System

Abstract

An object of a system according to an embodiment is to efficiently perform automatic generation of a medical document, error detection of a medical bill, and estimation of a medical cost.SOLUTION: The system according to the embodiment includes a medical document generation unit, an error detection unit, a cost estimation unit, a data collection unit, an analysis evaluation unit, and a proposal application unit. The medical document generation unit automatically generates a medical document using the generation AI. The error detection portion detects an input error or inconsistency of a receipt. The cost estimation unit estimates a medical treatment cost based on the treatment plan. The data collection unit collects data such as a medical record, a treatment history, and drug information of a patient. The assay unit analyzes the collected information and calculates a medical fee points. A proposal application part proposes the optimized medical examination plan and applies it to an actual medical examination process.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, tasks such as creating medical documents, detecting errors in medical prescriptions, and estimating medical costs were time-consuming and difficult to perform efficiently.

[0005] The system according to the embodiment aims to efficiently perform automatic generation of medical documents, error detection in medical prescriptions, and estimation of medical costs. [Means for solving the problem]

[0006] The system according to the embodiment comprises a medical document generation unit, an error detection unit, a cost estimation unit, a data collection unit, an analysis and evaluation unit, and a proposal application unit. The medical document generation unit automatically generates medical documents using a generation AI. The error detection unit detects input errors and inconsistencies in medical prescriptions. The cost estimation unit estimates medical costs based on treatment plans. The data collection unit collects data such as patient medical records, treatment history, and medication information. The analysis and evaluation unit analyzes the collected data and calculates medical fee points. The proposal application unit proposes optimized treatment plans and applies them to the actual medical treatment process. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently perform automatic generation of medical documents, error detection in medical prescriptions, and estimation of medical costs. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The revenue maximization system according to an embodiment of the present invention is a system that aims to streamline medical insurance claim processing in hospital management and proposes ways to maximize revenue based on the data. This revenue maximization system uses generative AI to automatically generate medical documents, detect errors and inconsistencies, and estimate medical costs, and also collects, analyzes, evaluates, proposes, and applies data to maximize medical fees. As a result, the revenue maximization system can improve the efficiency of medical insurance claim processing and maximize revenue in hospital management.

[0029] The revenue maximization system according to the embodiment includes a medical document generation unit, an error detection unit, a cost estimation unit, a data collection unit, an analysis and evaluation unit, and a proposal application unit. The medical document generation unit automatically generates medical documents using a generation AI. For example, the generation AI automatically creates medical documents and reports based on diagnosis results and treatment details. The generation AI can also generate medical documents based on prompts including diagnosis results and treatment details. Furthermore, the generation AI analyzes the doctor's input of diagnosis and treatment details and generates appropriate medical documents. The error detection unit detects input errors and inconsistencies in medical claims. For example, the generation AI analyzes input data for medical claims and detects inconsistencies or input errors between medical treatment details and billing details. The generation AI can also detect errors and inconsistencies based on input data for medical claims. Furthermore, the generation AI automatically detects input errors and inconsistencies in medical claims and prompts correction. The cost estimation unit estimates medical costs based on a treatment plan. For example, the generation AI analyzes input of a patient's medical records and treatment plan and calculates the costs of treatment. The generation AI can also estimate medical costs based on prompts containing medical records and treatment plans. Furthermore, the generation AI estimates the cost of patient treatment based on the treatment plan. The data collection unit collects data such as the patient's medical records, treatment history, and medication information. For example, the generation AI acquires data from electronic medical records and medication management systems and centrally manages it. The generation AI can also collect data based on prompts containing medical records, treatment history, and medication information. The generation AI also collects data such as the patient's medical records, treatment history, and medication information. The analysis and evaluation unit analyzes the collected data and calculates medical fee points. For example, the generation AI analyzes the patient's medical records and treatment history to evaluate the fee points for each medical procedure. The generation AI can also analyze data based on the collected data and calculate the fee points. Furthermore, the generation AI analyzes the collected data and calculates the medical fee points for each medical procedure. The proposal and application unit proposes an optimized treatment plan and applies it to the actual medical process. For example, the generation AI proposes an optimal treatment plan based on the analysis results, and the doctor uses it as a reference when providing medical care. The generative AI can also propose treatment plans based on the analysis results.Furthermore, the generation AI proposes optimized medical treatment plans to healthcare providers and applies them to actual medical treatment processes. As a result, the revenue maximization system according to the embodiment can realize efficient medical receipt operations and maximized profits in hospital management. For example, the automatic generation of medical documents reduces the burden on medical administration, and the detection of errors and inconsistencies improves the accuracy of medical receipt operations. In addition, estimating medical costs facilitates budget management, and data collection and analysis optimizes medical fees. Furthermore, the proposal of optimized medical treatment plans improves the quality of medical treatment and maximizes profits.

[0030] The medical document generation unit can create more detailed and personalized documents by taking into account the patient's past medical history and family history. For example, the medical document generation unit uses a generation AI to retrieve a patient's past medical history and family history from a database and generate personalized medical documents based on that information. For example, the unit emphasizes specific risk factors by taking into account the patient's past medical history and family medical history. The medical document generation unit also builds a system in which the generation AI takes into account the patient's past medical history and family history to generate personalized medical documents. For example, the unit creates documents that reflect individual treatment plans based on the patient's medical history and family history. Furthermore, the medical document generation unit develops algorithms that enable the generation AI to take into account the patient's past medical history and family history to generate personalized medical documents. For example, the unit analyzes a patient's past medical history and family history to create documents that reflect individual risk factors. This makes it possible to provide more appropriate medical documents to patients.

[0031] The medical document generation unit can convert a doctor's dictation into text in real time and generate documents on the spot. For example, the medical document generation unit will build a system in which a generation AI will recognize the doctor's dictation in real time and convert it into text. For example, what a doctor dictates during an examination will be instantly converted into text and saved as medical documents. The medical document generation unit will also build a system in which a generation AI will convert a doctor's dictation into text in real time and generate documents on the spot. For example, what a doctor dictates during an examination will be instantly converted into text and saved as medical documents. Furthermore, the medical document generation unit will develop an algorithm that will enable a generation AI to convert a doctor's dictation into text in real time and generate documents on the spot. For example, what a doctor dictates during an examination will be instantly converted into text and saved as medical documents. This will reduce the burden on doctors and enable faster document creation.

[0032] The medical document generation unit can apply the automatic medical document generation function to other medical-related documents, including surgical plans and discharge summaries. For example, the medical document generation unit uses generation AI to build a system that automatically generates surgical plans. For example, when the details and procedures of a surgery are input, the generation AI creates a surgical plan based on that information. The medical document generation unit also uses generation AI to build a system that automatically generates discharge summaries. For example, when the patient's condition at the time of discharge and future treatment plans are input, the generation AI creates a discharge summary based on that information. Furthermore, the medical document generation unit uses generation AI to develop algorithms for automatically generating other medical-related documents, including surgical plans and discharge summaries. For example, appropriate medical documents are generated based on the details of the surgery and the patient's condition at the time of discharge. This enables the automatic generation of a variety of medical-related documents, improving work efficiency.

[0033] The medical document generation unit can make the generated medical documents multilingual so that they can be used by international medical institutions. For example, the medical document generation unit builds a system that translates medical documents generated by the generation AI into multiple languages. For example, it can make them compatible with multiple languages ​​such as English, French, and Chinese. The medical document generation unit also builds a system that makes medical documents generated by the generation AI multilingual. For example, it can translate medical documents generated by the generation AI into multiple languages ​​so that they can be used by international medical institutions. Furthermore, the medical document generation unit develops an algorithm to make medical documents generated by the generation AI multilingual. For example, it can translate medical documents generated by the generation AI into multiple languages ​​so that they can be used by international medical institutions. This multilingual support makes them usable by international medical institutions.

[0034] The error detection unit learns from past error history and can prevent similar errors from occurring. For example, the generation AI learns from a database of past error history and builds a system that prevents similar errors from occurring. For example, it analyzes patterns of errors that have occurred in the past and proposes preventive measures. The error detection unit also learns from past error history and builds a system that prevents similar errors from occurring. For example, it proposes preventive measures for errors based on past error history. Furthermore, the error detection unit develops an algorithm that allows the generation AI to learn from past error history and prevent similar errors from occurring. For example, it analyzes past error history and proposes preventive measures for errors. In this way, by learning from past error history, it is possible to prevent similar errors from occurring.

[0035] The error detection unit can suggest specific correction methods to medical staff when an error is detected. For example, the error detection unit builds a system that suggests specific correction methods to medical staff when the generation AI detects an error. For example, it presents specific steps for correcting an input error. The error detection unit also builds a system that suggests specific correction methods to medical staff when the generation AI detects an error. For example, it presents steps for correcting an error. Furthermore, the error detection unit develops an algorithm to suggest specific correction methods to medical staff when the generation AI detects an error. For example, it presents steps for correcting an error. This reduces the burden on medical staff by suggesting specific correction methods when an error is detected.

[0036] The error detection unit can apply the error detection function to other medical tasks. For example, the error detection unit applies the error detection function of the generation AI to a medication management system to prevent medication errors and inventory management errors. For example, it detects inconsistencies in medication dosage or administration timing. The error detection unit also applies the error detection function of the generation AI to surgery schedule management to prevent schedule overlaps and errors. For example, it automatically checks surgery schedules and detects overlaps and errors. Furthermore, the error detection unit develops algorithms to apply the error detection function of the generation AI to other medical tasks. For example, it builds a system to prevent errors in medication management and surgery schedule management. In this way, the error detection function can be applied to other medical tasks, improving the accuracy of the entire operation.

[0037] When an error is detected, the error detection unit can compare it with other related databases to confirm the appropriateness of the correction. For example, when the generation AI detects an error, the error detection unit builds a system that compares the correction with medical guidelines to confirm the appropriateness of the correction. For example, it checks whether the medical treatment content complies with the guidelines. In addition, when the generation AI detects an error, the error detection unit builds a system that compares the correction with a drug database to confirm the appropriateness of the correction. For example, it checks whether the prescribed drug is appropriate. Furthermore, when the generation AI detects an error, the error detection unit develops an algorithm to compare the correction with other related databases to confirm the appropriateness of the correction. For example, it compares the correction with medical guidelines or a drug database to confirm the appropriateness of the correction. This makes it possible to confirm the appropriateness of the correction by comparing it with other related databases when an error is detected.

[0038] The cost estimation unit is able to predict the effectiveness of treatment and the patient's recovery rate, and propose cost-effective treatments. For example, the cost estimation unit constructs a system in which a generative AI predicts the effectiveness of treatment and the patient's recovery rate, and then proposes cost-effective treatments based on that. For example, it estimates costs taking into account the treatment period and recovery rate. The cost estimation unit also constructs a system in which a generative AI predicts the effectiveness of treatment and the patient's recovery rate, and then proposes cost-effective treatments. For example, it estimates costs taking into account the treatment period and recovery rate. The cost estimation unit also develops an algorithm in which a generative AI predicts the effectiveness of treatment and the patient's recovery rate, and then proposes cost-effective treatments. For example, it estimates costs taking into account the treatment period and recovery rate. This makes it possible to propose cost-effective treatments by predicting the effectiveness of treatment and the patient's recovery rate.

[0039] The cost estimation unit can apply the medical cost estimation function to other medical-related costs. For example, the cost estimation unit applies the medical cost estimation function of the generation AI to medical equipment maintenance costs, thereby achieving cost-efficient equipment management. For example, it performs estimations that take into account the frequency of equipment use and maintenance costs. The cost estimation unit also applies the medical cost estimation function of the generation AI to drug costs, thereby achieving cost-efficient drug management. For example, it performs estimations that take into account the frequency of drug use and inventory management. Furthermore, the cost estimation unit develops an algorithm for applying the medical cost estimation function of the generation AI to other medical-related costs. For example, it performs cost estimations that take into account the maintenance costs of medical equipment and drug costs. In this way, comprehensive cost management becomes possible by applying the medical cost estimation function to other medical-related costs.

[0040] The cost estimation unit can compare the estimated medical costs with other hospitals and medical institutions and provide a benchmark for cost reduction. The cost estimation unit, for example, builds a system that compares the medical costs estimated by the generation AI with other hospitals and medical institutions and provides a benchmark for cost reduction. For example, it compares the costs of the same treatment and proposes optimal cost reduction measures. The cost estimation unit also builds a system that compares the medical costs estimated by the generation AI with other hospitals and medical institutions and provides a benchmark for cost reduction. For example, it compares the costs of the same treatment and proposes optimal cost reduction measures. The cost estimation unit also develops an algorithm for comparing the medical costs estimated by the generation AI with other hospitals and medical institutions and providing a benchmark for cost reduction. For example, it compares the costs of the same treatment and proposes optimal cost reduction measures. This makes it possible to provide a benchmark for cost reduction by comparing medical costs with other hospitals and medical institutions.

[0041] The data collection unit can apply the data collection function to other medical-related data. For example, the data collection unit applies the data collection function of the generation AI to a patient's lifestyle data to collect data for health management. For example, it collects diet and exercise records. The data collection unit also applies the data collection function of the generation AI to genetic information to collect data for evaluating genetic risk factors. For example, it evaluates risk factors based on genetic information. Furthermore, the data collection unit develops algorithms for applying the data collection function of the generation AI to other medical-related data. For example, it collects a patient's lifestyle data and genetic information to perform comprehensive health management. This makes it possible to apply the data collection function to other medical-related data as well, enabling comprehensive health management.

[0042] The data collection unit can share the collected data with other medical institutions and research institutions to promote joint research and data analysis. For example, the data collection unit shares the data collected by the generative AI with other medical institutions and builds a system to promote joint research. For example, they share a database and conduct joint data analysis. The data collection unit also builds a system to share the data collected by the generative AI with other research institutions and builds a system to promote joint research. For example, they share a database and conduct joint data analysis. Furthermore, the data collection unit develops an algorithm to share the data collected by the generative AI with other medical institutions and research institutions and promote joint research and data analysis. For example, they share a database and conduct joint data analysis. In this way, by sharing the collected data with other medical institutions and research institutions, joint research and data analysis can be promoted.

[0043] The analysis and evaluation unit learns from past medical data and treatment results, and is able to make highly accurate evaluations. For example, the analysis and evaluation unit constructs a system in which the generation AI learns from a database about past medical data and treatment results, and makes highly accurate evaluations based on that information. For example, it predicts the current effectiveness of treatment based on past treatment results. The analysis and evaluation unit also constructs a system in which the generation AI learns from past medical data and treatment results, and makes highly accurate evaluations. For example, it predicts the current effectiveness of treatment based on past treatment results. Furthermore, the analysis and evaluation unit develops an algorithm that allows the generation AI to learn from past medical data and treatment results, and make highly accurate evaluations. For example, it predicts the current effectiveness of treatment based on past treatment results. In this way, by learning from past medical data and treatment results, more accurate evaluations become possible.

[0044] The analysis and evaluation department can automatically detect abnormal values ​​and outliers during data analysis, thereby improving the reliability of the analysis results. For example, the analysis and evaluation department builds a system in which the generation AI automatically detects abnormal values ​​and outliers during data analysis. For example, it checks the consistency and integrity of the data and eliminates outliers. The analysis and evaluation department also builds a system in which the generation AI automatically detects abnormal values ​​and outliers during data analysis, thereby improving the reliability of the analysis results. For example, it checks the consistency and integrity of the data and eliminates outliers. Furthermore, the analysis and evaluation department develops an algorithm in which the generation AI automatically detects abnormal values ​​and outliers during data analysis, thereby improving the reliability of the analysis results. For example, it checks the consistency and integrity of the data and eliminates outliers. This makes it possible to automatically detect abnormal values ​​and outliers during data analysis, thereby improving the reliability of the analysis results.

[0045] The analysis and evaluation unit can apply the data analysis function to other medical-related data. For example, the analysis and evaluation unit applies the data analysis function of the generation AI to a patient's lifestyle data to analyze data for health management. For example, it analyzes diet and exercise records. The analysis and evaluation unit also applies the data analysis function of the generation AI to genetic information to analyze data for evaluating genetic risk factors. For example, it evaluates risk factors based on genetic information. Furthermore, the analysis and evaluation unit develops algorithms for applying the data analysis function of the generation AI to other medical-related data. For example, it analyzes a patient's lifestyle data and genetic information to perform comprehensive health management. In this way, comprehensive health management becomes possible by applying the data analysis function to other medical-related data.

[0046] The Analysis and Evaluation Department can share the analyzed data with other medical institutions and research institutions, promoting joint research and data analysis. For example, the Analysis and Evaluation Department shares the data analyzed by the Generative AI with other medical institutions, building a system to promote joint research. For example, they share a database and perform joint data analysis. The Analysis and Evaluation Department also shares the data analyzed by the Generative AI with other research institutions, building a system to promote joint research. For example, they share a database and perform joint data analysis. Furthermore, the Analysis and Evaluation Department shares the data analyzed by the Generative AI with other medical institutions and research institutions, developing algorithms to promote joint research and data analysis. For example, they share a database and perform joint data analysis. In this way, by sharing the analyzed data with other medical institutions and research institutions, joint research and data analysis can be promoted.

[0047] The proposal application unit is capable of creating an optimal treatment plan by taking into consideration the patient's individual health condition and lifestyle habits when proposing a treatment plan. The proposal application unit, for example, builds a system in which a generating AI retrieves a patient's individual health condition and lifestyle habits from a database and creates an optimal treatment plan based on that. For example, it proposes a plan that takes into consideration the patient's diet and exercise records. The proposal application unit also builds a system in which a generating AI takes into consideration the patient's individual health condition and lifestyle habits and creates an optimal treatment plan. For example, it proposes a plan that takes into consideration the patient's diet and exercise records. The proposal application unit also develops an algorithm for the generating AI to create an optimal treatment plan by taking into consideration the patient's individual health condition and lifestyle habits. For example, it proposes a plan that takes into consideration the patient's diet and exercise records. This makes it possible to create an optimal treatment plan by taking into consideration the patient's individual health condition and lifestyle habits.

[0048] The proposal application unit can share the treatment plan proposed by the generative AI with other medical institutions and experts, and improve the plan based on feedback. For example, the proposal application unit builds a system for sharing the treatment plan proposed by the generative AI with other medical institutions and improving the plan based on feedback. For example, it can jointly evaluate the plan. The proposal application unit also builds a system for sharing the treatment plan proposed by the generative AI with other experts and improving the plan based on feedback. For example, it can jointly evaluate the plan. Furthermore, the proposal application unit develops an algorithm for sharing the treatment plan proposed by the generative AI with other medical institutions and experts, and improving the plan based on feedback. For example, it can jointly evaluate the plan. In this way, the treatment plan proposed by the generative AI can be shared with other medical institutions and experts, and the plan can be improved based on feedback, thereby improving the accuracy and effectiveness of the treatment plan.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The revenue maximization system can also be equipped with a data collection unit that collects patients' lifestyle data and analyzes the data for health management. For example, it can collect patients' dietary and exercise records to evaluate their health status. It can also monitor patients' sleep patterns and stress levels and provide health management advice. It can also propose individual health management plans based on the patient's lifestyle data. This allows for comprehensive health management for patients and contributes to maximizing the hospital's revenue.

[0051] The revenue maximization system can also be equipped with a task management function to improve the work efficiency of medical staff. For example, it can automatically optimize medical staff schedules to reduce duplication and waste in work. It can also set task priorities to support efficient work execution. It can also provide a resource management function to reduce the workload of medical staff. This improves the work efficiency of medical staff and contributes to maximizing hospital profits.

[0052] The revenue maximization system can also be equipped with an equipment management section to streamline the maintenance and management of medical equipment. For example, it can automatically manage the usage status and maintenance schedule of medical equipment to prevent equipment breakdowns. It can also analyze the frequency of use and lifespan of equipment to propose optimal maintenance plans. It can also evaluate the cost efficiency of medical equipment and achieve optimal equipment management. This will improve the efficiency of medical equipment maintenance and contribute to maximizing hospital revenue.

[0053] The revenue maximization system can also be equipped with a treatment effect monitoring unit that monitors the patient's treatment effect in real time and optimizes the treatment plan. For example, it can analyze the patient's vital signs and treatment progress in real time and adjust the treatment plan based on that. It can also evaluate the treatment effect and propose the optimal treatment method. It can also develop an optimization algorithm for the treatment plan based on the treatment effect. This maximizes the treatment effect and contributes to maximizing the hospital's profits.

[0054] The revenue maximization system can also be equipped with a data security unit to further strengthen the security of medical data. For example, it can encrypt patients' personal information and medical records to prevent unauthorized access. It can also back up data regularly to prevent data loss. It can also evaluate data security and optimize security measures. This strengthens the security of medical data and contributes to maximizing hospital revenue.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The medical document generation unit automatically generates medical documents using the generation AI. For example, the generation AI automatically creates medical documents and reports based on diagnosis results and treatment details. The generation AI can also generate medical documents based on prompts that include diagnosis results and treatment details. Furthermore, when a doctor inputs the diagnosis and treatment details, the generation AI analyzes them and generates appropriate medical documents. Step 2: The error detection unit detects input errors and inconsistencies in the receipt. For example, the generation AI analyzes the input data of the receipt and detects input errors and inconsistencies between the medical treatment details and the billing details. The generation AI can also detect errors and inconsistencies based on the input data of the receipt. Furthermore, the generation AI automatically detects input errors and inconsistencies in the receipt and prompts for correction. Step 3: The cost estimation unit estimates medical costs based on the treatment plan. For example, the generating AI inputs the patient's medical records and treatment plan, analyzes them, and calculates the cost of treatment. The generating AI can also estimate medical costs based on prompts that include the medical records and treatment plan. The generating AI then estimates the cost of patient treatment based on the treatment plan. Step 4: The data collection unit collects data such as the patient's medical records, treatment history, and medication information. For example, the generation AI obtains data from electronic medical records and medication management systems and manages it centrally. The generation AI can also collect data based on prompts that include medical records, treatment history, and medication information. The generation AI also collects data such as the patient's medical records, treatment history, and medication information. Step 5: The analysis and evaluation department analyzes the collected data and calculates medical fee points. For example, the generation AI analyzes the patient's medical records and treatment history and evaluates the fee points for each medical procedure. The generation AI can also analyze data based on the collected data and calculate fee points. Furthermore, the generation AI analyzes the collected data and calculates medical fee points for each medical procedure. Step 6: The proposal application unit proposes an optimized treatment plan and applies it to the actual medical treatment process. For example, the generation AI proposes an optimal treatment plan based on the analysis results, and the doctor uses it as a reference when providing treatment. The generation AI can also propose a treatment plan based on the analysis results. Furthermore, the generation AI proposes the optimized treatment plan to the healthcare provider and applies it to the actual medical treatment process.

[0057] (Example 2) The revenue maximization system according to an embodiment of the present invention is a system that aims to streamline medical insurance claim processing in hospital management and proposes ways to maximize revenue based on the data. This revenue maximization system uses generative AI to automatically generate medical documents, detect errors and inconsistencies, and estimate medical costs, and also collects, analyzes, evaluates, proposes, and applies data to maximize medical fees. As a result, the revenue maximization system can improve the efficiency of medical insurance claim processing and maximize revenue in hospital management.

[0058] The revenue maximization system according to the embodiment includes a medical document generation unit, an error detection unit, a cost estimation unit, a data collection unit, an analysis and evaluation unit, and a proposal application unit. The medical document generation unit automatically generates medical documents using a generation AI. For example, the generation AI automatically creates medical documents and reports based on diagnosis results and treatment details. The generation AI can also generate medical documents based on prompts including diagnosis results and treatment details. Furthermore, the generation AI analyzes the doctor's input of diagnosis and treatment details and generates appropriate medical documents. The error detection unit detects input errors and inconsistencies in medical claims. For example, the generation AI analyzes input data for medical claims and detects inconsistencies or input errors between medical treatment details and billing details. The generation AI can also detect errors and inconsistencies based on input data for medical claims. Furthermore, the generation AI automatically detects input errors and inconsistencies in medical claims and prompts correction. The cost estimation unit estimates medical costs based on a treatment plan. For example, the generation AI analyzes input of a patient's medical records and treatment plan and calculates the costs of treatment. The generation AI can also estimate medical costs based on prompts containing medical records and treatment plans. Furthermore, the generation AI estimates the cost of patient treatment based on the treatment plan. The data collection unit collects data such as the patient's medical records, treatment history, and medication information. For example, the generation AI acquires data from electronic medical records and medication management systems and centrally manages it. The generation AI can also collect data based on prompts containing medical records, treatment history, and medication information. The generation AI also collects data such as the patient's medical records, treatment history, and medication information. The analysis and evaluation unit analyzes the collected data and calculates medical fee points. For example, the generation AI analyzes the patient's medical records and treatment history to evaluate the fee points for each medical procedure. The generation AI can also analyze data based on the collected data and calculate the fee points. Furthermore, the generation AI analyzes the collected data and calculates the medical fee points for each medical procedure. The proposal and application unit proposes an optimized treatment plan and applies it to the actual medical process. For example, the generation AI proposes an optimal treatment plan based on the analysis results, and the doctor uses it as a reference when providing medical care. The generative AI can also propose treatment plans based on the analysis results.Furthermore, the generation AI proposes optimized medical treatment plans to healthcare providers and applies them to actual medical treatment processes. As a result, the revenue maximization system according to the embodiment can realize efficient medical receipt operations and maximized profits in hospital management. For example, the automatic generation of medical documents reduces the burden on medical administration, and the detection of errors and inconsistencies improves the accuracy of medical receipt operations. In addition, estimating medical costs facilitates budget management, and data collection and analysis optimizes medical fees. Furthermore, the proposal of optimized medical treatment plans improves the quality of medical treatment and maximizes profits.

[0059] The medical document generation unit can create more detailed and personalized documents by taking into account the patient's past medical history and family history. For example, the medical document generation unit uses a generation AI to retrieve a patient's past medical history and family history from a database and generate personalized medical documents based on that information. For example, the unit emphasizes specific risk factors by taking into account the patient's past medical history and family medical history. The medical document generation unit also builds a system in which the generation AI takes into account the patient's past medical history and family history to generate personalized medical documents. For example, the unit creates documents that reflect individual treatment plans based on the patient's medical history and family history. Furthermore, the medical document generation unit develops algorithms that enable the generation AI to take into account the patient's past medical history and family history to generate personalized medical documents. For example, the unit analyzes a patient's past medical history and family history to create documents that reflect individual risk factors. This makes it possible to provide more appropriate medical documents to patients.

[0060] The medical document generation unit can convert a doctor's dictation into text in real time and generate documents on the spot. For example, the medical document generation unit will build a system in which a generation AI will recognize the doctor's dictation in real time and convert it into text. For example, what a doctor dictates during an examination will be instantly converted into text and saved as medical documents. The medical document generation unit will also build a system in which a generation AI will convert a doctor's dictation into text in real time and generate documents on the spot. For example, what a doctor dictates during an examination will be instantly converted into text and saved as medical documents. Furthermore, the medical document generation unit will develop an algorithm that will enable a generation AI to convert a doctor's dictation into text in real time and generate documents on the spot. For example, what a doctor dictates during an examination will be instantly converted into text and saved as medical documents. This will reduce the burden on doctors and enable faster document creation.

[0061] The medical document generation unit uses an emotion estimation function to generate medical documents that reflect the patient's emotional state, thereby reducing the patient's psychological burden. For example, the medical document generation unit uses a generation AI to analyze the patient's emotional state in real time and generate emotionally considerate medical documents based on the results. For example, if the patient is feeling anxious, it uses expressions that give a sense of security. The medical document generation unit also builds a system in which the generation AI generates medical documents that reflect the patient's emotional state. For example, it analyzes the patient's emotional state and uses expressions that take emotions into consideration. Furthermore, the medical document generation unit develops an algorithm for the generation AI to generate medical documents that reflect the patient's emotional state. For example, it analyzes the patient's emotional state and uses expressions that take emotions into consideration. This reduces the patient's psychological burden and enables the provision of better medical services.

[0062] The medical document generation unit can apply the automatic medical document generation function to other medical-related documents, including surgical plans and discharge summaries. For example, the medical document generation unit uses generation AI to build a system that automatically generates surgical plans. For example, when the details and procedures of a surgery are input, the generation AI creates a surgical plan based on that information. The medical document generation unit also uses generation AI to build a system that automatically generates discharge summaries. For example, when the patient's condition at the time of discharge and future treatment plans are input, the generation AI creates a discharge summary based on that information. Furthermore, the medical document generation unit uses generation AI to develop algorithms for automatically generating other medical-related documents, including surgical plans and discharge summaries. For example, appropriate medical documents are generated based on the details of the surgery and the patient's condition at the time of discharge. This enables the automatic generation of a variety of medical-related documents, improving work efficiency.

[0063] The medical document generation unit can make the generated medical documents multilingual so that they can be used by international medical institutions. For example, the medical document generation unit builds a system that translates medical documents generated by the generation AI into multiple languages. For example, it can make them compatible with multiple languages ​​such as English, French, and Chinese. The medical document generation unit also builds a system that makes medical documents generated by the generation AI multilingual. For example, it can translate medical documents generated by the generation AI into multiple languages ​​so that they can be used by international medical institutions. Furthermore, the medical document generation unit develops an algorithm to make medical documents generated by the generation AI multilingual. For example, it can translate medical documents generated by the generation AI into multiple languages ​​so that they can be used by international medical institutions. This multilingual support makes them usable by international medical institutions.

[0064] The error detection unit learns from past error history and can prevent similar errors from occurring. For example, the generation AI learns from a database of past error history and builds a system that prevents similar errors from occurring. For example, it analyzes patterns of errors that have occurred in the past and proposes preventive measures. The error detection unit also learns from past error history and builds a system that prevents similar errors from occurring. For example, it proposes preventive measures for errors based on past error history. Furthermore, the error detection unit develops an algorithm that allows the generation AI to learn from past error history and prevent similar errors from occurring. For example, it analyzes past error history and proposes preventive measures for errors. In this way, by learning from past error history, it is possible to prevent similar errors from occurring.

[0065] The error detection unit can suggest specific correction methods to medical staff when an error is detected. For example, the error detection unit builds a system that suggests specific correction methods to medical staff when the generation AI detects an error. For example, it presents specific steps for correcting an input error. The error detection unit also builds a system that suggests specific correction methods to medical staff when the generation AI detects an error. For example, it presents steps for correcting an error. Furthermore, the error detection unit develops an algorithm to suggest specific correction methods to medical staff when the generation AI detects an error. For example, it presents steps for correcting an error. This reduces the burden on medical staff by suggesting specific correction methods when an error is detected.

[0066] The error detection unit can use the emotion estimation function to evaluate the stress level of medical staff when an error is detected and suggest stress reduction measures. The error detection unit, for example, uses the emotion estimation function to build a system that evaluates the stress level of medical staff when an error is detected. For example, if stress is high, it suggests relaxation methods. The error detection unit also uses the emotion estimation function to build a system that evaluates the stress level of medical staff when an error is detected and suggests stress reduction measures. For example, if stress is high, it suggests relaxation methods. The error detection unit also uses the emotion estimation function to develop an algorithm for evaluating the stress level of medical staff when an error is detected and suggesting stress reduction measures. For example, if stress is high, it suggests relaxation methods. In this way, the burden on medical staff can be reduced by evaluating the stress level of medical staff when an error is detected and suggesting stress reduction measures.

[0067] The error detection unit can apply the error detection function to other medical tasks. For example, the error detection unit applies the error detection function of the generation AI to a medication management system to prevent medication errors and inventory management errors. For example, it detects inconsistencies in medication dosage or administration timing. The error detection unit also applies the error detection function of the generation AI to surgery schedule management to prevent schedule overlaps and errors. For example, it automatically checks surgery schedules and detects overlaps and errors. Furthermore, the error detection unit develops algorithms to apply the error detection function of the generation AI to other medical tasks. For example, it builds a system to prevent errors in medication management and surgery schedule management. In this way, the error detection function can be applied to other medical tasks, improving the accuracy of the entire operation.

[0068] When an error is detected, the error detection unit can compare it with other related databases to confirm the appropriateness of the correction. For example, when the generation AI detects an error, the error detection unit builds a system that compares the correction with medical guidelines to confirm the appropriateness of the correction. For example, it checks whether the medical treatment content complies with the guidelines. In addition, when the generation AI detects an error, the error detection unit builds a system that compares the correction with a drug database to confirm the appropriateness of the correction. For example, it checks whether the prescribed drug is appropriate. Furthermore, when the generation AI detects an error, the error detection unit develops an algorithm to compare the correction with other related databases to confirm the appropriateness of the correction. For example, it compares the correction with medical guidelines or a drug database to confirm the appropriateness of the correction. This makes it possible to confirm the appropriateness of the correction by comparing it with other related databases when an error is detected.

[0069] The error detection unit can use the emotion estimation function to analyze the emotional reactions of medical staff after error detection and propose improvement measures for dealing with the error. The error detection unit, for example, uses the emotion estimation function to build a system that analyzes the emotional reactions of medical staff after error detection in real time. For example, it evaluates stress and anxiety levels. The error detection unit also uses the emotion estimation function to build a system that analyzes the emotional reactions of medical staff after error detection and proposes improvement measures for dealing with the error. For example, it evaluates stress and anxiety levels and proposes improvement measures. The error detection unit also uses the emotion estimation function to develop an algorithm for analyzing the emotional reactions of medical staff after error detection and proposing improvement measures for dealing with the error. For example, it evaluates stress and anxiety levels and proposes improvement measures. In this way, the burden on medical staff can be reduced by analyzing the emotional reactions of medical staff after error detection and proposing improvement measures for dealing with the error.

[0070] The cost estimation unit is able to predict the effectiveness of treatment and the patient's recovery rate, and propose cost-effective treatments. For example, the cost estimation unit constructs a system in which a generative AI predicts the effectiveness of treatment and the patient's recovery rate, and then proposes cost-effective treatments based on that. For example, it estimates costs taking into account the treatment period and recovery rate. The cost estimation unit also constructs a system in which a generative AI predicts the effectiveness of treatment and the patient's recovery rate, and then proposes cost-effective treatments. For example, it estimates costs taking into account the treatment period and recovery rate. The cost estimation unit also develops an algorithm in which a generative AI predicts the effectiveness of treatment and the patient's recovery rate, and then proposes cost-effective treatments. For example, it estimates costs taking into account the treatment period and recovery rate. This makes it possible to propose cost-effective treatments by predicting the effectiveness of treatment and the patient's recovery rate.

[0071] The cost estimation unit uses the emotion estimation function to estimate costs to reduce the patient's financial burden and can propose a treatment plan that takes the patient's emotional state into consideration. The cost estimation unit, for example, uses the emotion estimation function to build a system that estimates costs to reduce the patient's financial burden. For example, the emotional state of the patient is analyzed and a treatment method with a low financial burden is proposed. The cost estimation unit also uses the emotion estimation function to build a system that estimates costs to reduce the patient's financial burden and proposes a treatment plan that takes the patient's emotional state into consideration. For example, the emotional state of the patient is analyzed and a treatment method with a low financial burden is proposed. The cost estimation unit also uses the emotion estimation function to develop an algorithm for estimating costs to reduce the patient's financial burden and proposing a treatment plan that takes the patient's emotional state into consideration. For example, the emotional state of the patient is analyzed and a treatment method with a low financial burden is proposed. This makes it possible to propose a treatment plan that reduces the patient's financial burden and takes the patient's emotional state into consideration.

[0072] The cost estimation unit can apply the medical cost estimation function to other medical-related costs. For example, the cost estimation unit applies the medical cost estimation function of the generation AI to medical equipment maintenance costs, thereby achieving cost-efficient equipment management. For example, it performs estimations that take into account the frequency of equipment use and maintenance costs. The cost estimation unit also applies the medical cost estimation function of the generation AI to drug costs, thereby achieving cost-efficient drug management. For example, it performs estimations that take into account the frequency of drug use and inventory management. Furthermore, the cost estimation unit develops an algorithm for applying the medical cost estimation function of the generation AI to other medical-related costs. For example, it performs cost estimations that take into account the maintenance costs of medical equipment and drug costs. In this way, comprehensive cost management becomes possible by applying the medical cost estimation function to other medical-related costs.

[0073] The cost estimation unit can compare the estimated medical costs with other hospitals and medical institutions and provide a benchmark for cost reduction. The cost estimation unit, for example, builds a system that compares the medical costs estimated by the generation AI with other hospitals and medical institutions and provides a benchmark for cost reduction. For example, it compares the costs of the same treatment and proposes optimal cost reduction measures. The cost estimation unit also builds a system that compares the medical costs estimated by the generation AI with other hospitals and medical institutions and provides a benchmark for cost reduction. For example, it compares the costs of the same treatment and proposes optimal cost reduction measures. The cost estimation unit also develops an algorithm for comparing the medical costs estimated by the generation AI with other hospitals and medical institutions and providing a benchmark for cost reduction. For example, it compares the costs of the same treatment and proposes optimal cost reduction measures. This makes it possible to provide a benchmark for cost reduction by comparing medical costs with other hospitals and medical institutions.

[0074] The cost estimation unit can use the emotion estimation function to evaluate the emotional impact of the medical cost estimation results on patients and medical staff, and adjust the estimation results as necessary. The cost estimation unit, for example, uses the emotion estimation function to build a system that evaluates the emotional impact of the medical cost estimation results on patients and medical staff. For example, if the estimation results cause anxiety, the cost estimation unit corrects that part. The cost estimation unit also uses the emotion estimation function to build a system that evaluates the emotional impact of the medical cost estimation results on patients and medical staff, and adjusts the estimation results as necessary. For example, if the estimation results cause anxiety, the cost estimation unit corrects that part. The cost estimation unit also uses the emotion estimation function to develop an algorithm for evaluating the emotional impact of the medical cost estimation results on patients and medical staff, and adjusting the estimation results as necessary. For example, if the estimation results cause anxiety, the cost estimation unit corrects that part. In this way, the psychological burden can be reduced by evaluating the emotional impact of the medical cost estimation results on patients and medical staff, and adjusting the estimation results as necessary.

[0075] The data collection unit can use the emotion estimation function to evaluate the emotional states of patients and medical staff during data collection and optimize the data collection method. The data collection unit, for example, uses the emotion estimation function to build a system for evaluating the emotional states of patients and medical staff during data collection. For example, if data collection causes stress, the method is improved. The data collection unit also uses the emotion estimation function to build a system for evaluating the emotional states of patients and medical staff during data collection and optimizing the data collection method. For example, if data collection causes stress, the method is improved. The data collection unit also uses the emotion estimation function to develop an algorithm for evaluating the emotional states of patients and medical staff during data collection and optimizing the data collection method. For example, if data collection causes stress, the method is improved. In this way, by evaluating the emotional states of patients and medical staff during data collection and optimizing the data collection method, the efficiency and quality of data collection can be improved.

[0076] The data collection unit can apply the data collection function to other medical-related data. For example, the data collection unit applies the data collection function of the generation AI to a patient's lifestyle data to collect data for health management. For example, it collects diet and exercise records. The data collection unit also applies the data collection function of the generation AI to genetic information to collect data for evaluating genetic risk factors. For example, it evaluates risk factors based on genetic information. Furthermore, the data collection unit develops algorithms for applying the data collection function of the generation AI to other medical-related data. For example, it collects a patient's lifestyle data and genetic information to perform comprehensive health management. This makes it possible to apply the data collection function to other medical-related data as well, enabling comprehensive health management.

[0077] The data collection unit can share the collected data with other medical institutions and research institutions to promote joint research and data analysis. For example, the data collection unit shares the data collected by the generative AI with other medical institutions and builds a system to promote joint research. For example, they share a database and conduct joint data analysis. The data collection unit also builds a system to share the data collected by the generative AI with other research institutions and builds a system to promote joint research. For example, they share a database and conduct joint data analysis. Furthermore, the data collection unit develops an algorithm to share the data collected by the generative AI with other medical institutions and research institutions and promote joint research and data analysis. For example, they share a database and conduct joint data analysis. In this way, by sharing the collected data with other medical institutions and research institutions, joint research and data analysis can be promoted.

[0078] The data collection unit can use the emotion estimation function to analyze the emotional reactions of patients and medical staff during data collection and propose improvements to the data collection process. The data collection unit, for example, uses the emotion estimation function to build a system that analyzes the emotional reactions of patients and medical staff during data collection in real time. For example, it evaluates stress and anxiety levels. The data collection unit also uses the emotion estimation function to build a system that analyzes the emotional reactions of patients and medical staff during data collection and proposes improvements to the data collection process. For example, it evaluates stress and anxiety levels and proposes improvements. The data collection unit also uses the emotion estimation function to develop an algorithm for analyzing the emotional reactions of patients and medical staff during data collection and proposing improvements to the data collection process. For example, it evaluates stress and anxiety levels and proposes improvements. This makes it possible to analyze the emotional reactions of patients and medical staff during data collection and propose improvements to the data collection process, thereby improving the efficiency and quality of data collection.

[0079] The analysis and evaluation unit learns from past medical data and treatment results, and is able to make highly accurate evaluations. For example, the analysis and evaluation unit constructs a system in which the generation AI learns from a database about past medical data and treatment results, and makes highly accurate evaluations based on that information. For example, it predicts the current effectiveness of treatment based on past treatment results. The analysis and evaluation unit also constructs a system in which the generation AI learns from past medical data and treatment results, and makes highly accurate evaluations. For example, it predicts the current effectiveness of treatment based on past treatment results. Furthermore, the analysis and evaluation unit develops an algorithm that allows the generation AI to learn from past medical data and treatment results, and make highly accurate evaluations. For example, it predicts the current effectiveness of treatment based on past treatment results. In this way, by learning from past medical data and treatment results, more accurate evaluations become possible.

[0080] The analysis and evaluation department can automatically detect abnormal values ​​and outliers during data analysis, thereby improving the reliability of the analysis results. For example, the analysis and evaluation department builds a system in which the generation AI automatically detects abnormal values ​​and outliers during data analysis. For example, it checks the consistency and integrity of the data and eliminates outliers. The analysis and evaluation department also builds a system in which the generation AI automatically detects abnormal values ​​and outliers during data analysis, thereby improving the reliability of the analysis results. For example, it checks the consistency and integrity of the data and eliminates outliers. Furthermore, the analysis and evaluation department develops an algorithm in which the generation AI automatically detects abnormal values ​​and outliers during data analysis, thereby improving the reliability of the analysis results. For example, it checks the consistency and integrity of the data and eliminates outliers. This makes it possible to automatically detect abnormal values ​​and outliers during data analysis, thereby improving the reliability of the analysis results.

[0081] The analysis and evaluation unit can apply the data analysis function to other medical-related data. For example, the analysis and evaluation unit applies the data analysis function of the generation AI to a patient's lifestyle data to analyze data for health management. For example, it analyzes diet and exercise records. The analysis and evaluation unit also applies the data analysis function of the generation AI to genetic information to analyze data for evaluating genetic risk factors. For example, it evaluates risk factors based on genetic information. Furthermore, the analysis and evaluation unit develops algorithms for applying the data analysis function of the generation AI to other medical-related data. For example, it analyzes a patient's lifestyle data and genetic information to perform comprehensive health management. In this way, comprehensive health management becomes possible by applying the data analysis function to other medical-related data.

[0082] The Analysis and Evaluation Department can share the analyzed data with other medical institutions and research institutions, promoting joint research and data analysis. For example, the Analysis and Evaluation Department shares the data analyzed by the Generative AI with other medical institutions, building a system to promote joint research. For example, they share a database and perform joint data analysis. The Analysis and Evaluation Department also shares the data analyzed by the Generative AI with other research institutions, building a system to promote joint research. For example, they share a database and perform joint data analysis. Furthermore, the Analysis and Evaluation Department shares the data analyzed by the Generative AI with other medical institutions and research institutions, developing algorithms to promote joint research and data analysis. For example, they share a database and perform joint data analysis. In this way, by sharing the analyzed data with other medical institutions and research institutions, joint research and data analysis can be promoted.

[0083] The analysis and evaluation unit can use the emotion estimation function to analyze the emotional reactions of patients and medical staff to the analysis results and propose improvements to the analysis process. The analysis and evaluation unit, for example, uses the emotion estimation function to build a system that analyzes the emotional reactions of patients and medical staff to the analysis results in real time. For example, it evaluates the levels of stress and anxiety. The analysis and evaluation unit also uses the emotion estimation function to build a system that analyzes the emotional reactions of patients and medical staff to the analysis results and proposes improvements to the analysis process. For example, it evaluates the levels of stress and anxiety and proposes improvements. The analysis and evaluation unit also uses the emotion estimation function to develop an algorithm for analyzing the emotional reactions of patients and medical staff to the analysis results and proposing improvements to the analysis process. For example, it evaluates the levels of stress and anxiety and proposes improvements. In this way, by analyzing the emotional reactions of patients and medical staff to the analysis results and proposing improvements to the analysis process, it is possible to reduce psychological burden.

[0084] The proposal application unit is capable of creating an optimal treatment plan by taking into consideration the patient's individual health condition and lifestyle habits when proposing a treatment plan. The proposal application unit, for example, builds a system in which a generating AI retrieves a patient's individual health condition and lifestyle habits from a database and creates an optimal treatment plan based on that. For example, it proposes a plan that takes into consideration the patient's diet and exercise records. The proposal application unit also builds a system in which a generating AI takes into consideration the patient's individual health condition and lifestyle habits and creates an optimal treatment plan. For example, it proposes a plan that takes into consideration the patient's diet and exercise records. The proposal application unit also develops an algorithm for the generating AI to create an optimal treatment plan by taking into consideration the patient's individual health condition and lifestyle habits. For example, it proposes a plan that takes into consideration the patient's diet and exercise records. This makes it possible to create an optimal treatment plan by taking into consideration the patient's individual health condition and lifestyle habits.

[0085] The proposal application unit can use the emotion estimation function to evaluate the emotional impact of a proposed medical treatment plan on a patient and propose a plan that reduces the patient's psychological burden. The proposal application unit, for example, uses the emotion estimation function to build a system that evaluates the emotional impact of a proposed medical treatment plan on a patient. For example, if the plan causes anxiety, it modifies that part. The proposal application unit also uses the emotion estimation function to build a system that evaluates the emotional impact of a proposed medical treatment plan on a patient and proposes a plan that reduces the patient's psychological burden. For example, if the plan causes anxiety, it modifies that part. The proposal application unit also uses the emotion estimation function to develop an algorithm for evaluating the emotional impact of a proposed medical treatment plan on a patient and proposing a plan that reduces the patient's psychological burden. For example, if the plan causes anxiety, it modifies that part. This allows the system to evaluate the emotional impact of a proposed medical treatment plan on a patient and propose a plan that reduces the patient's psychological burden, thereby improving patient satisfaction.

[0086] The proposal application unit can share the treatment plan proposed by the generative AI with other medical institutions and experts, and improve the plan based on feedback. For example, the proposal application unit builds a system for sharing the treatment plan proposed by the generative AI with other medical institutions and improving the plan based on feedback. For example, it can jointly evaluate the plan. The proposal application unit also builds a system for sharing the treatment plan proposed by the generative AI with other experts and improving the plan based on feedback. For example, it can jointly evaluate the plan. Furthermore, the proposal application unit develops an algorithm for sharing the treatment plan proposed by the generative AI with other medical institutions and experts, and improving the plan based on feedback. For example, it can jointly evaluate the plan. In this way, the treatment plan proposed by the generative AI can be shared with other medical institutions and experts, and the plan can be improved based on feedback, thereby improving the accuracy and effectiveness of the treatment plan.

[0087] The proposal application unit uses the emotion estimation function to monitor the emotional reactions of patients and medical staff to the proposed medical plan in real time, and is able to continuously search for an optimal plan. The proposal application unit, for example, uses the emotion estimation function to build a system that monitors the emotional reactions of patients and medical staff to the proposed medical plan in real time. For example, the plan is adjusted based on the emotion score. The proposal application unit also uses the emotion estimation function to build a system that monitors the emotional reactions of patients and medical staff to the proposed medical plan in real time, and continuously searches for an optimal plan. For example, the plan is adjusted based on the emotion score. The proposal application unit also uses the emotion estimation function to monitor the emotional reactions of patients and medical staff to the proposed medical plan in real time, and develops an algorithm for continuously searching for an optimal plan. For example, the plan is adjusted based on the emotion score. In this way, the emotional reactions of patients and medical staff to the proposed medical plan can be monitored in real time, and the optimal plan can be continuously searched for, thereby improving the effectiveness and satisfaction of the medical plan.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The revenue maximization system can also be equipped with a data collection unit that collects patients' lifestyle data and analyzes the data for health management. For example, it can collect patients' dietary and exercise records to evaluate their health status. It can also monitor patients' sleep patterns and stress levels and provide health management advice. It can also propose individual health management plans based on the patient's lifestyle data. This allows for comprehensive health management for patients and contributes to maximizing the hospital's revenue.

[0090] The revenue maximization system can also be equipped with an emotion estimation function that monitors patients' emotional state in real time and reflects this in treatment plans. For example, it can analyze the anxiety and stress a patient feels during an examination and adjust the treatment plan accordingly. It can also suggest communication methods that take the patient's emotional state into account to reduce the patient's psychological burden. It can also suggest relaxation methods and stress reduction measures based on the patient's emotional state. This can improve patient satisfaction and contribute to maximizing hospital revenue.

[0091] The revenue maximization system can also be equipped with a task management function to improve the work efficiency of medical staff. For example, it can automatically optimize medical staff schedules to reduce duplication and waste in work. It can also set task priorities to support efficient work execution. It can also provide a resource management function to reduce the workload of medical staff. This improves the work efficiency of medical staff and contributes to maximizing hospital profits.

[0092] The revenue maximization system can also optimize medical fees by taking into account the patient's emotional state. For example, it can analyze the anxiety and stress a patient feels during treatment and adjust medical fees based on that. It can also propose treatment plans that take the patient's emotional state into account, thereby maximizing medical fees. It can also develop a medical fee optimization algorithm based on the patient's emotional state. This makes it possible to maximize hospital revenue while improving patient satisfaction.

[0093] The revenue maximization system can also be equipped with an equipment management section to streamline the maintenance and management of medical equipment. For example, it can automatically manage the usage status and maintenance schedule of medical equipment to prevent equipment breakdowns. It can also analyze the frequency of use and lifespan of equipment to propose optimal maintenance plans. It can also evaluate the cost efficiency of medical equipment and achieve optimal equipment management. This will improve the efficiency of medical equipment maintenance and contribute to maximizing hospital revenue.

[0094] The revenue maximization system can also propose treatment plans that take into account the patient's emotional state. For example, it can analyze the patient's emotional state in real time and adjust the treatment plan based on that. It can also propose treatment methods that take the patient's emotional state into account, reducing the patient's psychological burden. It can also develop an optimization algorithm for treatment plans based on the patient's emotional state. This makes it possible to maximize the hospital's revenue while improving patient satisfaction.

[0095] The revenue maximization system can also be equipped with a stress management function that monitors the stress levels of medical staff and reduces their workload. For example, it can analyze the stress levels of medical staff in real time and adjust their work based on that. It can also suggest relaxation methods and break times to reduce stress. Furthermore, it can develop an algorithm to optimize the workload based on the stress levels of medical staff. This reduces the workload of medical staff and contributes to maximizing the hospital's revenue.

[0096] The revenue maximization system can also be equipped with a treatment effect monitoring unit that monitors the patient's treatment effect in real time and optimizes the treatment plan. For example, it can analyze the patient's vital signs and treatment progress in real time and adjust the treatment plan based on that. It can also evaluate the treatment effect and propose the optimal treatment method. It can also develop an optimization algorithm for the treatment plan based on the treatment effect. This maximizes the treatment effect and contributes to maximizing the hospital's profits.

[0097] The revenue maximization system can also propose discharge plans that take into account the patient's emotional state. For example, it can analyze the patient's emotional state in real time and adjust the discharge plan based on that. It can also propose post-discharge follow-up plans that take the patient's emotional state into account, reducing the patient's psychological burden. Furthermore, it can develop an optimization algorithm for discharge plans based on the patient's emotional state. This makes it possible to maximize hospital revenue while improving patient satisfaction.

[0098] The revenue maximization system can also be equipped with a data security unit to further strengthen the security of medical data. For example, it can encrypt patients' personal information and medical records to prevent unauthorized access. It can also back up data regularly to prevent data loss. It can also evaluate data security and optimize security measures. This strengthens the security of medical data and contributes to maximizing hospital revenue.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The medical document generation unit automatically generates medical documents using the generation AI. For example, the generation AI automatically creates medical documents and reports based on diagnosis results and treatment details. The generation AI can also generate medical documents based on prompts that include diagnosis results and treatment details. Furthermore, when a doctor inputs the diagnosis and treatment details, the generation AI analyzes them and generates appropriate medical documents. Step 2: The error detection unit detects input errors and inconsistencies in the receipt. For example, the generation AI analyzes the input data of the receipt and detects input errors and inconsistencies between the medical treatment details and the billing details. The generation AI can also detect errors and inconsistencies based on the input data of the receipt. Furthermore, the generation AI automatically detects input errors and inconsistencies in the receipt and prompts for correction. Step 3: The cost estimation unit estimates medical costs based on the treatment plan. For example, the generating AI inputs the patient's medical records and treatment plan, analyzes them, and calculates the cost of treatment. The generating AI can also estimate medical costs based on prompts that include the medical records and treatment plan. The generating AI then estimates the cost of patient treatment based on the treatment plan. Step 4: The data collection unit collects data such as the patient's medical records, treatment history, and medication information. For example, the generation AI obtains data from electronic medical records and medication management systems and manages it centrally. The generation AI can also collect data based on prompts that include medical records, treatment history, and medication information. The generation AI also collects data such as the patient's medical records, treatment history, and medication information. Step 5: The analysis and evaluation department analyzes the collected data and calculates medical fee points. For example, the generation AI analyzes the patient's medical records and treatment history and evaluates the fee points for each medical procedure. The generation AI can also analyze data based on the collected data and calculate fee points. Furthermore, the generation AI analyzes the collected data and calculates medical fee points for each medical procedure. Step 6: The proposal application unit proposes an optimized treatment plan and applies it to the actual medical treatment process. For example, the generation AI proposes an optimal treatment plan based on the analysis results, and the doctor uses it as a reference when providing treatment. The generation AI can also propose a treatment plan based on the analysis results. Furthermore, the generation AI proposes the optimized treatment plan to the healthcare provider and applies it to the actual medical treatment process.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0103] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 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.

[0106] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0107] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0108] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0110] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0111] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0112] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0114] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0115] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0122] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0123] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0126] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0129] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0137] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0138] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0141] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0142] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0143] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0145] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0147] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0150] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0151] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0152] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0153] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0154] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0155] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0156] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0157] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0158] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0159] 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.

[0160] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0161] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0162] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0163] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0164] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0165] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0166] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0167] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0168] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a medical document generation unit that automatically generates medical documents using generative AI; An error detection unit that detects input errors and inconsistencies in prescriptions, a cost estimation unit that estimates medical costs based on a treatment plan; a data collection unit that collects data such as patient medical records, treatment history, and medication information; The analysis and evaluation department analyzes the collected data and calculates medical fee points; a proposal application unit that proposes an optimized medical plan and applies it to an actual medical treatment process; A system characterized by:

2. The medical document generation unit Using an emotion estimation function, the medical document is generated to reflect the emotional state of the patient, thereby reducing the psychological burden on the patient.

2. The system of claim 1.

3. The error detection unit Using emotion estimation functionality to assess the stress level of medical staff when an error is detected and suggest stress reduction measures 2. The system of claim 1.

4. The cost estimation unit Using an emotion estimation function, a cost estimation is performed to reduce the economic burden on the patient, and the treatment plan is proposed taking into account the emotional state of the patient.

2. The system of claim 1.

5. The data collection unit Using emotion estimation, the system analyzes the emotional responses of patients and medical staff during data collection and proposes improvements to the data collection process.

2. The system of claim 1.

6. The analysis and evaluation unit Using emotion estimation functionality, the system analyzes the emotional reactions of patients and medical staff to the analysis results and proposes improvements to the analysis process.

2. The system of claim 1.

7. The proposal application unit Using an emotion estimation function, the emotional impact of the proposed treatment plan on the patient is evaluated, and a plan that reduces the psychological burden on the patient is proposed.

2. The system of claim 1.

8. The proposal application unit Using emotion estimation functionality, the emotional reactions of the patient and medical staff to the proposed treatment plan are monitored in real time, and the optimal plan is continuously sought.

2. The system of claim 1.

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