System

Abstract

To provide a system according to an embodiment that automatically records and summarizes the details of a conversation during a medical examination, and efficiently creates a medical chart, a medical care specification, and a prescription.SOLUTION: A system according to an embodiment comprises a receiving unit, a creation unit, a determination unit, and a prescription unit. The receiving unit records the details of a conversation during a medical examination. The creation unit summarizes the details of the conversation recorded by the receiving unit to create a medical chart. The determination unit automatically determines points for every medical practice summarized by the creation unit to create a medical care specification. The prescription unit creates a prescription for drug on the basis of a symptom summarized by the creation unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

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

[0004] With conventional technology, creating medical records and medical statements after consultations, and writing prescriptions required a great deal of time and effort, placing a burden on doctors, medical administration, and patients.

[0005] The system according to the embodiment aims to automatically record and summarize the contents of conversations during consultations and efficiently create medical records, medical statements, and prescriptions. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a discrimination unit, and a prescription unit. The reception unit records the content of the conversation during the consultation. The generation unit summarizes the content of the conversation recorded by the reception unit to create a medical record. The discrimination unit automatically determines the points for each medical procedure summarized by the generation unit and creates a medical statement. The prescription unit creates a prescription for medicine based on the symptoms summarized by the generation unit. Effect of the Invention

[0007] The system according to the embodiment can automatically record and summarize conversations during consultations, and efficiently create medical records, medical statements, and prescriptions. [Brief description of the drawings]

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

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The medical treatment process efficiency system according to the embodiment of the present invention is a system in which the contents of conversations during consultations are recorded, a generation AI summarizes the conversations to create a medical record, automatically determines points, creates a medical statement, and creates a prescription for medicine. This system records all conversations between doctors and patients during consultations, and a generation AI summarizes the conversations to create a medical record. Furthermore, for each summarized medical procedure, the system automatically determines points from the official price and creates a medical statement. In addition, symptoms are inferred from the summary, a medicine appropriate for the symptoms is selected, and a prescription for medicine is created. For example, all conversations between doctors and patients during consultations are recorded. For example, conversations in which the doctor asks the patient about symptoms and conversations in which the doctor explains the results of the examination are included. This conversation is input to the generation AI. Next, the generation AI summarizes the input conversation. The generation AI analyzes the conversation, extracts important information, and creates a medical record. For example, the patient's symptoms, examination results, and doctor's diagnosis are summarized. Furthermore, for each summarized medical procedure, the system automatically determines points from the official price. Based on the summarized medical procedure, the generation AI determines the corresponding points from the official price list and creates a medical statement. For example, consultation fees, examination fees, and treatment fees are scored. Symptoms are also inferred from the summary, and the appropriate medicine is selected. The generation AI selects the appropriate medicine based on the summarized symptoms and writes a prescription. For example, for cold symptoms, antipyretics and cough suppressants are selected. This allows doctors to concentrate on the examination, and frees medical administration from the task of calculating points and creating medical statements. It also reduces the time patients have to wait to pay. For example, after the examination is over, the medical statement and prescription are issued immediately, allowing patients to pay smoothly. In this way, the medical process efficiency system can reduce the burden on doctors, medical administration, and patients.

[0029] The medical treatment process efficiency improvement system according to the embodiment includes a reception unit, a generation unit, a discrimination unit, and a prescription unit. The reception unit records the contents of the conversation during the examination. The contents of the conversation during the examination include, for example, the patient's symptoms, the doctor's diagnosis, and a treatment plan, but are not limited to such examples. The reception unit records the contents of the conversation using, for example, audio recording technology. The reception unit can also record the contents of the conversation as video using video recording technology. For example, the reception unit records the contents of the conversation with high quality using a microphone and a camera installed in the examination room. The generation unit uses a generation AI to summarize the contents of the conversation recorded by the reception unit and create a medical record. The generation AI analyzes the contents of the conversation using, for example, a text generation AI (for example, LLM), extracts important information, and summarizes it. The generation unit can also use the generation AI to pick out particularly important information from the contents of the conversation and create a medical record based on that. For example, the generation AI analyzes the contents of the conversation, summarizes the patient's symptoms, diagnosis results, treatment plan, and the like, and writes them in the medical record. The discrimination unit automatically determines the points for each medical procedure summarized by the generation unit, and creates a medical statement. The discrimination unit automatically determines the points corresponding to the summarized medical procedure based on, for example, a medical fee table. The discrimination unit can also use a generation AI to analyze the contents of the summarized medical procedure and determine the corresponding points. For example, the discrimination unit automatically calculates points such as consultation fees, examination fees, and treatment fees, and lists them on the medical bill. The prescription unit creates a prescription for medicine based on the symptoms summarized by the generation unit. The prescription unit can analyze the summarized symptoms and select an appropriate medicine using, for example, a generation AI. The prescription unit can also use a generation AI to select the most appropriate medicine from multiple medicines based on the patient's symptoms and create a prescription. For example, the prescription unit selects an antipyretic or cough medicine for cold symptoms and lists it on the prescription. As a result, the medical treatment process efficiency system according to the embodiment can reduce the burden on doctors, medical office staff, and patients.

[0030] The reception unit records the contents of the conversation during the examination. The contents of the conversation during the examination include, for example, the patient's symptoms, the doctor's diagnosis, and the treatment policy, but are not limited to such examples. The reception unit records the contents of the conversation using, for example, voice recording technology. Specifically, the conversation between the patient and the doctor is clearly recorded using a high-sensitivity microphone installed in the examination room. This makes it possible to record important information during the examination without omission. The reception unit can also record the contents of the conversation as video using video recording technology. For example, a high-resolution camera installed on the ceiling or wall of the examination room is used to record the state of the examination as video. This makes it possible to record not only the voice but also visual information such as the doctor's gestures and the patient's facial expressions. Furthermore, the reception unit can transmit these recorded data to a cloud server in real time and safely store them. This makes it possible to easily access the data after the examination and recheck it as necessary. The reception unit can also convert the recorded voice data into text data by using voice recognition technology in combination, thereby making subsequent processing more efficient. For example, the contents of the conversation can be automatically transcribed using a voice recognition engine, making it possible to smoothly process the generation unit and the discrimination unit. This allows the reception department to record the content of conversations during consultations from various angles and with high quality, thereby contributing to improving the efficiency of the entire system.

[0031] The generation unit uses the generation AI to summarize the conversation content recorded by the reception unit and create a medical record. The generation AI, for example, uses a text generation AI (for example, LLM) to analyze the conversation content, extract important information, and summarize it. Specifically, the generation AI uses natural language processing technology to extract important information such as the patient's symptoms, the doctor's diagnosis, and the treatment plan from the conversation content. For example, the generation AI can understand the context of the conversation and automatically identify the symptoms complained by the patient and the diagnosis results shown by the doctor. Furthermore, the generation AI automatically generates a concise and clear medical record based on the extracted information. The medical record includes basic information about the patient, details of the symptoms, diagnosis results, treatment plans, and information about prescribed drugs. The generation unit can also use the generation AI to pick out particularly important information from the conversation content and create a medical record based on that. For example, the generation AI analyzes the conversation content and summarizes the patient's symptoms, diagnosis results, treatment plans, etc., and writes them in the medical record. The generation AI can understand medical terms and specialized knowledge and create a medical record in appropriate expressions. This allows the generator to reduce the burden on doctors and significantly improve the efficiency of creating medical records. Furthermore, the generator can automatically store the generated medical records in the electronic medical record system, making them easily accessible to other medical staff. This improves the efficiency of the entire medical treatment process and contributes to improving the quality of medical care.

[0032] The discrimination unit automatically determines the points for each medical procedure summarized by the generation unit and creates a medical statement. The discrimination unit automatically determines the points corresponding to the summarized medical procedure based on, for example, the medical fee schedule. Specifically, the discrimination unit can use the generation AI to analyze the contents of the summarized medical procedure and determine the corresponding points. For example, the generation AI analyzes information such as consultation fees, examination fees, and treatment fees written in the medical record and automatically calculates the points corresponding to each medical procedure. Furthermore, the discrimination unit can always refer to the latest information on the medical fee schedule and determine accurate points. This allows the medical statement to be created quickly and accurately. This allows the discrimination unit to streamline the creation of the medical statement and reduce the burden on medical administration. Furthermore, the discrimination unit can automatically save the contents of the medical statement in the electronic medical record system so that other medical staff can easily access it. This can improve the efficiency of the entire medical process and contribute to improving the quality of medical care.

[0033] The prescription department creates a prescription for medicine based on the symptoms summarized by the generation department. The prescription department, for example, uses a generation AI to analyze the summarized symptoms and select an appropriate medicine. Specifically, the generation AI uses an algorithm to select the most effective and safe medicine based on the patient's symptoms and diagnosis results. For example, the generation AI considers the patient's age, sex, medical history, allergy information, etc. and selects the most effective and safe medicine. In addition, the generation AI can refer to the latest medical database and make the optimal prescription based on information such as the effects, side effects, and interactions of the medicine. Furthermore, the prescription department can use the generation AI to select the most appropriate medicine from multiple medicines based on the patient's symptoms and create a prescription. For example, the prescription department selects an antipyretic or cough suppressant for cold symptoms and writes it on the prescription. Furthermore, the prescription department can automatically save the generated prescription in the electronic medical record system so that other medical staff can easily access it. This can improve the efficiency of the entire medical treatment process and contribute to improving the quality of medical care.

[0034] The reception unit can record all conversations between the doctor and the patient during the consultation. The reception unit, for example, records the conversations during the consultation using audio recording technology. For example, the reception unit records the conversations in high quality using a microphone installed in the examination room. The reception unit can also record the conversations as video using video recording technology. For example, the reception unit records the conversations as video using a camera installed in the examination room. This allows detailed medical records to be created by recording all conversations during the consultation. 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 conversations during the consultation into AI and have the AI ​​execute the recording of the conversations.

[0035] The generation unit can summarize the recorded conversation content to create a medical record. The generation unit summarizes the recorded conversation content, for example, using a generation AI. The generation AI analyzes the conversation content, for example, using a text generation AI (for example, LLM), extracts important information, and summarizes it. The generation unit can also use the generation AI to pick out particularly important information from the conversation content and create a medical record based on it. For example, the generation AI analyzes the conversation content, summarizes the patient's symptoms, diagnosis results, treatment policy, etc., and writes them in the medical record. As a result, the medical treatment content can be efficiently organized by summarizing the recorded conversation content to create a medical record. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the generation unit can input the recorded conversation content into an AI and have the AI ​​execute the creation of a medical record.

[0036] The discrimination unit can automatically determine the points for each summarized medical procedure based on the official price and create a medical statement. The discrimination unit automatically determines the points corresponding to the summarized medical procedure based on, for example, a medical fee table. For example, the discrimination unit automatically calculates points for consultation fees, examination fees, treatment fees, etc., and lists them on the medical statement. The discrimination unit can also use a generation AI to analyze the contents of the summarized medical procedure and determine the corresponding points. For example, the discrimination unit uses a generation AI to analyze the contents of the summarized medical procedure and determine the corresponding points from the medical fee table. As a result, the points are automatically determined for each summarized medical procedure and a medical statement is created, thereby making the calculation of medical fees more efficient. Some or all of the above-mentioned processing in the discrimination unit may be performed using, for example, AI, or may be performed without using AI. For example, the discrimination unit can input the contents of the summarized medical procedure into AI and have the AI ​​execute the point determination.

[0037] The prescription unit can create a prescription for medicine based on the summarized symptoms. The prescription unit can use, for example, a generation AI to analyze the summarized symptoms and select an appropriate medicine. For example, the prescription unit can use the generation AI to select an optimal medicine from multiple medicines based on the patient's symptoms and create a prescription. For example, the prescription unit selects an antipyretic or cough suppressant for cold symptoms and writes it on the prescription. This makes it possible to prescribe an appropriate medicine by creating a prescription for medicine based on the summarized symptoms. Some or all of the above-mentioned processing in the prescription unit can be performed, for example, using AI or without AI. For example, the prescription unit can input the summarized symptoms into AI and have the AI ​​select the medicine and create the prescription.

[0038] The reception unit can analyze the contents of the conversation during the medical examination in real time and record the contents with important keywords emphasized. The reception unit uses AI, for example, to analyze the contents of the conversation during the medical examination in real time. For example, the reception unit analyzes the contents of the conversation in real time and extracts keywords related to symptoms. The reception unit can also analyze information necessary for diagnosis in real time and record important parts with emphasis. For example, the reception unit refers to the patient's past medical history and records related keywords with emphasis. As a result, by highlighting and recording important keywords, it becomes easier to understand the contents of the medical examination. A part or all of the above-mentioned 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 contents of the conversation during the medical examination into AI and have the AI ​​extract and emphasize important keywords.

[0039] The reception unit can record the contents of the conversation during the examination not only by voice but also by video, and can record non-verbal information as well. For example, the reception unit records the contents of the conversation during the examination using voice recording technology and video recording technology. For example, the reception unit uses a microphone and a camera installed in the examination room to record the contents of the conversation in high quality. The reception unit also uses video recording technology to record non-verbal information as well. For example, the reception unit records the patient's facial expressions and gestures on video and analyzes the non-verbal information. As a result, by recording non-verbal information as well, a more detailed medical record can be created. 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 contents of the conversation during the examination and non-verbal information into AI, and have AI perform recording and analysis.

[0040] The reception unit can record the contents of the conversation during the examination in multiple languages, so that it can also accommodate patients who speak different languages. The reception unit, for example, uses AI to record the contents of the conversation during the examination in multiple languages. For example, the reception unit translates the contents of the conversation in real time and records them in multiple languages. The reception unit can also automatically translate and record the contents of the examination based on the language setting of the patient. For example, the reception unit automatically translates and records the contents of the examination based on the language setting of the patient. This makes it possible to accommodate patients who speak different languages ​​by recording in multiple languages. 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 contents of the conversation during the examination into AI and have the AI ​​execute recording in multiple languages.

[0041] When recording the contents of the conversation during the examination, the reception unit can refer to the patient's past medical history and preferentially record highly relevant information. For example, when recording the contents of the conversation during the examination, the reception unit uses AI to refer to the patient's past medical history. For example, the reception unit refers to the patient's past medical history and preferentially records related information. The reception unit can also compare the past medical history with the current contents of the examination and record important information with emphasis. For example, the reception unit automatically classifies and records the contents of the examination based on the patient's past medical history. As a result, highly relevant information can be preferentially recorded by referring to the past medical history. A part or all of the above-mentioned 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 contents of the conversation during the examination to AI and cause AI to refer to the past medical history and preferentially record the information.

[0042] The generation unit can analyze the recorded conversation content, automatically extract information necessary for diagnosis, and reflect it in the medical record. The generation unit can, for example, use a generation AI to analyze the recorded conversation content, and automatically extract information necessary for diagnosis. The generation AI can, for example, use a text generation AI (for example, LLM) to analyze the conversation content and extract important information. The generation unit can also use the generation AI to pick up particularly important information from the conversation content and create a medical record based on it. For example, the generation AI can analyze the conversation content, extract the patient's symptoms, diagnosis results, treatment policy, etc., and write them in the medical record. This makes it easier to understand the medical treatment content by automatically extracting information necessary for diagnosis and reflecting it in the medical record. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using AI, or can be performed without using AI. For example, the generation unit can input the recorded conversation content into AI, and cause the AI ​​to extract information necessary for diagnosis and reflect it in the medical record.

[0043] The generation unit can analyze the recorded conversation content and integrate the opinions of multiple doctors to improve the reliability of the diagnosis. The generation unit, for example, uses a generation AI to analyze the recorded conversation content and integrate the opinions of multiple doctors. The generation AI, for example, uses a text generation AI (for example, LLM) to analyze the conversation content and integrate the diagnosis results of multiple doctors. The generation unit can also use the generation AI to analyze information necessary for diagnosis and integrate the opinions of multiple doctors to improve reliability. For example, the generation AI analyzes the conversation content and integrates the diagnosis results of multiple doctors to reflect them in the medical record. As a result, the reliability of the diagnosis is improved by integrating the opinions of multiple doctors. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the generation unit can input the recorded conversation content to the AI ​​and cause the AI ​​to execute the integration of the opinions of multiple doctors.

[0044] The generation unit can analyze the recorded conversation content, automatically classify the information necessary for diagnosis, and reflect it in the medical record. The generation unit can, for example, use a generation AI to analyze the recorded conversation content, and automatically classify the information necessary for diagnosis. The generation AI can, for example, use a text generation AI (for example, LLM) to analyze the conversation content and classify important information. The generation unit can also use the generation AI to pick up particularly important information from the conversation content and create a medical record based on it. For example, the generation AI can analyze the conversation content, classify the patient's symptoms, diagnosis results, treatment policy, etc., and write them in the medical record. This makes it easier to understand the medical treatment content by automatically classifying the information necessary for diagnosis and reflecting it in the medical record. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using an AI, or can be performed without using an AI. For example, the generation unit can input the recorded conversation content into an AI, and have the AI ​​classify the information necessary for diagnosis and reflect it in the medical record.

[0045] The generation unit can analyze the recorded conversation content and compare it with past medical data to improve the reliability of the diagnosis. The generation unit, for example, uses a generation AI to analyze the recorded conversation content and compare it with past medical data. The generation AI, for example, uses a text generation AI (for example, LLM) to analyze the conversation content and compare it with past medical data. The generation unit can also use the generation AI to analyze information necessary for diagnosis and improve reliability by comparing it with past medical data. For example, the generation AI analyzes the conversation content and reflects the diagnosis result in the chart by comparing it with past medical data. As a result, the reliability of the diagnosis is improved by comparing it with past medical data. A part or all of the above-mentioned processing in the generation unit may be performed by using an AI, for example, or may be performed without using an AI. For example, the generation unit can input the recorded conversation content to the AI ​​and cause the AI ​​to perform a comparison with past medical data.

[0046] The discrimination unit can improve the accuracy of the score by referring to past medical data for each summarized medical procedure. The discrimination unit, for example, uses a generation AI to analyze the summarized medical procedure and refer to past medical data. The generation AI, for example, uses a text generation AI (for example, LLM) to analyze the medical procedure and refer to past medical data. The discrimination unit can also use the generation AI to analyze information necessary for diagnosis and improve the accuracy of the score based on the past medical data. For example, the generation AI analyzes the summarized medical procedure and determines the score based on the past medical data. As a result, the accuracy of the score is improved by referring to the past medical data. A part or all of the above-mentioned processing in the discrimination unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the discrimination unit can input the summarized medical procedure to the AI ​​and cause the AI ​​to refer to the past medical data and determine the score.

[0047] The discrimination unit can automatically integrate the points of different medical departments for each summarized medical procedure to create a medical statement. The discrimination unit, for example, uses a generation AI to analyze the summarized medical procedure and automatically integrate the points of different medical departments. The generation AI, for example, uses a text generation AI (for example, LLM) to analyze the medical procedure and integrate the points of different medical departments. The discrimination unit can also use the generation AI to analyze information necessary for diagnosis and integrate the points of different medical departments to create a medical statement. For example, the generation AI analyzes the summarized medical procedure and integrates the points of different medical departments to write on the medical statement. As a result, the creation of the medical statement is made more efficient by integrating the points of different medical departments. A part or all of the above-mentioned processing in the discrimination unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the discrimination unit can input the summarized medical procedure to the AI ​​and cause the AI ​​to integrate the points of different medical departments.

[0048] The discrimination unit can determine the score for each summarized medical procedure by referring to the official price for each region. The discrimination unit, for example, uses a generation AI to analyze the summarized medical procedure and refer to the official price for each region. The generation AI, for example, uses a text generation AI (for example, LLM) to analyze the medical procedure and refer to the official price for each region. The discrimination unit can also use the generation AI to analyze information necessary for diagnosis and determine the score based on the official price for each region. For example, the generation AI analyzes the summarized medical procedure and determines the score based on the official price for each region. As a result, the score can be accurately determined by referring to the official price for each region. A part or all of the above-mentioned processing in the discrimination unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the discrimination unit can input the summarized medical procedure into the AI ​​and cause the AI ​​to refer to the official price for each region and determine the score.

[0049] The discrimination unit can customize and display the format of the medical statement for each summarized medical procedure. The discrimination unit, for example, uses a generation AI to analyze the summarized medical procedure and customize the format of the medical statement. The generation AI, for example, uses a text generation AI (for example, LLM) to analyze the medical procedure and customize the format of the medical statement. The discrimination unit can also use the generation AI to analyze information necessary for diagnosis and customize and display the format of the medical statement. For example, the generation AI analyzes the summarized medical procedure and customizes and displays the format of the medical statement. As a result, by customizing the format of the medical statement, a statement that is easy for patients to understand can be provided. Part or all of the above-mentioned processing in the discrimination unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the discrimination unit can input the summarized medical procedure to the AI ​​and have the AI ​​execute customization of the format of the medical statement.

[0050] The prescription unit can select the optimal medicine by referring to past prescription data based on the summarized symptoms. The prescription unit, for example, uses a generation AI to analyze the summarized symptoms and refer to past prescription data. The generation AI, for example, uses a text generation AI (for example, LLM) to analyze the symptoms and refer to past prescription data. The prescription unit can also use the generation AI to analyze information required for diagnosis and select the optimal medicine based on past prescription data. For example, the generation AI analyzes the summarized symptoms and selects the optimal medicine based on past prescription data. This makes it possible to select the optimal medicine by referring to past prescription data. Part or all of the above-mentioned processing in the prescription unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the prescription unit can input the summarized symptoms to the AI ​​and cause the AI ​​to refer to past prescription data and select a medicine.

[0051] The prescription unit can propose a combination of multiple drugs based on the summarized symptoms and create an optimal prescription. The prescription unit can use, for example, a generation AI to analyze the summarized symptoms and propose a combination of multiple drugs. The generation AI can use, for example, a text generation AI (for example, LLM) to analyze the symptoms and propose a combination of multiple drugs. The prescription unit can also use the generation AI to analyze information required for diagnosis, propose an optimal combination of drugs, and create a prescription. For example, the generation AI can analyze the summarized symptoms, propose a combination of multiple drugs, and write it on the prescription. This makes it possible to provide more effective treatment by proposing a combination of multiple drugs. Some or all of the above-mentioned processing in the prescription unit can be performed, for example, using AI, or can be performed without using AI. For example, the prescription unit can input summarized symptoms into AI and have the AI ​​execute the suggestion of a combination of multiple drugs and the creation of a prescription.

[0052] The prescription unit can select the most suitable medicine by referring to the stock information of medicines for each region based on the summarized symptoms. The prescription unit, for example, uses a generation AI to analyze the summarized symptoms and refers to the stock information of medicines for each region. The generation AI, for example, uses a text generation AI (for example, LLM) to analyze the symptoms and refers to the stock information of medicines for each region. The prescription unit can also use the generation AI to analyze information required for diagnosis and select the most suitable medicine based on the stock information of medicines for each region. For example, the generation AI analyzes the summarized symptoms and selects the most suitable medicine based on the stock information of medicines for each region. This allows the most suitable medicine to be selected by referring to the stock information of medicines for each region. A part or all of the above-mentioned processing in the prescription unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the prescription unit can input the summarized symptoms to an AI and cause the AI ​​to refer to the stock information of medicines for each region and select medicines.

[0053] The prescription unit can create an optimal prescription based on the summarized symptoms and taking into account the side effect information of the drug. The prescription unit, for example, uses a generation AI to analyze the summarized symptoms and take into account the side effect information of the drug. The generation AI, for example, uses a text generation AI (for example, LLM) to analyze the symptoms and take into account the side effect information of the drug. The prescription unit can also use the generation AI to analyze information necessary for diagnosis and create an optimal prescription based on the side effect information of the drug. For example, the generation AI analyzes the summarized symptoms and creates an optimal prescription based on the side effect information of the drug. As a result, a safer prescription can be created by taking into account the side effect information of the drug. Some or all of the above-mentioned processing in the prescription unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the prescription unit can input the summarized symptoms into the AI ​​and have the AI ​​take into account the side effect information of the drug and create a prescription.

[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] The medical treatment process efficiency system can further include a vital sign monitor unit that monitors the patient's vital signs in real time. The vital sign monitor unit measures and records the patient's vital signs, such as the patient's heart rate, blood pressure, and body temperature, in real time. This allows the patient's health condition to be continuously monitored during the examination, and if an abnormality is detected, the doctor can be immediately notified. For example, if the heart rate increases suddenly, the vital sign monitor unit sends an alert to the doctor to encourage a quick response. The vital sign monitor unit can also send the measured vital signs to the generation unit and automatically reflect them in the medical record. This makes the medical record more detailed and accurate. Furthermore, the vital sign monitor unit can analyze fluctuations in the patient's vital signs and use the results for long-term health management. For example, if there is an abnormal fluctuation compared to past vital sign data, the doctor is notified and appropriate diagnosis and treatment are supported.

[0056] The medical treatment process efficiency system may further include a lifestyle habit collection unit that collects lifestyle habit data of patients. The lifestyle habit collection unit, for example, collects and records data such as diet, exercise, and sleep of patients. This allows doctors to understand the lifestyle habits of patients and perform more appropriate diagnosis and treatment. For example, the lifestyle habit collection unit collects data entered by patients through smartphones or wearable devices and transmits it to the generation unit. The lifestyle habit collection unit can also analyze the collected data and evaluate health risks. For example, if the content of meals or the amount of exercise is insufficient, the doctor is notified and the doctor is encouraged to improve the lifestyle. Furthermore, the lifestyle habit collection unit can monitor the lifestyle habit data of patients over the long term and use it for health management. For example, the lifestyle habit collection unit compares the data with past data to analyze changes in lifestyle habits and support improvement of health conditions.

[0057] The medical process efficiency system may further include a social background collection unit that collects social background data of patients. The social background collection unit collects and records data such as the patient's occupation, home environment, and social support. This allows doctors to understand the patient's social background and perform more appropriate diagnosis and treatment. For example, the social background collection unit collects data entered by the patient and transmits it to the generation unit. The social background collection unit may also analyze the collected data and evaluate health risks. For example, if the occupation or home environment has an effect on health, the social background collection unit may notify the doctor and prompt him or her to take appropriate measures. Furthermore, the social background collection unit may monitor the patient's social background data over the long term and use it for health management. For example, the social background collection unit may analyze changes in the social background by comparing it with past data and support improvement of the health condition.

[0058] The medical treatment process efficiency system may further include an allergy management unit that manages allergy information of patients. The allergy management unit, for example, collects and records allergy information of patients. This allows doctors to understand the allergy information of patients and perform appropriate diagnosis and treatment. For example, the allergy management unit collects allergy information entered by patients and transmits it to the generation unit. The allergy management unit may also analyze the collected allergy information and reflect it in medical treatment. For example, it may select a medicine to be prescribed based on the allergy information to prevent allergic reactions. Furthermore, the allergy management unit may monitor the allergy information of patients over the long term and use it for health management. For example, it may compare it with past allergy information to analyze allergy fluctuations and support appropriate responses.

[0059] The medical treatment process efficiency system may further include an exercise data collection unit that collects exercise data of patients. The exercise data collection unit, for example, collects and records the amount and type of exercise of patients. This allows doctors to understand the exercise habits of patients and perform more appropriate diagnosis and treatment. For example, the exercise data collection unit collects data entered by patients through smartphones or wearable devices and transmits it to the generation unit. The exercise data collection unit may also analyze the collected data and evaluate health risks. For example, if the amount of exercise is insufficient, the exercise data collection unit may notify the doctor and encourage the patient to improve their exercise habits. Furthermore, the exercise data collection unit may monitor the patient's exercise data over the long term and use it for health management. For example, the exercise data collection unit may compare the patient's exercise data with past data to analyze changes in exercise habits and support improvements in health conditions.

[0060] The medical process efficiency system may further include a dietary data collection unit that collects dietary data of patients. The dietary data collection unit, for example, collects and records the dietary content and calorie intake of patients. This allows doctors to understand the dietary habits of patients and perform more appropriate diagnosis and treatment. For example, the dietary data collection unit collects data entered by patients through smartphones or wearable devices and transmits it to the generation unit. The dietary data collection unit may also analyze the collected data and evaluate health risks. For example, if the calorie intake is excessive, the doctor may be notified and the doctor may be encouraged to improve the dietary habits. Furthermore, the dietary data collection unit may monitor the dietary data of patients over the long term and use it for health management. For example, the dietary data collection unit may compare the dietary data with past data to analyze changes in dietary habits and support improvement of health conditions.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The reception department records the contents of the conversation during the consultation. The contents of the conversation during the consultation may include, for example, the patient's symptoms, the doctor's diagnosis, and treatment plans. The reception department records the contents of the conversation as audio or video using audio recording technology or video recording technology. For example, the contents of the conversation are recorded in high quality using a microphone and a camera installed in the examination room. Step 2: The generation unit uses a generation AI to summarize the conversation recorded by the reception unit and create a medical record. The generation AI uses a text generation AI (e.g., LLM) to analyze the conversation, extract important information, and summarize it. For example, the patient's symptoms, diagnosis, treatment plan, etc. are summarized and recorded in the medical record. Step 3: The discrimination unit automatically determines the points for each medical procedure summarized by the generation unit and creates a medical statement. The discrimination unit automatically determines the points corresponding to the summarized medical procedures based on the medical fee schedule. For example, it automatically calculates points for consultation fees, examination fees, treatment fees, etc., and lists them on the medical statement. Step 4: The prescribing unit creates a prescription for medicine based on the symptoms summarized by the generating unit. The prescribing unit uses the generating AI to analyze the summarized symptoms and select the appropriate medicine. For example, it selects an antipyretic or cough suppressant for cold symptoms and writes it on the prescription.

[0063] (Example 2) The medical treatment process efficiency system according to the embodiment of the present invention is a system in which the contents of conversations during consultations are recorded, a generation AI summarizes the conversations to create a medical record, automatically determines points, creates a medical statement, and creates a prescription for medicine. This system records all conversations between doctors and patients during consultations, and a generation AI summarizes the conversations to create a medical record. Furthermore, for each summarized medical procedure, the system automatically determines points from the official price and creates a medical statement. In addition, symptoms are inferred from the summary, a medicine appropriate for the symptoms is selected, and a prescription for medicine is created. For example, all conversations between doctors and patients during consultations are recorded. For example, conversations in which the doctor asks the patient about symptoms and conversations in which the doctor explains the results of the examination are included. This conversation is input to the generation AI. Next, the generation AI summarizes the input conversation. The generation AI analyzes the conversation, extracts important information, and creates a medical record. For example, the patient's symptoms, examination results, and doctor's diagnosis are summarized. Furthermore, for each summarized medical procedure, the system automatically determines points from the official price. Based on the summarized medical procedure, the generation AI determines the corresponding points from the official price list and creates a medical statement. For example, consultation fees, examination fees, and treatment fees are scored. Symptoms are also inferred from the summary, and the appropriate medicine is selected. The generation AI selects the appropriate medicine based on the summarized symptoms and writes a prescription. For example, for cold symptoms, antipyretics and cough suppressants are selected. This allows doctors to concentrate on the examination, and frees medical administration from the task of calculating points and creating medical statements. It also reduces the time patients have to wait to pay. For example, after the examination is over, the medical statement and prescription are issued immediately, allowing patients to pay smoothly. In this way, the medical process efficiency system can reduce the burden on doctors, medical administration, and patients.

[0064] The medical treatment process efficiency improvement system according to the embodiment includes a reception unit, a generation unit, a discrimination unit, and a prescription unit. The reception unit records the contents of the conversation during the examination. The contents of the conversation during the examination include, for example, the patient's symptoms, the doctor's diagnosis, and a treatment plan, but are not limited to such examples. The reception unit records the contents of the conversation using, for example, audio recording technology. The reception unit can also record the contents of the conversation as video using video recording technology. For example, the reception unit records the contents of the conversation with high quality using a microphone and a camera installed in the examination room. The generation unit uses a generation AI to summarize the contents of the conversation recorded by the reception unit and create a medical record. The generation AI analyzes the contents of the conversation using, for example, a text generation AI (for example, LLM), extracts important information, and summarizes it. The generation unit can also use the generation AI to pick out particularly important information from the contents of the conversation and create a medical record based on that. For example, the generation AI analyzes the contents of the conversation, summarizes the patient's symptoms, diagnosis results, treatment plan, and the like, and writes them in the medical record. The discrimination unit automatically determines the points for each medical procedure summarized by the generation unit, and creates a medical statement. The discrimination unit automatically determines the points corresponding to the summarized medical procedure based on, for example, a medical fee table. The discrimination unit can also use a generation AI to analyze the contents of the summarized medical procedure and determine the corresponding points. For example, the discrimination unit automatically calculates points such as consultation fees, examination fees, and treatment fees, and lists them on the medical bill. The prescription unit creates a prescription for medicine based on the symptoms summarized by the generation unit. The prescription unit can analyze the summarized symptoms and select an appropriate medicine using, for example, a generation AI. The prescription unit can also use a generation AI to select the most appropriate medicine from multiple medicines based on the patient's symptoms and create a prescription. For example, the prescription unit selects an antipyretic or cough medicine for cold symptoms and lists it on the prescription. As a result, the medical treatment process efficiency system according to the embodiment can reduce the burden on doctors, medical office staff, and patients.

[0065] The reception unit records the contents of the conversation during the examination. The contents of the conversation during the examination include, for example, the patient's symptoms, the doctor's diagnosis, and the treatment policy, but are not limited to such examples. The reception unit records the contents of the conversation using, for example, voice recording technology. Specifically, the conversation between the patient and the doctor is clearly recorded using a high-sensitivity microphone installed in the examination room. This makes it possible to record important information during the examination without omission. The reception unit can also record the contents of the conversation as video using video recording technology. For example, a high-resolution camera installed on the ceiling or wall of the examination room is used to record the state of the examination as video. This makes it possible to record not only the voice but also visual information such as the doctor's gestures and the patient's facial expressions. Furthermore, the reception unit can transmit these recorded data to a cloud server in real time and safely store them. This makes it possible to easily access the data after the examination and recheck it as necessary. The reception unit can also convert the recorded voice data into text data by using voice recognition technology in combination, thereby making subsequent processing more efficient. For example, the contents of the conversation can be automatically transcribed using a voice recognition engine, making it possible to smoothly process the generation unit and the discrimination unit. This allows the reception department to record the content of conversations during consultations in a high-quality and multifaceted manner, contributing to improved efficiency of the entire system.

[0066] The generation unit uses the generation AI to summarize the conversation content recorded by the reception unit and create a medical record. The generation AI, for example, uses a text generation AI (for example, LLM) to analyze the conversation content, extract important information, and summarize it. Specifically, the generation AI uses natural language processing technology to extract important information such as the patient's symptoms, the doctor's diagnosis, and the treatment plan from the conversation content. For example, the generation AI can understand the context of the conversation and automatically identify the symptoms complained by the patient and the diagnosis results shown by the doctor. Furthermore, the generation AI automatically generates a concise and clear medical record based on the extracted information. The medical record includes basic information about the patient, details of the symptoms, diagnosis results, treatment plans, and information about prescribed drugs. The generation unit can also use the generation AI to pick out particularly important information from the conversation content and create a medical record based on that. For example, the generation AI analyzes the conversation content and summarizes the patient's symptoms, diagnosis results, treatment plans, etc., and writes them in the medical record. The generation AI can understand medical terms and specialized knowledge and create a medical record in appropriate expressions. This allows the generator to reduce the burden on doctors and significantly improve the efficiency of creating medical records. Furthermore, the generator can automatically store the generated medical records in the electronic medical record system, making them easily accessible to other medical staff. This improves the efficiency of the entire medical treatment process and contributes to improving the quality of medical care.

[0067] The discrimination unit automatically determines the points for each medical procedure summarized by the generation unit and creates a medical statement. The discrimination unit automatically determines the points corresponding to the summarized medical procedure based on, for example, the medical fee schedule. Specifically, the discrimination unit can use the generation AI to analyze the contents of the summarized medical procedure and determine the corresponding points. For example, the generation AI analyzes information such as consultation fees, examination fees, and treatment fees written in the medical record and automatically calculates the points corresponding to each medical procedure. Furthermore, the discrimination unit can always refer to the latest information on the medical fee schedule and determine accurate points. This allows the medical statement to be created quickly and accurately. This allows the discrimination unit to streamline the creation of the medical statement and reduce the burden on medical administration. Furthermore, the discrimination unit can automatically save the contents of the medical statement in the electronic medical record system so that other medical staff can easily access it. This can improve the efficiency of the entire medical process and contribute to improving the quality of medical care.

[0068] The prescription department creates a prescription for medicine based on the symptoms summarized by the generation department. The prescription department, for example, uses a generation AI to analyze the summarized symptoms and select an appropriate medicine. Specifically, the generation AI uses an algorithm to select the most effective and safe medicine based on the patient's symptoms and diagnosis results. For example, the generation AI considers the patient's age, sex, medical history, allergy information, etc. and selects the most effective and safe medicine. In addition, the generation AI can refer to the latest medical database and make the optimal prescription based on information such as the effects, side effects, and interactions of the medicine. Furthermore, the prescription department can use the generation AI to select the most appropriate medicine from multiple medicines based on the patient's symptoms and create a prescription. For example, the prescription department selects an antipyretic or cough suppressant for cold symptoms and writes it on the prescription. Furthermore, the prescription department can automatically save the generated prescription in the electronic medical record system so that other medical staff can easily access it. This can improve the efficiency of the entire medical treatment process and contribute to improving the quality of medical care.

[0069] The reception unit can record all conversations between the doctor and the patient during the consultation. The reception unit, for example, records the conversations during the consultation using audio recording technology. For example, the reception unit records the conversations in high quality using a microphone installed in the examination room. The reception unit can also record the conversations as video using video recording technology. For example, the reception unit records the conversations as video using a camera installed in the examination room. This allows detailed medical records to be created by recording all conversations during the consultation. 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 conversations during the consultation into AI and have the AI ​​execute the recording of the conversations.

[0070] The generation unit can summarize the recorded conversation content to create a medical record. The generation unit summarizes the recorded conversation content, for example, using a generation AI. The generation AI analyzes the conversation content, for example, using a text generation AI (for example, LLM), extracts important information, and summarizes it. The generation unit can also use the generation AI to pick out particularly important information from the conversation content and create a medical record based on it. For example, the generation AI analyzes the conversation content, summarizes the patient's symptoms, diagnosis results, treatment policy, etc., and writes them in the medical record. As a result, the medical treatment content can be efficiently organized by summarizing the recorded conversation content to create a medical record. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the generation unit can input the recorded conversation content into an AI and have the AI ​​execute the creation of a medical record.

[0071] The discrimination unit can automatically determine the points for each summarized medical procedure based on the official price and create a medical statement. The discrimination unit automatically determines the points corresponding to the summarized medical procedure based on, for example, a medical fee table. For example, the discrimination unit automatically calculates points for consultation fees, examination fees, treatment fees, etc., and lists them on the medical statement. The discrimination unit can also use a generation AI to analyze the contents of the summarized medical procedure and determine the corresponding points. For example, the discrimination unit uses a generation AI to analyze the contents of the summarized medical procedure and determine the corresponding points from the medical fee table. As a result, the points are automatically determined for each summarized medical procedure and a medical statement is created, thereby making the calculation of medical fees more efficient. Some or all of the above-mentioned processing in the discrimination unit may be performed using, for example, AI, or may be performed without using AI. For example, the discrimination unit can input the contents of the summarized medical procedure into AI and have the AI ​​execute the point determination.

[0072] The prescription unit can create a prescription for medicine based on the summarized symptoms. The prescription unit can use, for example, a generation AI to analyze the summarized symptoms and select an appropriate medicine. For example, the prescription unit can use the generation AI to select an optimal medicine from multiple medicines based on the patient's symptoms and create a prescription. For example, the prescription unit selects an antipyretic or cough suppressant for cold symptoms and writes it on the prescription. This makes it possible to prescribe an appropriate medicine by creating a prescription for medicine based on the summarized symptoms. Some or all of the above-mentioned processing in the prescription unit can be performed, for example, using AI or without AI. For example, the prescription unit can input the summarized symptoms into AI and have the AI ​​select the medicine and create the prescription.

[0073] The reception unit can estimate the patient's emotions and adjust the method of recording the conversation content based on the estimated patient's emotions. The reception unit, for example, uses facial expression recognition technology to estimate the patient's emotions. For example, the reception unit analyzes the patient's facial expressions captured by a camera to estimate the emotions. The reception unit can also use voice analysis technology to analyze the tone and speed of the patient's voice and estimate the emotions. For example, the reception unit analyzes the tone and speed of the patient's voice to estimate the emotions. Furthermore, the reception unit adjusts the method of recording the conversation content based on the estimated patient's emotions. For example, if the patient is nervous, the reception unit analyzes the tone of the conversation and adds questions to relax the patient. Also, if the patient is relaxed, the reception unit can add questions to elicit detailed information. Furthermore, if the patient is in a hurry, the reception unit adjusts to record important information preferentially. As a result, a more appropriate medical record can be created by adjusting the method of recording the conversation content based on the patient's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit may input the contents of a conversation during a medical examination into the AI ​​and cause the AI ​​to record the contents of the conversation.

[0074] The reception unit can analyze the contents of the conversation during the medical examination in real time and record the contents with important keywords emphasized. The reception unit uses AI, for example, to analyze the contents of the conversation during the medical examination in real time. For example, the reception unit analyzes the contents of the conversation in real time and extracts keywords related to symptoms. The reception unit can also analyze information necessary for diagnosis in real time and record important parts with emphasis. For example, the reception unit refers to the patient's past medical history and records related keywords with emphasis. As a result, by highlighting and recording important keywords, it becomes easier to understand the contents of the medical examination. A part or all of the above-mentioned 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 contents of the conversation during the medical examination into AI and have the AI ​​extract and emphasize important keywords.

[0075] The reception unit can record the contents of the conversation during the examination not only by voice but also by video, and can record non-verbal information as well. For example, the reception unit records the contents of the conversation during the examination using voice recording technology and video recording technology. For example, the reception unit uses a microphone and a camera installed in the examination room to record the contents of the conversation in high quality. The reception unit also uses video recording technology to record non-verbal information as well. For example, the reception unit records the patient's facial expressions and gestures on video and analyzes the non-verbal information. As a result, by recording non-verbal information as well, a more detailed medical record can be created. 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 contents of the conversation during the examination and non-verbal information into AI, and have AI perform recording and analysis.

[0076] The reception unit can estimate the patient's emotions and determine the priority of conversations to be recorded based on the estimated patient's emotions. The reception unit, for example, uses facial expression recognition technology to estimate the patient's emotions. For example, the reception unit analyzes the patient's facial expressions captured by a camera to estimate the emotions. The reception unit can also use voice analysis technology to analyze the tone and speed of the patient's voice to estimate the emotions. For example, the reception unit analyzes the tone and speed of the patient's voice to estimate the emotions. Furthermore, the reception unit determines the priority of conversations to be recorded based on the estimated patient's emotions. For example, if the patient feels anxious, the reception unit preferentially records conversations to reassure the patient. Also, if the patient is relaxed, the reception unit can preferentially record conversations regarding detailed symptoms. Furthermore, if the patient is in a hurry, the reception unit preferentially records important diagnostic information. As a result, important information can be preferentially recorded by determining the priority of conversations based on the patient's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit may input the contents of a conversation during a medical examination into the AI, and have the AI ​​determine the priority order of the conversation.

[0077] The reception unit can record the contents of the conversation during the examination in multiple languages, so that it can also accommodate patients who speak different languages. The reception unit, for example, uses AI to record the contents of the conversation during the examination in multiple languages. For example, the reception unit translates the contents of the conversation in real time and records them in multiple languages. The reception unit can also automatically translate and record the contents of the examination based on the language setting of the patient. For example, the reception unit automatically translates and records the contents of the examination based on the language setting of the patient. This makes it possible to accommodate patients who speak different languages ​​by recording in multiple languages. 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 contents of the conversation during the examination into AI and have the AI ​​execute recording in multiple languages.

[0078] When recording the contents of the conversation during the examination, the reception unit can refer to the patient's past medical history and preferentially record highly relevant information. For example, when recording the contents of the conversation during the examination, the reception unit uses AI to refer to the patient's past medical history. For example, the reception unit refers to the patient's past medical history and preferentially records related information. The reception unit can also compare the past medical history with the current contents of the examination and record important information with emphasis. For example, the reception unit automatically classifies and records the contents of the examination based on the patient's past medical history. As a result, highly relevant information can be preferentially recorded by referring to the past medical history. A part or all of the above-mentioned 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 contents of the conversation during the examination to AI and cause AI to refer to the past medical history and preferentially record the information.

[0079] The generating unit can estimate the patient's emotion and adjust the expression method of the medical record based on the estimated emotion of the patient. The generating unit uses, for example, facial expression recognition technology to estimate the emotion of the patient. For example, the generating unit analyzes the facial expression of the patient captured by a camera and estimates the emotion. The generating unit can also use voice analysis technology to analyze the tone and speed of the patient's voice and estimate the emotion. For example, the generating unit analyzes the tone and speed of the patient's voice and estimates the emotion. Furthermore, the generating unit adjusts the expression method of the medical record based on the estimated emotion of the patient. For example, if the patient is nervous, the generating unit generates a concise and easy-to-understand medical record. Also, if the patient is relaxed, the generating unit can generate a medical record including detailed information. Furthermore, if the patient is in a hurry, the generating unit generates a medical record in which important information is written preferentially. As a result, a more appropriate medical record can be created by adjusting the expression method of the medical record based on the emotion of the patient. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI 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 generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit may cause AI to adjust the way the medical record is expressed.

[0080] The generation unit can analyze the recorded conversation content, automatically extract information necessary for diagnosis, and reflect it in the medical record. The generation unit can, for example, use a generation AI to analyze the recorded conversation content, and automatically extract information necessary for diagnosis. The generation AI can, for example, use a text generation AI (for example, LLM) to analyze the conversation content and extract important information. The generation unit can also use the generation AI to pick up particularly important information from the conversation content and create a medical record based on it. For example, the generation AI can analyze the conversation content, extract the patient's symptoms, diagnosis results, treatment policy, etc., and write them in the medical record. This makes it easier to understand the medical treatment content by automatically extracting information necessary for diagnosis and reflecting it in the medical record. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using AI, or can be performed without using AI. For example, the generation unit can input the recorded conversation content into AI, and cause the AI ​​to extract information necessary for diagnosis and reflect it in the medical record.

[0081] The generation unit can analyze the recorded conversation content and integrate the opinions of multiple doctors to improve the reliability of the diagnosis. The generation unit, for example, uses a generation AI to analyze the recorded conversation content and integrate the opinions of multiple doctors. The generation AI, for example, uses a text generation AI (for example, LLM) to analyze the conversation content and integrate the diagnosis results of multiple doctors. The generation unit can also use the generation AI to analyze information necessary for diagnosis and integrate the opinions of multiple doctors to improve reliability. For example, the generation AI analyzes the conversation content and integrates the diagnosis results of multiple doctors to reflect them in the medical record. As a result, the reliability of the diagnosis is improved by integrating the opinions of multiple doctors. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the generation unit can input the recorded conversation content to the AI ​​and cause the AI ​​to execute the integration of the opinions of multiple doctors.

[0082] The generating unit can estimate the patient's emotion and adjust the level of detail of the medical record based on the estimated patient's emotion. The generating unit uses, for example, facial expression recognition technology to estimate the patient's emotion. For example, the generating unit analyzes the facial expression of the patient captured by a camera to estimate the emotion. The generating unit can also use voice analysis technology to analyze the tone and speed of the patient's voice to estimate the emotion. For example, the generating unit analyzes the tone and speed of the patient's voice to estimate the emotion. Furthermore, the generating unit adjusts the level of detail of the medical record based on the estimated emotion of the patient. For example, if the patient is nervous, the generating unit generates a concise and easy-to-understand medical record. Also, if the patient is relaxed, the generating unit can generate a medical record including detailed information. Furthermore, if the patient is in a hurry, the generating unit generates a medical record in which important information is written preferentially. As a result, a more appropriate medical record can be created by adjusting the level of detail of the medical record based on the patient's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processes in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit may cause AI to adjust the level of detail of the medical record.

[0083] The generation unit can analyze the recorded conversation content, automatically classify the information necessary for diagnosis, and reflect it in the medical record. The generation unit can, for example, use a generation AI to analyze the recorded conversation content, and automatically classify the information necessary for diagnosis. The generation AI can, for example, use a text generation AI (for example, LLM) to analyze the conversation content and classify important information. The generation unit can also use the generation AI to pick up particularly important information from the conversation content and create a medical record based on it. For example, the generation AI can analyze the conversation content, classify the patient's symptoms, diagnosis results, treatment policy, etc., and write them in the medical record. This makes it easier to understand the medical treatment content by automatically classifying the information necessary for diagnosis and reflecting it in the medical record. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using an AI, or can be performed without using an AI. For example, the generation unit can input the recorded conversation content into an AI, and have the AI ​​classify the information necessary for diagnosis and reflect it in the medical record.

[0084] The generation unit can analyze the recorded conversation content and compare it with past medical data to improve the reliability of the diagnosis. The generation unit, for example, uses a generation AI to analyze the recorded conversation content and compare it with past medical data. The generation AI, for example, uses a text generation AI (for example, LLM) to analyze the conversation content and compare it with past medical data. The generation unit can also use the generation AI to analyze information necessary for diagnosis and improve reliability by comparing it with past medical data. For example, the generation AI analyzes the conversation content and reflects the diagnosis result in the chart by comparing it with past medical data. As a result, the reliability of the diagnosis is improved by comparing it with past medical data. A part or all of the above-mentioned processing in the generation unit may be performed by using an AI, for example, or may be performed without using an AI. For example, the generation unit can input the recorded conversation content to the AI ​​and cause the AI ​​to perform a comparison with past medical data.

[0085] The discrimination unit can estimate the patient's emotions and adjust the score discrimination criteria based on the estimated patient's emotions. The discrimination unit uses, for example, facial expression recognition technology to estimate the patient's emotions. For example, the discrimination unit analyzes the patient's facial expressions captured by a camera to estimate the emotions. The discrimination unit can also use voice analysis technology to analyze the tone and speed of the patient's voice to estimate the emotions. For example, the discrimination unit analyzes the tone and speed of the patient's voice to estimate the emotions. Furthermore, the discrimination unit adjusts the score discrimination criteria based on the estimated patient's emotions. For example, if the patient is nervous, the discrimination unit provides a concise and easy-to-understand score discrimination criterion. Also, if the patient is relaxed, the discrimination unit can provide a score discrimination criterion including detailed information. Furthermore, if the patient is in a hurry, the discrimination unit provides a score discrimination criterion that prioritizes important information. This allows a more appropriate medical statement to be created by adjusting the score discrimination criterion based on the patient's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the discrimination unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the discrimination unit may cause an AI to adjust the discrimination criteria for the score.

[0086] The discrimination unit can improve the accuracy of the score by referring to past medical data for each summarized medical procedure. The discrimination unit, for example, uses a generation AI to analyze the summarized medical procedure and refer to past medical data. The generation AI, for example, uses a text generation AI (for example, LLM) to analyze the medical procedure and refer to past medical data. The discrimination unit can also use the generation AI to analyze information necessary for diagnosis and improve the accuracy of the score based on the past medical data. For example, the generation AI analyzes the summarized medical procedure and determines the score based on the past medical data. As a result, the accuracy of the score is improved by referring to the past medical data. A part or all of the above-mentioned processing in the discrimination unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the discrimination unit can input the summarized medical procedure to the AI ​​and cause the AI ​​to refer to the past medical data and determine the score.

[0087] The discrimination unit can automatically integrate the points of different medical departments for each summarized medical procedure to create a medical statement. The discrimination unit, for example, uses a generation AI to analyze the summarized medical procedure and automatically integrate the points of different medical departments. The generation AI, for example, uses a text generation AI (for example, LLM) to analyze the medical procedure and integrate the points of different medical departments. The discrimination unit can also use the generation AI to analyze information necessary for diagnosis and integrate the points of different medical departments to create a medical statement. For example, the generation AI analyzes the summarized medical procedure and integrates the points of different medical departments to write on the medical statement. As a result, the creation of the medical statement is made more efficient by integrating the points of different medical departments. A part or all of the above-mentioned processing in the discrimination unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the discrimination unit can input the summarized medical procedure to the AI ​​and cause the AI ​​to integrate the points of different medical departments.

[0088] The discrimination unit can estimate the patient's emotions and adjust the display method of the medical statement based on the estimated patient's emotions. The discrimination unit, for example, uses facial expression recognition technology to estimate the patient's emotions. For example, the discrimination unit analyzes the patient's facial expression captured by a camera to estimate the emotions. The discrimination unit can also use voice analysis technology to analyze the tone and speed of the patient's voice to estimate the emotions. For example, the discrimination unit analyzes the tone and speed of the patient's voice to estimate the emotions. Furthermore, the discrimination unit adjusts the display method of the medical statement based on the estimated patient's emotions. For example, if the patient is nervous, the discrimination unit provides a concise and easy-to-understand medical statement. Also, if the patient is relaxed, the discrimination unit can provide a medical statement including detailed information. Furthermore, if the patient is in a hurry, the discrimination unit provides a medical statement that prioritizes important information. As a result, a more appropriate medical statement can be provided by adjusting the display method of the medical statement based on the patient's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit may cause AI to adjust the display method of the medical care statement.

[0089] The discrimination unit can determine the score for each summarized medical procedure by referring to the official price for each region. The discrimination unit, for example, uses a generation AI to analyze the summarized medical procedure and refer to the official price for each region. The generation AI, for example, uses a text generation AI (for example, LLM) to analyze the medical procedure and refer to the official price for each region. The discrimination unit can also use the generation AI to analyze information necessary for diagnosis and determine the score based on the official price for each region. For example, the generation AI analyzes the summarized medical procedure and determines the score based on the official price for each region. As a result, the score can be accurately determined by referring to the official price for each region. A part or all of the above-mentioned processing in the discrimination unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the discrimination unit can input the summarized medical procedure into the AI ​​and cause the AI ​​to refer to the official price for each region and determine the score.

[0090] The discrimination unit can customize and display the format of the medical statement for each summarized medical procedure. The discrimination unit, for example, uses a generation AI to analyze the summarized medical procedure and customize the format of the medical statement. The generation AI, for example, uses a text generation AI (for example, LLM) to analyze the medical procedure and customize the format of the medical statement. The discrimination unit can also use the generation AI to analyze information necessary for diagnosis and customize and display the format of the medical statement. For example, the generation AI analyzes the summarized medical procedure and customizes and displays the format of the medical statement. As a result, by customizing the format of the medical statement, a statement that is easy for patients to understand can be provided. Part or all of the above-mentioned processing in the discrimination unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the discrimination unit can input the summarized medical procedure to the AI ​​and have the AI ​​execute customization of the format of the medical statement.

[0091] The prescription unit can estimate the patient's emotions and adjust the way the prescription is expressed based on the estimated patient's emotions. The prescription unit, for example, uses facial expression recognition technology to estimate the patient's emotions. For example, the prescription unit analyzes the patient's facial expressions captured by a camera to estimate the emotions. The prescription unit can also use voice analysis technology to analyze the tone and speed of the patient's voice to estimate the emotions. For example, the prescription unit analyzes the tone and speed of the patient's voice to estimate the emotions. Furthermore, the prescription unit adjusts the way the prescription is expressed based on the estimated patient's emotions. For example, if the patient is nervous, the prescription unit generates a concise and easy-to-understand prescription. Also, if the patient is relaxed, the prescription unit can generate a prescription including detailed information. Furthermore, if the patient is in a hurry, the prescription unit generates a prescription that prioritizes important information. This allows a more appropriate prescription to be provided by adjusting the way the prescription is expressed based on the patient's emotions. The estimation of emotions is realized 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 prescription unit may be performed, for example, using AI, or may be performed without using AI. For example, the prescription unit may cause the AI ​​to adjust the way the prescription is expressed.

[0092] The prescription unit can select the optimal medicine by referring to past prescription data based on the summarized symptoms. The prescription unit, for example, uses a generation AI to analyze the summarized symptoms and refer to past prescription data. The generation AI, for example, uses a text generation AI (for example, LLM) to analyze the symptoms and refer to past prescription data. The prescription unit can also use the generation AI to analyze information required for diagnosis and select the optimal medicine based on past prescription data. For example, the generation AI analyzes the summarized symptoms and selects the optimal medicine based on past prescription data. This makes it possible to select the optimal medicine by referring to past prescription data. Part or all of the above-mentioned processing in the prescription unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the prescription unit can input the summarized symptoms to the AI ​​and cause the AI ​​to refer to past prescription data and select a medicine.

[0093] The prescription unit can propose a combination of multiple drugs based on the summarized symptoms and create an optimal prescription. The prescription unit can use, for example, a generation AI to analyze the summarized symptoms and propose a combination of multiple drugs. The generation AI can use, for example, a text generation AI (for example, LLM) to analyze the symptoms and propose a combination of multiple drugs. The prescription unit can also use the generation AI to analyze information required for diagnosis, propose an optimal combination of drugs, and create a prescription. For example, the generation AI can analyze the summarized symptoms, propose a combination of multiple drugs, and write it on the prescription. This makes it possible to provide more effective treatment by proposing a combination of multiple drugs. Some or all of the above-mentioned processing in the prescription unit can be performed, for example, using AI, or can be performed without using AI. For example, the prescription unit can input summarized symptoms into AI and have the AI ​​execute the suggestion of a combination of multiple drugs and the creation of a prescription.

[0094] The prescription unit can estimate the patient's emotions and adjust the level of detail of the prescription based on the estimated patient's emotions. The prescription unit, for example, uses facial expression recognition technology to estimate the patient's emotions. For example, the prescription unit analyzes the patient's facial expressions captured by a camera to estimate the emotions. The prescription unit can also use voice analysis technology to analyze the tone and speed of the patient's voice to estimate the emotions. For example, the prescription unit analyzes the tone and speed of the patient's voice to estimate the emotions. Furthermore, the prescription unit adjusts the level of detail of the prescription based on the estimated patient's emotions. For example, if the patient is nervous, the prescription unit generates a concise and easy-to-understand prescription. Also, if the patient is relaxed, the prescription unit can generate a prescription including detailed information. Furthermore, if the patient is in a hurry, the prescription unit generates a prescription that prioritizes important information. This allows a more appropriate prescription to be provided by adjusting the level of detail of the prescription based on the patient's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the prescription unit may be performed, for example, using AI, or may be performed without using AI. For example, the prescription unit may cause the AI ​​to adjust the level of detail of the prescription.

[0095] The prescription unit can select the most suitable medicine by referring to the stock information of medicines for each region based on the summarized symptoms. The prescription unit, for example, uses a generation AI to analyze the summarized symptoms and refers to the stock information of medicines for each region. The generation AI, for example, uses a text generation AI (for example, LLM) to analyze the symptoms and refers to the stock information of medicines for each region. The prescription unit can also use the generation AI to analyze information required for diagnosis and select the most suitable medicine based on the stock information of medicines for each region. For example, the generation AI analyzes the summarized symptoms and selects the most suitable medicine based on the stock information of medicines for each region. This allows the most suitable medicine to be selected by referring to the stock information of medicines for each region. A part or all of the above-mentioned processing in the prescription unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the prescription unit can input the summarized symptoms to an AI and cause the AI ​​to refer to the stock information of medicines for each region and select medicines.

[0096] The prescription unit can create an optimal prescription based on the summarized symptoms and taking into account the side effect information of the drug. The prescription unit, for example, uses a generation AI to analyze the summarized symptoms and take into account the side effect information of the drug. The generation AI, for example, uses a text generation AI (for example, LLM) to analyze the symptoms and take into account the side effect information of the drug. The prescription unit can also use the generation AI to analyze information necessary for diagnosis and create an optimal prescription based on the side effect information of the drug. For example, the generation AI analyzes the summarized symptoms and creates an optimal prescription based on the side effect information of the drug. As a result, a safer prescription can be created by taking into account the side effect information of the drug. Some or all of the above-mentioned processing in the prescription unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the prescription unit can input the summarized symptoms into the AI ​​and have the AI ​​take into account the side effect information of the drug and create a prescription.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The medical treatment process efficiency system can further include a vital sign monitor unit that monitors the patient's vital signs in real time. The vital sign monitor unit measures and records the patient's vital signs, such as the patient's heart rate, blood pressure, and body temperature, in real time. This allows the patient's health condition to be continuously monitored during the examination, and if an abnormality is detected, the doctor can be immediately notified. For example, if the heart rate increases suddenly, the vital sign monitor unit sends an alert to the doctor to encourage a quick response. The vital sign monitor unit can also send the measured vital signs to the generation unit and automatically reflect them in the medical record. This makes the medical record more detailed and accurate. Furthermore, the vital sign monitor unit can analyze fluctuations in the patient's vital signs and use the results for long-term health management. For example, if there is an abnormal fluctuation compared to past vital sign data, the doctor is notified and appropriate diagnosis and treatment are supported.

[0099] The medical treatment process efficiency system may further include a lifestyle habit collection unit that collects lifestyle habit data of patients. The lifestyle habit collection unit, for example, collects and records data such as diet, exercise, and sleep of patients. This allows doctors to understand the lifestyle habits of patients and perform more appropriate diagnosis and treatment. For example, the lifestyle habit collection unit collects data entered by patients through smartphones or wearable devices and transmits it to the generation unit. The lifestyle habit collection unit can also analyze the collected data and evaluate health risks. For example, if the content of meals or the amount of exercise is insufficient, the doctor is notified and the doctor is encouraged to improve the lifestyle. Furthermore, the lifestyle habit collection unit can monitor the lifestyle habit data of patients over the long term and use it for health management. For example, the lifestyle habit collection unit compares the data with past data to analyze changes in lifestyle habits and support improvement of health conditions.

[0100] The medical process efficiency system may further include an emotion adjustment unit that estimates the patient's emotion and adjusts the medical treatment content based on the estimated emotion. The emotion adjustment unit, for example, analyzes the patient's facial expression and tone of voice to estimate the emotion. This allows the doctor to understand the patient's emotional state and appropriately adjust the medical treatment content. For example, if the patient feels anxious, the emotion adjustment unit notifies the doctor and encourages the doctor to take measures to relax the patient. The emotion adjustment unit may also adjust the medical treatment content based on the estimated emotion. For example, if the patient is nervous, the emotion adjustment unit may briefly explain the medical treatment content to reassure the patient. Also, if the patient is relaxed, detailed information may be provided to deepen understanding. Furthermore, the emotion adjustment unit may monitor the patient's emotion data over the long term to improve the quality of medical treatment. For example, the emotion adjustment unit may compare the patient's emotion data with past emotion data to analyze the emotional fluctuations and optimize the medical treatment content.

[0101] The medical process efficiency system may further include a social background collection unit that collects social background data of patients. The social background collection unit collects and records data such as the patient's occupation, home environment, and social support. This allows doctors to understand the patient's social background and perform more appropriate diagnosis and treatment. For example, the social background collection unit collects data entered by the patient and transmits it to the generation unit. The social background collection unit may also analyze the collected data and evaluate health risks. For example, if the occupation or home environment has an effect on health, the social background collection unit may notify the doctor and prompt him or her to take appropriate measures. Furthermore, the social background collection unit may monitor the patient's social background data over the long term and use it for health management. For example, the social background collection unit may analyze changes in the social background by comparing it with past data and support improvement of the health condition.

[0102] The medical process efficiency system may further include an emotion adjustment unit that estimates the patient's emotions and adjusts the contents of the medical statement based on the estimated emotions. The emotion adjustment unit, for example, analyzes the patient's facial expressions and tone of voice to estimate the emotions. This allows the contents of the medical statement to be adjusted to match the patient's emotions. For example, if the patient is feeling anxious, the emotion adjustment unit simplifies the contents of the medical statement to make it easier to understand. Also, if the patient is relaxed, detailed information can be provided to deepen understanding. Furthermore, the emotion adjustment unit can monitor the patient's emotion data over the long term and optimize the contents of the medical statement. For example, it compares the patient's emotion data with past emotion data to analyze the fluctuations in emotion and adjust the contents of the medical statement.

[0103] The medical treatment process efficiency system may further include an allergy management unit that manages allergy information of patients. The allergy management unit, for example, collects and records allergy information of patients. This allows doctors to understand the allergy information of patients and perform appropriate diagnosis and treatment. For example, the allergy management unit collects allergy information entered by patients and transmits it to the generation unit. The allergy management unit may also analyze the collected allergy information and reflect it in medical treatment. For example, it may select a medicine to be prescribed based on the allergy information to prevent allergic reactions. Furthermore, the allergy management unit may monitor the allergy information of patients over the long term and use it for health management. For example, it may compare it with past allergy information to analyze allergy fluctuations and support appropriate responses.

[0104] The medical treatment process efficiency system may further include an emotion adjustment unit that estimates the patient's emotions and adjusts the contents of the prescription based on the estimated emotions. The emotion adjustment unit, for example, analyzes the patient's facial expressions and tone of voice to estimate the emotions. This allows the contents of the prescription to be adjusted to match the patient's emotions. For example, if the patient is feeling anxious, the emotion adjustment unit simplifies the contents of the prescription to make it easier to understand. Also, if the patient is relaxed, detailed information can be provided to deepen understanding. Furthermore, the emotion adjustment unit can monitor the patient's emotion data over the long term and optimize the contents of the prescription. For example, the emotion adjustment unit compares the patient's emotion data with past emotion data to analyze the fluctuations in emotion and adjust the contents of the prescription.

[0105] The medical treatment process efficiency system may further include an exercise data collection unit that collects exercise data of patients. The exercise data collection unit, for example, collects and records the amount and type of exercise of patients. This allows doctors to understand the exercise habits of patients and perform more appropriate diagnosis and treatment. For example, the exercise data collection unit collects data entered by patients through smartphones or wearable devices and transmits it to the generation unit. The exercise data collection unit may also analyze the collected data and evaluate health risks. For example, if the amount of exercise is insufficient, the exercise data collection unit may notify the doctor and encourage the patient to improve their exercise habits. Furthermore, the exercise data collection unit may monitor the patient's exercise data over the long term and use it for health management. For example, the exercise data collection unit may compare the patient's exercise data with past data to analyze changes in exercise habits and support improvements in health conditions.

[0106] The medical process efficiency system may further include an emotion adjustment unit that estimates the patient's emotion and adjusts the medical treatment content based on the estimated emotion. The emotion adjustment unit, for example, analyzes the patient's facial expression and tone of voice to estimate the emotion. This allows the doctor to understand the patient's emotional state and appropriately adjust the medical treatment content. For example, if the patient feels anxious, the emotion adjustment unit notifies the doctor and encourages the doctor to take measures to relax the patient. The emotion adjustment unit may also adjust the medical treatment content based on the estimated emotion. For example, if the patient is nervous, the emotion adjustment unit may briefly explain the medical treatment content to reassure the patient. Also, if the patient is relaxed, detailed information may be provided to deepen understanding. Furthermore, the emotion adjustment unit may monitor the patient's emotion data over the long term to improve the quality of medical treatment. For example, the emotion adjustment unit may compare the patient's emotion data with past emotion data to analyze the emotional fluctuations and optimize the medical treatment content.

[0107] The medical process efficiency system may further include a dietary data collection unit that collects dietary data of patients. The dietary data collection unit, for example, collects and records the dietary content and calorie intake of patients. This allows doctors to understand the dietary habits of patients and perform more appropriate diagnosis and treatment. For example, the dietary data collection unit collects data entered by patients through smartphones or wearable devices and transmits it to the generation unit. The dietary data collection unit may also analyze the collected data and evaluate health risks. For example, if the calorie intake is excessive, the doctor may be notified and the doctor may be encouraged to improve the dietary habits. Furthermore, the dietary data collection unit may monitor the dietary data of patients over the long term and use it for health management. For example, the dietary data collection unit may compare the dietary data with past data to analyze changes in dietary habits and support improvement of health conditions.

[0108] The process flow of the second embodiment will be briefly described below.

[0109] Step 1: The reception department records the contents of the conversation during the consultation. The contents of the conversation during the consultation may include, for example, the patient's symptoms, the doctor's diagnosis, and treatment plans. The reception department records the contents of the conversation as audio or video using audio recording technology or video recording technology. For example, the contents of the conversation are recorded in high quality using a microphone and a camera installed in the examination room. Step 2: The generation unit uses a generation AI to summarize the conversation recorded by the reception unit and create a medical record. The generation AI uses a text generation AI (e.g., LLM) to analyze the conversation, extract important information, and summarize it. For example, the patient's symptoms, diagnosis, treatment plan, etc. are summarized and recorded in the medical record. Step 3: The discrimination unit automatically determines the points for each medical procedure summarized by the generation unit and creates a medical statement. The discrimination unit automatically determines the points corresponding to the summarized medical procedures based on the medical fee schedule. For example, it automatically calculates points for consultation fees, examination fees, treatment fees, etc., and lists them on the medical statement. Step 4: The prescribing unit creates a prescription for medicine based on the symptoms summarized by the generating unit. The prescribing unit uses the generating AI to analyze the summarized symptoms and select the appropriate medicine. For example, it selects an antipyretic or cough suppressant for cold symptoms and writes it on the prescription.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0111] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0112] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0113] For example, the reception unit can record the contents of the conversation during the consultation using the microphone 38B and the camera 42 of the smart device 14. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and summarizes the contents of the conversation using a generation AI to create a medical record. The discrimination unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically determines the points for each summarized medical procedure and creates a medical statement. The prescription unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates a prescription for medicine based on the summarized symptoms. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] 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.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0121] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] 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.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0124] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0129] For example, the reception unit can record the contents of the conversation during the consultation using the microphone 238 and the camera 42 of the smart glasses 214. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and summarizes the contents of the conversation using a generation AI to create a medical record. The discrimination unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically determines the points for each summarized medical procedure and creates a medical statement. The prescription unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates a prescription for medicine based on the summarized symptoms. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0133] 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.

[0134] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0136] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0137] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0138] 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.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0140] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0141] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0144] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0145] For example, the reception unit can record the contents of the conversation during the consultation using the microphone 238 and the camera 42 of the headset type terminal 314. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and summarizes the contents of the conversation using a generation AI to create a medical record. The discrimination unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically determines the points for each summarized medical procedure and creates a medical statement. The prescription unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates a prescription for medicine based on the summarized symptoms. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0149] 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.

[0150] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0152] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0153] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0154] 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.

[0155] 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.

[0156] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0157] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0158] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0160] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0162] For example, the reception unit can record the contents of the conversation during the consultation using the microphone 238 and the camera 42 of the robot 414. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and summarizes the contents of the conversation using a generation AI to create a medical record. The discrimination unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically determines the points for each summarized medical procedure and creates a medical statement. The prescription unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates a prescription for medicine based on the summarized symptoms. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0163] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0164] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0165] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0166] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0167] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0168] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0170] 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.

[0171] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0172] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0173] 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.

[0174] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0175] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0176] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0177] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0178] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0179] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0180] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0181] (Appendix 1) A reception unit that records the contents of conversations during consultations; a generating unit that generates a medical record by summarizing the conversation content recorded by the reception unit; A determination unit that automatically determines the points for each medical procedure summarized by the generation unit and creates a medical care statement; a prescription unit that creates a prescription for medicine based on the symptoms summarized by the generation unit. A system characterized by: (Appendix 2) The reception unit is Record all conversations between doctor and patient during consultation 2. The system of claim 1, (Appendix 3) The generation unit is Summarize the recorded conversation to create a medical record 2. The system of claim 1, (Appendix 4) The discrimination unit is Automatically determine the points for each summarized medical procedure based on the official price and create a medical invoice 2. The system of claim 1, (Appendix 5) The prescription part comprises: Write a prescription for medication based on summarized symptoms 2. The system of claim 1, (Appendix 6) The reception unit is Infer patient emotion and adjust how conversations are recorded based on the inferred emotion 2. The system of claim 1, (Appendix 7) The reception unit is Analyze conversations during consultations in real time and record important keywords with emphasis 2. The system of claim 1, (Appendix 8) The reception unit is Record conversations during consultations not only with audio but also with video, including non-verbal information. 2. The system of claim 1, (Appendix 9) The reception unit is Estimate patient emotions and prioritize conversations to record based on the estimated patient emotions 2. The system of claim 1, (Appendix 10) The reception unit is Record conversations during consultations in multiple languages ​​to accommodate patients who speak different languages 2. The system of claim 1, (Appendix 11) The reception unit is When recording conversations during consultations, the system references the patient's past medical history to prioritize recording the most relevant information. 2. The system of claim 1, (Appendix 12) The generation unit is Infer patient emotions and adjust the presentation of medical records based on the inferred emotions 2. The system of claim 1, (Appendix 13) The generation unit is Analyze the recorded conversations, automatically extract information necessary for diagnosis, and reflect it in the medical record 2. The system of claim 1, (Appendix 14) The generation unit is Analyzing recorded conversations and synthesizing the opinions of multiple doctors to improve diagnostic reliability 2. The system of claim 1, (Appendix 15) The generation unit is Infer patient sentiment and adjust the level of detail in the medical record based on the inferred sentiment 2. The system of claim 1, (Appendix 16) The generation unit is Analyze the recorded conversations, automatically classify the information necessary for diagnosis, and reflect it in the medical record. 2. The system of claim 1, (Appendix 17) The generation unit is Analyze recorded conversations and compare them with past medical records to improve diagnostic reliability 2. The system of claim 1, (Appendix 18) The discrimination unit is Estimate the patient's emotions and adjust the score criteria based on the estimated patient's emotions. 2. The system of claim 1, (Appendix 19) The discrimination unit is For each summarized medical procedure, past medical data is referenced to improve the accuracy of the score 2. The system of claim 1, (Appendix 20) The discrimination unit is For each medical procedure summarized, the points for different medical departments are automatically integrated to create a medical bill. 2. The system of claim 1, (Appendix 21) The discrimination unit is Infer patient sentiment and adjust how medical bills are presented based on the inferred sentiment 2. The system of claim 1, (Appendix 22) The discrimination unit is For each medical procedure summarized, the points are determined by referring to the official price in each region. 2. The system of claim 1, (Appendix 23) The discrimination unit is Customize and display billing formats for each summarized medical procedure 2. The system of claim 1, (Appendix 24) The prescription part comprises: Infer patient emotions and adjust prescription wording based on the inferred emotions 2. The system of claim 1, (Appendix 25) The prescription part comprises: Based on summarized symptoms, the most appropriate medication is selected by referring to past prescription data. 2. The system of claim 1, (Appendix 26) The prescription part comprises: Based on the summarized symptoms, it proposes a combination of multiple drugs and creates the optimal prescription 2. The system of claim 1, (Appendix 27) The prescription part comprises: Inferring patient sentiment and adjusting prescription detail based on the inferred sentiment 2. The system of claim 1, (Appendix 28) The prescription part comprises: Based on summarized symptoms, select the most appropriate medication by referring to local drug inventory information 2. The system of claim 1, (Appendix 29) The prescription part comprises: Based on the summarized symptoms, the optimal prescription is created taking into account the side effects of the medication. 2. The system of claim 1, [Explanation of symbols]

[0182] 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 that records the contents of conversations during consultations; a generating unit that generates a medical record by summarizing the conversation content recorded by the reception unit; A determination unit that automatically determines the points for each medical procedure summarized by the generation unit and creates a medical care statement; a prescription unit that creates a prescription for medicine based on the symptoms summarized by the generation unit. A system characterized by:

2. The reception unit is Record all conversations between doctor and patient during consultation 2. The system of claim 1 .

3. The generation unit is Summarize the recorded conversation to create a medical record 2. The system of claim 1 .

4. The discrimination unit is Automatically determine the points for each summarized medical procedure based on the official price and create a medical invoice 2. The system of claim 1 .

5. The prescription part comprises: Write a prescription for medication based on summarized symptoms 2. The system of claim 1 .

6. The reception unit is Infer patient emotion and adjust how conversations are recorded based on the inferred emotion 2. The system of claim 1 .

7. The reception unit is Analyze conversations during consultations in real time and record important keywords with emphasis 2. The system of claim 1 .

8. The reception unit is Record conversations during consultations not only with audio but also with video, including non-verbal information.

2. The system of claim 1 .

9. The reception unit is Estimate patient emotions and prioritize conversations to record based on the estimated patient emotions 2. The system of claim 1 .

10. The reception unit is Record conversations during consultations in multiple languages ​​to accommodate patients who speak different languages 2. The system of claim 1 .

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