the user's emotion, thereby providing more appropriate analysis results. 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 these examples. 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 user's emotion data into the generation AI and cause the generation AI to optimize the length of the analysis.

[0091] During analysis, the analysis unit can set analysis priorities based on the time of data submission. The analysis unit, for example, prioritizes analysis of data with high urgency. For example, the analysis unit prioritizes analysis of data submitted earlier. The analysis unit can also prioritize analysis of data submitted more recently. This allows analysis to be performed efficiently by determining the analysis priorities based on the time of data submission. Some or all of the above-described 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 time of data submission to the generation AI and have the generation AI set the analysis priorities.

[0092] During analysis, the analysis unit can optimize the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. For example, the analysis unit postpones analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described 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 relevance of the data to the generation AI and cause the generation AI to optimize the order of analysis.

[0093] During analysis, the analysis unit can optimize the use of technical terms in the analysis according to the level of expertise of the medical professional. For example, the analysis unit uses detailed technical terms for medical professionals with high expertise. For example, the analysis unit uses concise terms for medical professionals with low expertise. The analysis unit can also adjust the way in which the analysis results are expressed according to the level of expertise of the medical professional. This makes it possible to provide analysis results that are easier to understand by adjusting the use of technical terms in the analysis according to the level of expertise of the medical professional. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the level of expertise of the medical professional into the generation AI and cause the generation AI to optimize the use of technical terms.

[0094] During analysis, the analysis unit can perform risk assessment based on the patient's genetic information. The analysis unit, for example, assesses the risk of a particular disease based on the patient's genetic information. For example, the analysis unit adjusts a treatment plan taking into account the patient's genetic information. The analysis unit can also suggest preventive measures using the patient's genetic information. This allows for more accurate risk assessment by taking into account the patient's genetic information. Some or all of the above-described 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 genetic information into the generation AI and have the generation AI perform a risk assessment.

[0095] The providing unit can estimate the user's emotion and optimize the presentation method of the information to be provided based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the presentation method of the information to be provided based on the estimated user's emotion. For example, the providing unit can provide detailed information when the patient is relaxed. The providing unit can also provide concise information when the patient is nervous. The providing unit can also provide information that focuses on the main points when the patient is in a hurry. This allows for adjusting the presentation method of the information to be provided based on the user's emotion, 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 may be performed using an AI, for example, or without an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to optimize the presentation method of the information.

[0096] The providing unit can optimize the level of detail of the provided information based on the importance of the analysis result when providing the information. For example, the providing unit provides detailed information for important analysis results. For example, the providing unit provides basic information for general analysis results. The providing unit can also quickly provide the gist of analysis results that are highly urgent. This allows information to be provided efficiently by adjusting the level of detail of the provided information based on the importance of the analysis results. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the importance of the analysis results to the generating AI and cause the generating AI to optimize the level of detail of the provided information.

[0097] The providing unit can apply an appropriate providing method depending on the category of the analysis results when providing the results. For example, the providing unit provides visual graphs or charts for image analysis results. For example, the providing unit provides summarized text information for text analysis results. The providing unit can also provide a detailed report including statistical data for numerical analysis results. This makes it possible to provide more appropriate information by applying different providing methods depending on the category of the analysis results. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the category of the analysis results into the generation AI and cause the generation AI to apply an appropriate providing method.

[0098] The providing unit can improve the accuracy of the information provided based on the medical professional's past feedback at the time of providing the information. The providing unit improves the accuracy of the information to be provided, for example, based on the medical professional's past feedback. For example, the providing unit adjusts the providing method by reflecting the medical professional's past feedback. The providing unit can also adjust the range of information to be provided by referring to the medical professional's past feedback. In this way, the accuracy of the information provided can be improved by referring to the medical professional's past feedback. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the medical professional's past feedback into the generating AI and cause the generating AI to improve the accuracy of the information provided.

[0099] The providing unit can provide comprehensive health information based on the patient's lifestyle data at the time of providing the information. The providing unit provides comprehensive health information, for example, by taking into account the patient's diet and exercise data. For example, the providing unit provides comprehensive health information by taking into account the patient's sleep patterns. The providing unit can also provide comprehensive health information by taking into account the patient's stress level. In this way, comprehensive health information can be provided by taking into account the patient's lifestyle data. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the patient's lifestyle data into the generating AI and cause the generating AI to provide comprehensive health information.

[0100] The providing unit can estimate the user's emotions and prioritize the information to be provided based on the estimated user's emotions. The providing unit, for example, estimates the user's emotions and determines the priority of the information to be provided based on the estimated user's emotions. For example, if the patient is relaxed, the providing unit can prioritize providing detailed information. Also, if the patient is nervous, the providing unit can prioritize providing basic information. Also, if the patient is in a hurry, the providing unit can prioritize providing the most important information. By determining the priority of the information to be provided based on the user's emotions, more important information can be prioritized. The emotion estimation is realized 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 providing unit may be performed using an AI, or may be performed without an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to set the priority of the information.

[0101] When providing the information, the providing unit can prioritize providing highly relevant information based on the patient's geographical location information. For example, if the patient is in a specific area, the providing unit prioritizes providing information related to that area. For example, if the patient is traveling, the providing unit prioritizes providing information related to the patient's destination. Furthermore, if the patient is in a specific medical facility, the providing unit can also prioritize providing information related to that facility. This allows for more appropriate information to be provided by prioritizing highly relevant information in consideration of the patient's geographical location information. Some or all of the above-described processing by the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the patient's geographical location information to the generating AI and cause the generating AI to provide highly relevant information.

[0102] The providing unit can analyze the patient's social media activity and provide related health information at the time of providing the data. The providing unit provides health-related information, for example, from the patient's social media posts. For example, the providing unit analyzes the patient's social media activity and infers the patient's health condition. The providing unit can also provide related health information by referring to the activity of the patient's friends on social media. In this way, related health information can be provided by analyzing the patient's social media activity. 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 patient's social media data into the generating AI and cause the generating AI to provide health information.

[0103] The providing unit can customize the method of providing information based on the patient's past feedback when providing the information. The providing unit, for example, adjusts the method of providing information based on the patient's past feedback. For example, the providing unit changes the type of information to be provided by reflecting the patient's past feedback. The providing unit can also adjust the frequency of information to be provided by referring to the patient's past feedback. In this way, the method of providing information can be customized by reflecting the patient's past feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the patient's past feedback data into the generating AI and cause the generating AI to customize the method of providing information.

[0104] At the time of provision, the providing unit can perform risk assessment based on the patient's family history and genetic information. The providing unit, for example, performs risk assessment taking into account the patient's family history. For example, the providing unit performs risk assessment taking into account the patient's genetic information. The providing unit can also perform comprehensive risk assessment by integrating the patient's family history and genetic information. This allows for more accurate risk assessment by taking into account the patient's family history and genetic information. Some or all of the above-described processing in the providing unit may be performed using, or without, AI, for example. For example, the providing unit can input the patient's family history and genetic information into the generating AI and cause the generating AI to perform risk assessment. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and provision 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 clinical data and electronic medical record information using the camera 42 and communication I / F 44 of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information in real time using a generation AI. The provision unit provides the analysis results to a medical professional, for example, by using the output device 40 of the smart device 14. The provision unit can also generate analysis results using the specific processing unit 290 of the data processing device 12 and provide them to a medical professional via the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and provision 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 clinical data and electronic medical record information using the camera 42 and communication I / F 44 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information in real time using a generation AI. The provision unit provides the analysis results to a medical professional, for example, by using the speaker 240 of the smart glasses 214. The provision unit can also generate analysis results using the specific processing unit 290 of the data processing device 12 and provide them to a medical professional via the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and provision unit described above 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 clinical data and electronic medical record information using the camera 42 and communication I / F 44 of the headset-type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information in real time using a generation AI. The provision unit provides the analysis results to the medical professional, for example, by using the speaker 240 of the headset-type terminal 314. The provision unit can also generate analysis results using the specific processing unit 290 of the data processing device 12 and provide them to the medical professional via the speaker 240 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and provision 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 clinical data and electronic medical record information using the camera 42 and communication I / F 44 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information in real time using a generation AI. The provision unit provides the analysis results to medical professionals, for example, by using the speaker 240 of the robot 414. The provision unit can also generate analysis results using the specific processing unit 290 of the data processing device 12 and provide them to medical professionals via the speaker 240 of the robot 414.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The collection unit can collect a patient's lifestyle data and integrate it with medical records to provide comprehensive health information. For example, the collection unit can collect a patient's dietary and exercise data and integrate it with medical records. The collection unit can also collect a patient's sleep patterns and integrate it with medical records. The collection unit can also collect a patient's stress level and integrate it with medical records. In this way, comprehensive health information can be provided by collecting a patient's lifestyle data and integrating it with medical records. 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 lifestyle data into a generation AI and have the generation AI integrate it with medical records.

[0107] The analysis unit can estimate the user's emotions and optimize the analysis presentation method based on the estimated user's emotions. For example, the analysis unit can provide detailed analysis results when the patient is relaxed. The analysis unit can also provide concise analysis results when the patient is nervous. Furthermore, the analysis unit can provide analysis results that focus on the main points when the patient is in a hurry. This allows for adjusting the analysis presentation method based on the user's emotions to provide more appropriate analysis results. 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 these 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 the user's emotion data into the generation AI and cause the generation AI to optimize the analysis presentation method.

[0108] The providing unit can optimize the level of detail of the provided information based on the importance of the analysis results when providing the information. For example, the providing unit provides detailed information for important analysis results. The providing unit can also provide basic information for general analysis results. Furthermore, the providing unit can quickly provide the gist of analysis results that are highly urgent. This allows information to be provided efficiently by adjusting the level of detail of the provided information based on the importance of the analysis results. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the importance of the analysis results to the generating AI and cause the generating AI to optimize the level of detail of the provided information.

[0109] When collecting data, the collection unit can prioritize collecting highly relevant data based on the patient's geographical location information. For example, if the patient is in a specific area, the collection unit can prioritize collecting data related to that area. Also, if the patient is traveling, the collection unit can prioritize collecting data related to the patient's destination. Furthermore, if the patient is in a specific medical facility, the collection unit can prioritize collecting data related to that facility. This allows for more appropriate data to be collected by prioritizing the collection of highly relevant data in consideration of the patient's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the patient's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0110] During analysis, the analysis unit can optimize the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a basic analysis on general data. Furthermore, the analysis unit can also perform a quick analysis on data with high urgency. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. 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 importance of the data to the generation AI and cause the generation AI to optimize the level of detail of the analysis.

[0111] The providing unit can estimate the user's emotions and optimize the presentation method of the information to be provided based on the estimated user's emotions. For example, the providing unit can provide detailed information when the patient is relaxed. The providing unit can also provide concise information when the patient is nervous. Furthermore, the providing unit can provide information that focuses on the main points when the patient is in a hurry. This allows for adjusting the presentation method of the information to be provided based on the user's emotions, thereby providing more appropriate information. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may 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 providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to optimize the presentation method of the information.

[0112] The collection unit can analyze the patient's social media activities and collect related health information when collecting data. For example, the collection unit can collect health-related information from the patient's social media posts. The collection unit can also analyze the patient's social media activities to infer their health status. Furthermore, the collection unit can also collect related health information by referring to the activities of the patient's friends on social media. In this way, related health information can be collected by analyzing the patient's social media activities. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using 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 health information.

[0113] The analysis unit can estimate the user's emotions and optimize the length of the analysis based on the estimated user's emotions. For example, the analysis unit can perform a detailed analysis if the patient is relaxed. The analysis unit can also perform a concise analysis if the patient is nervous. Furthermore, the analysis unit can perform a concise analysis if the patient is in a hurry. This allows for adjusting the length of the analysis based on the user's emotions, thereby providing more appropriate analysis results. 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 analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and cause the generation AI to optimize the length of the analysis.

[0114] The providing unit can customize the providing method based on the patient's past feedback when providing information. For example, the providing unit adjusts the method of providing information based on the patient's past feedback. The providing unit can also change the type of information to be provided by reflecting the patient's past feedback. Furthermore, the providing unit can also adjust the frequency of providing information by referring to the patient's past feedback. In this way, the providing method can be customized by reflecting the patient's past feedback. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the patient's past feedback data into the generating AI and cause the generating AI to customize the providing method.

[0115] The providing unit can estimate the user's emotions and prioritize the information to be provided based on the estimated user's emotions. For example, the providing unit can estimate the user's emotions and determine the priority of the information to be provided based on the estimated user's emotions. For example, if the patient is relaxed, the providing unit can prioritize providing detailed information. Also, if the patient is nervous, the providing unit can prioritize providing basic information. Furthermore, if the patient is in a hurry, the providing unit can prioritize providing the most important information. This allows for the priority of the information to be provided based on the user's emotions, thereby providing more important information preferentially. 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 providing unit can be performed using an AI, for example, or without an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to set the priority of the information.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The collection unit collects clinical data or information from electronic medical records. Clinical data includes patient vital signs, test results, treatment history, etc. The collection unit can obtain patient medical records from electronic medical records and test results directly from medical devices. It also obtains image data from medical imaging systems and collects patient vital signs in real time from monitoring systems. Step 2: The analysis unit uses the generation AI to analyze the information collected by the collection unit in real time. The analysis is performed based on the data update frequency and the type of analysis algorithm. The generation AI uses text generation AI (e.g., LLM) to analyze medical records and detect abnormalities. It can also use multimodal generation AI to analyze image data and detect abnormalities. Furthermore, the generation AI is used to analyze test results and assist in diagnosis. Step 3: The providing unit provides the results analyzed by the analyzing unit to the healthcare professional. The results may be provided in the form of a mobile app, a web portal, a voice assistant, or the like. For example, the providing unit may communicate the analysis results to the healthcare professional by voice using voice synthesis technology. The analysis results may also be displayed to the healthcare professional through a mobile app or a web portal. Furthermore, the providing unit may communicate the analysis results to the healthcare professional by voice using a voice assistant.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 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.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The 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.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 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.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0132] 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.

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

[0134] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0136] The data processing system 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0148] 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.

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

[0150] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0152] The data processing system 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0176] 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."

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] [Explanation of symbols]

[0190] 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 collection department that collects clinical data or electronic medical record information; an analysis unit that analyzes the information collected by the collection unit in real time; a providing unit that provides the results analyzed by the analyzing unit to a medical professional; Equipped with A system characterized by:

2. The collecting unit Collect patient medical records, test results, and image data 2. The system of claim 1.

3. The analysis unit Analyzing the collected information to detect or assist in the diagnosis of anomalies 2. The system of claim 1.

4. The providing unit Providing analysis results to medical professionals via voice 2. The system of claim 1.

5. The analysis unit Analyzing image data and detecting abnormalities 2. The system of claim 1.

6. The analysis unit Analyze medical records and assist with diagnosis 2. The system of claim 1.

7. The collecting unit Inferring user emotions and optimizing the timing of data collection based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit Analyze patient history and select appropriate data collection methods 2. The system of claim 1.

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