System

Abstract

An object of a system according to an embodiment is to efficiently provide a doctor with a past medical examination result at the time of a first medical examination.SOLUTION: A system includes a reception unit, an analysis unit, an extraction unit, and a provision unit. The reception part electronically inputs the past medical examination result by the patient. The analysis unit analyzes the information input by the reception unit. The extraction unit extracts important information from the information analyzed by the analysis unit. The providing unit provides the doctor with the information extracted by the extracting unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques require patients to verbally explain past examination results at their first visit to a new medical institution, which is cumbersome and inefficient.

[0005] The system according to the embodiment aims to efficiently provide past examination results to a doctor at the time of the first consultation. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, an extraction unit, and a provision unit. The reception unit allows a patient to electronically input past medical examination results. The analysis unit analyzes the information input by the reception unit. The extraction unit extracts important information from the information analyzed by the analysis unit. The provision unit provides the information extracted by the extraction unit to a doctor. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently provide past examination results to a doctor at the time of the first consultation. [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 system according to an embodiment of the present invention is a system for efficiently communicating past medical examination results to a doctor at the time of an initial consultation at a new medical institution. In this system, patients electronically input their past medical examination results, and AI analyzes the input results, extracts important information, and provides it to the doctor. For example, patients electronically input information such as past diagnoses, prescribed medications, and test results, and AI analyzes this information to extract important information and provide it to the doctor. This eliminates the need for patients to verbally explain their past medical examination results, making consultations more efficient. This system eliminates the need for patients to explain past medical examination results in detail, allowing doctors to quickly grasp the necessary information. For example, AI extracts information on a patient's past diagnoses and prescribed medications and provides it to the doctor, allowing consultations to proceed smoothly.

[0029] A medical information provision system according to an embodiment includes a reception unit, an analysis unit, an extraction unit, and a provision unit. The reception unit allows a patient to electronically input past medical examination results. The patient's past medical examination results include, but are not limited to, diagnosis results, prescriptions, and test results. The reception unit allows a patient to input medical examination results using, for example, a web form. The reception unit can also input medical examination results using a mobile app. The reception unit can also receive medical examination results via email. The analysis unit uses AI to analyze the information input by the reception unit. The analysis can be performed using, for example, data mining technology, but is not limited to, for example. For example, the analysis unit can analyze the input information using statistical analysis. The analysis unit can also analyze information using machine learning algorithms. The analysis unit can also analyze information using natural language processing technology. The extraction unit uses AI to extract important information from the information analyzed by the analysis unit. The important information can include, for example, information of high urgency and frequently occurring symptoms, but is not limited to, for example. For example, the extraction unit can extract information on past diagnoses and prescribed medications. The extraction unit can also extract important information from test results. Furthermore, the extraction unit can extract important information based on the patient's medical history. The providing unit provides the information extracted by the extraction unit to a doctor. The provision of information is performed, for example, by email, but is not limited to this example. For example, the providing unit can provide information to a doctor using a dashboard display. Furthermore, the providing unit can provide information to a doctor using an alert notification. Furthermore, the providing unit can provide information to a doctor using a mobile app. This allows the medical information providing system according to the embodiment to enable patients to efficiently communicate past examination results to doctors, thereby streamlining examinations.

[0030] The reception unit analyzes the patient's past medical history and suggests an input method. For example, the reception unit automatically displays medical examination results that the patient has frequently entered in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the patient has used in the past. The reception unit can also predict and suggest medical examination results to be used in a specific time period based on the patient's past medical history. This makes input work more efficient by suggesting the optimal input method based on the patient's past medical history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the patient's past medical examination history data into the generation AI and have the generation AI suggest the optimal input method.

[0031] The reception unit customizes input items based on the patient's current health condition and lifestyle habits when entering examination results. For example, when entering information about medications the patient is currently taking, the reception unit automatically displays related items. The reception unit can also add related input items based on the patient's lifestyle habits (smoking, drinking, etc.). The reception unit can also preferentially display necessary input items based on the patient's health condition (chronic disease, etc.). This makes input work more efficient by providing input items according to the patient's health condition and lifestyle habits. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data about the patient's health condition and lifestyle habits into the generation AI and have the generation AI customize the input items.

[0032] When inputting examination results, the reception unit selects an input means according to the patient's input method. For example, if the patient desires voice input, the reception unit provides a voice recognition function. Furthermore, if the patient desires text input, the reception unit can also provide keyboard input preferentially. Furthermore, if the patient desires image input, the reception unit can also provide an image recognition function. This provides the optimal input means according to the patient's input method, thereby streamlining the input work. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the patient's input method data into the generation AI and have the generation AI select the optimal input means.

[0033] When inputting medical examination results, the reception unit prioritizes inputting highly relevant information based on the patient's geographical location information. For example, if the patient lives in a specific area, the reception unit prioritizes inputting medical examination results related to that area. Furthermore, if the patient is traveling, the reception unit can prioritize inputting medical information for the patient's travel destination. Furthermore, if the patient is planning to move, the reception unit can prioritize inputting medical examination results related to the patient's new residence. This prioritizes inputting highly relevant information based on the patient's geographical location information, thereby streamlining the input work. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the patient's geographical location information data to the generation AI and cause the generation AI to prioritize inputting highly relevant information.

[0034] When inputting medical examination results, the reception unit analyzes the patient's social media activity and inputs related information. The reception unit inputs medical examination results based on, for example, health information shared by the patient on social media. The reception unit can also input related medical examination results based on the patient's social media activity. The reception unit can also input medical examination results by referring to the health information of the patient's friends on social media. This makes input work more efficient by inputting related information based on the patient's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the patient's social media activity data into the generation AI and have the generation AI input related information.

[0035] When entering examination results, the reception unit customizes the input method by reflecting the patient's past feedback. The reception unit, for example, suggests the optimal input method based on feedback previously entered by the patient. The reception unit can also customize the input interface based on the patient's past feedback. The reception unit can also adjust the input items by referring to the patient's feedback. This makes the input work more efficient by providing an input method based on the patient's past feedback. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the patient's past feedback data into the generation AI and have the generation AI customize the input method.

[0036] During analysis, the analysis unit adjusts the level of detail of the analysis based on the importance of the examination result. For example, the analysis unit performs a detailed analysis for important examination results. The analysis unit can also perform a simplified analysis for general examination results. The analysis unit can also perform a quick analysis for examination results with high urgency. This makes the analysis work more efficient by performing an analysis according to the importance of the examination result. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the examination result to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0037] During analysis, the analysis unit applies different analysis algorithms depending on the category of the examination results. The analysis unit selects an appropriate analysis algorithm based on, for example, the diagnosis name. The analysis unit can also select an appropriate analysis algorithm based on information about prescribed medications. The analysis unit can also select an appropriate analysis algorithm based on test results. This allows analysis to be performed according to the category of the examination results, making the analysis work more efficient. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the examination results into the generation AI and have the generation AI select an appropriate analysis algorithm.

[0038] During analysis, the analysis unit improves the accuracy of the analysis by referring to the patient's past analysis results. The analysis unit, for example, corrects the current analysis result based on the patient's past analysis results. The analysis unit can also understand the analysis trend from the patient's past analysis results and improve accuracy. The analysis unit can also adjust the analysis algorithm by referring to the patient's past analysis results. In this way, the accuracy of the analysis is improved by referring to the patient's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the patient's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0039] During analysis, the analysis unit determines the priority of analysis based on the time when the examination results were submitted. For example, the analysis unit prioritizes the analysis of the most recently submitted examination results. The analysis unit can also prioritize the analysis of examination results with a high urgency. The analysis unit can also postpone the analysis of examination results submitted earlier. This makes the analysis work more efficient by performing the analysis in priority order based on the time when the examination results were submitted. 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 data on the time when the examination results were submitted into the generation AI and have the generation AI determine the analysis priority.

[0040] During analysis, the analysis unit adjusts the order of analysis based on the relevance of the examination results. For example, the analysis unit prioritizes analysis of highly relevant examination results. The analysis unit can also postpone analysis of less relevant examination results. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the examination results. This makes the analysis work more efficient by performing the analysis in an order based on the relevance of the examination 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 relevance data of the examination results to the generation AI and have the generation AI adjust the order of analysis.

[0041] During analysis, the analysis unit adjusts the use of technical terminology in the analysis according to the patient's level of expertise. For example, if the patient is a medical professional, the analysis unit provides analysis results that use a lot of technical terminology. Furthermore, if the patient is a layperson, the analysis unit can also provide analysis results that avoid technical terminology. The analysis unit can also adjust the way the analysis results are presented according to the patient's level of expertise. This allows for a deeper understanding of the analysis results by providing analysis results that are appropriate for the patient's level of expertise. 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 level of expertise data into the generation AI and have the generation AI use technical terminology in the analysis.

[0042] During extraction, the extraction unit improves the accuracy of extraction based on the interrelationships between medical examination results. For example, the extraction unit extracts information on diagnosis names and prescribed medications by associating them with each other. The extraction unit can also extract information taking into account the interrelationships between test results and diagnosis names. The extraction unit can also analyze the interrelationships between medical examination results and extract highly accurate information. This improves the accuracy of extraction by taking into account the interrelationships between medical examination results. Some or all of the above-described processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input interrelationship data between medical examination results into the generation AI and cause the generation AI to improve the accuracy of extraction.

[0043] The extraction unit performs extraction while taking into consideration attribute information of the person submitting the medical examination results. For example, if the person submitting the medical examination results is a medical professional, the extraction unit prioritizes extracting specialized information. Furthermore, if the person submitting the medical examination results is a layperson, the extraction unit can also prioritize extracting general information. Furthermore, the extraction unit can extract optimal information based on the attribute information of the person submitting the medical examination results. This makes the extraction work more efficient by performing extraction based on the attribute information of the person submitting the medical examination results. Some or all of the above-described processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input attribute information data of the person submitting the medical examination results to the generation AI and cause the generation AI to perform extraction.

[0044] The extraction unit weights the extraction based on the frequency of submission of medical examination results during extraction. For example, the extraction unit prioritizes extraction of frequently submitted medical examination results. The extraction unit can also prioritize extraction of medical examination results that are submitted less frequently. The extraction unit can also dynamically adjust the weighting of the extraction based on the frequency of submission of medical examination results. This makes the extraction work more efficient by performing extraction with weighting based on the frequency of submission of medical examination results. Some or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input data on the frequency of submission of medical examination results to a generation AI and have the generation AI perform the weighting of the extraction.

[0045] During extraction, the extraction unit performs extraction based on the geographic distribution of medical examination results. For example, if a patient lives in a specific area, the extraction unit prioritizes extraction of medical examination results related to that area. Furthermore, if a patient is traveling, the extraction unit can prioritize extraction of medical information for the patient's travel destination. Furthermore, if a patient is planning to move, the extraction unit can prioritize extraction of medical examination results related to the patient's new residence. This makes extraction work more efficient by performing extraction based on the geographic distribution of medical examination results. Some or all of the above-described processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input geographic distribution data of medical examination results to the generation AI and have the generation AI perform the extraction.

[0046] During extraction, the extraction unit improves the accuracy of extraction by referring to literature related to the examination results. The extraction unit, for example, refers to academic papers related to the examination results to extract highly accurate information. The extraction unit can also extract appropriate information by referring to medical guidelines related to the examination results. The extraction unit can also extract highly accurate information by referring to past case studies related to the examination results. In this way, by referring to literature related to the examination results, the accuracy of extraction is improved. Some or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input literature data related to the examination results into the generation AI and cause the generation AI to improve the accuracy of extraction.

[0047] The extraction unit performs extraction taking into consideration the market value of the medical examination results. For example, the extraction unit prioritizes extraction of medical examination results with high market value. The extraction unit can also prioritize extraction of medical examination results with low market value. The extraction unit can also dynamically adjust the weighting of extraction based on the market value of the medical examination results. This makes the extraction work more efficient by performing extraction based on the market value of the medical examination results. Some or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input market value data of the medical examination results to the generation AI and have the generation AI perform the extraction.

[0048] The providing unit adjusts the level of detail of the provided information based on the importance of the examination result when providing the information. For example, the providing unit provides detailed information for important examination results. The providing unit can also provide simplified information for general examination results. The providing unit can also provide information quickly for examination results with high urgency. This allows for a deeper understanding of the information by providing information according to the importance of the examination result. 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 importance data of the examination result to the generating AI and cause the generating AI to adjust the level of detail of the provided information.

[0049] The providing unit applies different providing algorithms depending on the category of the examination results when providing the information. The providing unit selects an appropriate providing algorithm based on, for example, the diagnosis name. The providing unit can also select an appropriate providing algorithm based on information about prescribed medications. The providing unit can also select an appropriate providing algorithm based on test results. This provides information according to the category of the examination results, thereby deepening understanding of the information. 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 category data of the examination results to the generating AI and cause the generating AI to apply an appropriate providing algorithm.

[0050] At the time of provision, the provision unit improves the accuracy of provision by referring to the patient's past provision results. The provision unit, for example, corrects the current provision result based on the patient's past provision results. The provision unit can also understand provision trends from the patient's past provision results and improve accuracy. The provision unit can also adjust the provision algorithm by referring to the patient's past provision results. In this way, the accuracy of provision is improved by referring to the patient's past provision results. Some or all of the above-mentioned processing in the provision unit may be performed, for example, using AI or may be performed without using AI. For example, the provision unit can input the patient's past provision result data into the generation AI and cause the generation AI to improve the accuracy of provision.

[0051] At the time of providing, the providing unit determines the priority of provision based on the time of submission of the medical examination results. For example, the providing unit may provide the most recently submitted medical examination results with priority. The providing unit may also provide medical examination results with high urgency with priority. The providing unit may also provide medical examination results that were submitted earlier later. This allows for a deeper understanding of the information by providing information in priority order based on the time of submission of the medical examination results. 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 data on the time of submission of the medical examination results into the generating AI, and cause the generating AI to determine the priority of provision.

[0052] The providing unit adjusts the order of provision based on the relevance of the examination results when providing the information. For example, the providing unit prioritizes the provision of highly relevant examination results. The providing unit can also provide less relevant examination results later. The providing unit can also dynamically adjust the order of provision based on the relevance of the examination results. This allows for a deeper understanding of the information by providing information in an order based on the relevance of the examination results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input relevance data of the examination results to the generating AI and cause the generating AI to adjust the order of provision.

[0053] The providing unit adjusts the use of technical terminology in the provided information according to the patient's level of expertise. For example, if the patient is a medical professional, the providing unit provides information that uses a lot of technical terminology. Furthermore, if the patient is a layperson, the providing unit can also provide information that avoids technical terminology. The providing unit can also adjust the way the information is presented according to the patient's level of expertise. This allows for a deeper understanding of the information by providing information that is appropriate for the patient's level of expertise. 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 level of expertise data into the generating AI and cause the generating AI to use technical terminology in the provided information.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] When entering a patient's past medical examination results, the reception unit can collect data on the patient's lifestyle and daily activity and send it to the analysis unit. For example, by collecting data from a fitness tracker or smartwatch that the patient uses daily and analyzing it with the analysis unit, information such as the patient's exercise volume and sleep patterns can be reflected in the medical examination results. The reception unit can also collect data from the patient's food record app and send it to the analysis unit to reflect the patient's nutritional status and dietary trends in the medical examination results. Furthermore, the reception unit can collect data from an app that measures the patient's stress level and send it to the analysis unit to reflect the patient's mental health status in the medical examination results. This allows for more comprehensive medical examinations by incorporating the patient's lifestyle and daily activity data into the medical examination results.

[0056] The reception unit can analyze the patient's past medical history and take into account the patient's family history and genetic information when proposing an input method. For example, if the patient's family has a specific medical history, it can prioritize displaying input items related to that medical history. It can also suggest input items related to specific disease risks based on the patient's genetic information. Furthermore, it can predict and suggest medical items that may be required in the future based on the patient's family history and genetic information. This makes input work more efficient by proposing the optimal input method based on the patient's family history and genetic information.

[0057] When inputting medical examination results, the reception unit can take into account the patient's occupation and daily activity level when customizing input items based on the patient's current health condition and lifestyle. For example, if the patient does desk work, input items related to health risks associated with sitting for long periods of time can be added. Also, if the patient does physical labor, input items related to physical strain can be displayed preferentially. Furthermore, if the patient is elderly, input items related to health risks associated with aging can be customized. This makes input work more efficient by providing input items according to the patient's occupation and daily activity level.

[0058] When inputting examination results, the reception unit can take the patient's physical limitations into consideration when selecting an input means according to the patient's input method. For example, if the patient is visually impaired, voice input can be given priority. Also, if the patient is hearing impaired, text input can be given priority. Furthermore, if the patient has limitations in hand movement, input means using voice recognition or image recognition can be provided. This makes input work more efficient by providing the optimal input means according to the patient's physical limitations.

[0059] When inputting medical examination results, the reception unit can take the patient's travel history into consideration when preferentially inputting highly relevant information based on the patient's geographical location information. For example, it can prioritize inputting related medical examination results based on medical information from areas the patient has visited in the past. Also, if the patient travels frequently, it can prioritize inputting medical information from the destinations they have traveled to. Furthermore, if the patient plans to stay in a specific area for an extended period of time, it can prioritize inputting medical examination results related to that area. This makes input work more efficient by preferentially inputting highly relevant information based on the patient's travel history.

[0060] When inputting medical examination results, the reception unit can analyze the patient's social media activity and take the patient's online community activity into consideration when inputting related information. For example, if the patient is a member of a specific health-related online community, the reception unit can input medical examination results based on information shared in that community. The reception unit can also input related medical examination results based on health information obtained by the patient through activities in the online community. Furthermore, the reception unit can input medical examination results based on the health information of friends in the online community. This allows the input work to be more efficient by inputting related information based on the patient's online community activity.

[0061] When inputting medical examination results, the reception unit can take into account the frequency and content of the patient's feedback when customizing the input method by reflecting the patient's past feedback. For example, if the patient provides feedback frequently, the input interface can be customized in detail based on that feedback. Specific input items can also be added or deleted based on the patient's feedback. Furthermore, the input method can be optimized by reflecting improvements obtained from the patient's feedback. This makes input work more efficient by providing an input method based on the patient's past feedback.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The reception desk electronically inputs the patient's past medical examination results. The medical examination results input by the patient include diagnosis results, prescriptions, test results, etc. The reception desk can input or receive medical examination results using a web form, a mobile app, email, etc. Step 2: The analysis unit uses AI to analyze the information entered by the reception unit. The analysis is carried out using data mining technology, statistical analysis, machine learning algorithms, natural language processing technology, etc. Step 3: The extraction unit uses AI to extract important information from the information analyzed by the analysis unit. Important information includes information of high urgency, frequent symptoms, past diagnoses, prescribed medication information, important information from test results, and information based on the patient's medical history. Step 4: The providing unit provides the information extracted by the extracting unit to the doctor via email, dashboard display, alert notification, mobile app, etc.

[0064] (Example 2) A system according to an embodiment of the present invention is a system for efficiently communicating past medical examination results to a doctor at the time of an initial consultation at a new medical institution. In this system, patients electronically input their past medical examination results, and AI analyzes the input results, extracts important information, and provides it to the doctor. For example, patients electronically input information such as past diagnoses, prescribed medications, and test results, and AI analyzes this information to extract important information and provide it to the doctor. This eliminates the need for patients to verbally explain their past medical examination results, making consultations more efficient. This system eliminates the need for patients to explain past medical examination results in detail, allowing doctors to quickly grasp the necessary information. For example, AI extracts information on a patient's past diagnoses and prescribed medications and provides it to the doctor, allowing consultations to proceed smoothly.

[0065] A medical information provision system according to an embodiment includes a reception unit, an analysis unit, an extraction unit, and a provision unit. The reception unit allows a patient to electronically input past medical examination results. The patient's past medical examination results include, but are not limited to, diagnosis results, prescriptions, and test results. The reception unit allows a patient to input medical examination results using, for example, a web form. The reception unit can also input medical examination results using a mobile app. The reception unit can also receive medical examination results via email. The analysis unit uses AI to analyze the information input by the reception unit. The analysis can be performed using, for example, data mining technology, but is not limited to, for example. For example, the analysis unit can analyze the input information using statistical analysis. The analysis unit can also analyze information using machine learning algorithms. The analysis unit can also analyze information using natural language processing technology. The extraction unit uses AI to extract important information from the information analyzed by the analysis unit. The important information can include, for example, information of high urgency and frequently occurring symptoms, but is not limited to, for example. For example, the extraction unit can extract information on past diagnoses and prescribed medications. The extraction unit can also extract important information from test results. Furthermore, the extraction unit can extract important information based on the patient's medical history. The providing unit provides the information extracted by the extraction unit to a doctor. The provision of information is performed, for example, by email, but is not limited to this example. For example, the providing unit can provide information to a doctor using a dashboard display. Furthermore, the providing unit can provide information to a doctor using an alert notification. Furthermore, the providing unit can provide information to a doctor using a mobile app. This allows the medical information providing system according to the embodiment to enable patients to efficiently communicate past examination results to doctors, thereby streamlining examinations.

[0066] The reception unit estimates the patient's emotions and adjusts the display method of the input interface based on the estimated patient emotions. For example, if the patient is nervous, the reception unit provides an interface with subdued colors to reduce visual stress. Furthermore, if the patient is relaxed, the reception unit can provide an interface with bright colors to make input work more enjoyable. Furthermore, if the patient is tired, the reception unit can provide a simple, highly visible interface to make input work easier. This makes input work more comfortable by providing an interface that corresponds to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit may input image data of the patient taken with a camera into the generation AI and have the generation AI estimate the patient's emotions.

[0067] The reception unit analyzes the patient's past medical history and suggests an input method. For example, the reception unit automatically displays medical examination results that the patient has frequently entered in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the patient has used in the past. The reception unit can also predict and suggest medical examination results to be used in a specific time period based on the patient's past medical history. This makes input work more efficient by suggesting the optimal input method based on the patient's past medical history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the patient's past medical examination history data into the generation AI and have the generation AI suggest the optimal input method.

[0068] The reception unit customizes input items based on the patient's current health condition and lifestyle habits when entering examination results. For example, when entering information about medications the patient is currently taking, the reception unit automatically displays related items. The reception unit can also add related input items based on the patient's lifestyle habits (smoking, drinking, etc.). The reception unit can also preferentially display necessary input items based on the patient's health condition (chronic disease, etc.). This makes input work more efficient by providing input items according to the patient's health condition and lifestyle habits. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data about the patient's health condition and lifestyle habits into the generation AI and have the generation AI customize the input items.

[0069] When inputting examination results, the reception unit selects an input means according to the patient's input method. For example, if the patient desires voice input, the reception unit provides a voice recognition function. Furthermore, if the patient desires text input, the reception unit can also provide keyboard input preferentially. Furthermore, if the patient desires image input, the reception unit can also provide an image recognition function. This provides the optimal input means according to the patient's input method, thereby streamlining the input work. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the patient's input method data into the generation AI and have the generation AI select the optimal input means.

[0070] The reception unit estimates the patient's emotions and determines the priority of the medical examination results to be input based on the estimated patient emotions. For example, if the patient is feeling anxious, the reception unit prioritizes input of important medical examination results. Furthermore, if the patient is relaxed, the reception unit can also input detailed medical examination results. Furthermore, if the patient is in a hurry, the reception unit can prioritize input of the minimum necessary medical examination results. This makes the input process more efficient by inputting medical examination results in order of priority according to the patient's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit may input image data of the patient taken with a camera into the generation AI and have the generation AI estimate the patient's emotions.

[0071] When inputting medical examination results, the reception unit prioritizes inputting highly relevant information based on the patient's geographical location information. For example, if the patient lives in a specific area, the reception unit prioritizes inputting medical examination results related to that area. Furthermore, if the patient is traveling, the reception unit can prioritize inputting medical information for the patient's travel destination. Furthermore, if the patient is planning to move, the reception unit can prioritize inputting medical examination results related to the patient's new residence. This prioritizes inputting highly relevant information based on the patient's geographical location information, thereby streamlining the input work. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the patient's geographical location information data to the generation AI and cause the generation AI to prioritize inputting highly relevant information.

[0072] When inputting medical examination results, the reception unit analyzes the patient's social media activity and inputs related information. The reception unit inputs medical examination results based on, for example, health information shared by the patient on social media. The reception unit can also input related medical examination results based on the patient's social media activity. The reception unit can also input medical examination results by referring to the health information of the patient's friends on social media. This makes input work more efficient by inputting related information based on the patient's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the patient's social media activity data into the generation AI and have the generation AI input related information.

[0073] When entering examination results, the reception unit customizes the input method by reflecting the patient's past feedback. The reception unit, for example, suggests the optimal input method based on feedback previously entered by the patient. The reception unit can also customize the input interface based on the patient's past feedback. The reception unit can also adjust the input items by referring to the patient's feedback. This makes the input work more efficient by providing an input method based on the patient's past feedback. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the patient's past feedback data into the generation AI and have the generation AI customize the input method.

[0074] The analysis unit estimates the patient's emotions and adjusts the presentation method of the analysis based on the estimated patient's emotions. For example, if the patient is nervous, the analysis unit provides simple, highly visible analysis results. Furthermore, if the patient is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the patient is in a hurry, the analysis unit can provide analysis results that focus on the main points. This provides analysis results that correspond to the patient's emotions, thereby deepening understanding of the analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit may input image data of the patient taken with a camera into the generation AI and have the generation AI estimate the patient's emotions.

[0075] During analysis, the analysis unit adjusts the level of detail of the analysis based on the importance of the examination result. For example, the analysis unit performs a detailed analysis for important examination results. The analysis unit can also perform a simplified analysis for general examination results. The analysis unit can also perform a quick analysis for examination results with high urgency. This makes the analysis work more efficient by performing an analysis according to the importance of the examination result. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the examination result to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0076] During analysis, the analysis unit applies different analysis algorithms depending on the category of the examination results. The analysis unit selects an appropriate analysis algorithm based on, for example, the diagnosis name. The analysis unit can also select an appropriate analysis algorithm based on information about prescribed medications. The analysis unit can also select an appropriate analysis algorithm based on test results. This allows analysis to be performed according to the category of the examination results, making the analysis work more efficient. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the examination results into the generation AI and have the generation AI select an appropriate analysis algorithm.

[0077] During analysis, the analysis unit improves the accuracy of the analysis by referring to the patient's past analysis results. The analysis unit, for example, corrects the current analysis result based on the patient's past analysis results. The analysis unit can also understand the analysis trend from the patient's past analysis results and improve accuracy. The analysis unit can also adjust the analysis algorithm by referring to the patient's past analysis results. In this way, the accuracy of the analysis is improved by referring to the patient's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the patient's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0078] The analysis unit estimates the patient's emotions and adjusts the length of the analysis based on the estimated patient emotions. For example, if the patient is in a hurry, the analysis unit provides a short and concise analysis result. Furthermore, if the patient is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the patient is excited, the analysis unit can provide an analysis result with a visually stimulating effect. This provides analysis results tailored to the patient's emotions, deepening their understanding. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit may input image data of the patient captured by a camera into the generation AI and have the generation AI estimate the patient's emotions.

[0079] During analysis, the analysis unit determines the priority of analysis based on the time when the examination results were submitted. For example, the analysis unit prioritizes the analysis of the most recently submitted examination results. The analysis unit can also prioritize the analysis of examination results with a high urgency. The analysis unit can also postpone the analysis of examination results submitted earlier. This makes the analysis work more efficient by performing the analysis in priority order based on the time when the examination results were submitted. 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 data on the time when the examination results were submitted into the generation AI and have the generation AI determine the analysis priority.

[0080] During analysis, the analysis unit adjusts the order of analysis based on the relevance of the examination results. For example, the analysis unit prioritizes analysis of highly relevant examination results. The analysis unit can also postpone analysis of less relevant examination results. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the examination results. This makes the analysis work more efficient by performing the analysis in an order based on the relevance of the examination 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 relevance data of the examination results to the generation AI and have the generation AI adjust the order of analysis.

[0081] During analysis, the analysis unit adjusts the use of technical terminology in the analysis according to the patient's level of expertise. For example, if the patient is a medical professional, the analysis unit provides analysis results that use a lot of technical terminology. Furthermore, if the patient is a layperson, the analysis unit can also provide analysis results that avoid technical terminology. The analysis unit can also adjust the way the analysis results are presented according to the patient's level of expertise. This allows for a deeper understanding of the analysis results by providing analysis results that are appropriate for the patient's level of expertise. 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 level of expertise data into the generation AI and have the generation AI use technical terminology in the analysis.

[0082] The extraction unit estimates the patient's emotions and determines the priority of information to be extracted based on the estimated patient emotions. For example, if the patient is feeling anxious, the extraction unit prioritizes extracting important information. The extraction unit can also extract detailed information if the patient is relaxed. The extraction unit can also prioritize extracting the minimum necessary information if the patient is in a hurry. This improves the efficiency of the extraction process by extracting information in accordance with the patient's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the extraction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the extraction unit may input image data of the patient captured by a camera into the generation AI and cause the generation AI to estimate the patient's emotions.

[0083] During extraction, the extraction unit improves the accuracy of extraction based on the interrelationships between medical examination results. For example, the extraction unit extracts information on diagnosis names and prescribed medications by associating them with each other. The extraction unit can also extract information taking into account the interrelationships between test results and diagnosis names. The extraction unit can also analyze the interrelationships between medical examination results and extract highly accurate information. This improves the accuracy of extraction by taking into account the interrelationships between medical examination results. Some or all of the above-described processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input interrelationship data between medical examination results into the generation AI and cause the generation AI to improve the accuracy of extraction.

[0084] The extraction unit performs extraction while taking into consideration attribute information of the person submitting the medical examination results. For example, if the person submitting the medical examination results is a medical professional, the extraction unit prioritizes extracting specialized information. Furthermore, if the person submitting the medical examination results is a layperson, the extraction unit can also prioritize extracting general information. Furthermore, the extraction unit can extract optimal information based on the attribute information of the person submitting the medical examination results. This makes the extraction work more efficient by performing extraction based on the attribute information of the person submitting the medical examination results. Some or all of the above-described processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input attribute information data of the person submitting the medical examination results to the generation AI and cause the generation AI to perform extraction.

[0085] The extraction unit weights the extraction based on the frequency of submission of medical examination results during extraction. For example, the extraction unit prioritizes extraction of frequently submitted medical examination results. The extraction unit can also prioritize extraction of medical examination results that are submitted less frequently. The extraction unit can also dynamically adjust the weighting of the extraction based on the frequency of submission of medical examination results. This makes the extraction work more efficient by performing extraction with weighting based on the frequency of submission of medical examination results. Some or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input data on the frequency of submission of medical examination results to a generation AI and have the generation AI perform the weighting of the extraction.

[0086] The extraction unit estimates the patient's emotions and adjusts the display method of the extracted information based on the estimated patient emotions. For example, if the patient is nervous, the extraction unit provides a simple, highly visible display method. Furthermore, if the patient is relaxed, the extraction unit can provide a display method that includes detailed information. Furthermore, if the patient is in a hurry, the extraction unit can provide a display method that focuses on the main points. This allows for a deeper understanding of the information by providing information in a display method that matches the patient's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the extraction unit may be performed using, for example, AI, or without AI. For example, the extraction unit may input image data of the patient captured by a camera into the generation AI and cause the generation AI to estimate the patient's emotions.

[0087] During extraction, the extraction unit performs extraction based on the geographic distribution of medical examination results. For example, if a patient lives in a specific area, the extraction unit prioritizes extraction of medical examination results related to that area. Furthermore, if a patient is traveling, the extraction unit can prioritize extraction of medical information for the patient's travel destination. Furthermore, if a patient is planning to move, the extraction unit can prioritize extraction of medical examination results related to the patient's new residence. This makes extraction work more efficient by performing extraction based on the geographic distribution of medical examination results. Some or all of the above-described processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input geographic distribution data of medical examination results to the generation AI and have the generation AI perform the extraction.

[0088] During extraction, the extraction unit improves the accuracy of extraction by referring to literature related to the examination results. The extraction unit, for example, refers to academic papers related to the examination results to extract highly accurate information. The extraction unit can also extract appropriate information by referring to medical guidelines related to the examination results. The extraction unit can also extract highly accurate information by referring to past case studies related to the examination results. In this way, by referring to literature related to the examination results, the accuracy of extraction is improved. Some or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input literature data related to the examination results into the generation AI and cause the generation AI to improve the accuracy of extraction.

[0089] The extraction unit performs extraction taking into consideration the market value of the medical examination results. For example, the extraction unit prioritizes extraction of medical examination results with high market value. The extraction unit can also prioritize extraction of medical examination results with low market value. The extraction unit can also dynamically adjust the weighting of extraction based on the market value of the medical examination results. This makes the extraction work more efficient by performing extraction based on the market value of the medical examination results. Some or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input market value data of the medical examination results to the generation AI and have the generation AI perform the extraction.

[0090] The providing unit estimates the patient's emotions and adjusts the display method of the information to be provided based on the estimated patient emotions. For example, if the patient is nervous, the providing unit provides a simple, highly visible display method. Furthermore, if the patient is relaxed, the providing unit can also provide a display method that includes detailed information. Furthermore, if the patient is in a hurry, the providing unit can also provide a display method that focuses on the main points. This allows for a deeper understanding of the information by providing information in a display method that matches the patient's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit may input image data of the patient captured by a camera into the generation AI and cause the generation AI to estimate the patient's emotions.

[0091] The providing unit adjusts the level of detail of the provided information based on the importance of the examination result when providing the information. For example, the providing unit provides detailed information for important examination results. The providing unit can also provide simplified information for general examination results. The providing unit can also provide information quickly for examination results with high urgency. This allows for a deeper understanding of the information by providing information according to the importance of the examination result. 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 importance data of the examination result to the generating AI and cause the generating AI to adjust the level of detail of the provided information.

[0092] The providing unit applies different providing algorithms depending on the category of the examination results when providing the information. The providing unit selects an appropriate providing algorithm based on, for example, the diagnosis name. The providing unit can also select an appropriate providing algorithm based on information about prescribed medications. The providing unit can also select an appropriate providing algorithm based on test results. This provides information according to the category of the examination results, thereby deepening understanding of the information. 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 category data of the examination results to the generating AI and cause the generating AI to apply an appropriate providing algorithm.

[0093] At the time of provision, the provision unit improves the accuracy of provision by referring to the patient's past provision results. The provision unit, for example, corrects the current provision result based on the patient's past provision results. The provision unit can also understand provision trends from the patient's past provision results and improve accuracy. The provision unit can also adjust the provision algorithm by referring to the patient's past provision results. In this way, the accuracy of provision is improved by referring to the patient's past provision results. Some or all of the above-mentioned processing in the provision unit may be performed, for example, using AI or may be performed without using AI. For example, the provision unit can input the patient's past provision result data into the generation AI and cause the generation AI to improve the accuracy of provision.

[0094] The providing unit estimates the patient's emotions and determines the priority of information to be provided based on the estimated patient emotions. For example, if the patient is feeling anxious, the providing unit prioritizes providing important information. The providing unit can also provide detailed information if the patient is relaxed. The providing unit can also prioritize providing the minimum necessary information if the patient is in a hurry. This allows the patient to understand the information better by providing information in order of priority according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI 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-mentioned 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 may input image data of the patient taken with a camera into the generation AI and cause the generation AI to estimate the patient's emotions.

[0095] At the time of providing, the providing unit determines the priority of provision based on the time of submission of the medical examination results. For example, the providing unit may provide the most recently submitted medical examination results with priority. The providing unit may also provide medical examination results with high urgency with priority. The providing unit may also provide medical examination results that were submitted earlier later. This allows for a deeper understanding of the information by providing information in priority order based on the time of submission of the medical examination results. 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 data on the time of submission of the medical examination results into the generating AI, and cause the generating AI to determine the priority of provision.

[0096] The providing unit adjusts the order of provision based on the relevance of the examination results when providing the information. For example, the providing unit prioritizes the provision of highly relevant examination results. The providing unit can also provide less relevant examination results later. The providing unit can also dynamically adjust the order of provision based on the relevance of the examination results. This allows for a deeper understanding of the information by providing information in an order based on the relevance of the examination results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input relevance data of the examination results to the generating AI and cause the generating AI to adjust the order of provision.

[0097] The providing unit adjusts the use of technical terminology in the provided information according to the patient's level of expertise. For example, if the patient is a medical professional, the providing unit provides information that uses a lot of technical terminology. Furthermore, if the patient is a layperson, the providing unit can also provide information that avoids technical terminology. The providing unit can also adjust the way the information is presented according to the patient's level of expertise. This allows for a deeper understanding of the information by providing information that is appropriate for the patient's level of expertise. 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 level of expertise data into the generating AI and cause the generating AI to use technical terminology in the provided information. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, extraction unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit allows a patient to electronically input past medical examination results using the reception device 38 of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using AI. The extraction unit is realized by the specific processing unit 290 of the data processing device 12 and extracts important information from the analyzed information. The provision unit can provide the extracted information to a doctor using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, extraction unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit allows a patient to input past medical examination results by voice using the microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using AI. The extraction unit is realized by the specific processing unit 290 of the data processing device 12 and extracts important information from the analyzed information. The provision unit can provide the extracted information to a doctor using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, extraction unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit allows a patient to input past medical examination results by voice using the microphone 238 of the headset-type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using AI. The extraction unit is realized by the specific processing unit 290 of the data processing device 12 and extracts important information from the analyzed information. The provision unit can provide the extracted information to a doctor using the display 343 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, extraction unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit allows a patient to input past medical examination results by voice using the microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using AI. The extraction unit is realized by the specific processing unit 290 of the data processing device 12 and extracts important information from the analyzed information. The provision unit can provide the extracted information to a doctor using the speaker 240 of the robot 414.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] When entering a patient's past medical examination results, the reception unit can collect data on the patient's lifestyle and daily activity and send it to the analysis unit. For example, by collecting data from a fitness tracker or smartwatch that the patient uses daily and analyzing it with the analysis unit, information such as the patient's exercise volume and sleep patterns can be reflected in the medical examination results. The reception unit can also collect data from the patient's food record app and send it to the analysis unit to reflect the patient's nutritional status and dietary trends in the medical examination results. Furthermore, the reception unit can collect data from an app that measures the patient's stress level and send it to the analysis unit to reflect the patient's mental health status in the medical examination results. This allows for more comprehensive medical examinations by incorporating the patient's lifestyle and daily activity data into the medical examination results.

[0100] The reception unit can estimate the patient's emotions and adjust the voice guidance of the input interface based on the estimated patient's emotions. For example, if the patient is nervous, a voice guidance with a calm voice and a slow pace can be provided to relieve the patient's tension. If the patient is relaxed, a voice guidance with a bright and cheerful voice can be provided to make the input work more enjoyable. Furthermore, if the patient is tired, a simple and short voice guidance can be provided to speed up the input work. Thus, by providing a voice guidance according to the patient's emotions, the input work becomes more comfortable.

[0101] The reception unit can analyze the patient's past medical history and take into account the patient's family history and genetic information when proposing an input method. For example, if the patient's family has a specific medical history, it can prioritize displaying input items related to that medical history. It can also suggest input items related to specific disease risks based on the patient's genetic information. Furthermore, it can predict and suggest medical items that may be required in the future based on the patient's family history and genetic information. This makes input work more efficient by proposing the optimal input method based on the patient's family history and genetic information.

[0102] When inputting medical examination results, the reception unit can take into account the patient's occupation and daily activity level when customizing input items based on the patient's current health condition and lifestyle. For example, if the patient does desk work, input items related to health risks associated with sitting for long periods of time can be added. Also, if the patient does physical labor, input items related to physical strain can be displayed preferentially. Furthermore, if the patient is elderly, input items related to health risks associated with aging can be customized. This makes input work more efficient by providing input items according to the patient's occupation and daily activity level.

[0103] When inputting examination results, the reception unit can take the patient's physical limitations into consideration when selecting an input means according to the patient's input method. For example, if the patient is visually impaired, voice input can be given priority. Also, if the patient is hearing impaired, text input can be given priority. Furthermore, if the patient has limitations in hand movement, input means using voice recognition or image recognition can be provided. This makes input work more efficient by providing the optimal input means according to the patient's physical limitations.

[0104] The reception unit can estimate the patient's emotions and take the patient's past emotional history into consideration when determining the priority of medical examination results to be entered based on the estimated patient's emotions. For example, if the patient has previously prioritized inputting important medical examination results when feeling anxious, the reception unit can prioritize inputting important medical examination results again under similar circumstances. Furthermore, if the patient has previously input detailed medical examination results when feeling relaxed, the reception unit can also prioritize inputting detailed medical examination results again under similar circumstances. Furthermore, if the patient has previously input the minimum necessary medical examination results when in a hurry, the reception unit can also prioritize inputting the minimum necessary medical examination results again under similar circumstances. This makes the input process more efficient by inputting medical examination results in priority order based on the patient's past emotional history.

[0105] When inputting medical examination results, the reception unit can take the patient's travel history into consideration when preferentially inputting highly relevant information based on the patient's geographical location information. For example, it can prioritize inputting related medical examination results based on medical information from areas the patient has visited in the past. Also, if the patient travels frequently, it can prioritize inputting medical information from the destinations they have traveled to. Furthermore, if the patient plans to stay in a specific area for an extended period of time, it can prioritize inputting medical examination results related to that area. This makes input work more efficient by preferentially inputting highly relevant information based on the patient's travel history.

[0106] When inputting medical examination results, the reception unit can analyze the patient's social media activity and take the patient's online community activity into consideration when inputting related information. For example, if the patient is a member of a specific health-related online community, the reception unit can input medical examination results based on information shared in that community. The reception unit can also input related medical examination results based on health information obtained by the patient through activities in the online community. Furthermore, the reception unit can input medical examination results based on the health information of friends in the online community. This allows the input work to be more efficient by inputting related information based on the patient's online community activity.

[0107] When inputting medical examination results, the reception unit can take into account the frequency and content of the patient's feedback when customizing the input method by reflecting the patient's past feedback. For example, if the patient provides feedback frequently, the input interface can be customized in detail based on that feedback. Specific input items can also be added or deleted based on the patient's feedback. Furthermore, the input method can be optimized by reflecting improvements obtained from the patient's feedback. This makes input work more efficient by providing an input method based on the patient's past feedback.

[0108] The analysis unit can take into account the patient's emotional fluctuation patterns when estimating the patient's emotions and adjusting the way the analysis is presented based on the estimated patient's emotions. For example, if a simple analysis result was provided when the patient was tense in the past, a simple analysis result can be provided again in a similar situation. Also, if a detailed analysis result was provided when the patient was relaxed in the past, a detailed analysis result can be provided again in a similar situation. Furthermore, if a brief analysis result was provided when the patient was in a hurry in the past, a brief analysis result can be provided again in a similar situation. In this way, providing analysis results based on the patient's emotional fluctuation patterns deepens the patient's understanding of the analysis results.

[0109] The processing flow of the second embodiment will be briefly explained below.

[0110] Step 1: The reception desk electronically inputs the patient's past medical examination results. The medical examination results input by the patient include diagnosis results, prescriptions, test results, etc. The reception desk can input or receive medical examination results using a web form, a mobile app, email, etc. Step 2: The analysis unit uses AI to analyze the information entered by the reception unit. The analysis is carried out using data mining technology, statistical analysis, machine learning algorithms, natural language processing technology, etc. Step 3: The extraction unit uses AI to extract important information from the information analyzed by the analysis unit. Important information includes information of high urgency, frequent symptoms, past diagnoses, prescribed medication information, important information from test results, and information based on the patient's medical history. Step 4: The providing unit provides the information extracted by the extracting unit to the doctor via email, dashboard display, alert notification, mobile app, etc.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] 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.

[0130] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0145] 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.

[0146] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0162] 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.

[0163] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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, in order to avoid confusion and to 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.

[0181] 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.

[0182] [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit where patients electronically input their past medical examination results; an analysis unit that analyzes the information input by the reception unit; an extraction unit that extracts important information from the information analyzed by the analysis unit; a providing unit that provides the information extracted by the extracting unit to a doctor; Equipped with A system characterized by:

2. The reception unit Estimating the patient's emotions and adjusting the display method of the input interface based on the estimated patient's emotions 2. The system of claim 1.

3. The reception unit Analyzes the patient's past medical history and suggests input methods 2. The system of claim 1.

4. The reception unit Customize the input of consultation results based on the patient's current health status and lifestyle habits 2. The system of claim 1.

5. The reception unit When entering medical examination results, select the input method according to the patient's input method.

2. The system of claim 1.

6. The reception unit Estimate the patient's emotions and prioritize the consultation results to be entered based on the estimated patient emotions.

2. The system of claim 1.

7. The reception unit Prioritize relevant information during consultation entry based on the patient's geographic location 2. The system of claim 1.

8. The reception unit Analyze the patient's social media activity and enter relevant information when entering consultation results 2. The system of claim 1.

Images