system

Abstract

The system according to this embodiment aims to automate the input, checking, and correction of medical claims, and to propose the optimal treatment plan during medical treatment. [Solution] The system according to the embodiment comprises an input unit, a checking unit, a correction unit, an analysis unit, and a proposal unit. The input unit inputs the medical claim information. The checking unit checks the medical claim information entered by the input unit. The correction unit corrects the medical claim information checked by the checking unit. The analysis unit analyzes the data during medical treatment. The proposal unit proposes an appropriate treatment plan based on the data analyzed by the analysis unit.

Description

Technical Field

[0006] , , ,

[0005] , ,

[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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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, there is a problem that the input, check, and correction operations of receipts are time-consuming and the burden on medical staff is large.

[0005] The system according to the embodiment aims to automate the input, check, and correction of receipts and propose an optimal treatment plan during medical treatment.

Means for Solving the Problems

[0007] The system according to this embodiment can automate the input, checking, and correction of medical claims, and can propose the optimal treatment plan during medical treatment. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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) The medical office support system according to an embodiment of the present invention is a system that uses an autonomous AI agent to reduce the burden of medical office work and achieve both the maximization of medical fees and the optimal care for patients. This medical office support system automatically inputs, checks, and corrects medical claim forms. This significantly reduces the burden on medical office staff. In addition, it analyzes data in real time during consultations and proposes the optimal treatment plan to the doctor. This achieves both the maximization of medical fees and the optimal care for patients. For example, in the medical office support system, an autonomous AI agent inputs medical claim forms. When medical office staff input medical information, the AI ​​agent automatically inputs that information into the claim form. For example, when inputting information on medical treatment details or prescriptions, the AI ​​agent analyzes the information and inputs it into the claim form in the appropriate format. Next, the AI ​​agent checks the claim form. It checks in real time whether there are any errors in the entered claim form information and instructs corrections if there are any errors. For example, it checks whether the medical treatment details and prescription information match and whether it is within the scope of insurance coverage. Furthermore, the AI ​​agent corrects the claim form. If an error is detected, the AI ​​agent automatically corrects it. For example, if the medical treatment details and prescription information do not match, the AI ​​agent makes appropriate corrections to reflect the correct information in the medical claim. It also analyzes data in real time during the consultation and proposes the optimal treatment plan to the doctor. Based on the medical information, the AI ​​agent analyzes the data and proposes the optimal treatment plan. For example, based on the patient's symptoms and past medical history, it proposes the optimal treatment method and medication prescription. This system significantly reduces the burden on medical office staff while simultaneously maximizing medical reimbursement and providing optimal patient care. For example, automating the input, checking, and correction of medical claims reduces the working time of medical office staff and improves operational efficiency. Furthermore, proposing the optimal treatment plan during the consultation supports the doctor's decision-making and improves the quality of patient care. In this way, the medical office support system reduces the burden on medical office staff and enables both maximizing medical reimbursement and providing optimal patient care.

[0029] The medical office support system according to this embodiment comprises an input unit, a checking unit, a correction unit, an analysis unit, and a proposal unit. The input unit performs the input of medical claims. For example, when a medical office worker inputs medical information, the input unit automatically inputs that information into the medical claims form. For example, the input unit analyzes the information of medical treatment and prescriptions and inputs it into the medical claims form in an appropriate format. The input unit can, for example, analyze the medical treatment and input it into the medical claims form in an appropriate format. The input unit can also analyze prescription information and input it into the medical claims form in an appropriate format. Furthermore, the input unit can integrate the information of medical treatment and prescriptions and input it into the medical claims form in an appropriate format. The checking unit checks the medical claims information entered by the input unit. For example, the checking unit checks in real time whether there are any errors in the entered medical claims information. For example, the checking unit checks whether the information of medical treatment and prescriptions match and whether it is within the scope of insurance coverage. For example, the checking unit can check whether the information of medical treatment and prescriptions match. The checking unit can also check whether the information is within the scope of insurance coverage. Furthermore, the checking unit can also check whether the medical treatment details and prescription information match and whether it is within the scope of insurance coverage. The correction unit corrects the claim information checked by the checking unit. The correction unit automatically corrects, for example, incorrect inputs. The correction unit makes appropriate corrections, for example, if the medical treatment details and prescription information do not match, and reflects the correct information in the claim. The correction unit can make appropriate corrections, for example, if the medical treatment details and prescription information do not match. Furthermore, the correction unit can also reflect the correct information in the claim. Furthermore, the correction unit can make appropriate corrections, for example, if the medical treatment details and prescription information do not match, and reflect the correct information in the claim. The analysis unit analyzes data during medical treatment. The analysis unit, for example, analyzes data based on medical information and proposes the optimal treatment plan. The analysis unit proposes the optimal treatment method and prescription of medication, for example, based on the patient's symptoms and past medical history. The analysis unit can analyze the patient's symptoms and propose the optimal treatment method. Furthermore, the analysis department can propose the most suitable treatment method based on past medical history.Furthermore, the analysis unit can integrate the patient's symptoms and past medical history to propose the optimal treatment method. The proposal unit proposes the optimal treatment plan based on the data analyzed by the analysis unit. For example, the proposal unit proposes the optimal treatment plan based on medical information. For example, the proposal unit proposes the optimal treatment method and medication prescription based on the patient's symptoms and past medical history. For example, the proposal unit can analyze the patient's symptoms and propose the optimal treatment method. The proposal unit can also propose the optimal treatment method based on past medical history. Furthermore, the proposal unit can integrate the patient's symptoms and past medical history to propose the optimal treatment method. As a result, the medical office support system according to this embodiment can reduce the burden on medical office staff and achieve both the maximization of medical fees and optimal care for patients.

[0030] The input unit handles the input of medical claim forms. For example, when a medical office staff member enters medical information, the input unit automatically inputs that information into the claim form. Specifically, it analyzes information on medical treatment and prescriptions and inputs it into the claim form in the appropriate format. The analysis of medical treatment includes a process that uses natural language processing technology to extract necessary information from doctors' records and medical notes and converts it into a standardized code. For example, it extracts disease names and medical procedures from medical treatment records and inputs them into a format suitable for the claim form. When analyzing prescription information, it accurately extracts information such as drug names, dosages, and administration periods and reflects them in the claim form. Furthermore, the input unit can integrate medical treatment and prescription information and input it into the claim form while checking for inconsistencies. This significantly reduces the workload for medical office staff and prevents input errors. The input unit can also use speech recognition technology to transcribe medical treatment information dictated by doctors into text in real time and input it into the claim form. This reduces the burden on doctors and improves the efficiency of medical treatment. Furthermore, the input unit can be linked with the electronic medical record system to automatically acquire medical information and reflect it in the claims. This ensures consistency in medical information and prevents duplicate data entry.

[0031] The checking unit verifies the claim information entered by the input unit. For example, the checking unit checks in real time whether there are any errors in the entered claim information. Specifically, it checks whether the medical treatment details and prescription information match and whether it is within the scope of insurance coverage. When checking whether the medical treatment details and prescription information match, it verifies whether the medical treatment and prescribed medication correspond appropriately. For example, if a specific medication is prescribed for a specific medical treatment, it checks whether the combination is appropriate. Also, when checking whether it is within the scope of insurance coverage, it verifies whether the medical treatment and medication are covered by insurance based on the regulations of the insurance system. In this way, the checking unit can ensure the accuracy of the claim information and prevent fraudulent and erroneous claims. Furthermore, the checking unit verifies whether the contents of the claim are appropriate based on the medical treatment details and prescription information. For example, if the record of the medical treatment is incomplete or the prescription information is insufficient, it can issue a warning and prompt correction. The checking unit can use AI to learn from past data and patterns and perform more advanced checks. This allows the checking unit to improve the quality of medical claim information and streamline medical administration.

[0032] The correction unit corrects the claim information checked by the checking unit. For example, if an input error is detected, the correction unit automatically makes the correction. Specifically, if the medical treatment details and prescription information do not match, it makes the appropriate correction and reflects the correct information in the claim. For example, if the prescribed medication is incorrect for the medical treatment, the correction unit suggests an appropriate medication and corrects the claim information. Furthermore, if the medical treatment or medication is outside the scope of insurance coverage, it can suggest an alternative within the scope of insurance coverage and correct the claim information. The correction unit uses AI to learn past correction history and patterns, enabling it to make corrections more accurately and efficiently. This allows the correction unit to ensure the accuracy of claim information and improve the efficiency of medical administration. In addition, the correction unit also provides support functions for when medical administrators make corrections manually. For example, it highlights the areas that need correction and suggests corrections, reducing the burden on medical administrators. The correction unit also ensures traceability by recording the correction history so that it can be reviewed later. This allows the correction unit to improve the quality of medical claim information and enhance the reliability of medical administration.

[0033] The analysis department analyzes data during consultations. For example, it analyzes data based on medical information and proposes the optimal treatment plan. Specifically, it proposes the optimal treatment method and medication prescription based on the patient's symptoms and past medical history. The analysis department can use AI to analyze the patient's symptoms and propose the optimal treatment method. For example, when a patient's symptoms are entered, the AI ​​proposes the optimal treatment method based on past data and medical knowledge. It can also propose treatment methods that were effective for patients with similar symptoms based on past medical history. Furthermore, the analysis department can integrate the patient's symptoms and past medical history to propose the optimal treatment method. In this way, the analysis department can support doctors' consultations and provide patients with the best possible treatment. The analysis department can analyze data in real time and make immediate suggestions during consultations. This improves the efficiency of consultations and reduces patient waiting times. Furthermore, the analysis department also analyzes data after consultations to evaluate the effectiveness of treatment and use this information to improve future consultations. In this way, the analysis department can support continuous improvement of treatment and support patients' health management.

[0034] The Proposal Department proposes the optimal treatment plan based on data analyzed by the Analysis Department. Specifically, it proposes the optimal treatment plan based on medical information. For example, the Proposal Department proposes the optimal treatment method and medication prescription based on the patient's symptoms and past medical history. The Proposal Department can use AI to analyze the patient's symptoms and propose the optimal treatment method. For example, when the patient's symptoms are entered, the AI ​​proposes the optimal treatment method based on past data and medical knowledge. It can also propose treatment methods that have been effective for patients with similar symptoms based on past medical history. Furthermore, the Proposal Department can integrate the patient's symptoms and past medical history to propose the optimal treatment method. In this way, the Proposal Department can support physicians' consultations and provide patients with the optimal treatment. The Proposal Department can analyze data in real time and make immediate suggestions during consultations. This improves the efficiency of consultations and reduces patient waiting times. Furthermore, the Proposal Department also analyzes data after consultations to evaluate the effectiveness of treatment and use this information to improve future consultations. In this way, the Proposal Department can support continuous improvement of treatment and support patients' health management. The Proposal Department can also propose treatment plans tailored to the patient's lifestyle and individual needs. For example, the system considers the patient's lifestyle and allergy information to propose the most suitable treatment method. This allows the system to provide more personalized care to patients and maximize the effectiveness of treatment.

[0035] The detection unit can detect incorrect entries in medical claim forms. The detection unit checks, for example, in real time whether there are any errors in the entered medical claim information. The detection unit checks, for example, whether the medical treatment details and prescription information match and whether it is within the scope of insurance coverage. The detection unit can check, for example, whether the medical treatment details and prescription information match. The detection unit can also check whether it is within the scope of insurance coverage. Furthermore, the detection unit can also check whether the medical treatment details and prescription information match and whether it is within the scope of insurance coverage. This improves the accuracy of medical claim forms by detecting incorrect entries. Incorrect entry detection is performed, for example, using a generation AI. The generation AI receives medical claim information as input and detects incorrect entries. The generation AI checks, for example, whether the medical treatment details and prescription information match and whether it is within the scope of insurance coverage. If the generation AI detects an incorrect entry, it outputs that information.

[0036] The data collection unit can collect medical information. The data collection unit can collect medical information such as medical records, prescription information, and test results. For example, the data collection unit can collect medical records. Furthermore, the data collection unit can also collect prescription information. In addition, the data collection unit can collect test results. This allows for improved accuracy of data analysis by collecting medical information. The collection of medical information is performed, for example, using a generative AI. The generative AI receives medical information as input and collects it. The generative AI collects medical information such as medical records, prescription information, and test results. The generative AI outputs the collected medical information.

[0037] The information provider can provide information to physicians based on proposed treatment plans. For example, the information provider can propose the optimal treatment plan based on medical information and provide that information to physicians. For example, the information provider can propose the optimal treatment method and prescription of medication based on the patient's symptoms and past medical history and provide that information to physicians. For example, the information provider can analyze the patient's symptoms, propose the optimal treatment method, and provide that information to physicians. Furthermore, the information provider can propose the optimal treatment method based on past medical history and provide that information to physicians. In addition, the information provider can integrate the patient's symptoms and past medical history to propose the optimal treatment method and provide that information to physicians. In this way, by providing information to physicians based on proposed treatment plans, it is possible to support physicians' decision-making. Information provision is carried out using, for example, generative AI. The generative AI receives medical information as input and proposes the optimal treatment plan. The generative AI provides the proposed treatment plan to physicians.

[0038] The input unit can analyze information on medical treatment and prescriptions and input it into the claim form in a predetermined format. For example, the input unit can analyze the medical treatment and input it into the claim form in an appropriate format. The input unit can also analyze information on prescriptions and input it into the claim form in an appropriate format. Furthermore, the input unit can integrate the information on medical treatment and prescriptions and input it into the claim form in an appropriate format. This improves the accuracy of input by analyzing the information on medical treatment and prescriptions and inputting it into the claim form in an appropriate format. Some or all of the above processing in the input unit may be performed using, for example, a generation AI, or without a generation AI. For example, the input unit inputs the information on medical treatment and prescriptions into a generation AI, and the generation AI inputs it into the claim form in an appropriate format.

[0039] The checking unit can check whether the medical treatment details and prescription information match and whether it is within the scope of insurance coverage. The checking unit can, for example, check whether the medical treatment details and prescription information match. The checking unit can, for example, check whether the medical treatment details and prescription information match. The checking unit can also check whether it is within the scope of insurance coverage. Furthermore, the checking unit can also check whether the medical treatment details and prescription information match and whether it is within the scope of insurance coverage. This improves the accuracy of the medical claim by checking whether the medical treatment details and prescription information match and whether it is within the scope of insurance coverage. Some or all of the above processing in the checking unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the checking unit inputs the medical treatment details and prescription information into a generating AI, and the generating AI checks whether they match and whether it is within the scope of insurance coverage.

[0040] The correction unit can make appropriate corrections if the medical treatment details and prescription information do not match, and reflect the correct information in the claim form. For example, the correction unit can make appropriate corrections if the medical treatment details and prescription information do not match. The correction unit can make appropriate corrections if the medical treatment details and prescription information do not match. The correction unit can also reflect the correct information in the claim form. Furthermore, the correction unit can make appropriate corrections if the medical treatment details and prescription information do not match, and reflect the correct information in the claim form. This improves the accuracy of the claim form by making appropriate corrections if the medical treatment details and prescription information do not match. Some or all of the above processing in the correction unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the correction unit inputs the medical treatment details and prescription information into the generation AI, and the generation AI makes appropriate corrections.

[0041] The input unit can analyze past claims entry history and select a predetermined input method. For example, the input unit may prioritize suggesting input methods (voice, text, etc.) previously used by the user. For example, the input unit may predict and suggest an input method to be used during a specific time period based on the user's past input history. For example, the input unit may automatically display as candidates information on medical treatment details and prescriptions that the user has frequently entered in the past. This allows for the selection of the optimal input method by analyzing past claims entry history, thereby improving input efficiency. Some or all of the above-described processes in the input unit may be performed using, for example, a generation AI, or without a generation AI. For example, the input unit may input past claims entry history into a generation AI, and the generation AI may select the optimal input method.

[0042] The input unit can automatically classify medical treatment details and prescription information when entering claims, and input it in a predetermined format. For example, the input unit can automatically analyze medical treatment details, classify them into appropriate categories, and input them into the claims. The input unit can also automatically analyze prescription information and input it into the claims in an appropriate format. Furthermore, the input unit can integrate medical treatment details and prescription information and input it into the claims in an appropriate format. This improves the accuracy of input by automatically classifying medical treatment details and prescription information and inputting it in an appropriate format. Some or all of the above processing in the input unit may be performed using, for example, a generation AI, or without a generation AI. For example, the input unit inputs medical treatment details and prescription information into a generation AI, which automatically classifies and inputs it in an appropriate format.

[0043] The input unit can improve the accuracy of input based on detailed information about medical treatment when entering medical claim data. For example, the input unit can analyze detailed information about medical treatment and input it into the medical claim form in an appropriate format. The input unit can also perform corrections to improve the accuracy of input based on detailed information about medical treatment. Furthermore, the input unit can refer to detailed information about medical treatment and perform checks to prevent input errors. In this way, the accuracy of input can be improved by considering detailed information about medical treatment. Some or all of the above processing in the input unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the input unit inputs detailed information about medical treatment into a generating AI, and the generating AI improves the accuracy of the input.

[0044] The input unit can improve the accuracy of input by referring to relevant literature on medical treatment when entering data into a medical claim form. For example, the input unit can refer to relevant literature on medical treatment and input the data into the medical claim form in an appropriate format. The input unit can also refer to relevant literature on medical treatment and input the data into the medical claim form in an appropriate format. Furthermore, the input unit can perform corrections to improve the accuracy of input based on relevant literature on medical treatment. In addition, the input unit can refer to relevant literature on medical treatment and perform checks to prevent input errors. As a result, the accuracy of input can be improved by referring to relevant literature on medical treatment. Some or all of the above processing in the input unit may be performed using, for example, a generation AI, or without a generation AI. For example, the input unit inputs relevant literature on medical treatment into a generation AI, and the generation AI improves the accuracy of the input.

[0045] The checking unit can improve the accuracy of its checks based on the interrelationships of the claim information. For example, the checking unit can analyze the interrelationships of the claim information and perform appropriate checks. The checking unit can also perform corrections to improve the accuracy of the checks based on the interrelationships of the claim information. Furthermore, the checking unit can refer to the interrelationships of the claim information and perform checks to prevent checking errors. In this way, the accuracy of the checks can be improved by considering the interrelationships of the claim information. Some or all of the above processing in the checking unit may be performed using a generation AI, for example, or without a generation AI. For example, the checking unit inputs the interrelationships of the claim information into a generation AI, and the generation AI improves the accuracy of the checks.

[0046] The checking unit can perform a detailed analysis of whether the medical treatment details and prescription information match during the check. For example, the checking unit can analyze the medical treatment details and prescription information and check if they match. The checking unit can also perform appropriate corrections if the medical treatment details and prescription information do not match. Furthermore, the checking unit can perform corrections to improve the accuracy of the check based on the medical treatment details and prescription information. This improves the accuracy of the check by performing a detailed analysis of whether the medical treatment details and prescription information match. Some or all of the above processing in the checking unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the checking unit inputs the medical treatment details and prescription information into a generating AI, which then performs a detailed analysis.

[0047] The checking unit can perform checks while considering the geographical distribution of the claim information. For example, the checking unit can analyze the geographical distribution of the claim information and perform appropriate checks. The checking unit can also perform corrections to improve the accuracy of the checks based on the geographical distribution of the claim information. Furthermore, the checking unit can refer to the geographical distribution of the claim information and perform checks to prevent checking errors. In this way, the accuracy of the checks can be improved by considering the geographical distribution of the claim information. Some or all of the above processing in the checking unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the checking unit inputs the geographical distribution of the claim information into a generating AI, and the generating AI performs the checks.

[0048] The checking unit can improve the accuracy of its checks by referring to relevant literature related to the claim information during the checking process. For example, the checking unit can refer to relevant literature related to the claim information and perform appropriate checks. The checking unit can also refer to relevant literature related to the claim information and perform appropriate checks. Furthermore, the checking unit can make corrections to improve the accuracy of its checks based on the relevant literature related to the claim information. In addition, the checking unit can refer to relevant literature related to the claim information and perform checks to prevent checking errors. In this way, the accuracy of the checks can be improved by referring to relevant literature related to the claim information. Some or all of the above processing in the checking unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the checking unit inputs relevant literature related to the claim information into a generating AI, and the generating AI improves the accuracy of the checks.

[0049] The correction unit can select a predetermined correction method based on the past correction history of the claim information when making corrections. For example, the correction unit can analyze the past correction history of the claim information and select the optimal correction method. The correction unit can also perform corrections to improve the accuracy of the corrections based on the past correction history of the claim information. Furthermore, the correction unit can refer to the past correction history of the claim information and perform checks to prevent correction errors. This allows the correction unit to select the optimal correction method and improve the accuracy of the corrections by referring to the past correction history of the claim information. Some or all of the above processing in the correction unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the correction unit inputs the past correction history of the claim information into the generation AI, and the generation AI selects the optimal correction method.

[0050] The correction unit can perform predetermined corrections if the medical treatment details and prescription information do not match during the correction process. For example, the correction unit can analyze the medical treatment details and prescription information and perform appropriate corrections if they do not match. The correction unit can also perform automatic corrections using AI if the medical treatment details and prescription information do not match. Furthermore, the correction unit can perform adjustments to improve the accuracy of the corrections based on the medical treatment details and prescription information. This improves the accuracy of the medical claim by performing appropriate corrections when the medical treatment details and prescription information do not match. Some or all of the above-described processes in the correction unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the correction unit inputs the medical treatment details and prescription information into the generating AI, and the generating AI performs predetermined corrections.

[0051] The correction unit can perform corrections while considering the geographical distribution of the claim information. For example, the correction unit can analyze the geographical distribution of the claim information and perform appropriate corrections. The correction unit can also perform adjustments to improve the accuracy of the corrections based on the geographical distribution of the claim information. Furthermore, the correction unit can refer to the geographical distribution of the claim information and perform checks to prevent correction errors. In this way, the accuracy of the corrections can be improved by considering the geographical distribution of the claim information. Some or all of the above processing in the correction unit may be performed using a generation AI, for example, or without a generation AI. For example, the correction unit inputs the geographical distribution of the claim information into a generation AI, and the generation AI performs the corrections.

[0052] The correction unit can improve the accuracy of corrections by referring to relevant literature on the claim information during the correction process. For example, the correction unit can refer to relevant literature on the claim information and make appropriate corrections. The correction unit can also refer to relevant literature on the claim information and make appropriate corrections. Furthermore, the correction unit can perform adjustments to improve the accuracy of corrections based on the relevant literature on the claim information. In addition, the correction unit can refer to relevant literature on the claim information and perform checks to prevent correction errors. This allows for improved accuracy of corrections by referring to relevant literature on the claim information. Some or all of the above processing in the correction unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the correction unit inputs relevant literature on the claim information into the generating AI, and the generating AI improves the accuracy of the corrections.

[0053] The analysis unit can select a predetermined analysis method based on the past analysis history of medical information during the analysis. For example, the analysis unit can analyze the past analysis history of medical information and select the optimal analysis method. The analysis unit can also perform corrections to improve the accuracy of the analysis based on the past analysis history of medical information. Furthermore, the analysis unit can refer to the past analysis history of medical information and perform checks to prevent analysis errors. In this way, by referring to the past analysis history of medical information, the optimal analysis method can be selected and the accuracy of the analysis can be improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit inputs the past analysis history of medical information into a generation AI, and the generation AI selects the optimal analysis method.

[0054] The analysis unit can improve the accuracy of its analysis based on the interrelationships of medical information. For example, the analysis unit can analyze the interrelationships of medical information and perform an appropriate analysis. The analysis unit can also perform corrections to improve the accuracy of the analysis based on the interrelationships of medical information. Furthermore, the analysis unit can refer to the interrelationships of medical information and perform checks to prevent analysis errors. In this way, the accuracy of the analysis can be improved by considering the interrelationships of medical information. 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 interrelationships of medical information into a generating AI, and the generating AI improves the accuracy of the analysis.

[0055] The analysis unit can perform analysis based on the geographical distribution of medical information. For example, the analysis unit can analyze the geographical distribution of medical information and perform an appropriate analysis. The analysis unit can also perform corrections to improve the accuracy of the analysis based on the geographical distribution of medical information. Furthermore, the analysis unit can refer to the geographical distribution of medical information and perform checks to prevent analysis errors. In this way, the accuracy of the analysis can be improved by considering the geographical distribution of medical information. 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 geographical distribution of medical information into a generative AI, and the generative AI performs the analysis.

[0056] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on medical information during the analysis process. For example, the analysis unit can refer to relevant literature on medical information and perform an appropriate analysis. The analysis unit can also refer to relevant literature on medical information and perform an appropriate analysis. Furthermore, the analysis unit can make corrections to improve the accuracy of the analysis based on the relevant literature on medical information. In addition, the analysis unit can refer to relevant literature on medical information and perform checks to prevent analysis errors. In this way, the accuracy of the analysis can be improved by referring to relevant literature on medical information. Some or all of the above processes in the analysis unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the analysis unit inputs relevant literature on medical information into a generating AI, and the generating AI improves the accuracy of the analysis.

[0057] The proposal unit can select a predetermined proposal method based on the past proposal history of the treatment plan when making a proposal. For example, the proposal unit can analyze the past proposal history of the treatment plan and select the optimal proposal method. The proposal unit can also make corrections to improve the accuracy of the proposal based on the past proposal history of the treatment plan. Furthermore, the proposal unit can refer to the past proposal history of the treatment plan and perform checks to prevent proposal errors. In this way, by referring to the past proposal history of the treatment plan, the optimal proposal method can be selected and the accuracy of the proposal can be improved. Some or all of the above processing in the proposal unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the proposal unit inputs the past proposal history of the treatment plan into a generation AI, and the generation AI selects the optimal proposal method.

[0058] The suggestion unit can propose a predetermined treatment method based on the patient's symptoms and past medical history. For example, the suggestion unit can analyze the patient's symptoms and propose the optimal treatment method. The suggestion unit can also propose the optimal treatment method based on the patient's past medical history. Furthermore, the suggestion unit can integrate the patient's symptoms and past medical history to propose the optimal treatment method. This improves the accuracy of treatment by proposing the optimal treatment method based on the patient's symptoms and past medical history. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit inputs the patient's symptoms and past medical history into a generative AI, and the generative AI proposes a predetermined treatment method.

[0059] The proposal unit can make proposals based on the geographical distribution of treatment plans. For example, the proposal unit can analyze the geographical distribution of treatment plans and make appropriate proposals. The proposal unit can also make corrections to improve the accuracy of proposals based on the geographical distribution of treatment plans. Furthermore, the proposal unit can refer to the geographical distribution of treatment plans and perform checks to prevent proposal errors. This improves the accuracy of proposals by considering the geographical distribution of treatment plans. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit inputs the geographical distribution of treatment plans into a generative AI, and the generative AI makes proposals.

[0060] The proposal unit can improve the accuracy of its proposals by referring to relevant literature on treatment plans during the proposal process. For example, the proposal unit can refer to relevant literature on treatment plans and make appropriate proposals. The proposal unit can also refer to relevant literature on treatment plans and make appropriate proposals. Furthermore, the proposal unit can make corrections to improve the accuracy of its proposals based on the relevant literature on treatment plans. In addition, the proposal unit can refer to relevant literature on treatment plans and perform checks to prevent proposal errors. This allows for improved accuracy of proposals by referring to relevant literature on treatment plans. Some or all of the above processing in the proposal unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the proposal unit inputs relevant literature on treatment plans into a generating AI, and the generating AI improves the accuracy of the proposals.

[0061] The detection unit can select a predetermined detection method based on the past history of incorrect entries in the claim information when detection occurs. For example, the detection unit can analyze the past history of incorrect entries in the claim information and select the optimal detection method. The detection unit can also perform corrections to improve the accuracy of detection based on the past history of incorrect entries in the claim information. Furthermore, the detection unit can refer to the past history of incorrect entries in the claim information and perform checks to prevent detection errors. This allows the detection unit to select the optimal detection method and improve the accuracy of detection by referring to the past history of incorrect entries in the claim information. Some or all of the above processing in the detection unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the detection unit inputs the past history of incorrect entries in the claim information into the generating AI, and the generating AI selects the optimal detection method.

[0062] The detection unit can detect incorrect inputs based on the geographical distribution of the claim information during detection. The detection unit can, for example, analyze the geographical distribution of the claim information and detect appropriate incorrect inputs. The detection unit can, for example, analyze the geographical distribution of the claim information and detect appropriate incorrect inputs. The detection unit can also perform corrections to improve the accuracy of detection based on the geographical distribution of the claim information. Furthermore, the detection unit can refer to the geographical distribution of the claim information and perform checks to prevent detection errors. In this way, the accuracy of incorrect input detection can be improved by considering the geographical distribution of the claim information. Some or all of the above processing in the detection unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the detection unit inputs the geographical distribution of the claim information into the generating AI, and the generating AI detects incorrect inputs.

[0063] The data collection unit can select a predetermined collection method based on the past collection history of medical information at the time of collection. For example, the data collection unit can analyze the past collection history of medical information and select the optimal collection method. The data collection unit can also perform corrections to improve the accuracy of collection based on the past collection history of medical information. Furthermore, the data collection unit can refer to the past collection history of medical information and perform checks to prevent collection errors. This allows the data collection unit to select the optimal collection method and improve the accuracy of collection by referring to the past collection history of medical 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 past collection history of medical information into a generating AI, and the generating AI selects the optimal collection method.

[0064] The data collection unit can perform data collection based on the geographical distribution of medical information. For example, the data collection unit can analyze the geographical distribution of medical information and perform appropriate data collection. The data collection unit can also perform corrections to improve the accuracy of data collection based on the geographical distribution of medical information. Furthermore, the data collection unit can refer to the geographical distribution of medical information and perform checks to prevent data collection errors. This improves the accuracy of data collection by considering the geographical distribution of medical information. 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 the geographical distribution of medical information into a generating AI, and the generating AI performs the data collection.

[0065] The delivery unit can select a predetermined delivery method based on the past delivery history of the treatment plan at the time of delivery. For example, the delivery unit can analyze the past delivery history of the treatment plan and select the optimal delivery method. The delivery unit can also perform corrections to improve the accuracy of delivery based on the past delivery history of the treatment plan. Furthermore, the delivery unit can refer to the past delivery history of the treatment plan and perform checks to prevent delivery errors. In this way, by referring to the past delivery history of the treatment plan, the optimal delivery method can be selected and the accuracy of delivery can be improved. Some or all of the above processing in the delivery unit may be performed using, for example, a generation AI, or without a generation AI. For example, the delivery unit inputs the past delivery history of the treatment plan into a generation AI, and the generation AI selects the optimal delivery method.

[0066] The information provision unit can provide information based on the geographical distribution of treatment plans at the time of provision. For example, the information provision unit can analyze the geographical distribution of treatment plans and provide appropriate information. The information provision unit can also perform corrections to improve the accuracy of provision based on the geographical distribution of treatment plans. Furthermore, the information provision unit can refer to the geographical distribution of treatment plans and perform checks to prevent provision errors. In this way, the accuracy of information provision can be improved by considering the geographical distribution of treatment plans. Some or all of the above processing in the information provision unit may be performed using, for example, a generation AI, or without a generation AI. For example, the information provision unit inputs the geographical distribution of treatment plans into a generation AI, and the generation AI provides the information.

[0067] The information provider can improve the accuracy of information provision based on relevant literature for the treatment plan at the time of provision. For example, the information provider can refer to relevant literature for the treatment plan and provide appropriate information. The information provider can also refer to relevant literature for the treatment plan and provide appropriate information. Furthermore, the information provider can make corrections to improve the accuracy of information provision based on relevant literature for the treatment plan. In addition, the information provider can refer to relevant literature for the treatment plan and perform checks to prevent provision errors. This makes it possible to improve the accuracy of information provision by referring to relevant literature for the treatment plan. Some or all of the above processing in the information provider may be performed using, for example, a generating AI, or without using a generating AI. For example, the information provider inputs relevant literature for the treatment plan into a generating AI, and the generating AI improves the accuracy of information provision.

[0068] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0069] A medical administration support system can be equipped with a data collection unit that collects patient medical information. This unit can automatically collect, for example, patient medical records, prescription information, and test results. The unit can retrieve medical records from electronic medical records and prescription information from pharmacy systems. Furthermore, it can retrieve test results from testing laboratories and integrate this information into a database. This eliminates the need for medical administrators to manually collect information and improves data consistency and accuracy.

[0070] Medical administration support systems can include a visualization unit that visualizes the results of analysis of medical information. For example, the visualization unit can display medical information as graphs and charts, making it intuitively understandable to doctors. The visualization unit can also display the progression of a patient's symptoms and the effectiveness of treatment over time, providing doctors with reference when deciding on treatment plans. Furthermore, the visualization unit can compare data from multiple patients to identify common trends and patterns. This makes it easier for doctors to make data-driven decisions, thereby improving the quality of medical care.

[0071] A medical administration support system can include a sharing section that allows for real-time sharing of patient medical information. For example, this sharing section can store medical information on the cloud, allowing doctors and medical staff to access it when needed. The sharing section can share medical information using secure communication methods, preventing information leaks. Furthermore, with the patient's consent, the sharing section can share information with other medical institutions to facilitate collaborative care. This streamlines information sharing between medical institutions and ensures consistent patient care.

[0072] A medical administration support system can be equipped with a treatment plan generation unit that automatically generates treatment plans based on patient medical information. For example, the treatment plan generation unit analyzes the patient's symptoms and past medical history to propose the optimal treatment plan. The unit suggests medication prescriptions and treatment methods tailored to the patient's symptoms, which the physician can then use as a reference. Furthermore, the treatment plan generation unit can present multiple treatment plans, allowing the physician to choose the most suitable one. This supports the physician's decision-making regarding treatment policies and enables the provision of the most appropriate treatment for the patient.

[0073] A medical office support system can be equipped with a voice input unit that uses speech recognition technology to input medical information. For example, the voice input unit can recognize in real time what a doctor says during a consultation and input it as medical information. The voice input unit can input information about consultations and prescriptions by voice and save it as text data. Furthermore, the voice input unit can learn the doctor's speech patterns to improve the accuracy of speech recognition. This allows doctors to input medical information without using their hands, improving the efficiency of medical practice.

[0074] A medical office support system can include a template input section that allows for the input of medical information using templates. For example, when entering information such as medical treatment details or prescriptions, the template input section uses pre-prepared templates. The template input section allows for the creation of medical claims simply by selecting a template appropriate to the medical treatment and entering the necessary information. Furthermore, the template input section allows for the customization of templates, enabling them to be modified to meet the needs of physicians. This simplifies the input of medical information and reduces input errors.

[0075] The following briefly describes the processing flow for example form 1.

[0076] Step 1: The input unit performs the input of medical claim information. For example, when a medical office staff member enters medical information, the input unit automatically inputs that information into the medical claim form. The input unit can analyze the medical treatment details and prescription information and input it into the medical claim form in the appropriate format. It can also integrate the medical treatment details and prescription information and input it into the medical claim form in the appropriate format. Step 2: The checking unit checks the claim information entered by the input unit. For example, it checks in real time whether there are any errors in the entered claim information. It can check whether the medical treatment details and prescription information match and whether it is within the scope of insurance coverage. Step 3: The correction unit corrects the claim information checked by the checking unit. For example, if an incorrect entry is detected, it will be corrected automatically. If the medical treatment details and prescription information do not match, it will make appropriate corrections and reflect the correct information in the claim. Step 4: The analysis department analyzes data during consultations. For example, they analyze data based on medical information and propose the optimal treatment plan. Based on the patient's symptoms and past medical history, they can suggest the most suitable treatment methods and medication prescriptions. Step 5: The proposal department proposes the optimal treatment plan based on the data analyzed by the analysis department. For example, it proposes the optimal treatment plan based on medical information. Based on the patient's symptoms and past medical history, it can propose the most suitable treatment method and medication prescription.

[0077] (Example of form 2) The medical office support system according to an embodiment of the present invention is a system that uses an autonomous AI agent to reduce the burden of medical office work and achieve both the maximization of medical fees and the optimal care for patients. This medical office support system automatically inputs, checks, and corrects medical claim forms. This significantly reduces the burden on medical office staff. In addition, it analyzes data in real time during consultations and proposes the optimal treatment plan to the doctor. This achieves both the maximization of medical fees and the optimal care for patients. For example, in the medical office support system, an autonomous AI agent inputs medical claim forms. When medical office staff input medical information, the AI ​​agent automatically inputs that information into the claim form. For example, when inputting information on medical treatment details or prescriptions, the AI ​​agent analyzes the information and inputs it into the claim form in the appropriate format. Next, the AI ​​agent checks the claim form. It checks in real time whether there are any errors in the entered claim form information and instructs corrections if there are any errors. For example, it checks whether the medical treatment details and prescription information match and whether it is within the scope of insurance coverage. Furthermore, the AI ​​agent corrects the claim form. If an error is detected, the AI ​​agent automatically corrects it. For example, if the medical treatment details and prescription information do not match, the AI ​​agent makes appropriate corrections to reflect the correct information in the medical claim. It also analyzes data in real time during the consultation and proposes the optimal treatment plan to the doctor. Based on the medical information, the AI ​​agent analyzes the data and proposes the optimal treatment plan. For example, based on the patient's symptoms and past medical history, it proposes the optimal treatment method and medication prescription. This system significantly reduces the burden on medical office staff while simultaneously maximizing medical reimbursement and providing optimal patient care. For example, automating the input, checking, and correction of medical claims reduces the working time of medical office staff and improves operational efficiency. Furthermore, proposing the optimal treatment plan during the consultation supports the doctor's decision-making and improves the quality of patient care. In this way, the medical office support system reduces the burden on medical office staff and enables both maximizing medical reimbursement and providing optimal patient care.

[0078] The medical office support system according to this embodiment comprises an input unit, a checking unit, a correction unit, an analysis unit, and a proposal unit. The input unit performs the input of medical claims. For example, when a medical office worker inputs medical information, the input unit automatically inputs that information into the medical claims form. For example, the input unit analyzes the information of medical treatment and prescriptions and inputs it into the medical claims form in an appropriate format. The input unit can, for example, analyze the medical treatment and input it into the medical claims form in an appropriate format. The input unit can also analyze prescription information and input it into the medical claims form in an appropriate format. Furthermore, the input unit can integrate the information of medical treatment and prescriptions and input it into the medical claims form in an appropriate format. The checking unit checks the medical claims information entered by the input unit. For example, the checking unit checks in real time whether there are any errors in the entered medical claims information. For example, the checking unit checks whether the information of medical treatment and prescriptions match and whether it is within the scope of insurance coverage. For example, the checking unit can check whether the information of medical treatment and prescriptions match. The checking unit can also check whether the information is within the scope of insurance coverage. Furthermore, the checking unit can also check whether the medical treatment details and prescription information match and whether it is within the scope of insurance coverage. The correction unit corrects the claim information checked by the checking unit. The correction unit automatically corrects, for example, incorrect inputs. The correction unit makes appropriate corrections, for example, if the medical treatment details and prescription information do not match, and reflects the correct information in the claim. The correction unit can make appropriate corrections, for example, if the medical treatment details and prescription information do not match. Furthermore, the correction unit can also reflect the correct information in the claim. Furthermore, the correction unit can make appropriate corrections, for example, if the medical treatment details and prescription information do not match, and reflect the correct information in the claim. The analysis unit analyzes data during medical treatment. The analysis unit, for example, analyzes data based on medical information and proposes the optimal treatment plan. The analysis unit proposes the optimal treatment method and prescription of medication, for example, based on the patient's symptoms and past medical history. The analysis unit can analyze the patient's symptoms and propose the optimal treatment method. Furthermore, the analysis department can propose the most suitable treatment method based on past medical history.Furthermore, the analysis unit can integrate the patient's symptoms and past medical history to propose the optimal treatment method. The proposal unit proposes the optimal treatment plan based on the data analyzed by the analysis unit. For example, the proposal unit proposes the optimal treatment plan based on medical information. For example, the proposal unit proposes the optimal treatment method and medication prescription based on the patient's symptoms and past medical history. For example, the proposal unit can analyze the patient's symptoms and propose the optimal treatment method. The proposal unit can also propose the optimal treatment method based on past medical history. Furthermore, the proposal unit can integrate the patient's symptoms and past medical history to propose the optimal treatment method. As a result, the medical office support system according to this embodiment can reduce the burden on medical office staff and achieve both the maximization of medical fees and optimal care for patients.

[0079] The input unit handles the input of medical claim forms. For example, when a medical office staff member enters medical information, the input unit automatically inputs that information into the claim form. Specifically, it analyzes information on medical treatment and prescriptions and inputs it into the claim form in the appropriate format. The analysis of medical treatment includes a process that uses natural language processing technology to extract necessary information from doctors' records and medical notes and converts it into a standardized code. For example, it extracts disease names and medical procedures from medical treatment records and inputs them into a format suitable for the claim form. When analyzing prescription information, it accurately extracts information such as drug names, dosages, and administration periods and reflects them in the claim form. Furthermore, the input unit can integrate medical treatment and prescription information and input it into the claim form while checking for inconsistencies. This significantly reduces the workload for medical office staff and prevents input errors. The input unit can also use speech recognition technology to transcribe medical treatment information dictated by doctors into text in real time and input it into the claim form. This reduces the burden on doctors and improves the efficiency of medical treatment. Furthermore, the input unit can be linked with the electronic medical record system to automatically acquire medical information and reflect it in the claims. This ensures consistency in medical information and prevents duplicate data entry.

[0080] The checking unit verifies the claim information entered by the input unit. For example, the checking unit checks in real time whether there are any errors in the entered claim information. Specifically, it checks whether the medical treatment details and prescription information match and whether it is within the scope of insurance coverage. When checking whether the medical treatment details and prescription information match, it verifies whether the medical treatment and prescribed medication correspond appropriately. For example, if a specific medication is prescribed for a specific medical treatment, it checks whether the combination is appropriate. Also, when checking whether it is within the scope of insurance coverage, it verifies whether the medical treatment and medication are covered by insurance based on the regulations of the insurance system. In this way, the checking unit can ensure the accuracy of the claim information and prevent fraudulent and erroneous claims. Furthermore, the checking unit verifies whether the contents of the claim are appropriate based on the medical treatment details and prescription information. For example, if the record of the medical treatment is incomplete or the prescription information is insufficient, it can issue a warning and prompt correction. The checking unit can use AI to learn from past data and patterns and perform more advanced checks. This allows the checking unit to improve the quality of medical claim information and streamline medical administration.

[0081] The correction unit corrects the claim information checked by the checking unit. For example, if an input error is detected, the correction unit automatically makes the correction. Specifically, if the medical treatment details and prescription information do not match, it makes the appropriate correction and reflects the correct information in the claim. For example, if the prescribed medication is incorrect for the medical treatment, the correction unit suggests an appropriate medication and corrects the claim information. Furthermore, if the medical treatment or medication is outside the scope of insurance coverage, it can suggest an alternative within the scope of insurance coverage and correct the claim information. The correction unit uses AI to learn past correction history and patterns, enabling it to make corrections more accurately and efficiently. This allows the correction unit to ensure the accuracy of claim information and improve the efficiency of medical administration. In addition, the correction unit also provides support functions for when medical administrators make corrections manually. For example, it highlights the areas that need correction and suggests corrections, reducing the burden on medical administrators. The correction unit also ensures traceability by recording the correction history so that it can be reviewed later. This allows the correction unit to improve the quality of medical claim information and enhance the reliability of medical administration.

[0082] The analysis department analyzes data during consultations. For example, it analyzes data based on medical information and proposes the optimal treatment plan. Specifically, it proposes the optimal treatment method and medication prescription based on the patient's symptoms and past medical history. The analysis department can use AI to analyze the patient's symptoms and propose the optimal treatment method. For example, when a patient's symptoms are entered, the AI ​​proposes the optimal treatment method based on past data and medical knowledge. It can also propose treatment methods that were effective for patients with similar symptoms based on past medical history. Furthermore, the analysis department can integrate the patient's symptoms and past medical history to propose the optimal treatment method. In this way, the analysis department can support doctors' consultations and provide patients with the best possible treatment. The analysis department can analyze data in real time and make immediate suggestions during consultations. This improves the efficiency of consultations and reduces patient waiting times. Furthermore, the analysis department also analyzes data after consultations to evaluate the effectiveness of treatment and use this information to improve future consultations. In this way, the analysis department can support continuous improvement of treatment and support patients' health management.

[0083] The Proposal Department proposes the optimal treatment plan based on data analyzed by the Analysis Department. Specifically, it proposes the optimal treatment plan based on medical information. For example, the Proposal Department proposes the optimal treatment method and medication prescription based on the patient's symptoms and past medical history. The Proposal Department can use AI to analyze the patient's symptoms and propose the optimal treatment method. For example, when the patient's symptoms are entered, the AI ​​proposes the optimal treatment method based on past data and medical knowledge. It can also propose treatment methods that have been effective for patients with similar symptoms based on past medical history. Furthermore, the Proposal Department can integrate the patient's symptoms and past medical history to propose the optimal treatment method. In this way, the Proposal Department can support physicians' consultations and provide patients with the optimal treatment. The Proposal Department can analyze data in real time and make immediate suggestions during consultations. This improves the efficiency of consultations and reduces patient waiting times. Furthermore, the Proposal Department also analyzes data after consultations to evaluate the effectiveness of treatment and use this information to improve future consultations. In this way, the Proposal Department can support continuous improvement of treatment and support patients' health management. The Proposal Department can also propose treatment plans tailored to the patient's lifestyle and individual needs. For example, the system considers the patient's lifestyle and allergy information to propose the most suitable treatment method. This allows the system to provide more personalized care to patients and maximize the effectiveness of treatment.

[0084] The detection unit can detect incorrect entries in medical claim forms. The detection unit checks, for example, in real time whether there are any errors in the entered medical claim information. The detection unit checks, for example, whether the medical treatment details and prescription information match and whether it is within the scope of insurance coverage. The detection unit can check, for example, whether the medical treatment details and prescription information match. The detection unit can also check whether it is within the scope of insurance coverage. Furthermore, the detection unit can also check whether the medical treatment details and prescription information match and whether it is within the scope of insurance coverage. This improves the accuracy of medical claim forms by detecting incorrect entries. Incorrect entry detection is performed, for example, using a generation AI. The generation AI receives medical claim information as input and detects incorrect entries. The generation AI checks, for example, whether the medical treatment details and prescription information match and whether it is within the scope of insurance coverage. If the generation AI detects an incorrect entry, it outputs that information.

[0085] The data collection unit can collect medical information. The data collection unit can collect medical information such as medical records, prescription information, and test results. For example, the data collection unit can collect medical records. Furthermore, the data collection unit can also collect prescription information. In addition, the data collection unit can collect test results. This allows for improved accuracy of data analysis by collecting medical information. The collection of medical information is performed, for example, using a generative AI. The generative AI receives medical information as input and collects it. The generative AI collects medical information such as medical records, prescription information, and test results. The generative AI outputs the collected medical information.

[0086] The information provider can provide information to physicians based on proposed treatment plans. For example, the information provider can propose the optimal treatment plan based on medical information and provide that information to physicians. For example, the information provider can propose the optimal treatment method and prescription of medication based on the patient's symptoms and past medical history and provide that information to physicians. For example, the information provider can analyze the patient's symptoms, propose the optimal treatment method, and provide that information to physicians. Furthermore, the information provider can propose the optimal treatment method based on past medical history and provide that information to physicians. In addition, the information provider can integrate the patient's symptoms and past medical history to propose the optimal treatment method and provide that information to physicians. In this way, by providing information to physicians based on proposed treatment plans, it is possible to support physicians' decision-making. Information provision is carried out using, for example, generative AI. The generative AI receives medical information as input and proposes the optimal treatment plan. The generative AI provides the proposed treatment plan to physicians.

[0087] The input unit can analyze information on medical treatment and prescriptions and input it into the claim form in a predetermined format. For example, the input unit can analyze the medical treatment and input it into the claim form in an appropriate format. The input unit can also analyze information on prescriptions and input it into the claim form in an appropriate format. Furthermore, the input unit can integrate the information on medical treatment and prescriptions and input it into the claim form in an appropriate format. This improves the accuracy of input by analyzing the information on medical treatment and prescriptions and inputting it into the claim form in an appropriate format. Some or all of the above processing in the input unit may be performed using, for example, a generation AI, or without a generation AI. For example, the input unit inputs the information on medical treatment and prescriptions into a generation AI, and the generation AI inputs it into the claim form in an appropriate format.

[0088] The checking unit can check whether the medical treatment details and prescription information match and whether it is within the scope of insurance coverage. The checking unit can, for example, check whether the medical treatment details and prescription information match. The checking unit can, for example, check whether the medical treatment details and prescription information match. The checking unit can also check whether it is within the scope of insurance coverage. Furthermore, the checking unit can also check whether the medical treatment details and prescription information match and whether it is within the scope of insurance coverage. This improves the accuracy of the medical claim by checking whether the medical treatment details and prescription information match and whether it is within the scope of insurance coverage. Some or all of the above processing in the checking unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the checking unit inputs the medical treatment details and prescription information into a generating AI, and the generating AI checks whether they match and whether it is within the scope of insurance coverage.

[0089] The correction unit can make appropriate corrections if the medical treatment details and prescription information do not match, and reflect the correct information in the claim form. For example, the correction unit can make appropriate corrections if the medical treatment details and prescription information do not match. The correction unit can make appropriate corrections if the medical treatment details and prescription information do not match. The correction unit can also reflect the correct information in the claim form. Furthermore, the correction unit can make appropriate corrections if the medical treatment details and prescription information do not match, and reflect the correct information in the claim form. This improves the accuracy of the claim form by making appropriate corrections if the medical treatment details and prescription information do not match. Some or all of the above processing in the correction unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the correction unit inputs the medical treatment details and prescription information into the generation AI, and the generation AI makes appropriate corrections.

[0090] The input unit can estimate the user's emotions and adjust the timing of claim entry based on the estimated emotions. For example, if the user is stressed, the input unit will delay the entry timing and wait until the user is relaxed. For example, if the user is relaxed, the input unit will speed up the entry timing to efficiently enter the claim. For example, if the user is in a hurry, the input unit will optimize the entry timing to quickly enter the claim. This reduces the user's burden by adjusting the timing of claim entry based on 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 input unit may be performed using a generative AI, or not using a generative AI. For example, the input unit inputs the user's emotion data into a generative AI, and the generative AI adjusts the entry timing.

[0091] The input unit can analyze past claims entry history and select a predetermined input method. For example, the input unit may prioritize suggesting input methods (voice, text, etc.) previously used by the user. For example, the input unit may predict and suggest an input method to be used during a specific time period based on the user's past input history. For example, the input unit may automatically display as candidates information on medical treatment details and prescriptions that the user has frequently entered in the past. This allows for the selection of the optimal input method by analyzing past claims entry history, thereby improving input efficiency. Some or all of the above-described processes in the input unit may be performed using, for example, a generation AI, or without a generation AI. For example, the input unit may input past claims entry history into a generation AI, and the generation AI may select the optimal input method.

[0092] The input unit can automatically classify medical treatment details and prescription information when entering claims, and input it in a predetermined format. For example, the input unit can automatically analyze medical treatment details, classify them into appropriate categories, and input them into the claims. The input unit can also automatically analyze prescription information and input it into the claims in an appropriate format. Furthermore, the input unit can integrate medical treatment details and prescription information and input it into the claims in an appropriate format. This improves the accuracy of input by automatically classifying medical treatment details and prescription information and inputting it in an appropriate format. Some or all of the above processing in the input unit may be performed using, for example, a generation AI, or without a generation AI. For example, the input unit inputs medical treatment details and prescription information into a generation AI, which automatically classifies and inputs it in an appropriate format.

[0093] The input unit can estimate the user's emotions and determine the priority of the claims to be entered based on the estimated emotions. For example, if the user is stressed, the input unit will postpone less important claims. For example, if the user is relaxed, the input unit will prioritize entering high-importance claims. For example, if the user is in a hurry, the input unit will prioritize entering urgent claims. In this way, important claims can be prioritized by determining the priority of claims based on 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 input unit may be performed using a generative AI, or not using a generative AI. For example, the input unit inputs user emotion data into a generative AI, and the generative AI determines the priority of the claims.

[0094] The input unit can improve the accuracy of input based on detailed information about medical treatment when entering medical claim data. For example, the input unit can analyze detailed information about medical treatment and input it into the medical claim form in an appropriate format. The input unit can also perform corrections to improve the accuracy of input based on detailed information about medical treatment. Furthermore, the input unit can refer to detailed information about medical treatment and perform checks to prevent input errors. In this way, the accuracy of input can be improved by considering detailed information about medical treatment. Some or all of the above processing in the input unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the input unit inputs detailed information about medical treatment into a generating AI, and the generating AI improves the accuracy of the input.

[0095] The input unit can improve the accuracy of input by referring to relevant literature on medical treatment when entering data into a medical claim form. For example, the input unit can refer to relevant literature on medical treatment and input the data into the medical claim form in an appropriate format. The input unit can also refer to relevant literature on medical treatment and input the data into the medical claim form in an appropriate format. Furthermore, the input unit can perform corrections to improve the accuracy of input based on relevant literature on medical treatment. In addition, the input unit can refer to relevant literature on medical treatment and perform checks to prevent input errors. As a result, the accuracy of input can be improved by referring to relevant literature on medical treatment. Some or all of the above processing in the input unit may be performed using, for example, a generation AI, or without a generation AI. For example, the input unit inputs relevant literature on medical treatment into a generation AI, and the generation AI improves the accuracy of the input.

[0096] The checking unit can estimate the user's emotions and adjust the checking criteria based on the estimated emotions. For example, if the user is stressed, the checking unit will loosen the checking criteria to reduce the user's burden. For example, if the user is relaxed, the checking unit will tighten the checking criteria to improve accuracy. For example, if the user is in a hurry, the checking unit will optimize the checking criteria to perform the check quickly. In this way, the user's burden can be reduced by adjusting the checking criteria based on 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 checking unit may be performed using a generative AI, or not using a generative AI. For example, the checking unit inputs the user's emotion data into a generative AI, and the generative AI adjusts the checking criteria.

[0097] The checking unit can improve the accuracy of its checks based on the interrelationships of the claim information. For example, the checking unit can analyze the interrelationships of the claim information and perform appropriate checks. The checking unit can also perform corrections to improve the accuracy of the checks based on the interrelationships of the claim information. Furthermore, the checking unit can refer to the interrelationships of the claim information and perform checks to prevent checking errors. In this way, the accuracy of the checks can be improved by considering the interrelationships of the claim information. Some or all of the above processing in the checking unit may be performed using a generation AI, for example, or without a generation AI. For example, the checking unit inputs the interrelationships of the claim information into a generation AI, and the generation AI improves the accuracy of the checks.

[0098] The checking unit can perform a detailed analysis of whether the medical treatment details and prescription information match during the check. For example, the checking unit can analyze the medical treatment details and prescription information and check if they match. The checking unit can also perform appropriate corrections if the medical treatment details and prescription information do not match. Furthermore, the checking unit can perform corrections to improve the accuracy of the check based on the medical treatment details and prescription information. This improves the accuracy of the check by performing a detailed analysis of whether the medical treatment details and prescription information match. Some or all of the above processing in the checking unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the checking unit inputs the medical treatment details and prescription information into a generating AI, which then performs a detailed analysis.

[0099] The checking unit can estimate the user's emotions and adjust the order in which the check results are displayed based on the estimated emotions. For example, if the user is stressed, the checking unit will postpone displaying less important check results. For example, if the user is relaxed, the checking unit will prioritize displaying more important check results. For example, if the user is in a hurry, the checking unit will prioritize displaying more urgent check results. This reduces the user's burden by adjusting the order in which the check results are displayed based on 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 checking unit may be performed using a generative AI, or not using a generative AI. For example, the checking unit inputs the user's emotion data into a generative AI, and the generative AI adjusts the order in which the check results are displayed.

[0100] The checking unit can perform checks while considering the geographical distribution of the claim information. For example, the checking unit can analyze the geographical distribution of the claim information and perform appropriate checks. The checking unit can also perform corrections to improve the accuracy of the checks based on the geographical distribution of the claim information. Furthermore, the checking unit can refer to the geographical distribution of the claim information and perform checks to prevent checking errors. In this way, the accuracy of the checks can be improved by considering the geographical distribution of the claim information. Some or all of the above processing in the checking unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the checking unit inputs the geographical distribution of the claim information into a generating AI, and the generating AI performs the checks.

[0101] The checking unit can improve the accuracy of its checks by referring to relevant literature related to the claim information during the checking process. For example, the checking unit can refer to relevant literature related to the claim information and perform appropriate checks. The checking unit can also refer to relevant literature related to the claim information and perform appropriate checks. Furthermore, the checking unit can make corrections to improve the accuracy of its checks based on the relevant literature related to the claim information. In addition, the checking unit can refer to relevant literature related to the claim information and perform checks to prevent checking errors. In this way, the accuracy of the checks can be improved by referring to relevant literature related to the claim information. Some or all of the above processing in the checking unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the checking unit inputs relevant literature related to the claim information into a generating AI, and the generating AI improves the accuracy of the checks.

[0102] The correction unit can estimate the user's emotions and adjust the correction method based on the estimated emotions. For example, if the user is stressed, the correction unit simplifies the correction method to reduce the user's burden. For example, if the user is relaxed, the correction unit makes the correction method more detailed to improve accuracy. For example, if the user is in a hurry, the correction unit optimizes the correction method and performs the correction quickly. In this way, the user's burden can be reduced by adjusting the correction method based on 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 correction unit may be performed using a generative AI, or not using a generative AI. For example, the correction unit inputs the user's emotion data into a generative AI, and the generative AI adjusts the correction method.

[0103] The correction unit can select a predetermined correction method based on the past correction history of the claim information when making corrections. For example, the correction unit can analyze the past correction history of the claim information and select the optimal correction method. The correction unit can also perform corrections to improve the accuracy of the corrections based on the past correction history of the claim information. Furthermore, the correction unit can refer to the past correction history of the claim information and perform checks to prevent correction errors. This allows the correction unit to select the optimal correction method and improve the accuracy of the corrections by referring to the past correction history of the claim information. Some or all of the above processing in the correction unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the correction unit inputs the past correction history of the claim information into the generation AI, and the generation AI selects the optimal correction method.

[0104] The correction unit can perform predetermined corrections if the medical treatment details and prescription information do not match during the correction process. For example, the correction unit can analyze the medical treatment details and prescription information and perform appropriate corrections if they do not match. The correction unit can also perform automatic corrections using AI if the medical treatment details and prescription information do not match. Furthermore, the correction unit can perform adjustments to improve the accuracy of the corrections based on the medical treatment details and prescription information. This improves the accuracy of the medical claim by performing appropriate corrections when the medical treatment details and prescription information do not match. Some or all of the above-described processes in the correction unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the correction unit inputs the medical treatment details and prescription information into the generating AI, and the generating AI performs predetermined corrections.

[0105] The editing unit can estimate the user's emotions and determine the priority of corrections based on the estimated emotions. For example, if the user is stressed, the editing unit will postpone less important corrections. For example, if the user is relaxed, the editing unit will prioritize more important corrections. For example, if the user is in a hurry, the editing unit will prioritize more urgent corrections. In this way, important corrections can be prioritized by determining the priority of corrections based on 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 editing unit may be performed using a generative AI, or not using a generative AI. For example, the editing unit inputs user emotion data into a generative AI, and the generative AI determines the priority of corrections.

[0106] The correction unit can perform corrections while considering the geographical distribution of the claim information. For example, the correction unit can analyze the geographical distribution of the claim information and perform appropriate corrections. The correction unit can also perform adjustments to improve the accuracy of the corrections based on the geographical distribution of the claim information. Furthermore, the correction unit can refer to the geographical distribution of the claim information and perform checks to prevent correction errors. In this way, the accuracy of the corrections can be improved by considering the geographical distribution of the claim information. Some or all of the above processing in the correction unit may be performed using a generation AI, for example, or without a generation AI. For example, the correction unit inputs the geographical distribution of the claim information into a generation AI, and the generation AI performs the corrections.

[0107] The correction unit can improve the accuracy of corrections by referring to relevant literature on the claim information during the correction process. For example, the correction unit can refer to relevant literature on the claim information and make appropriate corrections. The correction unit can also refer to relevant literature on the claim information and make appropriate corrections. Furthermore, the correction unit can perform adjustments to improve the accuracy of corrections based on the relevant literature on the claim information. In addition, the correction unit can refer to relevant literature on the claim information and perform checks to prevent correction errors. This allows for improved accuracy of corrections by referring to relevant literature on the claim information. Some or all of the above processing in the correction unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the correction unit inputs relevant literature on the claim information into the generating AI, and the generating AI improves the accuracy of the corrections.

[0108] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit provides a simplified data analysis method. For example, if the user is relaxed, the analysis unit provides a detailed data analysis method. For example, if the user is in a hurry, the analysis unit provides a rapid data analysis method. This reduces the user's burden by adjusting the data analysis method based on 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 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 adjusts the data analysis method.

[0109] The analysis unit can select a predetermined analysis method based on the past analysis history of medical information during the analysis. For example, the analysis unit can analyze the past analysis history of medical information and select the optimal analysis method. The analysis unit can also perform corrections to improve the accuracy of the analysis based on the past analysis history of medical information. Furthermore, the analysis unit can refer to the past analysis history of medical information and perform checks to prevent analysis errors. In this way, by referring to the past analysis history of medical information, the optimal analysis method can be selected and the accuracy of the analysis can be improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit inputs the past analysis history of medical information into a generation AI, and the generation AI selects the optimal analysis method.

[0110] The analysis unit can improve the accuracy of its analysis based on the interrelationships of medical information. For example, the analysis unit can analyze the interrelationships of medical information and perform an appropriate analysis. The analysis unit can also perform corrections to improve the accuracy of the analysis based on the interrelationships of medical information. Furthermore, the analysis unit can refer to the interrelationships of medical information and perform checks to prevent analysis errors. In this way, the accuracy of the analysis can be improved by considering the interrelationships of medical information. 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 interrelationships of medical information into a generating AI, and the generating AI improves the accuracy of the analysis.

[0111] The analysis unit can estimate the user's emotions and determine the priority of analyses based on the estimated emotions. For example, if the user is stressed, the analysis unit will postpone less important analyses. For example, if the user is relaxed, the analysis unit will prioritize high-importance analyses. For example, if the user is in a hurry, the analysis unit will prioritize urgent analyses. In this way, important analyses can be prioritized by determining the priority of analyses based on 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 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 determines the priority of analyses.

[0112] The analysis unit can perform analysis based on the geographical distribution of medical information. For example, the analysis unit can analyze the geographical distribution of medical information and perform an appropriate analysis. The analysis unit can also perform corrections to improve the accuracy of the analysis based on the geographical distribution of medical information. Furthermore, the analysis unit can refer to the geographical distribution of medical information and perform checks to prevent analysis errors. In this way, the accuracy of the analysis can be improved by considering the geographical distribution of medical information. 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 geographical distribution of medical information into a generative AI, and the generative AI performs the analysis.

[0113] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on medical information during the analysis process. For example, the analysis unit can refer to relevant literature on medical information and perform an appropriate analysis. The analysis unit can also refer to relevant literature on medical information and perform an appropriate analysis. Furthermore, the analysis unit can make corrections to improve the accuracy of the analysis based on the relevant literature on medical information. In addition, the analysis unit can refer to relevant literature on medical information and perform checks to prevent analysis errors. In this way, the accuracy of the analysis can be improved by referring to relevant literature on medical information. Some or all of the above processes in the analysis unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the analysis unit inputs relevant literature on medical information into a generating AI, and the generating AI improves the accuracy of the analysis.

[0114] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will present concise and easy-to-understand suggestions. If the user is relaxed, the suggestion unit will present suggestions that include detailed information. If the user is in a hurry, the suggestion unit will present quick and to-the-point suggestions. By adjusting the way suggestions are presented based on the user's emotions, the burden on the user can be reduced. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using a generative AI, or not. For example, the suggestion unit inputs user emotion data into a generative AI, and the generative AI adjusts the way suggestions are presented.

[0115] The proposal unit can select a predetermined proposal method based on the past proposal history of the treatment plan when making a proposal. For example, the proposal unit can analyze the past proposal history of the treatment plan and select the optimal proposal method. The proposal unit can also make corrections to improve the accuracy of the proposal based on the past proposal history of the treatment plan. Furthermore, the proposal unit can refer to the past proposal history of the treatment plan and perform checks to prevent proposal errors. In this way, by referring to the past proposal history of the treatment plan, the optimal proposal method can be selected and the accuracy of the proposal can be improved. Some or all of the above processing in the proposal unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the proposal unit inputs the past proposal history of the treatment plan into a generation AI, and the generation AI selects the optimal proposal method.

[0116] The suggestion unit can propose a predetermined treatment method based on the patient's symptoms and past medical history. For example, the suggestion unit can analyze the patient's symptoms and propose the optimal treatment method. The suggestion unit can also propose the optimal treatment method based on the patient's past medical history. Furthermore, the suggestion unit can integrate the patient's symptoms and past medical history to propose the optimal treatment method. This improves the accuracy of treatment by proposing the optimal treatment method based on the patient's symptoms and past medical history. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit inputs the patient's symptoms and past medical history into a generative AI, and the generative AI proposes a predetermined treatment method.

[0117] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit will postpone less important suggestions. For example, if the user is relaxed, the suggestion unit will prioritize more important suggestions. For example, if the user is in a hurry, the suggestion unit will prioritize more urgent suggestions. In this way, by determining the priority of suggestions based on the user's emotions, important suggestions can be prioritized. 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 suggestion unit may be performed using a generative AI, or not using a generative AI. For example, the suggestion unit inputs user emotion data into a generative AI, and the generative AI determines the priority of suggestions.

[0118] The proposal unit can make proposals based on the geographical distribution of treatment plans. For example, the proposal unit can analyze the geographical distribution of treatment plans and make appropriate proposals. The proposal unit can also make corrections to improve the accuracy of proposals based on the geographical distribution of treatment plans. Furthermore, the proposal unit can refer to the geographical distribution of treatment plans and perform checks to prevent proposal errors. This improves the accuracy of proposals by considering the geographical distribution of treatment plans. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit inputs the geographical distribution of treatment plans into a generative AI, and the generative AI makes proposals.

[0119] The proposal unit can improve the accuracy of its proposals by referring to relevant literature on treatment plans during the proposal process. For example, the proposal unit can refer to relevant literature on treatment plans and make appropriate proposals. The proposal unit can also refer to relevant literature on treatment plans and make appropriate proposals. Furthermore, the proposal unit can make corrections to improve the accuracy of its proposals based on the relevant literature on treatment plans. In addition, the proposal unit can refer to relevant literature on treatment plans and perform checks to prevent proposal errors. This allows for improved accuracy of proposals by referring to relevant literature on treatment plans. Some or all of the above processing in the proposal unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the proposal unit inputs relevant literature on treatment plans into a generating AI, and the generating AI improves the accuracy of the proposals.

[0120] The detection unit can estimate the user's emotions and adjust the error detection method based on the estimated user emotions. For example, if the user is stressed, the detection unit simplifies the error detection method to reduce the user's burden. For example, if the user is relaxed, the detection unit makes the error detection method more detailed to improve accuracy. For example, if the user is in a hurry, the detection unit optimizes the error detection method to perform detection quickly. In this way, the user's burden can be reduced by adjusting the error detection method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the detection unit may be performed using a generative AI, or not using a generative AI. For example, the detection unit inputs the user's emotion data into the generative AI, and the generative AI adjusts the error detection method.

[0121] The detection unit can select a predetermined detection method based on the past history of incorrect entries in the claim information when detection occurs. For example, the detection unit can analyze the past history of incorrect entries in the claim information and select the optimal detection method. The detection unit can also perform corrections to improve the accuracy of detection based on the past history of incorrect entries in the claim information. Furthermore, the detection unit can refer to the past history of incorrect entries in the claim information and perform checks to prevent detection errors. This allows the detection unit to select the optimal detection method and improve the accuracy of detection by referring to the past history of incorrect entries in the claim information. Some or all of the above processing in the detection unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the detection unit inputs the past history of incorrect entries in the claim information into the generating AI, and the generating AI selects the optimal detection method.

[0122] The detection unit can estimate the user's emotions and determine the priority of input errors based on the estimated emotions. For example, if the user is stressed, the detection unit will postpone detecting low-priority input errors. For example, if the user is relaxed, the detection unit will prioritize detecting high-priority input errors. For example, if the user is in a hurry, the detection unit will prioritize detecting high-urgency input errors. In this way, by determining the priority of input errors based on the user's emotions, important input errors can be detected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using 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 detection unit may be performed using a generative AI, or not using a generative AI. For example, the detection unit inputs user emotion data into a generative AI, and the generative AI determines the priority of input errors.

[0123] The detection unit can detect incorrect inputs based on the geographical distribution of the claim information during detection. The detection unit can, for example, analyze the geographical distribution of the claim information and detect appropriate incorrect inputs. The detection unit can, for example, analyze the geographical distribution of the claim information and detect appropriate incorrect inputs. The detection unit can also perform corrections to improve the accuracy of detection based on the geographical distribution of the claim information. Furthermore, the detection unit can refer to the geographical distribution of the claim information and perform checks to prevent detection errors. In this way, the accuracy of incorrect input detection can be improved by considering the geographical distribution of the claim information. Some or all of the above processing in the detection unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the detection unit inputs the geographical distribution of the claim information into the generating AI, and the generating AI detects incorrect inputs.

[0124] The data collection unit can estimate the user's emotions and adjust the method of collecting medical information based on the estimated emotions. For example, if the user is stressed, the data collection unit simplifies the collection method to reduce the user's burden. For example, if the user is relaxed, the data collection unit provides a detailed collection method. For example, if the user is in a hurry, the data collection unit provides a rapid collection method. This reduces the user's burden by adjusting the method of collecting medical information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 using a generative AI. For example, the data collection unit inputs the user's emotion data into a generative AI, and the generative AI adjusts the collection method.

[0125] The data collection unit can select a predetermined collection method based on the past collection history of medical information at the time of collection. For example, the data collection unit can analyze the past collection history of medical information and select the optimal collection method. The data collection unit can also perform corrections to improve the accuracy of collection based on the past collection history of medical information. Furthermore, the data collection unit can refer to the past collection history of medical information and perform checks to prevent collection errors. This allows the data collection unit to select the optimal collection method and improve the accuracy of collection by referring to the past collection history of medical 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 past collection history of medical information into a generating AI, and the generating AI selects the optimal collection method.

[0126] The data collection unit can estimate the user's emotions and determine the priority of collecting medical information based on the estimated emotions. For example, if the user is stressed, the data collection unit will postpone the collection of less important information. For example, if the user is relaxed, the data collection unit will prioritize the collection of highly important information. For example, if the user is in a hurry, the data collection unit will prioritize the collection of highly urgent information. In this way, by determining the priority of collecting medical information based on the user's emotions, important information can be collected preferentially. 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 determines the collection priority.

[0127] The data collection unit can perform data collection based on the geographical distribution of medical information. For example, the data collection unit can analyze the geographical distribution of medical information and perform appropriate data collection. The data collection unit can also perform corrections to improve the accuracy of data collection based on the geographical distribution of medical information. Furthermore, the data collection unit can refer to the geographical distribution of medical information and perform checks to prevent data collection errors. This improves the accuracy of data collection by considering the geographical distribution of medical information. 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 the geographical distribution of medical information into a generating AI, and the generating AI performs the data collection.

[0128] The information provider can estimate the user's emotions and adjust the method of information delivery based on the estimated emotions. For example, if the user is stressed, the information provider will provide concise and easy-to-understand information. For example, if the user is relaxed, the information provider will provide detailed information. For example, if the user is in a hurry, the information provider will provide quick and to-the-point information. By adjusting the method of information delivery based on the user's emotions, the burden on the user can be reduced. 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 information provider may be performed using a generative AI, or not using a generative AI. For example, the information provider inputs the user's emotion data into a generative AI, and the generative AI adjusts the method of information delivery.

[0129] The delivery unit can select a predetermined delivery method based on the past delivery history of the treatment plan at the time of delivery. For example, the delivery unit can analyze the past delivery history of the treatment plan and select the optimal delivery method. The delivery unit can also perform corrections to improve the accuracy of delivery based on the past delivery history of the treatment plan. Furthermore, the delivery unit can refer to the past delivery history of the treatment plan and perform checks to prevent delivery errors. In this way, by referring to the past delivery history of the treatment plan, the optimal delivery method can be selected and the accuracy of delivery can be improved. Some or all of the above processing in the delivery unit may be performed using, for example, a generation AI, or without a generation AI. For example, the delivery unit inputs the past delivery history of the treatment plan into a generation AI, and the generation AI selects the optimal delivery method.

[0130] The information delivery unit can estimate the user's emotions and determine the priority of information delivery based on the estimated emotions. For example, if the user is stressed, the information delivery unit will postpone providing less important information. For example, if the user is relaxed, the information delivery unit will prioritize providing highly important information. For example, if the user is in a hurry, the information delivery unit will prioritize providing highly urgent information. In this way, by determining the priority of information delivery based on the user's emotions, important information can be provided preferentially. 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 information delivery unit may be performed using a generative AI, or not using a generative AI. For example, the information delivery unit inputs user emotion data into a generative AI, and the generative AI determines the priority of information delivery.

[0131] The information provision unit can provide information based on the geographical distribution of treatment plans at the time of provision. For example, the information provision unit can analyze the geographical distribution of treatment plans and provide appropriate information. The information provision unit can also perform corrections to improve the accuracy of provision based on the geographical distribution of treatment plans. Furthermore, the information provision unit can refer to the geographical distribution of treatment plans and perform checks to prevent provision errors. In this way, the accuracy of information provision can be improved by considering the geographical distribution of treatment plans. Some or all of the above processing in the information provision unit may be performed using, for example, a generation AI, or without a generation AI. For example, the information provision unit inputs the geographical distribution of treatment plans into a generation AI, and the generation AI provides the information.

[0132] The information provider can improve the accuracy of information provision based on relevant literature for the treatment plan at the time of provision. For example, the information provider can refer to relevant literature for the treatment plan and provide appropriate information. The information provider can also refer to relevant literature for the treatment plan and provide appropriate information. Furthermore, the information provider can make corrections to improve the accuracy of information provision based on relevant literature for the treatment plan. In addition, the information provider can refer to relevant literature for the treatment plan and perform checks to prevent provision errors. This makes it possible to improve the accuracy of information provision by referring to relevant literature for the treatment plan. Some or all of the above processing in the information provider may be performed using, for example, a generating AI, or without using a generating AI. For example, the information provider inputs relevant literature for the treatment plan into a generating AI, and the generating AI improves the accuracy of information provision.

[0133] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0134] A medical administration support system can be equipped with a data collection unit that collects patient medical information. This unit can automatically collect, for example, patient medical records, prescription information, and test results. The unit can retrieve medical records from electronic medical records and prescription information from pharmacy systems. Furthermore, it can retrieve test results from testing laboratories and integrate this information into a database. This eliminates the need for medical administrators to manually collect information and improves data consistency and accuracy.

[0135] Medical administration support systems can be equipped with an emotion estimation function that estimates a patient's emotions. This function can, for example, estimate emotions from a patient's facial expressions and voice, and understand the patient's stress level during treatment. If the emotion estimation function detects that a patient is stressed, it can provide this information to the doctor, allowing for adjustments to the treatment plan. Furthermore, if the patient is relaxed, the emotion estimation function can provide information to facilitate a smoother treatment. This allows for treatment tailored to the patient's emotions, thereby improving patient satisfaction.

[0136] Medical administration support systems can include a visualization unit that visualizes the results of analysis of medical information. For example, the visualization unit can display medical information as graphs and charts, making it intuitively understandable to doctors. The visualization unit can also display the progression of a patient's symptoms and the effectiveness of treatment over time, providing doctors with reference when deciding on treatment plans. Furthermore, the visualization unit can compare data from multiple patients to identify common trends and patterns. This makes it easier for doctors to make data-driven decisions, thereby improving the quality of medical care.

[0137] A medical administration support system can include a sharing section that allows for real-time sharing of patient medical information. For example, this sharing section can store medical information on the cloud, allowing doctors and medical staff to access it when needed. The sharing section can share medical information using secure communication methods, preventing information leaks. Furthermore, with the patient's consent, the sharing section can share information with other medical institutions to facilitate collaborative care. This streamlines information sharing between medical institutions and ensures consistent patient care.

[0138] A medical administration support system can be equipped with a treatment plan generation unit that automatically generates treatment plans based on patient medical information. For example, the treatment plan generation unit analyzes the patient's symptoms and past medical history to propose the optimal treatment plan. The unit suggests medication prescriptions and treatment methods tailored to the patient's symptoms, which the physician can then use as a reference. Furthermore, the treatment plan generation unit can present multiple treatment plans, allowing the physician to choose the most suitable one. This supports the physician's decision-making regarding treatment policies and enables the provision of the most appropriate treatment for the patient.

[0139] A medical administration support system may include a consultation adjustment unit that estimates a patient's emotions and adjusts the progress of the consultation based on those emotions. For example, if a patient is feeling stressed, the consultation adjustment unit may slow down the progress of the consultation to give the patient time to relax. If the patient is relaxed, the consultation adjustment unit may make the consultation proceed smoothly and efficiently. If the patient is in a hurry, the consultation adjustment unit may expedite the progress of the consultation to save the patient time. This can improve patient satisfaction by providing consultation that is tailored to the patient's emotions.

[0140] A medical office support system can be equipped with a voice input unit that uses speech recognition technology to input medical information. For example, the voice input unit can recognize in real time what a doctor says during a consultation and input it as medical information. The voice input unit can input information about consultations and prescriptions by voice and save it as text data. Furthermore, the voice input unit can learn the doctor's speech patterns to improve the accuracy of speech recognition. This allows doctors to input medical information without using their hands, improving the efficiency of medical practice.

[0141] A medical administration support system may include a display adjustment unit that estimates a patient's emotions and adjusts how medical information is displayed based on those emotions. For example, if a patient is stressed, the display adjustment unit will display medical information concisely to reduce the patient's burden. If a patient is relaxed, the display adjustment unit will display detailed medical information to make it easier for the patient to understand. If a patient is in a hurry, the display adjustment unit will quickly display medical information that highlights the key points. By displaying medical information in accordance with the patient's emotions, patient satisfaction can be improved.

[0142] A medical office support system can include a template input section that allows for the input of medical information using templates. For example, when entering information such as medical treatment details or prescriptions, the template input section uses pre-prepared templates. The template input section allows for the creation of medical claims simply by selecting a template appropriate to the medical treatment and entering the necessary information. Furthermore, the template input section allows for the customization of templates, enabling them to be modified to meet the needs of physicians. This simplifies the input of medical information and reduces input errors.

[0143] A medical administration support system may include a prioritization unit that estimates a patient's emotions and determines the priority of medical information based on those emotions. For example, if a patient is stressed, the prioritization unit will postpone less important medical information. If a patient is relaxed, the unit will prioritize displaying more important medical information. If a patient is in a hurry, the unit will prioritize displaying more urgent medical information. This allows for the rapid provision of important information by prioritizing medical information according to the patient's emotions.

[0144] The following briefly describes the processing flow for example form 2.

[0145] Step 1: The input unit performs the input of medical claim information. For example, when a medical office staff member enters medical information, the input unit automatically inputs that information into the medical claim form. The input unit can analyze the medical treatment details and prescription information and input it into the medical claim form in the appropriate format. It can also integrate the medical treatment details and prescription information and input it into the medical claim form in the appropriate format. Step 2: The checking unit checks the claim information entered by the input unit. For example, it checks in real time whether there are any errors in the entered claim information. It can check whether the medical treatment details and prescription information match and whether it is within the scope of insurance coverage. Step 3: The correction unit corrects the claim information checked by the checking unit. For example, if an incorrect entry is detected, it will be corrected automatically. If the medical treatment details and prescription information do not match, it will make appropriate corrections and reflect the correct information in the claim. Step 4: The analysis department analyzes data during consultations. For example, they analyze data based on medical information and propose the optimal treatment plan. Based on the patient's symptoms and past medical history, they can suggest the most suitable treatment methods and medication prescriptions. Step 5: The proposal department proposes the optimal treatment plan based on the data analyzed by the analysis department. For example, it proposes the optimal treatment plan based on medical information. Based on the patient's symptoms and past medical history, it can propose the most suitable treatment method and medication prescription.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the input unit, checking unit, correction unit, analysis unit, proposal unit, detection unit, collection unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the input unit is implemented by the control unit 46A of the smart device 14, which automatically inputs medical information into the claim form when a medical office worker enters medical information. The checking unit is implemented by the identification processing unit 290 of the data processing unit 12, which checks in real time whether there are any errors in the entered claim form information. The correction unit is implemented by the control unit 46A of the smart device 14, which automatically corrects any input errors that are detected. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the data based on the medical information and proposes an optimal treatment plan. The proposal unit is implemented by the control unit 46A of the smart device 14, which proposes an optimal treatment plan based on the data analyzed by the analysis unit. The detection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and detects incorrect input of medical claims. The collection unit is implemented, for example, by the control unit 46A of the smart device 14, and collects medical information. The provision unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides information to the physician based on the proposed treatment plan. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0150] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the input unit, checking unit, correction unit, analysis unit, proposal unit, detection unit, collection unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the input unit is implemented by the control unit 46A of the smart glasses 214, which automatically inputs medical information into the claim form when a medical office worker enters medical information. The checking unit is implemented by the identification processing unit 290 of the data processing unit 12, which checks in real time whether there are any errors in the entered claim form information. The correction unit is implemented by the control unit 46A of the smart glasses 214, which automatically corrects any input errors that are detected. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the data based on the medical information and proposes an optimal treatment plan. The proposal unit is implemented by the control unit 46A of the smart glasses 214, which proposes an optimal treatment plan based on the data analyzed by the analysis unit. The detection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and detects incorrect input of medical claims. The collection unit is implemented, for example, by the control unit 46A of the smart glasses 214, and collects medical information. The provision unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides information to the physician based on the proposed treatment plan. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0166] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] Each of the multiple elements described above, including the input unit, checking unit, correction unit, analysis unit, proposal unit, detection unit, collection unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the input unit is implemented by the control unit 46A of the headset terminal 314, which automatically inputs medical information into the claim form when a medical office worker enters medical information. The checking unit is implemented by the identification processing unit 290 of the data processing unit 12, which checks in real time whether there are any errors in the entered claim form information. The correction unit is implemented by the control unit 46A of the headset terminal 314, which automatically corrects any input errors that are detected. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the data based on the medical information and proposes an optimal treatment plan. The proposal unit is implemented by the control unit 46A of the headset terminal 314, which proposes an optimal treatment plan based on the data analyzed by the analysis unit. The detection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and detects incorrect input of medical claims. The collection unit is implemented, for example, by the control unit 46A of the headset terminal 314, and collects medical information. The provision unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides information to the physician based on the proposed treatment plan. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0182] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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).

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.).

[0195] 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.

[0196] 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.

[0197] 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.

[0198] Each of the multiple elements described above, including the input unit, checking unit, correction unit, analysis unit, proposal unit, detection unit, collection unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the input unit is implemented by the control unit 46A of the robot 414, which automatically inputs medical information into the claim form when a medical office worker enters medical information. The checking unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which checks in real time whether there are any errors in the entered claim form information. The correction unit is implemented by, for example, the control unit 46A of the robot 414, which automatically corrects any input errors that are detected. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which analyzes the data based on the medical information and proposes an optimal treatment plan. The proposal unit is implemented by, for example, the control unit 46A of the robot 414, which proposes an optimal treatment plan based on the data analyzed by the analysis unit. The detection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and detects incorrect input of medical claims. The collection unit is implemented, for example, by the control unit 46A of the robot 414, and collects medical information. The provision unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides information to the physician based on the proposed treatment plan. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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."

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] (Note 1) An input section for entering medical claim information, A checking unit that checks the claim information entered by the input unit, A correction unit that corrects the claim information checked by the aforementioned checking unit, The analysis department analyzes data during medical treatment, The system includes a proposal unit that proposes an appropriate treatment plan based on the data analyzed by the analysis unit. A system characterized by the following features. (Note 2) It includes a detection unit that detects incorrect input in medical claim forms. The system described in Appendix 1, characterized by the features described herein. (Note 3) It is equipped with a collection unit for collecting medical information. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a department that provides information to doctors based on proposed treatment plans. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned input unit is The system analyzes information on medical treatment and prescriptions, and inputs it into the medical claim form in a prescribed format. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned checking unit is Check if the medical treatment details and prescription information match, and whether it is covered by insurance. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned modification section is, If the medical treatment details and prescription information do not match, make appropriate corrections and reflect the correct information in the medical claim. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned input unit is The system estimates the user's emotions and adjusts the timing of medical claim entry based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned input unit is Analyze past medical claim entry history and select a predetermined entry method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned input unit is When entering medical claim information, the system automatically categorizes the medical treatment details and prescription information and inputs it in a specified format. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned input unit is The system estimates the user's emotions and determines the priority of the medical claim forms to be entered based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned input unit is When entering medical claims, improve the accuracy of the input based on detailed information about the medical treatment. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned input unit is When entering medical claims, improve the accuracy of the input based on relevant literature related to the medical treatment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned checking unit is The system estimates the user's emotions and adjusts the check criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned checking unit is During the checking process, the accuracy of the checks is improved based on the interrelationships of the medical claim information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned checking unit is During the check, we analyze whether the medical treatment details and prescription information match. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned checking unit is It estimates the user's emotions and adjusts the order in which the check results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned checking unit is During the check, the check is performed based on the geographical distribution of the medical claim information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned checking unit is During the check, improve the accuracy of the check based on relevant literature from the medical claim information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned modification section is, It estimates the user's emotions and adjusts the correction method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned modification section is, When making corrections, the system selects a predetermined correction method based on the past correction history of the medical claim information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned modification section is, If the medical treatment details and prescription information do not match during the correction process, the prescribed corrections will be made. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned modification section is, It estimates user sentiment and determines the priority of modifications based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned modification section is, When making corrections, the corrections will be made based on the geographical distribution of the medical claim information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned modification section is, When making corrections, improve the accuracy of the corrections based on relevant literature in the claims information. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned analysis unit is We estimate user sentiment and adjust the data analysis method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned analysis unit is During the analysis, a predetermined analysis method is selected based on the past analysis history of medical information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned analysis unit is During analysis, improve the accuracy of the analysis based on the interrelationships of medical information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned analysis unit is We estimate the user's emotions and prioritize the analysis based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned analysis unit is During the analysis, the analysis will be conducted based on the geographical distribution of medical information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned analysis unit is During analysis, improve the accuracy of the analysis based on relevant literature regarding clinical information. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned proposal section is, When making a proposal, a predetermined proposal method is selected based on the history of past treatment plan proposals. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned proposal section is, When making a proposal, we suggest a specific treatment method based on the patient's symptoms and past medical history. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned proposal section is, When making a proposal, the proposal should be based on the geographical distribution of the treatment plan. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned proposal section is, When making a proposal, improve the accuracy of the proposal based on relevant literature for the treatment plan. The system described in Appendix 1, characterized by the features described herein. (Note 38) The detection unit is The system estimates the user's emotions and adjusts the error detection method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The detection unit is Upon detection, a predetermined detection method is selected based on the past history of incorrect entries in the medical claim information. The system described in Appendix 1, characterized by the features described herein. (Note 40) The detection unit is The system estimates the user's emotions and determines the priority of incorrect inputs based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The detection unit is When detection occurs, incorrect entries are detected based on the geographical distribution of the medical claim information. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned collection unit is Estimate the user's emotion and adjust the method of collecting medical information based on the estimated user's emotion The system according to appended note 1, characterized in that (Appended note 43) The collection unit When collecting, select a predetermined collection method based on the past collection history of medical information The system according to appended note 1, characterized in that (Appended note 44) The collection unit Estimate the user's emotion and determine the priority of collecting medical information based on the estimated user's emotion The system according to appended note 1, characterized in that (Appended note 45) The collection unit When collecting, perform collection based on the geographical distribution of medical information The system according to appended note 1, characterized in that (Appended note 46) The providing unit Estimate the user's emotion and adjust the method of providing information based on the estimated user's emotion The system according to appended note 1, characterized in that <00​​​​​​​​​​​​​​​​​​​​​​​​​​​​​ The system according to Appendix 1, characterized in that...

Explanation of Signs

[0218] 10, 210, 310, 410 Data processing system 12 Data processing device 14 Smart device 214 Smart glasses 314 Headset-type terminal 414 Robot

Claims

1. An input section for entering medical claim information, A checking unit that checks the claim information entered by the input unit, A correction unit that corrects the claim information checked by the aforementioned checking unit, The analysis department analyzes data during medical treatment, The system includes a proposal unit that proposes an appropriate treatment plan based on the data analyzed by the analysis unit. A system characterized by the following features.

2. It includes a detection unit that detects incorrect input in medical claim forms. The system according to feature 1.

3. It is equipped with a collection unit for collecting medical information. The system according to feature 1.

4. It includes a department that provides information to doctors based on proposed treatment plans. The system according to feature 1.

5. The aforementioned input unit is The system analyzes information on medical treatment and prescriptions, and inputs it into the medical claim form in a prescribed format. The system according to feature 1.

6. The aforementioned checking unit is Check if the medical treatment details and prescription information match, and whether it is covered by insurance. The system according to feature 1.

7. The aforementioned modification section is, If the medical treatment details and prescription information do not match, make appropriate corrections and reflect the correct information in the medical claim. The system according to feature 1.

8. The aforementioned input unit is The system estimates the user's emotions and adjusts the timing of medical claim entry based on those emotions. The system according to feature 1.

9. The aforementioned input unit is Analyze past medical claim entry history and select a predetermined entry method. The system according to feature 1.

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