system

Abstract

The system according to this embodiment aims to automatically analyze information related to diagnosis and treatment and assign appropriate medical codes. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a registration unit. The collection unit collects diagnostic data. The analysis unit analyzes the data collected by the collection unit and assigns appropriate medical codes. The registration unit registers the medical codes assigned by the analysis unit in a database.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, since the work of assigning diagnoses and treatments to appropriate medical codes is performed manually, there is a risk of input errors and delays in the process.

[0005] The system according to the embodiment aims to automatically analyze information related to diagnoses and treatments and assign appropriate medical codes.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a registration unit. The collection unit collects diagnostic data. The analysis unit analyzes the data collected by the collection unit and assigns an appropriate medical code. The registration unit registers the medical code assigned by the analysis unit in a database. [Effects of the Invention]

[0007] The system according to this embodiment can automatically analyze information related to diagnosis and treatment and assign appropriate medical codes. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 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". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An AI agent system for automating medical coding according to an embodiment of the present invention is a system that automates the assignment of medical codes (ICD, CPT, ATC, etc.) for diagnosis and treatment. This system uses natural language processing and machine learning techniques to automatically analyze information related to diagnosis and treatment and assign accurate medical codes. The AI ​​agent system for automating medical coding continuously learns and improves its judgment based on the latest medical information. Since the codes generated by the AI ​​are immediately registered in the database, it helps to streamline the entire medical process. For example, the AI ​​agent system for automating medical coding collects data such as patient medical records and test results. This data is input into the AI ​​agent. Next, the AI ​​agent analyzes the input data and assigns the appropriate medical code. For example, if diagnostic data for bronchitis is input, the AI ​​agent assigns ICD code J20.9. Furthermore, the AI ​​agent registers the assigned medical code in the database. This eliminates the need for medical staff to manually enter codes. This improves work efficiency and reduces the burden on medical staff. Automation reduces the risk of coding errors and improves the accuracy and speed of insurance claims. Furthermore, the use of standardized codes enhances data management and makes it easier to maintain consistency across the entire healthcare institution. This makes it possible to improve both the quality of medical care and the efficiency of management simultaneously. For example, when cardiac catheterization data is entered, the AI ​​agent assigns CPT code 93453. Similarly, when drug data is entered, the AI ​​agent assigns ATC code N02BE01. This allows medical staff to focus on diagnosis and treatment, improving the quality of patient care. In addition, the AI ​​agent continuously learns and improves its judgment based on the latest medical information. For example, when new diagnostic methods or treatments are introduced, the AI ​​agent can learn that information and assign the appropriate medical code. This streamlines the medical process and improves the overall operation of the healthcare institution. Thus, the AI ​​agent system for automating medical coding can improve the efficiency and accuracy of medical processes by automating the collection, analysis, and registration of diagnostic data.

[0029] The AI ​​agent system for automating medical coding according to the embodiment comprises a collection unit, an analysis unit, and a registration unit. The collection unit collects diagnostic data. Diagnostic data includes, but is not limited to, image data, text data, and numerical data. The collection unit collects data such as patient medical records and test results. The collection unit can acquire data from, for example, an electronic medical record system. The collection unit can also scan patient medical records and convert them into digital data. For example, the collection unit reads patient medical records with a scanner and saves them as image data. The collection unit can also convert image data into text data using OCR technology. The analysis unit analyzes the data collected by the collection unit and assigns appropriate medical codes. The analysis unit analyzes text data using, for example, natural language processing technology. The analysis unit analyzes data using, for example, machine learning algorithms and assigns appropriate medical codes. For example, the analysis unit assigns ICD code J20.9 to diagnostic data for bronchitis. For example, the analysis unit assigns CPT code 93453 to data for cardiac catheterization. The analysis unit assigns, for example, the ATC code N02BE01 to drug data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit inputs diagnostic data into the generating AI, and the generating AI assigns an appropriate medical code. The registration unit registers the medical code assigned by the analysis unit into a database. The registration unit registers the medical code into, for example, a relational database. The registration unit can also register the medical code into, for example, a NoSQL database. The registration unit checks the integrity of the data when registering the medical code into the database. Some or all of the above-described processes in the registration unit may be performed using, for example, a generating AI, or without a generating AI. For example, the registration unit registers the medical code assigned by the generating AI into a database. As a result, the AI ​​agent system for automating medical coding according to the embodiment can improve the efficiency and accuracy of medical processes by automating the collection, analysis, and registration of diagnostic data.

[0030] The data collection unit collects diagnostic data. This includes, but is not limited to, image data, text data, and numerical data. The unit collects data such as patient medical records and test results. Specifically, it can acquire data from electronic medical record systems. Electronic medical record systems store patient medical records, test results, prescription information, etc., in digital format, and the data collection unit automatically acquires this data. The data collection unit can also scan patient medical records and convert them into digital data. For example, the data collection unit scans patient medical records and saves them as image data. Furthermore, the data collection unit can convert image data into text data using OCR (Optical Character Recognition) technology. OCR technology extracts character information from scanned image data and saves it as text data, and can accurately extract data from handwritten medical records and printed documents. This allows the data collection unit to efficiently collect and digitize diverse formats of diagnostic data. In addition, the data collection unit can collect data not only from patient medical records but also from testing equipment. For example, CT scan and MRI image data, blood test results, and other data can be collected and integrated for analysis. This allows the data collection unit to collect comprehensive diagnostic data and provide it to the analysis unit.

[0031] The analysis unit analyzes the data collected by the collection unit and assigns appropriate medical codes. For example, the analysis unit analyzes text data using natural language processing (NLP) technology. NLP is a technology that extracts meaning from text data and identifies information for assigning medical codes. For example, it extracts disease names and treatment details from text data such as medical certificates and prescriptions and identifies the corresponding medical codes. The analysis unit also analyzes data using machine learning algorithms and assigns appropriate medical codes. Machine learning algorithms can learn from large amounts of diagnostic data and assign medical codes to new data with high accuracy. For example, it assigns ICD code J20.9 to diagnostic data for bronchitis. ICD code stands for International Classification of Diseases, a standard classification system for assigning unique codes to diseases and symptoms. The analysis unit analyzes the diagnostic data and identifies the appropriate ICD code. It also assigns CPT code 93453 to cardiac catheterization data. CPT code stands for Current Procedural Terminology, a standard classification system for assigning unique codes to medical procedures and services. The analysis unit analyzes the test data and identifies the appropriate CPT code. Furthermore, it assigns the ATC code N02BE01 to the drug data. The ATC code is an abbreviation for Anatomical Therapeutic Chemical Classification System, a standard classification system for assigning unique codes to drugs. The analysis unit analyzes the drug data and identifies the appropriate ATC code. Some or all of the above processing in the analysis unit may be performed using a generating AI, or not using a generating AI. For example, the analysis unit inputs diagnostic data into a generating AI, and the generating AI assigns the appropriate medical code. The generating AI can learn from a large amount of medical data and assign medical codes with high accuracy. This allows the analysis unit to analyze the collected data quickly and accurately and assign the appropriate medical code.

[0032] The registration unit registers the medical codes assigned by the analysis unit into a database. For example, the registration unit registers the medical codes into a relational database. A relational database is a database management system that manages data in a tabular format and maintains data integrity by linking multiple tables. By registering medical codes into a relational database and linking them with other medical data, the registration unit can maintain data consistency and integrity. The registration unit can also register medical codes into a NoSQL database. Unlike relational databases, NoSQL databases are database management systems that have a flexible data model and can process large amounts of data at high speed. The registration unit can register medical codes into a NoSQL database to improve scalability and performance. Furthermore, when registering medical codes into the database, the registration unit checks the integrity of the data. Data integrity checking is a process to ensure that the data is accurate and consistent, and is important to prevent data duplication and inconsistencies. The registration unit can ensure that the medical codes registered in the database are accurate and maintain data integrity. Some or all of the above processes in the registration unit may be performed using generative AI, or not. For example, the registration unit registers the medical codes assigned by the generation AI into the database. The generation AI automatically performs data integrity checks, ensuring that accurate medical codes are registered in the database. This allows the registration unit to efficiently and accurately register the medical codes assigned by the analysis unit into the database, automating the management of medical data.

[0033] The data collection unit can collect data such as patient medical records and test results. For example, the data collection unit can obtain patient medical records from an electronic medical record system. The data collection unit can also, for example, scan patient medical records and save them as image data. The data collection unit can also, for example, convert image data into text data using OCR technology. The data collection unit can also, for example, obtain patient test results from an electronic medical record system. By collecting patient medical records and test results, the comprehensiveness of diagnostic data is improved. Some or all of the above processing in the data collection unit may be performed using, for example, a generating AI, or without a generating AI. For example, the data collection unit inputs patient medical records into a generating AI, and the generating AI analyzes the medical records and collects data.

[0034] The analysis unit can analyze the collected data and assign appropriate medical codes. The analysis unit can, for example, analyze text data using natural language processing technology. The analysis unit can, for example, analyze data using machine learning algorithms and assign appropriate medical codes. The analysis unit can, for example, assign ICD code J20.9 to bronchitis diagnostic data. The analysis unit can, for example, assign CPT code 93453 to cardiac catheterization data. The analysis unit can, for example, assign ATC code N02BE01 to drug data. This improves the accuracy of medical coding by analyzing the collected data and assigning appropriate medical codes. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit inputs diagnostic data into a generative AI, and the generative AI assigns appropriate medical codes.

[0035] The registration unit can register assigned medical codes in a database. For example, the registration unit can register medical codes in a relational database. The registration unit can also register medical codes in a NoSQL database. For example, the registration unit checks the integrity of the data when registering medical codes in a database. This streamlines the management of medical data by registering assigned medical codes in a database. Some or all of the above processes in the registration unit may be performed using, for example, a generation AI, or without a generation AI. For example, the registration unit registers medical codes assigned by a generation AI in a database.

[0036] The analysis unit can continuously learn and improve its judgment based on the latest medical information. The analysis unit can continuously learn using, for example, machine learning algorithms. The analysis unit can learn the latest medical information using, for example, deep learning technology. As a result, the accuracy of assigning medical codes improves through continuous learning. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit inputs the latest medical information into a generative AI, and the generative AI performs learning.

[0037] The analysis unit can assign the ICD code J20.9 to bronchitis diagnostic data. The analysis unit analyzes the bronchitis diagnostic data using, for example, natural language processing technology. The analysis unit analyzes the bronchitis diagnostic data using, for example, a machine learning algorithm and assigns the ICD code J20.9. This improves the accuracy of medical coding by assigning the correct ICD code to the bronchitis diagnostic data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit inputs the bronchitis diagnostic data into a generative AI, and the generative AI assigns the ICD code J20.9.

[0038] The analysis unit can assign the CPT code 93453 to cardiac catheterization data. The analysis unit analyzes the cardiac catheterization data using, for example, natural language processing technology. The analysis unit analyzes the cardiac catheterization data using, for example, machine learning algorithms and assigns the CPT code 93453. This improves the accuracy of medical coding by assigning the correct CPT code to the cardiac catheterization data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit inputs the cardiac catheterization data into a generative AI, and the generative AI assigns the CPT code 93453.

[0039] The analysis unit can assign the ATC code N02BE01 to drug data. The analysis unit analyzes the drug data using, for example, natural language processing technology. The analysis unit analyzes the drug data using, for example, a machine learning algorithm and assigns the ATC code N02BE01. This improves the accuracy of medical coding by assigning the correct ATC code to the drug data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit inputs the drug data into a generative AI, and the generative AI assigns the ATC code N02BE01.

[0040] The data collection unit can analyze the patient's past medical records and select the optimal data collection method. For example, the data collection unit selects the most effective data collection method based on the patient's past medical records. For example, the data collection unit prioritizes the collection of specific diagnostic data from the patient's past medical records. For example, the data collection unit analyzes the patient's past medical records and customizes the data collection method. This allows the optimal data collection method to be selected by analyzing the patient's past medical records. Some or all of the above processes in the data collection unit may be performed using, for example, a generating AI, or without a generating AI. For example, the data collection unit inputs the patient's past medical records into a generating AI, and the generating AI selects the optimal data collection method.

[0041] The data collection unit can filter the collected diagnostic data based on the patient's current medical condition and treatment plan. For example, the data collection unit can collect only the necessary diagnostic data based on the patient's current medical condition. For example, the data collection unit can prioritize the collection of relevant diagnostic data based on the patient's treatment plan. For example, the data collection unit can filter out unnecessary data considering the patient's medical condition and treatment plan. This allows the collection of only the necessary diagnostic data by filtering based on the patient's current medical condition and treatment plan. Some or all of the above processing in the data collection unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the data collection unit inputs data on the patient's medical condition and treatment plan into a generating AI, and the generating AI performs the filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the patient's geographical location information when collecting diagnostic data. For example, the data collection unit prioritizes the collection of region-specific diagnostic data based on the patient's geographical location information. For example, the data collection unit collects relevant diagnostic data by considering the patient's geographical location information. For example, the data collection unit selects the optimal collection method based on the patient's geographical location information. This improves the efficiency of data collection by prioritizing the collection of highly relevant data by considering the patient's geographical location information. Some or all of the above processing in the data collection unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the data collection unit inputs the patient's geographical location information into a generating AI, and the generating AI selects highly relevant data.

[0043] The data collection unit can analyze the patient's social media activity and collect relevant data when collecting diagnostic data. For example, the data collection unit analyzes the patient's social media activity and collects relevant diagnostic data. For example, the data collection unit collects information about the patient's health status from the patient's social media activity. For example, the data collection unit selects the optimal data collection method based on the patient's social media activity. This allows the collection of relevant diagnostic data by analyzing the patient's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit inputs the patient's social media activity data into a generative AI, and the generative AI collects the relevant data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the diagnostic data during the analysis. For example, the analysis unit performs a detailed analysis on important diagnostic data. For example, the analysis unit performs a simplified analysis on less important diagnostic data. The analysis unit adjusts the level of detail of the analysis according to the importance of the diagnostic data. This improves the efficiency of the analysis by adjusting the level of detail of the analysis based on the importance of the diagnostic data. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit inputs the importance of the diagnostic data to the generating AI, and the generating AI adjusts the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of the diagnostic data during analysis. For example, the analysis unit selects the optimal analysis algorithm depending on the category of the diagnostic data. For example, the analysis unit applies different analysis algorithms based on the category of the diagnostic data. For example, the analysis unit customizes the analysis algorithm depending on the category of the diagnostic data. By applying different analysis algorithms depending on the category of the diagnostic data, the accuracy of the analysis is improved. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit inputs the category of the diagnostic data into the generative AI, and the generative AI applies the optimal analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on the submission timing of diagnostic data during analysis. For example, the analysis unit prioritizes the analysis of the data submitted earliest based on the submission timing of the diagnostic data. For example, the analysis unit determines the order of analysis considering the submission timing of the diagnostic data. For example, the analysis unit adjusts the priority of analysis based on the submission timing of the diagnostic data. This improves the efficiency of analysis by determining the priority of analysis based on the submission timing of the diagnostic data. Some or all of the above processes in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit inputs the submission timing of the diagnostic data into the generating AI, and the generating AI determines the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the diagnostic data during the analysis. For example, the analysis unit prioritizes the analysis of the most relevant data based on the relevance of the diagnostic data. For example, the analysis unit determines the order of analysis considering the relevance of the diagnostic data. For example, the analysis unit adjusts the order of analysis based on the relevance of the diagnostic data. This improves the efficiency of the analysis by adjusting the order of analysis based on the relevance of the diagnostic data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit inputs the relevance of the diagnostic data into a generative AI, and the generative AI adjusts the order of analysis.

[0048] The registration unit can select the optimal registration method by referring to past registration data during registration. For example, the registration unit selects the optimal registration method based on past registration data. For example, the registration unit prioritizes the registration of relevant data by referring to past registration data. For example, the registration unit analyzes past registration data and customizes the registration method. This allows the optimal registration method to be selected by referring to past registration data. Some or all of the above processes in the registration unit may be performed using, for example, a generation AI, or without a generation AI. For example, the registration unit inputs past registration data into a generation AI, and the generation AI selects the optimal registration method.

[0049] The registration unit can apply different registration algorithms depending on the category of the diagnostic data during registration. For example, the registration unit selects the optimal registration algorithm depending on the category of the diagnostic data. For example, the registration unit applies different registration algorithms based on the category of the diagnostic data. For example, the registration unit customizes the registration algorithm depending on the category of the diagnostic data. This improves the accuracy of registration by applying different registration algorithms depending on the category of the diagnostic data. Some or all of the above processes in the registration unit may be performed using, for example, a generative AI, or without a generative AI. For example, the registration unit inputs the category of the diagnostic data into the generative AI, and the generative AI applies the optimal registration algorithm.

[0050] The registration unit can weight the registered data based on when the diagnostic data was submitted. For example, the registration unit prioritizes registering the data submitted earliest based on when the diagnostic data was submitted. For example, the registration unit weights the registered data considering when the diagnostic data was submitted. For example, the registration unit adjusts the priority of the registered data based on when the diagnostic data was submitted. This improves the efficiency of registration by weighting the registered data based on when the diagnostic data was submitted. Some or all of the above processing in the registration unit may be performed using a generation AI, for example, or without a generation AI. For example, the registration unit inputs the submission dates of the diagnostic data into the generation AI, and the generation AI weights the registered data.

[0051] The registration unit can improve the accuracy of registration by referring to relevant literature for the diagnostic data during registration. For example, the registration unit improves the accuracy of registration by referring to relevant literature for the diagnostic data. For example, the registration unit selects the optimal registration method based on relevant literature for the diagnostic data. For example, the registration unit verifies the accuracy of the registration data by referring to relevant literature for the diagnostic data. As a result, the accuracy of registration is improved by referring to relevant literature for the diagnostic data. Some or all of the above processes in the registration unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the registration unit inputs relevant literature for the diagnostic data into a generating AI, and the generating AI improves the accuracy of registration.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The analysis unit can adjust the analysis results when analyzing diagnostic data, taking into account the patient's lifestyle data. For example, if the patient is a smoker, the analysis can provide results that increase the risk of smoking-related diseases. Also, if the patient exercises regularly, the analysis can provide results that take into account the health benefits of exercise. Furthermore, it can also assess the risk of diet-related diseases by considering the patient's dietary data. In this way, by considering the patient's lifestyle data, more individualized analysis results can be provided.

[0054] The data collection unit can determine the type of data to collect when collecting diagnostic data, taking into account the patient's genetic information. For example, if a patient has a specific gene mutation, data to assess the risk of diseases associated with that mutation can be prioritized for collection. Furthermore, data related to diseases common in the family can be collected, taking into account the patient's family history. Additionally, data to assess the risk of diseases that may develop in the future can be collected based on the patient's genetic information. This allows for the collection of more accurate diagnostic data by considering the patient's genetic information.

[0055] The data collection unit can prioritize the collection of data related to region-specific diseases by considering the patient's geographical location when collecting diagnostic data. For example, it can collect data related to infectious diseases prevalent in a particular region. It can also collect data to assess the risk of diseases related to environmental factors by considering regional environmental factors (e.g., air pollution and water quality). Furthermore, it can collect data related to medical resources by considering regional medical resources (e.g., the number of hospitals and clinics). This allows for the collection of more region-specific diagnostic data by considering the patient's geographical location.

[0056] The data collection unit can analyze patients' social media activity and collect information related to their health status when collecting diagnostic data. For example, it can extract information about a patient's health status from the content they post on social media. It can also analyze patients' health interests and behavioral patterns based on their social media activity. Furthermore, it can identify health-related risk factors based on patients' social media activity. As a result, analyzing patients' social media activity allows for the collection of more comprehensive diagnostic data.

[0057] The data collection unit can prioritize the collection of relevant data when collecting diagnostic data, taking into account the patient's treatment plan. For example, if a patient is receiving a specific treatment, data related to that treatment can be prioritized. It can also collect data to evaluate the progress of treatment based on the patient's treatment plan. Furthermore, it can collect data to evaluate the effectiveness of treatment, taking the patient's treatment plan into consideration. This allows for the collection of more effective diagnostic data by considering the patient's treatment plan.

[0058] The data collection unit can analyze a patient's past medical records and select the optimal data collection method when collecting diagnostic data. For example, it can select the most effective collection method based on the patient's past medical records. It can also prioritize the collection of specific diagnostic data from the patient's past medical records. Furthermore, it can customize the data collection method by analyzing the patient's past medical records. This allows for the selection of the optimal data collection method by analyzing the patient's past medical records.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The data collection unit collects diagnostic data. Diagnostic data includes image data, text data, and numerical data. The data collection unit collects data such as patient medical records and test results. For example, the data collection unit can acquire data from electronic medical record systems or scan patient medical records and save them as image data. It can also convert image data into text data using OCR technology. Step 2: The analysis unit analyzes the data collected by the collection unit and assigns appropriate medical codes. The analysis unit analyzes the data using natural language processing technology and machine learning algorithms. For example, it may assign the ICD code J20.9 to bronchitis diagnostic data or the CPT code 93453 to cardiac catheterization data. It can also assign the ATC code N02BE01 to drug data. Processing in the analysis unit may also be performed using generative AI. Step 3: The registration unit registers the medical codes assigned by the analysis unit into the database. The registration unit can register medical codes into relational databases or NoSQL databases. The registration unit registers medical codes into the database while checking data integrity. Processing in the registration unit may also be performed using generation AI.

[0061] (Example of form 2) An AI agent system for automating medical coding according to an embodiment of the present invention is a system that automates the assignment of medical codes (ICD, CPT, ATC, etc.) for diagnosis and treatment. This system uses natural language processing and machine learning techniques to automatically analyze information related to diagnosis and treatment and assign accurate medical codes. The AI ​​agent system for automating medical coding continuously learns and improves its judgment based on the latest medical information. Since the codes generated by the AI ​​are immediately registered in the database, it helps to streamline the entire medical process. For example, the AI ​​agent system for automating medical coding collects data such as patient medical records and test results. This data is input into the AI ​​agent. Next, the AI ​​agent analyzes the input data and assigns the appropriate medical code. For example, if diagnostic data for bronchitis is input, the AI ​​agent assigns ICD code J20.9. Furthermore, the AI ​​agent registers the assigned medical code in the database. This eliminates the need for medical staff to manually enter codes. This improves work efficiency and reduces the burden on medical staff. Automation reduces the risk of coding errors and improves the accuracy and speed of insurance claims. Furthermore, the use of standardized codes enhances data management and makes it easier to maintain consistency across the entire healthcare institution. This makes it possible to improve both the quality of medical care and the efficiency of management simultaneously. For example, when cardiac catheterization data is entered, the AI ​​agent assigns CPT code 93453. Similarly, when drug data is entered, the AI ​​agent assigns ATC code N02BE01. This allows medical staff to focus on diagnosis and treatment, improving the quality of patient care. In addition, the AI ​​agent continuously learns and improves its judgment based on the latest medical information. For example, when new diagnostic methods or treatments are introduced, the AI ​​agent can learn that information and assign the appropriate medical code. This streamlines the medical process and improves the overall operation of the healthcare institution. Thus, the AI ​​agent system for automating medical coding can improve the efficiency and accuracy of medical processes by automating the collection, analysis, and registration of diagnostic data.

[0062] The AI ​​agent system for automating medical coding according to the embodiment comprises a collection unit, an analysis unit, and a registration unit. The collection unit collects diagnostic data. Diagnostic data includes, but is not limited to, image data, text data, and numerical data. The collection unit collects data such as patient medical records and test results. The collection unit can acquire data from, for example, an electronic medical record system. The collection unit can also scan patient medical records and convert them into digital data. For example, the collection unit reads patient medical records with a scanner and saves them as image data. The collection unit can also convert image data into text data using OCR technology. The analysis unit analyzes the data collected by the collection unit and assigns appropriate medical codes. The analysis unit analyzes text data using, for example, natural language processing technology. The analysis unit analyzes data using, for example, machine learning algorithms and assigns appropriate medical codes. For example, the analysis unit assigns ICD code J20.9 to diagnostic data for bronchitis. For example, the analysis unit assigns CPT code 93453 to data for cardiac catheterization. The analysis unit assigns, for example, the ATC code N02BE01 to drug data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit inputs diagnostic data into the generating AI, and the generating AI assigns an appropriate medical code. The registration unit registers the medical code assigned by the analysis unit into a database. The registration unit registers the medical code into, for example, a relational database. The registration unit can also register the medical code into, for example, a NoSQL database. The registration unit checks the integrity of the data when registering the medical code into the database. Some or all of the above-described processes in the registration unit may be performed using, for example, a generating AI, or without a generating AI. For example, the registration unit registers the medical code assigned by the generating AI into a database. As a result, the AI ​​agent system for automating medical coding according to the embodiment can improve the efficiency and accuracy of medical processes by automating the collection, analysis, and registration of diagnostic data.

[0063] The data collection unit collects diagnostic data. This includes, but is not limited to, image data, text data, and numerical data. The unit collects data such as patient medical records and test results. Specifically, it can acquire data from electronic medical record systems. Electronic medical record systems store patient medical records, test results, prescription information, etc., in digital format, and the data collection unit automatically acquires this data. The data collection unit can also scan patient medical records and convert them into digital data. For example, the data collection unit scans patient medical records and saves them as image data. Furthermore, the data collection unit can convert image data into text data using OCR (Optical Character Recognition) technology. OCR technology extracts character information from scanned image data and saves it as text data, and can accurately extract data from handwritten medical records and printed documents. This allows the data collection unit to efficiently collect and digitize diverse formats of diagnostic data. In addition, the data collection unit can collect data not only from patient medical records but also from testing equipment. For example, CT scan and MRI image data, blood test results, and other data can be collected and integrated for analysis. This allows the data collection unit to collect comprehensive diagnostic data and provide it to the analysis unit.

[0064] The analysis unit analyzes the data collected by the collection unit and assigns appropriate medical codes. For example, the analysis unit analyzes text data using natural language processing (NLP) technology. NLP is a technology that extracts meaning from text data and identifies information for assigning medical codes. For example, it extracts disease names and treatment details from text data such as medical certificates and prescriptions and identifies the corresponding medical codes. The analysis unit also analyzes data using machine learning algorithms and assigns appropriate medical codes. Machine learning algorithms can learn from large amounts of diagnostic data and assign medical codes to new data with high accuracy. For example, it assigns ICD code J20.9 to diagnostic data for bronchitis. ICD code stands for International Classification of Diseases, a standard classification system for assigning unique codes to diseases and symptoms. The analysis unit analyzes the diagnostic data and identifies the appropriate ICD code. It also assigns CPT code 93453 to cardiac catheterization data. CPT code stands for Current Procedural Terminology, a standard classification system for assigning unique codes to medical procedures and services. The analysis unit analyzes the test data and identifies the appropriate CPT code. Furthermore, it assigns the ATC code N02BE01 to the drug data. The ATC code is an abbreviation for Anatomical Therapeutic Chemical Classification System, a standard classification system for assigning unique codes to drugs. The analysis unit analyzes the drug data and identifies the appropriate ATC code. Some or all of the above processing in the analysis unit may be performed using a generating AI, or not using a generating AI. For example, the analysis unit inputs diagnostic data into a generating AI, and the generating AI assigns the appropriate medical code. The generating AI can learn from a large amount of medical data and assign medical codes with high accuracy. This allows the analysis unit to analyze the collected data quickly and accurately and assign the appropriate medical code.

[0065] The registration unit registers the medical codes assigned by the analysis unit into a database. For example, the registration unit registers the medical codes into a relational database. A relational database is a database management system that manages data in a tabular format and maintains data integrity by linking multiple tables. By registering medical codes into a relational database and linking them with other medical data, the registration unit can maintain data consistency and integrity. The registration unit can also register medical codes into a NoSQL database. Unlike relational databases, NoSQL databases are database management systems that have a flexible data model and can process large amounts of data at high speed. The registration unit can register medical codes into a NoSQL database to improve scalability and performance. Furthermore, when registering medical codes into the database, the registration unit checks the integrity of the data. Data integrity checking is a process to ensure that the data is accurate and consistent, and is important to prevent data duplication and inconsistencies. The registration unit can ensure that the medical codes registered in the database are accurate and maintain data integrity. Some or all of the above processes in the registration unit may be performed using generative AI, or not. For example, the registration unit registers the medical codes assigned by the generation AI into the database. The generation AI automatically performs data integrity checks, ensuring that accurate medical codes are registered in the database. This allows the registration unit to efficiently and accurately register the medical codes assigned by the analysis unit into the database, automating the management of medical data.

[0066] The data collection unit can collect data such as patient medical records and test results. For example, the data collection unit can obtain patient medical records from an electronic medical record system. The data collection unit can also, for example, scan patient medical records and save them as image data. The data collection unit can also, for example, convert image data into text data using OCR technology. The data collection unit can also, for example, obtain patient test results from an electronic medical record system. By collecting patient medical records and test results, the comprehensiveness of diagnostic data is improved. Some or all of the above processing in the data collection unit may be performed using, for example, a generating AI, or without a generating AI. For example, the data collection unit inputs patient medical records into a generating AI, and the generating AI analyzes the medical records and collects data.

[0067] The analysis unit can analyze the collected data and assign appropriate medical codes. The analysis unit can, for example, analyze text data using natural language processing technology. The analysis unit can, for example, analyze data using machine learning algorithms and assign appropriate medical codes. The analysis unit can, for example, assign ICD code J20.9 to bronchitis diagnostic data. The analysis unit can, for example, assign CPT code 93453 to cardiac catheterization data. The analysis unit can, for example, assign ATC code N02BE01 to drug data. This improves the accuracy of medical coding by analyzing the collected data and assigning appropriate medical codes. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit inputs diagnostic data into a generative AI, and the generative AI assigns appropriate medical codes.

[0068] The registration unit can register assigned medical codes in a database. For example, the registration unit can register medical codes in a relational database. The registration unit can also register medical codes in a NoSQL database. For example, the registration unit checks the integrity of the data when registering medical codes in a database. This streamlines the management of medical data by registering assigned medical codes in a database. Some or all of the above processes in the registration unit may be performed using, for example, a generation AI, or without a generation AI. For example, the registration unit registers medical codes assigned by a generation AI in a database.

[0069] The analysis unit can continuously learn and improve its judgment based on the latest medical information. The analysis unit can continuously learn using, for example, machine learning algorithms. The analysis unit can learn the latest medical information using, for example, deep learning technology. As a result, the accuracy of assigning medical codes improves through continuous learning. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit inputs the latest medical information into a generative AI, and the generative AI performs learning.

[0070] The analysis unit can assign the ICD code J20.9 to bronchitis diagnostic data. The analysis unit analyzes the bronchitis diagnostic data using, for example, natural language processing technology. The analysis unit analyzes the bronchitis diagnostic data using, for example, a machine learning algorithm and assigns the ICD code J20.9. This improves the accuracy of medical coding by assigning the correct ICD code to the bronchitis diagnostic data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit inputs the bronchitis diagnostic data into a generative AI, and the generative AI assigns the ICD code J20.9.

[0071] The analysis unit can assign the CPT code 93453 to cardiac catheterization data. The analysis unit analyzes the cardiac catheterization data using, for example, natural language processing technology. The analysis unit analyzes the cardiac catheterization data using, for example, machine learning algorithms and assigns the CPT code 93453. This improves the accuracy of medical coding by assigning the correct CPT code to the cardiac catheterization data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit inputs the cardiac catheterization data into a generative AI, and the generative AI assigns the CPT code 93453.

[0072] The analysis unit can assign the ATC code N02BE01 to drug data. The analysis unit analyzes the drug data using, for example, natural language processing technology. The analysis unit analyzes the drug data using, for example, a machine learning algorithm and assigns the ATC code N02BE01. This improves the accuracy of medical coding by assigning the correct ATC code to the drug data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit inputs the drug data into a generative AI, and the generative AI assigns the ATC code N02BE01.

[0073] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection and wait until the user is relaxed. If the user is relaxed, the data collection unit can immediately collect the data. If the user is in a hurry, the data collection unit can quickly collect the data. This improves the efficiency of data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using a generative AI, or not. For example, the data collection unit inputs the user's emotion data into a generative AI, and the generative AI estimates the emotions.

[0074] The data collection unit can analyze the patient's past medical records and select the optimal data collection method. For example, the data collection unit selects the most effective data collection method based on the patient's past medical records. For example, the data collection unit prioritizes the collection of specific diagnostic data from the patient's past medical records. For example, the data collection unit analyzes the patient's past medical records and customizes the data collection method. This allows the optimal data collection method to be selected by analyzing the patient's past medical records. Some or all of the above processes in the data collection unit may be performed using, for example, a generating AI, or without a generating AI. For example, the data collection unit inputs the patient's past medical records into a generating AI, and the generating AI selects the optimal data collection method.

[0075] The data collection unit can filter the collected diagnostic data based on the patient's current medical condition and treatment plan. For example, the data collection unit can collect only the necessary diagnostic data based on the patient's current medical condition. For example, the data collection unit can prioritize the collection of relevant diagnostic data based on the patient's treatment plan. For example, the data collection unit can filter out unnecessary data considering the patient's medical condition and treatment plan. This allows the collection of only the necessary diagnostic data by filtering based on the patient's current medical condition and treatment plan. Some or all of the above processing in the data collection unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the data collection unit inputs data on the patient's medical condition and treatment plan into a generating AI, and the generating AI performs the filtering.

[0076] The data collection unit can estimate the user's emotions and determine the priority of diagnostic data to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit will prioritize collecting important diagnostic data. For example, if the user is relaxed, the data collection unit will collect detailed diagnostic data. For example, if the user is in a hurry, the data collection unit will prioritize diagnostic data that can be collected quickly. This improves the efficiency of data collection by determining the priority of diagnostic data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using a generative AI, or not using a generative AI. For example, the data collection unit inputs the user's emotion data into a generative AI, and the generative AI estimates the emotions.

[0077] The data collection unit can prioritize the collection of highly relevant data by considering the patient's geographical location information when collecting diagnostic data. For example, the data collection unit prioritizes the collection of region-specific diagnostic data based on the patient's geographical location information. For example, the data collection unit collects relevant diagnostic data by considering the patient's geographical location information. For example, the data collection unit selects the optimal collection method based on the patient's geographical location information. This improves the efficiency of data collection by prioritizing the collection of highly relevant data by considering the patient's geographical location information. Some or all of the above processing in the data collection unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the data collection unit inputs the patient's geographical location information into a generating AI, and the generating AI selects highly relevant data.

[0078] The data collection unit can analyze the patient's social media activity and collect relevant data when collecting diagnostic data. For example, the data collection unit analyzes the patient's social media activity and collects relevant diagnostic data. For example, the data collection unit collects information about the patient's health status from the patient's social media activity. For example, the data collection unit selects the optimal data collection method based on the patient's social media activity. This allows the collection of relevant diagnostic data by analyzing the patient's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit inputs the patient's social media activity data into a generative AI, and the generative AI collects the relevant data.

[0079] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit provides a simple and easy-to-understand analysis result. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. For example, if the user is in a hurry, the analysis unit provides a concise analysis result. By adjusting the presentation of the analysis according to the user's emotions, the analysis results become easier to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit inputs the user's emotion data into a generative AI, and the generative AI estimates the emotion.

[0080] The analysis unit can adjust the level of detail of the analysis based on the importance of the diagnostic data during the analysis. For example, the analysis unit performs a detailed analysis on important diagnostic data. For example, the analysis unit performs a simplified analysis on less important diagnostic data. The analysis unit adjusts the level of detail of the analysis according to the importance of the diagnostic data. This improves the efficiency of the analysis by adjusting the level of detail of the analysis based on the importance of the diagnostic data. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit inputs the importance of the diagnostic data to the generating AI, and the generating AI adjusts the level of detail of the analysis.

[0081] The analysis unit can apply different analysis algorithms depending on the category of the diagnostic data during analysis. For example, the analysis unit selects the optimal analysis algorithm depending on the category of the diagnostic data. For example, the analysis unit applies different analysis algorithms based on the category of the diagnostic data. For example, the analysis unit customizes the analysis algorithm depending on the category of the diagnostic data. By applying different analysis algorithms depending on the category of the diagnostic data, the accuracy of the analysis is improved. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit inputs the category of the diagnostic data into the generative AI, and the generative AI applies the optimal analysis algorithm.

[0082] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit provides a short, concise analysis. For example, if the user is relaxed, the analysis unit provides a detailed analysis. For example, if the user is excited, the analysis unit provides a visually stimulating analysis. By adjusting the length of the analysis according to the user's emotions, the analysis results become easier to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit inputs user emotion data into a generative AI, and the generative AI estimates the emotions.

[0083] The analysis unit can determine the priority of analysis based on the submission timing of diagnostic data during analysis. For example, the analysis unit prioritizes the analysis of the data submitted earliest based on the submission timing of the diagnostic data. For example, the analysis unit determines the order of analysis considering the submission timing of the diagnostic data. For example, the analysis unit adjusts the priority of analysis based on the submission timing of the diagnostic data. This improves the efficiency of analysis by determining the priority of analysis based on the submission timing of the diagnostic data. Some or all of the above processes in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit inputs the submission timing of the diagnostic data into the generating AI, and the generating AI determines the priority of analysis.

[0084] The analysis unit can adjust the order of analysis based on the relevance of the diagnostic data during the analysis. For example, the analysis unit prioritizes the analysis of the most relevant data based on the relevance of the diagnostic data. For example, the analysis unit determines the order of analysis considering the relevance of the diagnostic data. For example, the analysis unit adjusts the order of analysis based on the relevance of the diagnostic data. This improves the efficiency of the analysis by adjusting the order of analysis based on the relevance of the diagnostic data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit inputs the relevance of the diagnostic data into a generative AI, and the generative AI adjusts the order of analysis.

[0085] The registration unit can estimate the user's emotions and adjust the registration method based on the estimated emotions. For example, if the user is nervous, the registration unit provides a simple and highly visible registration method. For example, if the user is relaxed, the registration unit provides a detailed registration method. For example, if the user is in a hurry, the registration unit provides a method that allows for quick registration. This improves the efficiency of registration by adjusting the registration method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the registration unit may be performed using a generative AI, or not using a generative AI. For example, the registration unit inputs the user's emotion data into a generative AI, and the generative AI estimates the emotion.

[0086] The registration unit can select the optimal registration method by referring to past registration data during registration. For example, the registration unit selects the optimal registration method based on past registration data. For example, the registration unit prioritizes the registration of relevant data by referring to past registration data. For example, the registration unit analyzes past registration data and customizes the registration method. This allows the optimal registration method to be selected by referring to past registration data. Some or all of the above processes in the registration unit may be performed using, for example, a generation AI, or without a generation AI. For example, the registration unit inputs past registration data into a generation AI, and the generation AI selects the optimal registration method.

[0087] The registration unit can apply different registration algorithms depending on the category of the diagnostic data during registration. For example, the registration unit selects the optimal registration algorithm depending on the category of the diagnostic data. For example, the registration unit applies different registration algorithms based on the category of the diagnostic data. For example, the registration unit customizes the registration algorithm depending on the category of the diagnostic data. This improves the accuracy of registration by applying different registration algorithms depending on the category of the diagnostic data. Some or all of the above processes in the registration unit may be performed using, for example, a generative AI, or without a generative AI. For example, the registration unit inputs the category of the diagnostic data into the generative AI, and the generative AI applies the optimal registration algorithm.

[0088] The registration unit can estimate the user's emotions and determine registration priorities based on the estimated emotions. For example, if the user is stressed, the registration unit will prioritize registering important data. For example, if the user is relaxed, the registration unit will register detailed data. For example, if the user is in a hurry, the registration unit will prioritize data that can be registered quickly. This improves registration efficiency by determining registration priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the registration unit may be performed using a generative AI, or not using a generative AI. For example, the registration unit inputs the user's emotion data into a generative AI, and the generative AI estimates the emotions.

[0089] The registration unit can weight the registered data based on when the diagnostic data was submitted. For example, the registration unit prioritizes registering the data submitted earliest based on when the diagnostic data was submitted. For example, the registration unit weights the registered data considering when the diagnostic data was submitted. For example, the registration unit adjusts the priority of the registered data based on when the diagnostic data was submitted. This improves the efficiency of registration by weighting the registered data based on when the diagnostic data was submitted. Some or all of the above processing in the registration unit may be performed using a generation AI, for example, or without a generation AI. For example, the registration unit inputs the submission dates of the diagnostic data into the generation AI, and the generation AI weights the registered data.

[0090] The registration unit can improve the accuracy of registration by referring to relevant literature for the diagnostic data during registration. For example, the registration unit improves the accuracy of registration by referring to relevant literature for the diagnostic data. For example, the registration unit selects the optimal registration method based on relevant literature for the diagnostic data. For example, the registration unit verifies the accuracy of the registration data by referring to relevant literature for the diagnostic data. As a result, the accuracy of registration is improved by referring to relevant literature for the diagnostic data. Some or all of the above processes in the registration unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the registration unit inputs relevant literature for the diagnostic data into a generating AI, and the generating AI improves the accuracy of registration.

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

[0092] The analysis unit can adjust the analysis results when analyzing diagnostic data, taking into account the patient's lifestyle data. For example, if the patient is a smoker, the analysis can provide results that increase the risk of smoking-related diseases. Also, if the patient exercises regularly, the analysis can provide results that take into account the health benefits of exercise. Furthermore, it can also assess the risk of diet-related diseases by considering the patient's dietary data. In this way, by considering the patient's lifestyle data, more individualized analysis results can be provided.

[0093] The data collection unit can determine the type of data to collect when collecting diagnostic data, taking into account the patient's genetic information. For example, if a patient has a specific gene mutation, data to assess the risk of diseases associated with that mutation can be prioritized for collection. Furthermore, data related to diseases common in the family can be collected, taking into account the patient's family history. Additionally, data to assess the risk of diseases that may develop in the future can be collected based on the patient's genetic information. This allows for the collection of more accurate diagnostic data by considering the patient's genetic information.

[0094] The analysis unit can estimate the patient's emotions when analyzing diagnostic data and adjust the presentation method of the analysis results based on the estimated emotions. For example, if the patient is feeling anxious, the analysis results can be presented in a simple and easy-to-understand format. If the patient is relaxed, detailed analysis results can be provided. Furthermore, if the patient is in a hurry, concise analysis results that get straight to the point can be provided. In this way, by adjusting the presentation method of the analysis results according to the patient's emotions, it becomes easier for the patient to understand the results.

[0095] The data collection unit can prioritize the collection of data related to region-specific diseases by considering the patient's geographical location when collecting diagnostic data. For example, it can collect data related to infectious diseases prevalent in a particular region. It can also collect data to assess the risk of diseases related to environmental factors by considering regional environmental factors (e.g., air pollution and water quality). Furthermore, it can collect data related to medical resources by considering regional medical resources (e.g., the number of hospitals and clinics). This allows for the collection of more region-specific diagnostic data by considering the patient's geographical location.

[0096] The analysis unit can estimate the patient's emotions when analyzing diagnostic data and determine the priority of the analysis based on those emotions. For example, if the patient is stressed, important diagnostic data can be prioritized for analysis. If the patient is relaxed, a more detailed analysis can be performed. Furthermore, if the patient is in a hurry, data that can be analyzed quickly can be prioritized. This improves the efficiency of the analysis by prioritizing it according to the patient's emotions.

[0097] The data collection unit can analyze patients' social media activity and collect information related to their health status when collecting diagnostic data. For example, it can extract information about a patient's health status from the content they post on social media. It can also analyze patients' health interests and behavioral patterns based on their social media activity. Furthermore, it can identify health-related risk factors based on patients' social media activity. As a result, analyzing patients' social media activity allows for the collection of more comprehensive diagnostic data.

[0098] The analysis unit can estimate the patient's emotions when analyzing diagnostic data and adjust the level of detail of the analysis based on the estimated emotions. For example, if the patient is anxious, it can provide simple and easy-to-understand analysis results. If the patient is relaxed, it can provide detailed analysis results. Furthermore, if the patient is in a hurry, it can provide concise analysis results that get straight to the point. By adjusting the level of detail of the analysis according to the patient's emotions, it makes it easier for the patient to understand the analysis results.

[0099] The data collection unit can prioritize the collection of relevant data when collecting diagnostic data, taking into account the patient's treatment plan. For example, if a patient is receiving a specific treatment, data related to that treatment can be prioritized. It can also collect data to evaluate the progress of treatment based on the patient's treatment plan. Furthermore, it can collect data to evaluate the effectiveness of treatment, taking the patient's treatment plan into consideration. This allows for the collection of more effective diagnostic data by considering the patient's treatment plan.

[0100] The analysis unit can estimate the patient's emotions when analyzing diagnostic data and adjust the presentation of the analysis based on the estimated emotions. For example, if the patient is tense, it can provide simple and easy-to-understand analysis results. If the patient is relaxed, it can provide detailed analysis results. Furthermore, if the patient is in a hurry, it can provide concise analysis results that get straight to the point. By adjusting the presentation of the analysis according to the patient's emotions, it makes the analysis results easier for the patient to understand.

[0101] The data collection unit can analyze a patient's past medical records and select the optimal data collection method when collecting diagnostic data. For example, it can select the most effective collection method based on the patient's past medical records. It can also prioritize the collection of specific diagnostic data from the patient's past medical records. Furthermore, it can customize the data collection method by analyzing the patient's past medical records. This allows for the selection of the optimal data collection method by analyzing the patient's past medical records.

[0102] The following briefly describes the processing flow for example form 2.

[0103] Step 1: The data collection unit collects diagnostic data. Diagnostic data includes image data, text data, and numerical data. The data collection unit collects data such as patient medical records and test results. For example, the data collection unit can acquire data from electronic medical record systems or scan patient medical records and save them as image data. It can also convert image data into text data using OCR technology. Step 2: The analysis unit analyzes the data collected by the collection unit and assigns appropriate medical codes. The analysis unit analyzes the data using natural language processing technology and machine learning algorithms. For example, it may assign the ICD code J20.9 to bronchitis diagnostic data or the CPT code 93453 to cardiac catheterization data. It can also assign the ATC code N02BE01 to drug data. Processing in the analysis unit may also be performed using generative AI. Step 3: The registration unit registers the medical codes assigned by the analysis unit into the database. The registration unit can register medical codes into relational databases or NoSQL databases. The registration unit registers medical codes into the database while checking data integrity. Processing in the registration unit may also be performed using generation AI.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0106] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] Each of the multiple elements described above, including the data collection unit, analysis unit, and registration unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects patient medical records and test results using the camera 42 and communication I / F 44 of the smart device 14. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, which analyzes the data using natural language processing technology and machine learning algorithms and assigns appropriate medical codes. The registration unit is implemented in the identification processing unit 290 of the data processing unit 12, which registers the assigned medical codes in the database 24. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0108] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0109] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0111] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0112] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0114] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0115] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0116] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0117] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0118] In the smart glasses 214, 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0122] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed 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 necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0123] Each of the multiple elements described above, including the data collection unit, analysis unit, and registration unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects patient medical records and test results using the camera 42 and communication I / F 44 of the smart glasses 214. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, which analyzes the data using natural language processing technology and machine learning algorithms and assigns appropriate medical codes. The registration unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, which registers the assigned medical codes in the database 24. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0124] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0125] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0126] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0127] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0128] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0130] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0131] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0132] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0133] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0134] In the headset terminal 314, 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 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0136] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0138] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0139] Each of the multiple elements described above, including the data collection unit, analysis unit, and registration unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects patient medical records and test results using the camera 42 and communication I / F 44 of the headset terminal 314. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, which analyzes the data using natural language processing technology and machine learning algorithms and assigns appropriate medical codes. The registration unit is implemented in the identification processing unit 290 of the data processing unit 12, which registers the assigned medical codes in the database 24. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0140] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0141] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0142] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0143] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0144] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0146] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0147] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0148] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0149] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed 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 necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements described above, including the data collection unit, analysis unit, and registration unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects patient medical records and test results using the camera 42 and communication I / F 44 of the robot 414. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, which analyzes the data using natural language processing technology and machine learning algorithms and assigns appropriate medical codes. The registration unit is implemented in the identification processing unit 290 of the data processing unit 12, which registers the assigned medical codes in the database 24. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0157] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0158] Figure 9 shows the emotion map 400, in 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 the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0159] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0160] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0161] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0162] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it 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 emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0163] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0164] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0165] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0166] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0167] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0168] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0169] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0170] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0171] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0172] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0173] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0174] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0175] (Note 1) A data collection unit that collects diagnostic data, An analysis unit analyzes the data collected by the aforementioned collection unit and assigns an appropriate medical code, The system includes a registration unit that registers the medical code assigned by the analysis unit into a database. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect data such as patient medical records and test results. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed and the appropriate medical code is assigned. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned registration unit is Register the assigned medical code in the database. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Continuously learn and improve your decision-making based on the latest medical information. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, Assign ICD code J20.9 to diagnostic data for bronchitis. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, Assign CPT code 93453 to cardiac catheterization data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, Assign the ATC code N02BE01 to the drug data. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of diagnostic data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Analyze the patient's past medical records and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting diagnostic data, filtering is performed based on the patient's current medical condition and treatment plan. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and determines the priority of diagnostic data to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting diagnostic data, the collection of highly relevant data is prioritized, taking into account the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When collecting diagnostic data, analyze patients' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the diagnostic data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the diagnostic data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the diagnostic data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the diagnostic data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned registration unit is The system estimates the user's emotions and adjusts the registration process based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned registration unit is During registration, the system will refer to past registration data to select the most suitable registration method. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned registration unit is During registration, different registration algorithms are applied depending on the category of the diagnostic data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned registration unit is The system estimates user sentiment and determines registration priority based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned registration unit is During registration, registration data is weighted based on when the diagnostic data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned registration unit is During registration, we improve the accuracy of registration by referring to relevant literature on diagnostic data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects diagnostic data, An analysis unit analyzes the data collected by the aforementioned collection unit and assigns an appropriate medical code, The system includes a registration unit that registers the medical code assigned by the analysis unit into a database. A system characterized by the following features.

2. The aforementioned collection unit is Collect data such as patient medical records and test results. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed and the appropriate medical code is assigned. The system according to feature 1.

4. The aforementioned registration unit is Register the assigned medical code in the database. The system according to feature 1.

5. The aforementioned analysis unit, Continuously learn and improve your decision-making based on the latest medical information. The system according to feature 1.

6. The aforementioned analysis unit, Assign ICD code J20.9 to diagnostic data for bronchitis. The system according to feature 1.

7. The aforementioned analysis unit, Assign CPT code 93453 to cardiac catheterization data. The system according to feature 1.

8. The aforementioned analysis unit, Assign the ATC code N02BE01 to the drug data. The system according to feature 1.

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