System

Abstract

An object of the system according to the embodiment is to automatically create a letter of introduction, unify the format, and perform an error check.SOLUTION: A system according to an embodiment includes a referral letter creation unit, a format unification unit, and an error check unit. The referral letter creation unit automatically creates a referral letter using the generation AI. The format unifying unit converts the referral letter created by the referral letter creation unit into a unified format. The error check unit performs an error check on the content of the referral letter converted by the format unifying 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] With previous technology, creating hospital referral letters was time-consuming and resource-intensive, and posed challenges such as incorrect or missing information and inconsistent formatting.

[0005] The system according to the embodiment aims to automatically create letters of introduction, standardize their formats, and perform error checking. [Means for solving the problem]

[0006] The system according to the embodiment includes a referral letter creation unit, a format standardization unit, and an error check unit. The referral letter creation unit automatically creates referral letters using a generation AI. The format standardization unit converts the referral letters created by the referral letter creation unit into a standardized format. The error check unit checks for errors in the contents of the referral letters converted by the format standardization unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically create letters of introduction, standardize formats, and perform error checking. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The automatic referral letter creation system according to an embodiment of the present invention uses generation AI to automatically create referral letters and provides a unified format and error checks, thereby improving the operational efficiency of medical institutions and the quality of patient care.

[0029] An automatic referral letter creation system according to an embodiment includes a referral letter creation unit, a format unification unit, and an error check unit. The referral letter creation unit automatically creates referral letters using a generation AI. For example, the generation AI analyzes information such as a patient's medical records, test results, and treatment history to generate an appropriate referral letter. The generation AI can also create referral letters using technologies such as text generation AI (e.g., GPT-3) and BERT. The format unification unit converts the referral letters created by the referral letter creation unit into a unified format. For example, if the generation AI creates referral letters based on a unified format, the receiving medical institution can easily understand the information. The error check unit checks for errors in the contents of the referral letters converted by the format unification unit. For example, the generation AI analyzes the contents of the referral letter and checks for errors or omissions. If errors are found, the error check unit suggests corrections. As a result, the automatic referral letter creation system according to an embodiment automatically creates referral letters, provides a unified format, and checks for errors, thereby improving the work efficiency of medical institutions and the quality of patient care.

[0030] The referral letter creation unit can analyze a patient's past medical records and create a referral letter proposing the most appropriate treatment or specialist. In the referral letter creation unit, for example, a generating AI analyzes a patient's past medical records and creates a referral letter proposing the most appropriate treatment. For example, the most appropriate treatment is proposed based on past treatment history and test results. In addition, the referral letter creation unit analyzes a patient's medical records and creates a referral letter proposing a specialist. For example, an appropriate specialist is introduced based on specific symptoms and medical history. In addition, the referral letter creation unit analyzes a patient's medical records and creates a referral letter presenting treatment options. For example, multiple treatments are proposed and the patient can choose. This improves the effectiveness of patient treatment by proposing the most appropriate treatment or specialist based on the patient's past medical records.

[0031] The referral letter creation unit can create a referral letter including personalized advice by taking into account the patient's lifestyle and environmental information. In the referral letter creation unit, for example, a generation AI analyzes the patient's lifestyle and creates a referral letter including personalized advice. For example, advice for maintaining health is provided based on dietary and exercise habits. In addition, the referral letter creation unit analyzes the patient's environmental information and creates a referral letter including personalized advice. For example, advice for reducing stress is provided by taking into account the living environment and work environment. In addition, the referral letter creation unit integrates the patient's lifestyle and environmental information and creates a referral letter including personalized advice. For example, specific advice is provided for preventing and improving lifestyle-related diseases. This supports the patient's health management by providing personalized advice that takes into account the patient's lifestyle and environmental information.

[0032] The referral letter creation unit automatically generates referral letters in different languages, making it possible to accommodate international patients. For example, the referral letter creation unit uses a generation AI to automatically translate Japanese referral letters into English, making it possible to accommodate international patients. For example, it generates a referral letter to be sent to an English-speaking medical institution. The referral letter creation unit also has a multilingual function in the generation AI, automatically generating a referral letter tailored to the patient's native language. For example, it provides a referral letter translated into the patient's native language, such as Chinese or Spanish. The referral letter creation unit also uses a generation AI to accurately translate medical and technical terms in different languages, creating a referral letter that is easy for international patients to understand. For example, it performs accurate translations while referring to a medical terminology dictionary. This allows referral letters to be automatically generated in different languages, making it possible to accommodate international patients.

[0033] The referral letter creation unit can create referral letters that include easy-to-understand explanations for the patient's family and caregivers. For example, the generation AI creates referral letters for the patient's family and caregivers that include easy-to-understand explanations that avoid technical jargon. For example, medical terminology is replaced with general language. The referral letter creation unit also creates referral letters that explain treatment plans and key points of care in an easy-to-understand manner for the patient's family and caregivers. For example, the generation AI briefly summarizes the treatment procedure and points to note. The referral letter creation unit also creates referral letters for the patient's family and caregivers that include care methods and points to note in daily life. For example, specific advice on diet and exercise is provided. This improves the quality of care by providing easy-to-understand explanations for the patient's family and caregivers.

[0034] The format unification unit can learn the differences in formats between medical institutions and automatically generate a format optimized for each medical institution. For example, the format unification unit's generation AI learns the format of each medical institution and automatically generates a referral letter optimized for that format. For example, it creates a referral letter that matches the format of a specific hospital. The format unification unit also analyzes the differences in formats between medical institutions and automatically selects the optimal format. For example, it prepares multiple format templates and selects the appropriate one. The format unification unit also builds a flexible system where the generation AI learns the formats of medical institutions and can respond to format changes. For example, it can also respond when a new format is added. This allows the generation AI to learn the differences in formats between medical institutions and provide an optimized format, making information sharing smoother.

[0035] The format unification unit can automatically add the medical institution's logo and specific design elements to the referral letter format. For example, the generation AI automatically adds the medical institution's logo to the referral letter format. For example, the hospital's logo is placed in the header of the referral letter. The format unification unit also automatically adds specific design elements to the referral letter format. For example, the color scheme and font of the medical institution are used. The format unification unit also reflects the medical institution's brand elements in the referral letter format. For example, the hospital's mission statement and slogan are included in the referral letter. This allows the visual consistency of the referral letters to be maintained by automatically adding the medical institution's logo and design elements.

[0036] The format unification unit can automatically generate formats specialized for different medical fields. For example, the generation AI in the format unification unit automatically generates a format specialized for dentistry. For example, it creates a referral letter that includes information about dental treatment. In addition, the generation AI in the format unification unit automatically generates a format specialized for ophthalmology. For example, it creates a referral letter that includes vision test results and information about ophthalmological treatment. In addition, the generation AI in the format unification unit automatically generates formats specialized for different medical fields. For example, it provides formats corresponding to each field, such as internal medicine, surgery, and dermatology. By providing formats specialized for different medical fields, it becomes possible to share information appropriate for each field.

[0037] The format unification unit adds visual elements to the referral letter format, making the information easier to understand intuitively. In the format unification unit, for example, the generation AI adds graphs to the referral letter format to visually display the patient's test results. For example, blood test results are shown in a graph. In addition, the format unification unit adds charts to the referral letter format to visually display the treatment process. For example, the progress of treatment is shown in a chart. In addition, the format unification unit adds visual elements to the referral letter format to make the information easier to understand intuitively. For example, a timeline showing changes in symptoms is added. In this way, adding visual elements makes the information easier to understand intuitively.

[0038] The error check unit can compare the contents of the referral letter with other medical databases and perform error checks based on the latest medical information. For example, the generating AI compares the contents of the referral letter with other medical databases and performs error checks based on the latest medical information. For example, it refers to the latest treatment and drug information to correct errors. The error check unit also has the generating AI refer to medical databases to check for errors in the contents of the referral letter. For example, it checks the accuracy of the diagnosis and treatment method. The error check unit also has the generating AI link with other medical databases to update the contents of the referral letter based on the latest medical information. For example, it corrects the contents based on new treatment guidelines. This makes it possible to check for errors based on the latest medical information by comparing with other medical databases.

[0039] The error check unit can analyze the contents of the referral letter and automatically correct incorrect use of medical terminology and ambiguous expressions. For example, the generation AI analyzes the contents of the referral letter and automatically corrects incorrect use of medical terminology. For example, it replaces incorrect terminology with correct terminology. The error check unit also analyzes the contents of the referral letter and corrects ambiguous expressions to clearer ones. For example, it clearly describes specific symptoms and treatment methods. The error check unit also analyzes the contents of the referral letter and automatically corrects incorrect use of medical terminology and ambiguous expressions. For example, it replaces technical terms with general terms. This automatically corrects incorrect use of medical terminology and ambiguous expressions, improving the accuracy of the referral letter.

[0040] The error checking unit can translate the contents of a referral letter into different languages ​​and check the accuracy of the translation. For example, the error checking unit has the generation AI translate the contents of a referral letter into different languages ​​and check the accuracy of the translation. For example, it checks the contents of a referral letter translated into English. The error checking unit also has a multilingual error checking function in the generation AI and checks the accuracy of the translation. For example, it checks whether the translated content matches the original content. The error checking unit also analyzes the contents of a referral letter translated by the generation AI into different languages ​​and corrects mistranslations and unnatural expressions. For example, it checks the accurate translation of medical terminology. This makes it possible to error check the accuracy of referral letters translated into different languages, making it possible to accommodate international patients.

[0041] The error check unit can analyze the contents of the referral letter and revise it to make it easier for the patient's family and caregivers to understand. For example, the generation AI analyzes the contents of the referral letter and revise it to make it easier for the patient's family and caregivers to understand. For example, technical terms are replaced with more general terms. The error check unit also analyzes the contents of the referral letter and revise it to make it easier to understand the treatment plan and key points of care. For example, it concisely summarizes the treatment procedure and points to note. The error check unit also analyzes the contents of the referral letter and revise it to make it easier to understand for the patient's family and caregivers. For example, it provides specific advice on diet and exercise. This improves the quality of care by revising it to make it easier for the patient's family and caregivers to understand.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The referral letter creation unit can analyze a patient's past medical records and create a referral letter proposing the most appropriate treatment or specialist. For example, the generation AI analyzes a patient's past medical records and creates a referral letter proposing the most appropriate treatment. For example, it proposes the most appropriate treatment based on past treatment history and test results. The referral letter creation unit also analyzes a patient's medical records and creates a referral letter proposing a specialist. For example, it refers an appropriate specialist based on specific symptoms and medical history. The referral letter creation unit also analyzes a patient's medical records and creates a referral letter presenting treatment options. For example, it proposes multiple treatments and allows the patient to choose. This improves the effectiveness of patient treatment by proposing the most appropriate treatment or specialist based on the patient's past medical records.

[0044] The referral letter creation unit can create a referral letter including personalized advice by taking into account the patient's lifestyle and environmental information. For example, the generation AI analyzes the patient's lifestyle and creates a referral letter including personalized advice. For example, it provides advice for maintaining health based on dietary and exercise habits. The referral letter creation unit also analyzes the patient's environmental information and creates a referral letter including personalized advice. For example, it provides advice for reducing stress by taking into account the living environment and work environment. The referral letter creation unit also integrates the patient's lifestyle and environmental information and creates a referral letter including personalized advice. For example, it provides specific advice for preventing and improving lifestyle-related diseases. This supports the patient's health management by providing personalized advice that takes into account the patient's lifestyle and environmental information.

[0045] The referral letter creation unit can automatically generate referral letters in different languages, making it possible to accommodate international patients. For example, the generation AI can automatically translate a Japanese referral letter into English, making it possible to accommodate international patients. For example, it generates a referral letter to be sent to an English-speaking medical institution. The referral letter creation unit also has a multilingual function in the generation AI, automatically generating a referral letter tailored to the patient's native language. For example, it provides a referral letter translated into the patient's native language, such as Chinese or Spanish. The referral letter creation unit also uses the generation AI to accurately translate medical and technical terms in different languages, creating referral letters that are easy for international patients to understand. For example, it performs accurate translations while referring to a medical terminology dictionary. This allows referral letters to be automatically generated in different languages, making it possible to accommodate international patients.

[0046] The referral letter creation unit can create referral letters that include easy-to-understand explanations for the patient's family and caregivers. For example, the generation AI creates referral letters for the patient's family and caregivers that include easy-to-understand explanations that avoid technical jargon. For example, medical terminology is replaced with general language. The referral letter creation unit also creates referral letters that explain treatment plans and key points of care in an easy-to-understand manner for the patient's family and caregivers. For example, the referral letter creation unit briefly summarizes the treatment procedure and points to note. The referral letter creation unit also creates referral letters for the patient's family and caregivers that include care methods and points to note in daily life. For example, specific advice on diet and exercise is provided. This improves the quality of care by providing easy-to-understand explanations for the patient's family and caregivers.

[0047] The format unification unit can learn the differences in formats between medical institutions and automatically generate a format optimized for each medical institution. For example, the generation AI learns the format of each medical institution and automatically generates a referral letter optimized for that format. For example, it creates a referral letter tailored to the format of a specific hospital. The format unification unit also analyzes the differences in formats between medical institutions and automatically selects the optimal format. For example, it prepares multiple format templates and selects the appropriate one. The format unification unit also builds a flexible system where the generation AI learns the formats of medical institutions and can respond to format changes. For example, it can also respond when a new format is added. This allows the generation AI to learn the differences in formats between medical institutions and provide an optimized format, making information sharing smoother.

[0048] The format unification unit can automatically add the medical institution's logo and specific design elements to the referral letter format. For example, the generation AI automatically adds the medical institution's logo to the referral letter format. For example, the hospital's logo is placed in the header of the referral letter. The format unification unit also automatically adds specific design elements to the referral letter format. For example, the color scheme and font of the medical institution are used. The format unification unit also allows the generation AI to reflect the medical institution's brand elements in the referral letter format. For example, the hospital's mission statement and slogan are included in the referral letter. This allows the medical institution's logo and design elements to be automatically added, thereby maintaining visual consistency among referral letters.

[0049] The format unification unit can automatically generate formats specialized for different medical fields. For example, the generation AI automatically generates a format specialized for dentistry. For example, it creates a referral letter that includes information about dental treatment. The format unification unit also automatically generates a format specialized for ophthalmology. For example, it creates a referral letter that includes vision test results and information about ophthalmology treatment. The format unification unit also automatically generates formats specialized for different medical fields. For example, it provides formats corresponding to each field, such as internal medicine, surgery, and dermatology. By providing formats specialized for different medical fields, it becomes possible to share information appropriate for each field.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The referral letter creation unit automatically creates a referral letter using generation AI. For example, the generation AI analyzes information such as the patient's medical records, test results, and treatment history to create an appropriate referral letter. The generation AI can also create referral letters using technologies such as text generation AI (e.g., GPT-3) and BERT. Step 2: The format unification unit converts the referral letters created by the referral letter creation unit into a unified format. For example, if the generation AI creates referral letters based on a unified format, the receiving medical institution can easily understand the information. Step 3: The error check section checks the contents of the referral letter converted by the format unification section for errors. For example, the generation AI analyzes the contents of the referral letter and checks for errors or omissions. If errors are found, it suggests corrections.

[0052] (Example 2) The automatic referral letter creation system according to an embodiment of the present invention uses generation AI to automatically create referral letters and provides a unified format and error checks, thereby improving the operational efficiency of medical institutions and the quality of patient care.

[0053] An automatic referral letter creation system according to an embodiment includes a referral letter creation unit, a format unification unit, and an error check unit. The referral letter creation unit automatically creates referral letters using a generation AI. For example, the generation AI analyzes information such as a patient's medical records, test results, and treatment history to generate an appropriate referral letter. The generation AI can also create referral letters using technologies such as text generation AI (e.g., GPT-3) and BERT. The format unification unit converts the referral letters created by the referral letter creation unit into a unified format. For example, if the generation AI creates referral letters based on a unified format, the receiving medical institution can easily understand the information. The error check unit checks for errors in the contents of the referral letters converted by the format unification unit. For example, the generation AI analyzes the contents of the referral letter and checks for errors or omissions. If errors are found, the error check unit suggests corrections. As a result, the automatic referral letter creation system according to an embodiment automatically creates referral letters, provides a unified format, and checks for errors, thereby improving the work efficiency of medical institutions and the quality of patient care.

[0054] The referral letter creation unit can estimate the patient's emotional state and create a referral letter that includes considerations for reducing the patient's psychological burden. In the referral letter creation unit, for example, the generation AI analyzes the patient's medical records and past medical data to estimate the patient's emotional state. For example, the generation AI estimates the patient's emotions based on descriptions of the patient's words and facial expressions in the medical records and includes considerations in the referral letter. In addition, in the referral letter creation unit, the generation AI analyzes the patient's audio data and video data to estimate the patient's emotional state. For example, the generation AI estimates emotions based on conversations and video recordings during the consultation and adds a message to the referral letter to reduce the patient's psychological burden. In addition, the generation AI considers the patient's lifestyle and environmental information to estimate the patient's emotional state. For example, the generation AI analyzes the patient's living environment and stress factors and includes advice to reduce the patient's psychological burden in the referral letter. In this way, the patient's psychological burden can be reduced by creating a referral letter that takes the patient's emotional state into consideration.

[0055] The referral letter creation unit can analyze a patient's past medical records and create a referral letter proposing the most appropriate treatment or specialist. In the referral letter creation unit, for example, a generating AI analyzes a patient's past medical records and creates a referral letter proposing the most appropriate treatment. For example, the most appropriate treatment is proposed based on past treatment history and test results. In addition, the referral letter creation unit analyzes a patient's medical records and creates a referral letter proposing a specialist. For example, an appropriate specialist is introduced based on specific symptoms and medical history. In addition, the referral letter creation unit analyzes a patient's medical records and creates a referral letter presenting treatment options. For example, multiple treatments are proposed and the patient can choose. This improves the effectiveness of patient treatment by proposing the most appropriate treatment or specialist based on the patient's past medical records.

[0056] The referral letter creation unit can create a referral letter including personalized advice by taking into account the patient's lifestyle and environmental information. In the referral letter creation unit, for example, a generation AI analyzes the patient's lifestyle and creates a referral letter including personalized advice. For example, advice for maintaining health is provided based on dietary and exercise habits. In addition, the referral letter creation unit analyzes the patient's environmental information and creates a referral letter including personalized advice. For example, advice for reducing stress is provided by taking into account the living environment and work environment. In addition, the referral letter creation unit integrates the patient's lifestyle and environmental information and creates a referral letter including personalized advice. For example, specific advice is provided for preventing and improving lifestyle-related diseases. This supports the patient's health management by providing personalized advice that takes into account the patient's lifestyle and environmental information.

[0057] The referral letter creation unit automatically generates referral letters in different languages, making it possible to accommodate international patients. For example, the referral letter creation unit uses a generation AI to automatically translate Japanese referral letters into English, making it possible to accommodate international patients. For example, it generates a referral letter to be sent to an English-speaking medical institution. The referral letter creation unit also has a multilingual function in the generation AI, automatically generating a referral letter tailored to the patient's native language. For example, it provides a referral letter translated into the patient's native language, such as Chinese or Spanish. The referral letter creation unit also uses a generation AI to accurately translate medical and technical terms in different languages, creating a referral letter that is easy for international patients to understand. For example, it performs accurate translations while referring to a medical terminology dictionary. This allows referral letters to be automatically generated in different languages, making it possible to accommodate international patients.

[0058] The referral letter creation unit can create referral letters that include easy-to-understand explanations for the patient's family and caregivers. For example, the generation AI creates referral letters for the patient's family and caregivers that include easy-to-understand explanations that avoid technical jargon. For example, medical terminology is replaced with general language. The referral letter creation unit also creates referral letters that explain treatment plans and key points of care in an easy-to-understand manner for the patient's family and caregivers. For example, the generation AI briefly summarizes the treatment procedure and points to note. The referral letter creation unit also creates referral letters for the patient's family and caregivers that include care methods and points to note in daily life. For example, specific advice on diet and exercise is provided. This improves the quality of care by providing easy-to-understand explanations for the patient's family and caregivers.

[0059] The referral letter creation unit can estimate the patient's emotions and create a referral letter that includes an encouraging and reassuring message that corresponds to the emotion. In the referral letter creation unit, for example, the generation AI estimates the patient's emotions and creates a referral letter that includes an encouraging message. For example, if the patient is feeling anxious, a reassuring message is added. In addition, the referral letter creation unit analyzes the patient's emotions and creates a referral letter that includes a message that elicits positive emotions. For example, a message praising the patient's efforts is added. In addition, the referral letter creation unit considers the patient's emotions and creates a referral letter that includes advice and words of encouragement that correspond to the emotion. For example, a message that encourages a positive attitude toward treatment is added. In this way, the psychological burden on the patient can be reduced by providing a message that corresponds to the patient's emotions.

[0060] The format unification unit can learn the differences in formats between medical institutions and automatically generate a format optimized for each medical institution. For example, the format unification unit's generation AI learns the format of each medical institution and automatically generates a referral letter optimized for that format. For example, it creates a referral letter that matches the format of a specific hospital. The format unification unit also analyzes the differences in formats between medical institutions and automatically selects the optimal format. For example, it prepares multiple format templates and selects the appropriate one. The format unification unit also builds a flexible system where the generation AI learns the formats of medical institutions and can respond to format changes. For example, it can also respond when a new format is added. This allows the generation AI to learn the differences in formats between medical institutions and provide an optimized format, making information sharing smoother.

[0061] The format unification unit can automatically add the medical institution's logo and specific design elements to the referral letter format. For example, the generation AI automatically adds the medical institution's logo to the referral letter format. For example, the hospital's logo is placed in the header of the referral letter. The format unification unit also automatically adds specific design elements to the referral letter format. For example, the color scheme and font of the medical institution are used. The format unification unit also reflects the medical institution's brand elements in the referral letter format. For example, the hospital's mission statement and slogan are included in the referral letter. This allows the visual consistency of the referral letters to be maintained by automatically adding the medical institution's logo and design elements.

[0062] The format unification unit can reflect the patient's emotional state and provide a reader-friendly design. For example, the generation AI analyzes the patient's emotional state and reflects the results in the format. For example, if the patient is feeling anxious, a design that gives a sense of security can be adopted. The format unification unit also adjusts the color and font of the format by taking the patient's emotional state into consideration. For example, it uses colors that have a relaxing effect. The format unification unit also provides a reader-friendly design based on the patient's emotional state. For example, it adds icons and illustrations that correspond to the emotion. This makes it possible to create referral letters that are reader-friendly by providing a design that reflects the patient's emotional state.

[0063] The format unification unit can automatically generate formats specialized for different medical fields. For example, the generation AI in the format unification unit automatically generates a format specialized for dentistry. For example, it creates a referral letter that includes information about dental treatment. In addition, the generation AI in the format unification unit automatically generates a format specialized for ophthalmology. For example, it creates a referral letter that includes vision test results and information about ophthalmological treatment. In addition, the generation AI in the format unification unit automatically generates formats specialized for different medical fields. For example, it provides formats corresponding to each field, such as internal medicine, surgery, and dermatology. By providing formats specialized for different medical fields, it becomes possible to share information appropriate for each field.

[0064] The format unification unit adds visual elements to the referral letter format, making the information easier to understand intuitively. In the format unification unit, for example, the generation AI adds graphs to the referral letter format to visually display the patient's test results. For example, blood test results are shown in a graph. In addition, the format unification unit adds charts to the referral letter format to visually display the treatment process. For example, the progress of treatment is shown in a chart. In addition, the format unification unit adds visual elements to the referral letter format to make the information easier to understand intuitively. For example, a timeline showing changes in symptoms is added. In this way, adding visual elements makes the information easier to understand intuitively.

[0065] The format unification unit incorporates an emotion estimation function into the referral letter format, and can automatically select colors and fonts according to the patient's emotions. For example, the format unification unit uses a generation AI to estimate the patient's emotions and automatically selects the color of the format based on the result. For example, a color that has a relaxing effect is used. The format unification unit also uses a generation AI to analyze the patient's emotions and automatically selects the font of the format based on the result. For example, a font that is easy to read is used. The format unification unit also provides a format in which the generation AI reflects the patient's emotional state. For example, design elements according to the emotion are added. In this way, by incorporating the emotion estimation function, colors and fonts according to the patient's emotions can be automatically selected.

[0066] The error check unit can analyze the contents of the referral letter, perform an error check based on the emotional state of the patient, and make emotionally considerate correction suggestions. For example, the generation AI analyzes the contents of the referral letter and performs an error check that takes the patient's emotional state into consideration. For example, if the patient is feeling anxious, the error check unit makes correction suggestions that give the patient a sense of security. The error check unit also analyzes the patient's emotional state and makes emotionally considerate correction suggestions. For example, it corrects negative expressions to positive expressions. The error check unit also analyzes the contents of the referral letter and makes emotionally considerate correction suggestions. For example, it adds an encouraging message that corresponds to the patient's emotions. In this way, by performing an error check that takes the patient's emotional state into consideration, it becomes possible to make emotionally considerate correction suggestions.

[0067] The error check unit can compare the contents of the referral letter with other medical databases and perform error checks based on the latest medical information. For example, the generating AI compares the contents of the referral letter with other medical databases and performs error checks based on the latest medical information. For example, it refers to the latest treatment and drug information to correct errors. The error check unit also has the generating AI refer to medical databases to check for errors in the contents of the referral letter. For example, it checks the accuracy of the diagnosis and treatment method. The error check unit also has the generating AI link with other medical databases to update the contents of the referral letter based on the latest medical information. For example, it corrects the contents based on new treatment guidelines. This makes it possible to check for errors based on the latest medical information by comparing with other medical databases.

[0068] The error check unit can analyze the contents of the referral letter and automatically correct incorrect use of medical terminology and ambiguous expressions. For example, the generation AI analyzes the contents of the referral letter and automatically corrects incorrect use of medical terminology. For example, it replaces incorrect terminology with correct terminology. The error check unit also analyzes the contents of the referral letter and corrects ambiguous expressions to clearer ones. For example, it clearly describes specific symptoms and treatment methods. The error check unit also analyzes the contents of the referral letter and automatically corrects incorrect use of medical terminology and ambiguous expressions. For example, it replaces technical terms with general terms. This automatically corrects incorrect use of medical terminology and ambiguous expressions, improving the accuracy of the referral letter.

[0069] The error checking unit can translate the contents of a referral letter into different languages ​​and check the accuracy of the translation. For example, the error checking unit has the generation AI translate the contents of a referral letter into different languages ​​and check the accuracy of the translation. For example, it checks the contents of a referral letter translated into English. The error checking unit also has a multilingual error checking function in the generation AI and checks the accuracy of the translation. For example, it checks whether the translated content matches the original content. The error checking unit also analyzes the contents of a referral letter translated by the generation AI into different languages ​​and corrects mistranslations and unnatural expressions. For example, it checks the accurate translation of medical terminology. This makes it possible to error check the accuracy of referral letters translated into different languages, making it possible to accommodate international patients.

[0070] The error check unit can analyze the contents of the referral letter and revise it to make it easier for the patient's family and caregivers to understand. For example, the generation AI analyzes the contents of the referral letter and revise it to make it easier for the patient's family and caregivers to understand. For example, technical terms are replaced with more general terms. The error check unit also analyzes the contents of the referral letter and revise it to make it easier to understand the treatment plan and key points of care. For example, it concisely summarizes the treatment procedure and points to note. The error check unit also analyzes the contents of the referral letter and revise it to make it easier to understand for the patient's family and caregivers. For example, it provides specific advice on diet and exercise. This improves the quality of care by revising it to make it easier for the patient's family and caregivers to understand.

[0071] The error check unit can analyze the contents of the referral letter and use the emotion estimation function to make correction suggestions based on the patient's emotions. For example, the error check unit uses a generation AI to analyze the contents of the referral letter and use the emotion estimation function to make correction suggestions that take the patient's emotions into consideration. For example, if the patient is feeling anxious, the error check unit makes correction suggestions that give the patient a sense of security. The error check unit also uses a generation AI to analyze the patient's emotions and make correction suggestions that take the patient's emotions into consideration. For example, it corrects negative expressions to positive expressions. The error check unit also uses a generation AI to analyze the contents of the referral letter and makes correction suggestions that add an encouraging message that corresponds to the patient's emotions. For example, it adds a message that encourages a positive attitude toward treatment. In this way, by using the emotion estimation function, it is possible to make correction suggestions that take the patient's emotions into consideration.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The referral letter creation unit can estimate the patient's emotional state and create a referral letter that includes considerations to reduce the patient's psychological burden. For example, the generation AI analyzes the patient's medical records and past medical data to estimate the patient's emotional state. For example, the generation AI can estimate the patient's emotions based on descriptions of the patient's words and facial expressions in the medical records and include considerations in the referral letter. In addition, to estimate the patient's emotional state, the generation AI analyzes the patient's audio data and video data. For example, the generation AI can estimate emotions based on conversations and video recordings during the consultation and add a message to the referral letter to reduce the patient's psychological burden. In addition, the generation AI can take into account the patient's lifestyle and environmental information to estimate the patient's emotional state. For example, the generation AI can analyze the patient's living environment and stress factors and include advice to reduce the patient's psychological burden in the referral letter. In this way, the generation AI can create a referral letter that takes the patient's emotional state into consideration and reduce the patient's psychological burden.

[0074] The referral letter creation unit can analyze a patient's past medical records and create a referral letter proposing the most appropriate treatment or specialist. For example, the generation AI analyzes a patient's past medical records and creates a referral letter proposing the most appropriate treatment. For example, it proposes the most appropriate treatment based on past treatment history and test results. The referral letter creation unit also analyzes a patient's medical records and creates a referral letter proposing a specialist. For example, it refers an appropriate specialist based on specific symptoms and medical history. The referral letter creation unit also analyzes a patient's medical records and creates a referral letter presenting treatment options. For example, it proposes multiple treatments and allows the patient to choose. This improves the effectiveness of patient treatment by proposing the most appropriate treatment or specialist based on the patient's past medical records.

[0075] The referral letter creation unit can create a referral letter including personalized advice by taking into account the patient's lifestyle and environmental information. For example, the generation AI analyzes the patient's lifestyle and creates a referral letter including personalized advice. For example, it provides advice for maintaining health based on dietary and exercise habits. The referral letter creation unit also analyzes the patient's environmental information and creates a referral letter including personalized advice. For example, it provides advice for reducing stress by taking into account the living environment and work environment. The referral letter creation unit also integrates the patient's lifestyle and environmental information and creates a referral letter including personalized advice. For example, it provides specific advice for preventing and improving lifestyle-related diseases. This supports the patient's health management by providing personalized advice that takes into account the patient's lifestyle and environmental information.

[0076] The referral letter creation unit can automatically generate referral letters in different languages, making it possible to accommodate international patients. For example, the generation AI can automatically translate a Japanese referral letter into English, making it possible to accommodate international patients. For example, it generates a referral letter to be sent to an English-speaking medical institution. The referral letter creation unit also has a multilingual function in the generation AI, automatically generating a referral letter tailored to the patient's native language. For example, it provides a referral letter translated into the patient's native language, such as Chinese or Spanish. The referral letter creation unit also uses the generation AI to accurately translate medical and technical terms in different languages, creating referral letters that are easy for international patients to understand. For example, it performs accurate translations while referring to a medical terminology dictionary. This allows referral letters to be automatically generated in different languages, making it possible to accommodate international patients.

[0077] The referral letter creation unit can create referral letters that include easy-to-understand explanations for the patient's family and caregivers. For example, the generation AI creates referral letters for the patient's family and caregivers that include easy-to-understand explanations that avoid technical jargon. For example, medical terminology is replaced with general language. The referral letter creation unit also creates referral letters that explain treatment plans and key points of care in an easy-to-understand manner for the patient's family and caregivers. For example, the referral letter creation unit briefly summarizes the treatment procedure and points to note. The referral letter creation unit also creates referral letters for the patient's family and caregivers that include care methods and points to note in daily life. For example, specific advice on diet and exercise is provided. This improves the quality of care by providing easy-to-understand explanations for the patient's family and caregivers.

[0078] The referral letter creation unit can estimate the patient's emotions and create a referral letter that includes an encouraging and reassuring message that corresponds to the emotion. For example, the generation AI estimates the patient's emotions and creates a referral letter that includes an encouraging message. For example, if the patient is feeling anxious, a reassuring message is added. The referral letter creation unit also analyzes the patient's emotions and creates a referral letter that includes a message that elicits positive emotions. For example, a message praising the patient's efforts is added. The referral letter creation unit also takes the patient's emotions into consideration and creates a referral letter that includes advice and words of encouragement that correspond to the emotion. For example, a message that encourages a positive attitude toward treatment is added. In this way, the psychological burden on the patient can be reduced by providing a message that corresponds to the patient's emotions.

[0079] The format unification unit can learn the differences in formats between medical institutions and automatically generate a format optimized for each medical institution. For example, the generation AI learns the format of each medical institution and automatically generates a referral letter optimized for that format. For example, it creates a referral letter tailored to the format of a specific hospital. The format unification unit also analyzes the differences in formats between medical institutions and automatically selects the optimal format. For example, it prepares multiple format templates and selects the appropriate one. The format unification unit also builds a flexible system where the generation AI learns the formats of medical institutions and can respond to format changes. For example, it can also respond when a new format is added. This allows the generation AI to learn the differences in formats between medical institutions and provide an optimized format, making information sharing smoother.

[0080] The format unification unit can automatically add the medical institution's logo and specific design elements to the referral letter format. For example, the generation AI automatically adds the medical institution's logo to the referral letter format. For example, the hospital's logo is placed in the header of the referral letter. The format unification unit also automatically adds specific design elements to the referral letter format. For example, the color scheme and font of the medical institution are used. The format unification unit also allows the generation AI to reflect the medical institution's brand elements in the referral letter format. For example, the hospital's mission statement and slogan are included in the referral letter. This allows the medical institution's logo and design elements to be automatically added, thereby maintaining visual consistency among referral letters.

[0081] The format unification unit can reflect the patient's emotional state and provide a reader-friendly design. For example, the generation AI analyzes the patient's emotional state and reflects the results in the format. For example, if the patient is feeling anxious, a design that gives a sense of security can be adopted. The format unification unit also adjusts the color and font of the format by having the generation AI take the patient's emotional state into consideration. For example, it uses colors that have a relaxing effect. The format unification unit also provides a reader-friendly design based on the patient's emotional state. For example, it adds icons and illustrations that correspond to the emotion. This allows for a referral letter to be created that is reader-friendly by providing a design that reflects the patient's emotional state.

[0082] The format unification unit can automatically generate formats specialized for different medical fields. For example, the generation AI automatically generates a format specialized for dentistry. For example, it creates a referral letter that includes information about dental treatment. The format unification unit also automatically generates a format specialized for ophthalmology. For example, it creates a referral letter that includes vision test results and information about ophthalmology treatment. The format unification unit also automatically generates formats specialized for different medical fields. For example, it provides formats corresponding to each field, such as internal medicine, surgery, and dermatology. By providing formats specialized for different medical fields, it becomes possible to share information appropriate for each field.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The referral letter creation unit automatically creates a referral letter using generation AI. For example, the generation AI analyzes information such as the patient's medical records, test results, and treatment history to create an appropriate referral letter. The generation AI can also create referral letters using technologies such as text generation AI (e.g., GPT-3) and BERT. Step 2: The format unification unit converts the referral letters created by the referral letter creation unit into a unified format. For example, if the generation AI creates referral letters based on a unified format, the receiving medical institution can easily understand the information. Step 3: The error check section checks the contents of the referral letter converted by the format unification section for errors. For example, the generation AI analyzes the contents of the referral letter and checks for errors or omissions. If errors are found, it suggests corrections.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0098] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

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

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the robot 414, 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. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0131] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0133] The data processing system 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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]

[0152] 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 referral letter creation unit that automatically creates referral letters using generation AI; a format unification unit that converts the letters of introduction created by the letter of introduction creation unit into a unified format; an error check unit that checks for errors in the content of the letter of introduction converted by the format unification unit; A system characterized by:

2. The letter of introduction preparation unit Estimate the patient's emotional state and prepare a referral letter that includes considerations to reduce the patient's psychological burden.

2. The system of claim 1.

3. The letter of introduction preparation unit Analyzing a patient's medical history and generating referrals to suggest the most appropriate treatment or specialist 2. The system of claim 1.

4. The letter of introduction preparation unit Prepare a referral letter with personalized advice, taking into account the patient's lifestyle and environmental information 2. The system of claim 1.

5. The letter of introduction preparation unit Automatically generate referral letters in different languages ​​to accommodate international patients 2. The system of claim 1.

6. The letter of introduction preparation unit Prepare referral letters with easy-to-understand instructions for the patient's family and caregivers 2. The system of claim 1.

7. The letter of introduction preparation unit Estimate the patient's emotions and create a referral letter with emotionally appropriate encouraging and reassuring messages 2. The system of claim 1.

8. The format unification unit Learns the differences in formats between medical institutions and automatically generates formats optimized for each medical institution 2. The system of claim 1.

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