System

Abstract

An object of a system according to an embodiment is to improve the efficiency of a medical insurance claim process and to reduce a burden on a medical worker.SOLUTION: A system includes a data input unit, an analysis unit, and a mail generation unit. The data input unit inputs disease data of a patient. The analyzing unit analyzes the disease data of the patient input by the data input unit. The mail generation unit generates an insurance claim request mail based on the data analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made the medical insurance claim process complicated, requiring time and effort from medical professionals.

[0005] The system according to the embodiment aims to streamline the medical insurance claim process and reduce the burden on medical professionals. [Means for solving the problem]

[0006] The system according to the embodiment includes a data input unit, an analysis unit, and an email generation unit. The data input unit inputs disease data of a patient. The analysis unit analyzes the disease data of the patient input by the data input unit. The email generation unit generates an insurance claim request email based on the data analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can streamline the medical insurance claim process and reduce the burden on medical professionals. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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) In the medical insurance claim support system according to an embodiment of the present invention, medical professionals input patient disease data, and a generation AI generates an insurance claim request email based on the latest medical research. This enables the medical insurance claim support system to improve the accuracy of insurance claims, increase approval rates, and significantly reduce the time required for document creation.

[0029] A medical insurance claim support system according to an embodiment includes a data input unit, an analysis unit, and an email generation unit. The data input unit inputs a patient's disease data, including, for example, basic patient information, diagnosis results, treatment details, and the medications and medical devices used. The analysis unit analyzes the patient's disease data input by the data input unit. For example, the generation AI analyzes the disease data using data mining technology and selects appropriate diagnosis and treatment codes. The generation AI also analyzes data patterns using machine learning algorithms to improve the accuracy of insurance claims. The email generation unit generates an insurance claim request email based on the data analyzed by the analysis unit. For example, the generation AI automatically generates an insurance claim request email based on the latest medical research. The generation AI can also generate an insurance claim request email with an appropriate format and necessary fields. This allows the medical insurance claim support system to improve the accuracy of insurance claims, increase approval rates, and significantly reduce the time required for document creation.

[0030] The data entry unit checks the consistency and accuracy of data in real time as it is entered, and can automatically correct input errors. For example, when a medical professional enters a patient's disease data, the generation AI analyzes the input content in real time and checks the consistency and accuracy of the data. For example, if there is a discrepancy between the patient's age and the diagnosed disease name, the generation AI automatically displays a warning and prompts the user to make corrections. The data entry unit also has a function to automatically correct input errors. For example, the generation AI detects typos and numerical errors and automatically corrects them. This improves the accuracy of data entry and reduces input errors.

[0031] When entering a patient's disease data, the data entry unit can automatically refer to past medical history and family history to complete relevant information. For example, when a medical professional enters a patient's disease data, the data entry unit's generation AI automatically refers to past medical history and completes relevant information. For example, if the patient has been diagnosed with the same disease in the past, that information is automatically displayed. The data entry unit also has the function of automatically referring to family history and completing relevant information. For example, it provides additional diagnostic information taking into account family medical history and genetic factors. This improves the accuracy of data entry by referring to past medical history and family history.

[0032] The data input unit can add a voice input function, allowing medical professionals to input patient disease data by voice. The data input unit, for example, introduces voice recognition technology to enable medical professionals to input patient disease data by voice. For example, the medical professional simply speaks into a microphone, and the generative AI converts the voice into text and enters it into a database. The data input unit also uses the voice input function to build a system that converts voice to text in real time. For example, text is displayed on the screen simultaneously with voice input. This improves convenience for medical professionals with the voice input function.

[0033] The data input unit can provide a function that allows patients to input and update their own disease data through a smartphone app. For example, the data input unit can provide a function that allows patients to input and update their own disease data through a smartphone app. For example, diagnosis results and treatment details can be entered into the app, and the generation AI can automatically save them in a database. The data input unit can also build a system that allows patients to update their own disease data in real time using a smartphone app. For example, new diagnosis information and treatment details can be entered through the app and reflected in the database. This allows patients to input and update data themselves, improving the accuracy of the data.

[0034] The analysis unit can generate insurance claim request emails taking into account not only the latest medical research, but also local insurance systems and regulations. For example, when the generation AI generates an insurance claim request email based on the latest medical research, the analysis unit also takes local insurance systems and regulations into account. For example, it automatically references local insurance coverage and regulations and reflects appropriate content in the email. The analysis unit also has a function to adjust the content of insurance claim request emails taking into account local insurance systems and regulations. For example, it optimizes the content of the email based on local legal requirements and industry standards. This improves the accuracy of insurance claims by taking into account local insurance systems and regulations.

[0035] When generating an insurance claim request email, the analysis unit can analyze past insurance claim data and automatically select the format most likely to be approved. For example, the generation AI in the analysis unit analyzes past insurance claim data and automatically selects the format most likely to be approved. For example, it identifies a format with a high approval rate based on past data and applies that format to the email. The analysis unit also has a function to optimize the content of the insurance claim request email based on past insurance claim data. For example, it refers to past success stories and selects the optimal format and necessary items. This improves the approval rate by selecting the optimal format based on past data.

[0036] The analysis unit supports multiple languages ​​when generating an insurance claim request email, and can simultaneously generate invoices in different languages. For example, when the generation AI generates an insurance claim request email, the analysis unit supports multiple languages ​​and simultaneously generates invoices in different languages. For example, invoices are generated in multiple languages, such as English, French, and Chinese. In addition, the analysis unit has a function to generate invoices in different languages ​​using a translation engine for multilingual support. For example, the generation AI automatically translates and generates invoices in multiple languages. This allows for multilingual support, making it possible to generate invoices in different languages.

[0037] The analysis unit can automatically add visual elements when generating an insurance claim request email to make it visually easier to understand. For example, when the generation AI generates an insurance claim request email, the analysis unit automatically adds visual elements (e.g., graphs and charts) to make it visually easier to understand. For example, it adds a graph showing the progress of treatment or a chart showing the breakdown of costs. The analysis unit also has a function that uses visual elements to make the contents of the insurance claim request email visually easier to understand. For example, the generation AI automatically generates graphs and charts and inserts them into the email. In this way, adding visual elements makes it visually easier to understand.

[0038] The analysis unit can analyze not only the latest medical research but also insurance companies' past approval and denial data to improve the accuracy of insurance claims. For example, in order for the generative AI to improve the accuracy of insurance claims, the analysis unit analyzes not only the latest medical research but also insurance companies' past approval and denial data. For example, it identifies diagnosis and treatment codes that are likely to be approved based on past data. The analysis unit also has a function to optimize the content of insurance claim request emails based on the insurance company's past approval and denial data. For example, it refers to past success stories and selects the most appropriate diagnosis and treatment codes. In this way, analyzing past data improves the accuracy of insurance claims.

[0039] The analysis unit can reflect medical professional feedback in real time when selecting diagnostic codes and treatment codes. For example, the analysis unit reflects medical professional feedback in real time when the generation AI selects diagnostic codes and treatment codes. For example, the generation AI adjusts diagnostic codes and treatment codes based on feedback entered by medical professionals. The analysis unit also has a function to optimize the content of insurance claim request emails based on feedback from medical professionals. For example, it reflects feedback and selects more appropriate diagnostic codes and treatment codes. In this way, the accuracy of insurance claims is improved by reflecting feedback from medical professionals.

[0040] The analysis unit can automatically compare and analyze the regulations and requirements of different insurance companies to improve the accuracy of insurance claims. For example, the generation AI automatically compares and analyzes the regulations and requirements of different insurance companies to improve the accuracy of insurance claims. For example, it selects the optimal diagnosis and treatment codes based on the regulations of each insurance company. The analysis unit also has a function to optimize the content of insurance claim request emails based on the regulations and requirements of different insurance companies. For example, it compares the regulations of each insurance company and reflects the optimal content in the email. This improves the accuracy of insurance claims by comparing and analyzing the regulations and requirements of different insurance companies.

[0041] The analysis unit can refer to success stories from other medical institutions and propose the optimal billing method to improve the accuracy of insurance claims. For example, the generation AI of the analysis unit refers to success stories from other medical institutions and proposes the optimal billing method to improve the accuracy of insurance claims. For example, it selects the optimal diagnosis code and treatment code based on success stories. The analysis unit also has a function to optimize the content of insurance claim request emails based on success stories from other medical institutions. For example, it refers to success stories and proposes the optimal billing method. In this way, the accuracy of insurance claims is improved by referring to success stories from other medical institutions.

[0042] The email generation unit can automatically refer to similar past documents and select the optimal template when creating a document. For example, when creating a document, the generation AI in the email generation unit automatically refers to similar past documents and selects the optimal template. For example, it selects the optimal template based on past success stories, making document creation more efficient. The email generation unit also has a function to optimize the content of insurance claim request emails based on similar past documents. For example, it refers to past data and selects the optimal format and necessary items. This improves the efficiency of document creation by referring to similar past documents.

[0043] The email generation unit can improve quality by automatically checking grammar and applying style guidelines when creating documents. For example, when the generation AI creates a document, the email generation unit automatically checks grammar and applies style guidelines to improve quality. For example, it automatically corrects grammatical errors and generates a document that complies with style guidelines. The email generation unit also has a function to optimize the content of insurance claim request emails based on grammar checks and the application of style guidelines. For example, the generation AI automatically checks the quality of the document and suggests corrections. As a result, the quality of the document is improved by checking grammar and applying style guidelines.

[0044] The email generation unit also supports voice input and handwriting input when creating documents, improving convenience for medical professionals. For example, when the generation AI creates a document, the email generation unit also supports voice input and handwriting input, improving convenience for medical professionals. For example, a medical professional can simply give voice instructions and the generation AI can automatically generate a document. The email generation unit also supports handwriting input and has a function that automatically converts handwritten input by a medical professional into text. For example, handwriting input can be done using a tablet, and the generation AI automatically converts it into text. This improves convenience for medical professionals by supporting voice input and handwriting input.

[0045] The email generation unit can automatically refer to relevant laws, regulations, and guidelines when creating a document and add appropriate wording. For example, when creating a document, the generation AI automatically refers to relevant laws, regulations, and guidelines and adds appropriate wording. For example, it automatically inserts wording based on the latest laws and regulations. The email generation unit also has a function to optimize the content of insurance claim request emails based on relevant laws, regulations, and guidelines. For example, the generation AI automatically checks legal requirements and adds appropriate wording. This improves the legality of documents by referring to relevant laws, regulations, and guidelines.

[0046] Furthermore, medical insurance claim support systems automatically optimize medical staff schedules, reducing their burden. For example, generative AI automatically optimizes medical staff schedules, reducing their burden. For example, it generates an optimal schedule by taking into account each staff member's workload and break times. The system also has the ability to adjust staff schedules in real time and respond to sudden changes. For example, it automatically readjusts schedules to accommodate sudden vacations or shift changes. This optimizes medical staff schedules, reducing their burden.

[0047] Furthermore, the medical insurance claim support system automatically prioritizes tasks for medical staff, supporting efficient work. For example, generative AI automatically prioritizes tasks for medical staff, supporting efficient work. For example, it prioritizes tasks based on urgency and importance, promoting efficient work. The system also has the function of monitoring task progress in real time and reprioritizing tasks as necessary. For example, it dynamically changes priorities according to task progress and suggests the optimal work order. This supports efficient work by prioritizing tasks.

[0048] Furthermore, the medical insurance claim support system automatically coordinates collaboration with other medical institutions to reduce the burden on medical staff. For example, the generative AI in the medical insurance claim support system automatically coordinates collaboration with other medical institutions to reduce the burden on medical staff. For example, it automatically handles procedures for transferring patients to other hospitals and referrals to specialists. The system also has functions to smoothly share data and exchange information with other medical institutions. For example, it shares patient medical information with other medical institutions to strengthen collaboration. This automatically coordinates collaboration with other medical institutions, reducing the burden on medical staff.

[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 data input unit can use voice recognition technology to support voice input when entering patient disease data. For example, medical professionals can enter basic patient information and diagnosis results by voice, and the generative AI converts the voice into text and stores it in a database. The voice input function can also convert voice into text in real time and display the input content on the screen. This makes the input work of medical professionals more efficient.

[0051] When entering a patient's disease data, the data entry unit can automatically refer to past medical history and family history to supplement the relevant information. For example, if the patient has been diagnosed with the same disease in the past, that information will be automatically displayed. It can also automatically refer to family history to provide additional diagnostic information that takes genetic factors into account. This improves the accuracy of data entry by referencing past medical history and family history.

[0052] The analysis unit can generate insurance claim request emails that take into account not only the latest medical research but also local insurance systems and regulations. For example, it automatically references local insurance coverage and regulations and reflects appropriate content in the email. It can also optimize the content of emails based on local legal requirements and industry standards. This improves the accuracy of insurance claims by taking local insurance systems and regulations into account.

[0053] When generating an insurance claim request email, the analysis unit can analyze past insurance claim data and automatically select the format most likely to be approved. For example, it can identify the format with the highest approval rate based on past data and apply that format to the email. It can also refer to past success stories to select the optimal format and required items. This improves the approval rate by selecting the optimal format based on past data.

[0054] The analysis unit supports multiple languages ​​when generating insurance claim request emails, and can simultaneously generate invoices in different languages. For example, it generates invoices in multiple languages, such as English, French, and Chinese. It can also generate invoices in different languages ​​using a translation engine. This allows for multilingual support, making it possible to generate invoices in different languages.

[0055] When generating an insurance claim request email, the analysis unit can automatically add visual elements to make it easier to understand visually. For example, a graph showing the progress of treatment or a chart showing a breakdown of costs can be added. Visual elements can also be used to make the contents of the insurance claim request email easier to understand visually. By adding visual elements, the email becomes easier to understand visually.

[0056] To improve the accuracy of insurance claims, the analysis unit can analyze not only the latest medical research but also insurance companies' past approval and denial data. For example, it can identify diagnostic and treatment codes that are likely to be approved based on past data. It can also refer to past success stories to select the most appropriate diagnostic and treatment codes. This analysis of past data improves the accuracy of insurance claims.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The data input unit inputs the patient's disease data, including basic information about the patient, diagnosis results, treatment details, and the medicines and medical devices used. Step 2: The analysis unit analyzes the patient's disease data entered by the data input unit. For example, the generation AI uses data mining technology to analyze the disease data and select appropriate diagnostic and treatment codes. The generation AI also uses machine learning algorithms to analyze data patterns and improve the accuracy of insurance claims. Step 3: The email generation unit generates an insurance claim request email based on the data analyzed by the analysis unit. For example, the generation AI can automatically generate an insurance claim request email based on the latest medical research. The generation AI can also generate an insurance claim request email with the appropriate format and necessary items.

[0059] (Example 2) In the medical insurance claim support system according to an embodiment of the present invention, medical professionals input patient disease data, and a generation AI generates an insurance claim request email based on the latest medical research. This enables the medical insurance claim support system to improve the accuracy of insurance claims, increase approval rates, and significantly reduce the time required for document creation.

[0060] A medical insurance claim support system according to an embodiment includes a data input unit, an analysis unit, and an email generation unit. The data input unit inputs a patient's disease data, including, for example, basic patient information, diagnosis results, treatment details, and the medications and medical devices used. The analysis unit analyzes the patient's disease data input by the data input unit. For example, the generation AI analyzes the disease data using data mining technology and selects appropriate diagnosis and treatment codes. The generation AI also analyzes data patterns using machine learning algorithms to improve the accuracy of insurance claims. The email generation unit generates an insurance claim request email based on the data analyzed by the analysis unit. For example, the generation AI automatically generates an insurance claim request email based on the latest medical research. The generation AI can also generate an insurance claim request email with an appropriate format and necessary fields. This allows the medical insurance claim support system to improve the accuracy of insurance claims, increase approval rates, and significantly reduce the time required for document creation.

[0061] The data entry unit checks the consistency and accuracy of data in real time as it is entered, and can automatically correct input errors. For example, when a medical professional enters a patient's disease data, the generation AI analyzes the input content in real time and checks the consistency and accuracy of the data. For example, if there is a discrepancy between the patient's age and the diagnosed disease name, the generation AI automatically displays a warning and prompts the user to make corrections. The data entry unit also has a function to automatically correct input errors. For example, the generation AI detects typos and numerical errors and automatically corrects them. This improves the accuracy of data entry and reduces input errors.

[0062] When entering a patient's disease data, the data entry unit can automatically refer to past medical history and family history to complete relevant information. For example, when a medical professional enters a patient's disease data, the data entry unit's generation AI automatically refers to past medical history and completes relevant information. For example, if the patient has been diagnosed with the same disease in the past, that information is automatically displayed. The data entry unit also has the function of automatically referring to family history and completing relevant information. For example, it provides additional diagnostic information taking into account family medical history and genetic factors. This improves the accuracy of data entry by referring to past medical history and family history.

[0063] The data input unit can use the emotion estimation function to estimate the patient's emotional state and provide advice to reduce stress and anxiety. For example, when the data input unit inputs the patient's disease data, the generative AI uses the emotion estimation function to analyze the patient's emotional state in real time. For example, it analyzes the patient's facial expressions and tone of voice to estimate the level of stress and anxiety. The data input unit also has a function to provide advice to reduce stress and anxiety. For example, it suggests relaxation methods and counseling. This allows the system to take the patient's emotional state into consideration and provide advice to reduce stress and anxiety.

[0064] The data input unit can add a voice input function, allowing medical professionals to input patient disease data by voice. The data input unit, for example, introduces voice recognition technology to enable medical professionals to input patient disease data by voice. For example, the medical professional simply speaks into a microphone, and the generative AI converts the voice into text and enters it into a database. The data input unit also uses the voice input function to build a system that converts voice to text in real time. For example, text is displayed on the screen simultaneously with voice input. This improves convenience for medical professionals with the voice input function.

[0065] The data input unit can provide a function that allows patients to input and update their own disease data through a smartphone app. For example, the data input unit can provide a function that allows patients to input and update their own disease data through a smartphone app. For example, diagnosis results and treatment details can be entered into the app, and the generation AI can automatically save them in a database. The data input unit can also build a system that allows patients to update their own disease data in real time using a smartphone app. For example, new diagnosis information and treatment details can be entered through the app and reflected in the database. This allows patients to input and update data themselves, improving the accuracy of the data.

[0066] The data input unit uses an emotion estimation function to monitor the patient's emotions in real time when entering data and provide input support. For example, when a patient enters disease data, the data input unit uses the emotion estimation function to monitor the patient's emotions in real time. For example, if the patient is feeling anxious, the generation AI provides input support to make the input process smoother. The data input unit also has a function that uses the emotion estimation function to provide input support according to the patient's emotional state. For example, it performs input completion and error checking to support the patient's input. This allows the unit to monitor the patient's emotions and provide input support, improving the smoothness of input.

[0067] The analysis unit can generate insurance claim request emails taking into account not only the latest medical research, but also local insurance systems and regulations. For example, when the generation AI generates an insurance claim request email based on the latest medical research, the analysis unit also takes local insurance systems and regulations into account. For example, it automatically references local insurance coverage and regulations and reflects appropriate content in the email. The analysis unit also has a function to adjust the content of insurance claim request emails taking into account local insurance systems and regulations. For example, it optimizes the content of the email based on local legal requirements and industry standards. This improves the accuracy of insurance claims by taking into account local insurance systems and regulations.

[0068] When generating an insurance claim request email, the analysis unit can analyze past insurance claim data and automatically select the format most likely to be approved. For example, the generation AI in the analysis unit analyzes past insurance claim data and automatically selects the format most likely to be approved. For example, it identifies a format with a high approval rate based on past data and applies that format to the email. The analysis unit also has a function to optimize the content of the insurance claim request email based on past insurance claim data. For example, it refers to past success stories and selects the optimal format and necessary items. This improves the approval rate by selecting the optimal format based on past data.

[0069] The analysis unit can use the emotion estimation function to automatically select phrases that will evoke positive emotions in the insurance company representative. For example, when the generation AI generates an insurance claim request email, the analysis unit uses the emotion estimation function to select phrases that will evoke positive emotions in the insurance company representative. For example, polite expressions and words of gratitude are automatically inserted. The analysis unit also has a function to use the emotion estimation function to select phrases that will evoke positive emotions in the insurance company representative. For example, phrases that include positive expressions and words of gratitude are automatically selected. This selection of positive phrases improves the approval rate of insurance claims.

[0070] The analysis unit supports multiple languages ​​when generating an insurance claim request email, and can simultaneously generate invoices in different languages. For example, when the generation AI generates an insurance claim request email, the analysis unit supports multiple languages ​​and simultaneously generates invoices in different languages. For example, invoices are generated in multiple languages, such as English, French, and Chinese. In addition, the analysis unit has a function to generate invoices in different languages ​​using a translation engine for multilingual support. For example, the generation AI automatically translates and generates invoices in multiple languages. This allows for multilingual support, making it possible to generate invoices in different languages.

[0071] The analysis unit can automatically add visual elements when generating an insurance claim request email to make it visually easier to understand. For example, when the generation AI generates an insurance claim request email, the analysis unit automatically adds visual elements (e.g., graphs and charts) to make it visually easier to understand. For example, it adds a graph showing the progress of treatment or a chart showing the breakdown of costs. The analysis unit also has a function that uses visual elements to make the contents of the insurance claim request email visually easier to understand. For example, the generation AI automatically generates graphs and charts and inserts them into the email. In this way, adding visual elements makes it visually easier to understand.

[0072] The analysis unit can use the emotion estimation function to adjust the content of insurance claim request emails so that they are easier for patients and medical professionals to understand. For example, when the generation AI generates an insurance claim request email, the analysis unit uses the emotion estimation function to adjust the content to make it easier for patients and medical professionals to understand. For example, by replacing technical terms with simpler language and using polite language. The analysis unit also has a function to use the emotion estimation function to adjust the content of insurance claim request emails to make them easier for patients and medical professionals to understand. For example, the generation AI automatically adjusts the difficulty level of the document and converts it into an easy-to-read format. This allows for smoother communication by adjusting the content to make it easier for patients and medical professionals to understand.

[0073] The analysis unit can analyze not only the latest medical research but also insurance companies' past approval and denial data to improve the accuracy of insurance claims. For example, in order for the generative AI to improve the accuracy of insurance claims, the analysis unit analyzes not only the latest medical research but also insurance companies' past approval and denial data. For example, it identifies diagnosis and treatment codes that are likely to be approved based on past data. The analysis unit also has a function to optimize the content of insurance claim request emails based on the insurance company's past approval and denial data. For example, it refers to past success stories and selects the most appropriate diagnosis and treatment codes. In this way, analyzing past data improves the accuracy of insurance claims.

[0074] The analysis unit can reflect medical professional feedback in real time when selecting diagnostic codes and treatment codes. For example, the analysis unit reflects medical professional feedback in real time when the generation AI selects diagnostic codes and treatment codes. For example, the generation AI adjusts diagnostic codes and treatment codes based on feedback entered by medical professionals. The analysis unit also has a function to optimize the content of insurance claim request emails based on feedback from medical professionals. For example, it reflects feedback and selects more appropriate diagnostic codes and treatment codes. In this way, the accuracy of insurance claims is improved by reflecting feedback from medical professionals.

[0075] The analysis unit can use the emotion estimation function to select diagnostic codes and treatment codes that will evoke positive emotions in insurance company representatives. For example, when the generation AI selects diagnostic codes and treatment codes, the analysis unit uses the emotion estimation function to select codes that will evoke positive emotions in insurance company representatives. For example, it selects codes that include positive expressions and words of gratitude. The analysis unit also has a function to use the emotion estimation function to select diagnostic codes and treatment codes that will evoke positive emotions in insurance company representatives. For example, the generation AI automatically selects codes that evoke positive emotions. This selection of codes that evoke positive emotions improves the approval rate of insurance claims.

[0076] The analysis unit can automatically compare and analyze the regulations and requirements of different insurance companies to improve the accuracy of insurance claims. For example, the generation AI automatically compares and analyzes the regulations and requirements of different insurance companies to improve the accuracy of insurance claims. For example, it selects the optimal diagnosis and treatment codes based on the regulations of each insurance company. The analysis unit also has a function to optimize the content of insurance claim request emails based on the regulations and requirements of different insurance companies. For example, it compares the regulations of each insurance company and reflects the optimal content in the email. This improves the accuracy of insurance claims by comparing and analyzing the regulations and requirements of different insurance companies.

[0077] The analysis unit can refer to success stories from other medical institutions and propose the optimal billing method to improve the accuracy of insurance claims. For example, the generation AI of the analysis unit refers to success stories from other medical institutions and proposes the optimal billing method to improve the accuracy of insurance claims. For example, it selects the optimal diagnosis code and treatment code based on success stories. The analysis unit also has a function to optimize the content of insurance claim request emails based on success stories from other medical institutions. For example, it refers to success stories and proposes the optimal billing method. In this way, the accuracy of insurance claims is improved by referring to success stories from other medical institutions.

[0078] The analysis unit can use the emotion estimation function to select diagnostic codes and treatment codes that take into account the emotions of patients and medical professionals in order to improve the accuracy of insurance claims. For example, when the generation AI selects diagnostic codes and treatment codes, the analysis unit uses the emotion estimation function to consider the emotions of patients and medical professionals. For example, it selects codes that elicit positive emotions. The analysis unit also has a function to use the emotion estimation function to select diagnostic codes and treatment codes that take into account the emotions of patients and medical professionals. For example, the generation AI automatically selects codes that take emotions into account. This improves the accuracy of insurance claims by taking into account the emotions of patients and medical professionals.

[0079] The email generation unit can automatically refer to similar past documents and select the optimal template when creating a document. For example, when creating a document, the generation AI in the email generation unit automatically refers to similar past documents and selects the optimal template. For example, it selects the optimal template based on past success stories, making document creation more efficient. The email generation unit also has a function to optimize the content of insurance claim request emails based on similar past documents. For example, it refers to past data and selects the optimal format and necessary items. This improves the efficiency of document creation by referring to similar past documents.

[0080] The email generation unit can improve quality by automatically checking grammar and applying style guidelines when creating documents. For example, when the generation AI creates a document, the email generation unit automatically checks grammar and applies style guidelines to improve quality. For example, it automatically corrects grammatical errors and generates a document that complies with style guidelines. The email generation unit also has a function to optimize the content of insurance claim request emails based on grammar checks and the application of style guidelines. For example, the generation AI automatically checks the quality of the document and suggests corrections. As a result, the quality of the document is improved by checking grammar and applying style guidelines.

[0081] The email generation unit can use the emotion estimation function to provide advice to reduce stress to medical professionals when creating documents. For example, the email generation unit uses the emotion estimation function to provide advice to reduce stress to medical professionals when the generation AI is creating documents. For example, it can suggest breathing techniques for relaxation or stress management methods. The email generation unit also has a function that uses the emotion estimation function to monitor the emotional state of medical professionals in real time and provide advice to reduce stress. For example, the generation AI can automatically analyze the emotional state and provide appropriate advice. This improves work efficiency by providing advice to reduce stress to medical professionals.

[0082] The email generation unit also supports voice input and handwriting input when creating documents, improving convenience for medical professionals. For example, when the generation AI creates a document, the email generation unit also supports voice input and handwriting input, improving convenience for medical professionals. For example, a medical professional can simply give voice instructions and the generation AI can automatically generate a document. The email generation unit also supports handwriting input and has a function that automatically converts handwritten input by a medical professional into text. For example, handwriting input can be done using a tablet, and the generation AI automatically converts it into text. This improves convenience for medical professionals by supporting voice input and handwriting input.

[0083] The email generation unit can automatically refer to relevant laws, regulations, and guidelines when creating a document and add appropriate wording. For example, when creating a document, the generation AI automatically refers to relevant laws, regulations, and guidelines and adds appropriate wording. For example, it automatically inserts wording based on the latest laws and regulations. The email generation unit also has a function to optimize the content of insurance claim request emails based on relevant laws, regulations, and guidelines. For example, the generation AI automatically checks legal requirements and adds appropriate wording. This improves the legality of documents by referring to relevant laws, regulations, and guidelines.

[0084] The email generation unit uses the emotion estimation function to monitor the emotions of medical professionals in real time when creating documents, thereby optimizing work efficiency. For example, when the generation AI is creating documents, the email generation unit uses the emotion estimation function to monitor the emotions of medical professionals in real time, thereby optimizing work efficiency. For example, if the medical professional is feeling stressed, the generation AI provides advice on how to relax. The email generation unit also has a function that uses the emotion estimation function to optimize work efficiency based on the emotional state of the medical professional. For example, the generation AI automatically analyzes the emotional state and suggests appropriate work methods. In this way, work efficiency is optimized by monitoring the emotions of medical professionals.

[0085] Furthermore, medical insurance claim support systems automatically optimize medical staff schedules, reducing their burden. For example, generative AI automatically optimizes medical staff schedules, reducing their burden. For example, it generates an optimal schedule by taking into account each staff member's workload and break times. The system also has the ability to adjust staff schedules in real time and respond to sudden changes. For example, it automatically readjusts schedules to accommodate sudden vacations or shift changes. This optimizes medical staff schedules, reducing their burden.

[0086] Furthermore, the medical insurance claim support system automatically prioritizes tasks for medical staff, supporting efficient work. For example, generative AI automatically prioritizes tasks for medical staff, supporting efficient work. For example, it prioritizes tasks based on urgency and importance, promoting efficient work. The system also has the function of monitoring task progress in real time and reprioritizing tasks as necessary. For example, it dynamically changes priorities according to task progress and suggests the optimal work order. This supports efficient work by prioritizing tasks.

[0087] Furthermore, the medical insurance claim support system uses an emotion estimation function to monitor the emotional state of medical staff in real time and provide advice to reduce stress. For example, the generative AI uses the emotion estimation function to monitor the emotional state of medical staff in real time and provide advice to reduce stress. For example, it suggests breathing techniques for relaxation and stress management methods. The system also has a function to suggest appropriate times to take a break or refresh depending on the emotional state. For example, if the stress level is high, it will notify the staff to take a break. This improves work efficiency by monitoring the emotional state of medical staff and providing advice to reduce stress.

[0088] Furthermore, the medical insurance claim support system automatically coordinates collaboration with other medical institutions to reduce the burden on medical staff. For example, the generative AI in the medical insurance claim support system automatically coordinates collaboration with other medical institutions to reduce the burden on medical staff. For example, it automatically handles procedures for transferring patients to other hospitals and referrals to specialists. The system also has functions to smoothly share data and exchange information with other medical institutions. For example, it shares patient medical information with other medical institutions to strengthen collaboration. This automatically coordinates collaboration with other medical institutions, reducing the burden on medical staff.

[0089] Furthermore, the medical insurance claim support system monitors the patient's emotional state in real time and suggests appropriate care. For example, in the medical insurance claim support system, generative AI monitors the patient's emotional state in real time and suggests appropriate care. For example, if the patient is feeling anxious, it will suggest music or meditation to help them relax. The system also has a function to suggest appropriate treatment or counseling depending on the patient's emotional state. For example, it will suggest the optimal treatment method or timing for counseling based on the patient's emotional state. This improves the quality of patient care by monitoring the patient's emotional state and suggesting appropriate care.

[0090] Furthermore, the medical insurance claim support system uses an emotion estimation function to support emotional communication between medical staff and patients, improving the quality of care. For example, the generative AI uses an emotion estimation function to support emotional communication between medical staff and patients, improving the quality of care. For example, it monitors the patient's emotional state in real time and suggests appropriate communication methods. The system also has a function to monitor the emotional state of medical staff and provide advice to facilitate smooth communication with patients. For example, it provides advice to encourage the expression of empathy and the sharing of emotions. This improves the quality of care by supporting emotional communication between medical staff and patients.

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

[0092] The data input unit can use voice recognition technology to support voice input when entering patient disease data. For example, medical professionals can enter basic patient information and diagnosis results by voice, and the generative AI converts the voice into text and stores it in a database. The voice input function can also convert voice into text in real time and display the input content on the screen. This makes the input work of medical professionals more efficient.

[0093] When entering a patient's disease data, the data entry unit can automatically refer to past medical history and family history to supplement the relevant information. For example, if the patient has been diagnosed with the same disease in the past, that information will be automatically displayed. It can also automatically refer to family history to provide additional diagnostic information that takes genetic factors into account. This improves the accuracy of data entry by referencing past medical history and family history.

[0094] The data input unit can use the emotion estimation function to estimate the patient's emotional state and provide advice to reduce stress and anxiety. For example, it can analyze the patient's facial expressions and tone of voice to estimate the level of stress and anxiety. It can also suggest relaxation methods and counseling. This allows it to take the patient's emotional state into consideration and provide advice to reduce stress and anxiety.

[0095] The analysis unit can generate insurance claim request emails that take into account not only the latest medical research but also local insurance systems and regulations. For example, it automatically references local insurance coverage and regulations and reflects appropriate content in the email. It can also optimize the content of emails based on local legal requirements and industry standards. This improves the accuracy of insurance claims by taking local insurance systems and regulations into account.

[0096] When generating an insurance claim request email, the analysis unit can analyze past insurance claim data and automatically select the format most likely to be approved. For example, it can identify the format with the highest approval rate based on past data and apply that format to the email. It can also refer to past success stories to select the optimal format and required items. This improves the approval rate by selecting the optimal format based on past data.

[0097] The analysis unit can use its emotion estimation function to automatically select phrases that will evoke positive emotions in insurance company representatives. For example, it can automatically insert polite expressions and words of gratitude. It can also automatically select phrases that include positive expressions and words of gratitude. This selection of positive phrases improves the approval rate of insurance claims.

[0098] The analysis unit supports multiple languages ​​when generating insurance claim request emails, and can simultaneously generate invoices in different languages. For example, it generates invoices in multiple languages, such as English, French, and Chinese. It can also generate invoices in different languages ​​using a translation engine. This allows for multilingual support, making it possible to generate invoices in different languages.

[0099] When generating an insurance claim request email, the analysis unit can automatically add visual elements to make it easier to understand visually. For example, a graph showing the progress of treatment or a chart showing a breakdown of costs can be added. Visual elements can also be used to make the contents of the insurance claim request email easier to understand visually. By adding visual elements, the email becomes easier to understand visually.

[0100] The analysis unit can use its emotion estimation function to adjust the content of insurance claim request emails to make them easier for patients and medical professionals to understand. For example, it can replace technical terms with simpler language and use more polite expressions. It can also adjust the difficulty level of the document and convert it into an easy-to-read format. This allows for smoother communication by adjusting the content to make it easier for patients and medical professionals to understand.

[0101] To improve the accuracy of insurance claims, the analysis unit can analyze not only the latest medical research but also insurance companies' past approval and denial data. For example, it can identify diagnostic and treatment codes that are likely to be approved based on past data. It can also refer to past success stories to select the most appropriate diagnostic and treatment codes. This analysis of past data improves the accuracy of insurance claims.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The data input unit inputs the patient's disease data, including basic information about the patient, diagnosis results, treatment details, and the medicines and medical devices used. Step 2: The analysis unit analyzes the patient's disease data entered by the data input unit. For example, the generation AI uses data mining technology to analyze the disease data and select appropriate diagnostic and treatment codes. The generation AI also uses machine learning algorithms to analyze data patterns and improve the accuracy of insurance claims. Step 3: The email generation unit generates an insurance claim request email based on the data analyzed by the analysis unit. For example, the generation AI can automatically generate an insurance claim request email based on the latest medical research. The generation AI can also generate an insurance claim request email with the appropriate format and necessary items.

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

[0105] 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.

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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]

[0171] 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 data input unit for inputting disease data of a patient; an analysis unit that analyzes the disease data of the patient input by the data input unit; an email generation unit that generates an insurance claim request email based on the data analyzed by the analysis unit; A system characterized by:

2. The data input unit Check the consistency and accuracy of the patient's disease data in real time as it is entered, and automatically correct any input errors.

2. The system of claim 1.

3. The analysis unit Generate the insurance claim request email by taking into account not only the latest medical research but also local insurance systems and regulations.

2. The system of claim 1.

4. The email generation unit When creating a document, the system automatically references similar documents from the past and selects the most suitable template.

2. The system of claim 1.

5. The data input unit Estimating the patient's emotional state and providing advice to reduce stress and anxiety 2. The system of claim 1.

6. The analysis unit Automatically selects phrases that will evoke positive emotions from insurance company representatives 2. The system of claim 1.

7. The email generation unit Providing advice to reduce stress for healthcare professionals when writing documentation 2. The system of claim 1.

8. The system comprises: Monitor the emotional state of medical staff in real time and provide advice to reduce stress 2. The system of claim 1.

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