System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting basic information and a medical record of a patient; means for transmitting the basic information and the medical record of the patient to a server; means for analyzing received information and generating a referral letter; means for adapting the generated referral letter to a unified format and performing an error check; means for confirming and correcting the referral letter at a terminal; and means for electronically transmitting and storing a final referral letter to a medical institution.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] Currently, medical institutions spend a great deal of time and resources preparing referral letters. Furthermore, referral letters often contain incorrect or incomplete information, and have inconsistent formats. This reduces the efficiency of medical care and the quality of patient care. The present invention aims to solve these problems, streamline the referral letter preparation process, and improve the operational efficiency of medical institutions. [Means for solving the problem]

[0005] The present invention solves these problems with a system that includes a means for inputting basic patient information and medical records, a means for transmitting the basic patient information and medical records to a server, a means for analyzing the received information and generating a referral letter, a means for adapting the generated referral letter to a unified format and performing error checks, a means for confirming and correcting the referral letter on a terminal, and a means for electronically transmitting and storing the final referral letter to the medical institution. Furthermore, the system also includes a means for importing patient information in cooperation with an electronic medical record system and encrypting and transmitting the generated referral letter, thereby improving the accuracy and security of the information.

[0006] "Basic patient information" refers to information necessary to identify an individual patient and understand their current medical status, such as name, age, gender, address, contact information, medical history, current diagnosis, and details of symptoms.

[0007] "Medical records" are medical data such as the details of a patient's past medical treatments, treatment history, prescribed medications, and test results.

[0008] A "server" is a central control device for receiving, analyzing, storing, and transmitting data.

[0009] A "letter of referral" is a document used by a doctor when referring a patient to another medical institution, and it contains the patient's symptoms, medical history, current treatment, etc.

[0010] The "unified format" is a standard document format that allows the contents of the letter of introduction to be expressed in a consistent format.

[0011] "Error checking" is the process of checking whether there are any errors or omissions in the input information or the generated document.

[0012] A "terminal" is a computer or smart device used by a doctor or medical staff member.

[0013] An "electronic medical record system" is a system that electronically manages patient medical records.

[0014] "Patient capture" is the process of obtaining patient medical data from the electronic medical record system.

[0015] "Encryption" is the technique of converting data into a secret format that cannot be deciphered by third parties.

[0016] "Sending" is the process of transferring data or documents to a specific destination.

[0017] "Storage" means that data such as generated referral letters are kept in a database or the like so that they can be referenced or reused later. [Brief explanation of the drawings]

[0018] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] 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, a 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), and an APU (Accelerated Processing Unit).

[0022] 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.

[0023] 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.

[0024] 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), Bluetooth (registered trademark), etc.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0030] 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.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] The present invention is a system for efficiently creating referral letters automatically, and supports the process of inputting and analyzing basic patient information and medical records, and generating and sending referral letters. Specific embodiments of this system are described below.

[0040] Entering and submitting patient data

[0041] First, the user (doctor or medical staff) enters the patient's basic information and medical records into a dedicated terminal. This terminal is linked to the electronic health record system (EHR), and the necessary medical records can be automatically retrieved. For example, if a doctor enters "Yamada Taro, 45 years old, male, lower back pain" into the terminal, this information will automatically retrieve detailed medical records from the EHR system.

[0042] The device then packages the entered data and retrieved medical records and sends them to a server over a secure, encrypted channel, preserving patient privacy and data integrity.

[0043] Information analysis and referral generation

[0044] The server analyzes the received data and generates a referral letter using natural language processing (NLP) technology. Specifically, the server inputs the necessary information into the AI ​​model and drafts an appropriate referral letter based on the patient's symptoms and treatment details. For example, based on the information "Yamada Taro, 45 years old, chronic back pain, currently undergoing rehabilitation," it generates a sentence such as "Yamada Taro suffers from chronic back pain and is currently undergoing rehabilitation and drug treatment. A referral to an orthopedic surgeon for further detailed examination is required."

[0045] Formatting consistency and error checking

[0046] The generated letter of introduction is then adapted to a unified format by the server. A template engine is used to embed the generated text into a standard document. An error-checking program is then launched to detect typos and missing information. For example, the sentence "Yamada Taro suffers from chronic back pain" is automatically checked for typos and missing information.

[0047] Checking and correcting the letter of introduction

[0048] Next, the user (doctor or medical staff) checks the generated referral letter on the terminal. They check the contents of the referral letter on the screen and manually correct it if necessary. For example, they can enter additional information such as "An additional consultation with an orthopedic surgeon is recommended."

[0049] Send and save referrals

[0050] Finally, the terminal electronically sends the final referral letter to the designated hospital or medical institution via the server. The transmission is done via email or a medical information exchange system. At the same time, the server stores a copy of the generated referral letter in a database for future reference or reuse. For example, the terminal sends the referral letter to the email address of an orthopedic department, and the server stores the referral letter in a database.

[0051] In this way, the system of the present invention streamlines the referral process and ensures accuracy and consistency of information, improving the quality of patient care and helping medical institutions operate more efficiently.

[0052] The processing flow will be explained below.

[0053] Step 1:

[0054] The user (doctor or medical staff) uses the terminal to enter basic patient information, such as name, age, sex, address, contact information, medical history, current diagnosis information, and details of symptoms.

[0055] Step 2:

[0056] Based on the entered data, the device connects to the electronic health record system (EHR) to retrieve the patient's medical records, and calls the API to obtain the necessary information.

[0057] Step 3:

[0058] The terminal packages the entered basic patient information and the acquired medical records into a single packet, reformatting the data as necessary.

[0059] Step 4:

[0060] The device encrypts the packaged data and sends it over a secure channel to the server, using an API to handle the transmission.

[0061] Step 5:

[0062] The server analyzes the received data and generates a draft referral letter using natural language processing (NLP) technology. Specifically, the necessary information is input into an AI model, which automatically generates a letter appropriate to the patient's condition.

[0063] Step 6:

[0064] The server adapts the generated letter of introduction to a unified format and uses a template engine to embed the generated text into a template document.

[0065] Step 7:

[0066] The server starts an error check program to automatically detect typos, incorrect information, and missing information in the generated letter of introduction.

[0067] Step 8:

[0068] The user (doctor or medical staff) checks the referral letter generated on the terminal, confirms the contents on the screen, and manually corrects them if necessary.

[0069] Step 9:

[0070] The terminal converts the modified referral into a printable format, for example, PDF format, and sends it to a printer.

[0071] Step 10:

[0072] The terminal electronically transmits the final version of the referral letter to the designated hospital or medical institution via the server, via email or a medical information exchange system.

[0073] Step 11:

[0074] The server stores a copy of the generated referral in its database for future reference and reuse.

[0075] Example 1

[0076] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0077] In today's medical field, creating referral letters is a time-consuming, labor-intensive process that is prone to errors. Information can also be inconsistent and inaccurate, potentially affecting appropriate patient care. Furthermore, ensuring the security of referral letter storage and transmission is also an issue.

[0078] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0079] In this invention, the server includes a means for inputting basic information and medical records of a patient, a means for transmitting the basic information and medical records of the patient to the server, a means for analyzing the received information and generating a referral letter using natural language processing technology, and a means for adapting the generated referral letter to a unified format and performing error checking, thereby enabling automatic generation of referral letters, improved accuracy and consistency, and secure storage and transmission of data.

[0080] "Patient basic information" refers to basic personal information such as the patient's name, age, gender, and symptoms.

[0081] "Medical records" refer to detailed medical records, such as a patient's medical history, current treatment, medications, and rehabilitation status.

[0082] "Server" refers to a central processing unit for analyzing patient information, generating referral letters, and storing and transmitting data.

[0083] "Terminal" refers to an electronic device used by doctors and medical staff to enter patient basic information and medical records, and to review and correct referral letters.

[0084] "Natural language processing technology" refers to technology that enables computers to understand and process human language, including text analysis and automatic generation.

[0085] A "generative AI model" is an artificial intelligence model trained for a specific task, in this case the automated generation of letters of introduction.

[0086] "Unified format" refers to a standard document format that ensures that referral documents are output in a consistent format.

[0087] "Error checking" refers to the process of detecting and correcting typographical errors and missing information in the generated referral letters.

[0088] An "electronic medical record system" refers to a system that electronically manages and stores patient medical records.

[0089] "Encryption" refers to a technology that converts information using a specific algorithm to protect data and prevent unauthorized access by third parties.

[0090] A "secure path" refers to a communication path through which data is sent and received securely, using encryption and authentication technologies.

[0091] This invention is a system for making referral letters more efficient by utilizing basic patient information and medical records. Specific embodiments for implementing this system are described below.

[0092] Entering and submitting patient data

[0093] First, the user (doctor or medical staff) uses a dedicated terminal to input basic information and medical records of the patient, including details such as the patient's name, age, gender, and current symptoms.

[0094] As a specific example, a user enters "Yamada Taro, 45 years old, male, lower back pain" into the terminal. This input information is linked to the electronic medical record system (EHR), and more detailed medical records are automatically retrieved.

[0095] The device then packages the entered data and retrieved medical records, encrypts them using a secure protocol, and transmits them to a server, ensuring data integrity and privacy.

[0096] Information analysis and referral generation

[0097] The server analyzes the received data and generates a referral letter using natural language processing (NLP) technology. Specifically, it inputs the necessary information as prompt sentences into the generative AI model and drafts an appropriate referral letter.

[0098] For example, the server might use the following prompt:

[0099] Patient demographics:

[0100] Name: Yamada Taro

[0101] Age: 45

[0102] Gender: Male

[0103] Symptom: Lower back pain

[0104] Medical Records:

[0105] Under rehabilitation and drug treatment

[0106] Generate a testimonial:

[0107] Based on this prompt, the AI ​​model generates the following sentence: "Taro Yamada suffers from chronic lower back pain and is currently undergoing rehabilitation and medication. He needs to be referred to an orthopedic surgeon for further detailed examination."

[0108] Formatting consistency and error checking

[0109] The server uses a template engine to adapt the generated letters of introduction to a unified format, ensuring that they are output in a consistent format. It also runs an error-checking program to detect typos and missing information and suggest corrections.

[0110] Checking and correcting the letter of introduction

[0111] The user (doctor or medical staff) can review the referral letter generated on the terminal. They can check the contents on the screen and manually make corrections if necessary. For example, they can manually enter additional information such as "Additional consultation with an orthopedic surgeon is recommended."

[0112] Send and save referrals

[0113] The terminal then electronically transmits the final version of the referral letter to the designated hospital or medical institution via a server, using email or a medical information exchange system as the transmission method.

[0114] The server stores a copy of the referral in a database for future reference or reuse. For example, the device sends the referral to the orthopedic department's email address, and the server stores the referral in a database.

[0115] In this way, the system of the present invention streamlines the referral process, ensures accuracy and consistency of information, improves the quality of patient care, and helps medical institutions operate more efficiently.

[0116] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0117] Step 1: Enter patient demographics and medical records

[0118] The user (doctor or medical staff) uses a dedicated terminal to input basic information and medical records of the patient. In this input step, details such as the patient's name, age, gender, and current symptoms are entered.

[0119] As a specific operation, the user inputs "Yamada Taro, 45 years old, male, lower back pain" into the terminal. This is the input.

[0120] Step 2: Automated patient data capture and packaging

[0121] The terminal will then link with the electronic health record system (EHR) based on the basic information entered, and automatically retrieve the patient's detailed medical records. Specifically, the terminal will query the EHR system for records for "Yamada Taro." This will retrieve detailed medical records for "under rehabilitation and drug treatment."

[0122] Input: Basic information entered by the user

[0123] Output: Data package containing detailed medical records

[0124] Step 3: Send patient data

[0125] The device packages the acquired data, encrypts it, and sends it to the server. Specifically, the device sends it using a secure protocol.

[0126] Input: Data package containing detailed medical records

[0127] Output: Sending encrypted data

[0128] Step 4: Analyze the data and generate a draft letter of introduction

[0129] The server analyzes the received data and uses natural language processing (NLP) techniques to generate appropriate prompts, which are then fed into a generative AI model, which then drafts a referral letter based on the prompts.

[0130] Example prompt sentence:

[0131] Patient demographics:

[0132] Name: Yamada Taro

[0133] Age: 45

[0134] Gender: Male

[0135] Symptom: Lower back pain

[0136] Medical Records:

[0137] Under rehabilitation and drug treatment

[0138] Generate a testimonial:

[0139] Input: Detailed medical records

[0140] Output: Draft letter of introduction

[0141] Step 5: Formatting and Error Checking

[0142] The server uses a template engine to adapt the generated letters of introduction to a unified format, and also runs an error-checking program to detect typos and missing information.

[0143] Input: Draft letter of introduction

[0144] Output: Error checked referral letter

[0145] Step 6: Review and correct the referral letter

[0146] The user (doctor or medical staff) checks the referral generated on the device and manually corrects it if necessary. Specifically, the user enters information about additional consultations or treatments. For example, the user adds information such as "An additional consultation with an orthopedic surgeon is recommended."

[0147] Input: Error-checked referral letter

[0148] Output: Revised version of the referral letter

[0149] Step 7: Send and save your referral

[0150] The terminal electronically sends the final referral letter to the designated hospital or medical institution via the server. Specifically, the terminal sends the letter via email or a medical information exchange system. At the same time, the server stores a copy of the generated referral letter in a database.

[0151] Input: Revised version of the referral letter

[0152] Output: Referrals sent and referrals saved

[0153] These processing steps streamline the referral process and ensure accuracy and consistency of information.

[0154] (Application example 1)

[0155] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0156] The current process for creating referrals and industrial product inspection reports requires a lot of manual input and correction, which is time-consuming and labor-intensive. It is also prone to errors and it is difficult to maintain consistency of information. In the industrial sector, in particular, there is a demand for fast and accurate production of inspection reports, and a system as efficient as the creation of referrals is required.

[0157] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0158] In this invention, the server includes means for inputting basic patient information and medical records, means for transmitting the basic patient information and medical records to the server, means for analyzing the received information and generating a referral letter, means for adapting the generated referral letter to a unified format and performing error checks, means for checking and correcting the referral letter on a terminal, means for electronically transmitting the final referral letter to a medical institution and storing it, means for inputting inspection data of industrial products and transmitting it to the server, means for analyzing the received inspection data of industrial products and generating an inspection report, means for adapting the generated inspection report to a unified format and performing error checks, means for checking and correcting the inspection report on a terminal, and means for electronically transmitting the final inspection report to a relevant department and storing it, thereby enabling efficient and accurate generation of referral letters and inspection reports in medical institutions and industrial fields.

[0159] "Basic patient information" refers to basic personal information necessary to identify a patient, such as the patient's name, age, gender, and contact information.

[0160] "Medical records" refers to medical-related information such as details of examinations, prescriptions, treatment history, and test results conducted for patients at medical institutions.

[0161] "Server" means a central processing unit for receiving, analyzing, storing, and transmitting data over a network.

[0162] A "referral letter" is an official document created to refer a patient to another medical institution or specialist.

[0163] A "uniform format" is a standard for organizing documents and data into a specific standard format.

[0164] "Error checking" is the process of verifying the accuracy of documents or data and detecting typographical errors or incomplete information.

[0165] A "terminal" is a device for inputting, displaying, and modifying data. Examples include personal computers and tablets.

[0166] "Industrial product inspection data" refers to data on the quality and performance of inspected items collected at factories and manufacturing sites.

[0167] "Inspection report" means an official document summarizing the results of an inspection and including conclusions regarding the quality and performance of the inspected item.

[0168] "Related departments" are internal departments related to the inspection results and quality control of industrial products.

[0169] These definitions provide clearer explanations of key terms found in the claims and facilitate a better understanding of the invention.

[0170] The present invention is a system for efficiently creating letters of introduction and inspection reports for industrial products. Specific embodiments of this system will be described below.

[0171] First, the user (doctor or medical staff) enters the patient's basic information and medical records into a dedicated terminal. This terminal is linked to the electronic health record system (EHR) and can automatically retrieve the necessary medical records. For example, if a doctor enters "Patient A, 45 years old, male, lower back pain" into the terminal, this information will automatically retrieve detailed medical records from the EHR system.

[0172] The device then packages the entered data and retrieved medical records and sends them to a server over a secure, encrypted channel, preserving patient privacy and data integrity.

[0173] The server analyzes the received data and generates a referral letter using natural language processing (NLP) technology. Specifically, the server inputs the necessary information into the AI ​​model and drafts an appropriate referral letter based on the patient's symptoms and treatment details. For example, based on the information "Patient A, 45 years old, chronic lower back pain, undergoing rehabilitation," it generates a sentence such as "Patient A suffers from chronic lower back pain and is currently undergoing rehabilitation and drug treatment. A referral to an orthopedic surgeon is required for further detailed examination."

[0174] The generated referral letter is adapted to a unified format by the server. A template engine is used to embed the generated text into a standard document. An error-checking program is then launched to detect typos and missing information. For example, the sentence "Patient A suffers from chronic lower back pain" is automatically checked for typos and missing information.

[0175] Next, the user (doctor or medical staff) checks the generated referral letter on the terminal. They check the contents of the referral letter on the screen and manually correct it if necessary. For example, they can enter additional information such as "An additional consultation with an orthopedic surgeon is recommended."

[0176] Finally, the terminal electronically sends the final referral letter to the designated medical institution via the server. The transmission is done via email or a medical information exchange system. At the same time, the server stores a copy of the generated referral letter in a database for future reference or reuse. For example, the terminal sends the referral letter to the email address of the orthopedic department, and the server stores the referral letter in the database.

[0177] A similar process can also be used to automatically generate inspection reports using inspection data from industrial products. Inspectors use their smartphones to input inspection data and send it to a server. The server analyzes the received data and generates an appropriate inspection report. The generated inspection report is then adapted to a unified format and checked for errors. The inspector reviews the report and makes any necessary corrections. Finally, the report is electronically sent to the relevant department and stored.

[0178] As a specific example, if an inspector enters data for item ID "12345" and records it as "Passed," the server can analyze this data and generate an inspection report such as "Inspection results performed by inspector A, item ID: 12345, result: Passed."

[0179] Examples of prompts to input to a generative AI model include:

[0180] Inspection Data:

[0181] Inspector: Inspector A

[0182] Item ID: 12345

[0183] Result: Passed

[0184] Based on this, generate an inspection report in the following format:

[0185] ----------------------

[0186] Inspection Report:

[0187] Inspector: Inspector A

[0188] Item ID: 12345

[0189] Test result: Passed

[0190] ----------------------

[0191] In this way, the system of the present invention streamlines the process of creating referral letters and test reports, ensuring accuracy and consistency of information, thereby helping to improve operational efficiency and quality in medical institutions and industrial fields.

[0192] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0193] Step 1: User enters basic information and medical records

[0194] The user (doctor or medical staff) uses the terminal to input basic information and medical records about the patient. For example, the information input in this step might be "Patient A, 45 years old, male, with lower back pain." The terminal is linked to the electronic medical record system (EHR), and automatically obtains detailed medical records based on the input information. The input data is temporarily stored in the terminal.

[0195] Step 2: The device sends the data to the server

[0196] The terminal compiles the entered basic patient information and the acquired medical records, packages them, and sends them to the server. The transmission is carried out via a secure, encrypted communication channel. The input data is encrypted before being sent to the server. This ensures patient privacy and data integrity.

[0197] Step 3: The server analyzes the data and generates a referral.

[0198] The server analyzes the received data and generates a referral letter using natural language processing (NLP) technology. Based on the input basic information and medical records, the AI ​​model generates an appropriate draft referral letter. For example, from the data "Patient A, 45 years old, chronic back pain, undergoing rehabilitation," a referral letter may be generated that reads, "Patient A suffers from chronic back pain and is currently undergoing rehabilitation and drug treatment. He or she will need to be referred to an orthopedic surgeon for further detailed examination." The generated referral letter is stored on the server.

[0199] Step 4: The server standardizes the format of the letters of introduction and checks for errors

[0200] The server uses a template engine to adapt the generated letter of introduction to a standardized format. The generated text is converted into a standard document format. An error check program is then run to detect typos and missing information in the letter of introduction. Once the error check is complete, the letter of introduction is saved back to the server.

[0201] Step 5: User confirms and modifies the referral letter

[0202] The user (doctor or medical staff) uses the terminal to check the generated referral letter. They check the details of the referral letter displayed on the screen and manually correct it if necessary. For example, they can enter additional information such as "An orthopedic surgeon recommends an additional consultation." The corrected referral letter is temporarily saved on the terminal.

[0203] Step 6: The device sends and saves the final version of the introduction letter

[0204] After confirmation and correction, the final referral letter is sent electronically to the designated medical institution via the server. The transmission route is email or a medical information exchange system. At the same time, the server stores a copy of the generated and sent referral letter in a database for future reference and reuse.

[0205] Step 7: Inspectors enter the inspection data for industrial products

[0206] Inspectors use their smartphones to input inspection data for industrial products. For example, the item ID is "12345" and the inspection result is "Passed." The input data is temporarily saved on the smartphone.

[0207] Step 8: The smartphone sends the data to the server

[0208] The smartphone packages the entered test data and sends it to the server via a secure, encrypted communication channel. The entered data is sent to the server in encrypted form and stored there.

[0209] Step 9: The server analyzes the inspection data and generates an inspection report.

[0210] The server analyzes the received inspection data and generates an inspection report. Based on the input inspection data, the AI ​​model generates an appropriate draft inspection report. For example, based on the data item ID "12345" and inspection result "Passed," an inspection report such as "Inspection results performed by inspector A, item ID: 12345, result: Passed" is generated. The generated report is saved on the server.

[0211] Step 10: The server unifies the format of the inspection report and checks for errors.

[0212] The server uses a template engine to adapt the generated inspection report to a standardized format. The generated text is converted into a standard document format. An error check program is then run to detect typographical errors and missing information in the inspection report. Once the error check is complete, the report is saved back to the server.

[0213] Step 11: Inspector reviews and corrects the inspection report

[0214] The inspector uses a smartphone to check the generated inspection report. He / she carefully reviews the contents of the inspection report displayed on the screen and manually corrects them as necessary. For example, he / she enters additional information such as "additional quality checks are required." The corrected inspection report is temporarily saved on the smartphone.

[0215] Step 12: Your smartphone sends and saves the final inspection report

[0216] After confirmation and corrections are completed, the final inspection report is sent electronically to the relevant department via the smartphone server, via email or the internal information exchange system. At the same time, the server stores a copy of the generated and sent inspection report in a database for future reference and reuse.

[0217] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0218] The present invention combines a system for streamlining the automatic creation of referral letters with an emotion engine that recognizes the user's emotions. This system supports the process of inputting and analyzing basic patient information and medical records, generating and sending referral letters, and further adjusts the process by taking the user's emotional state into consideration. Specific embodiments of this system are described below.

[0219] Entering and submitting patient data

[0220] First, the user (doctor or medical staff) enters the patient's basic information and medical records into a dedicated terminal. This terminal is linked to the electronic health record system (EHR), and the necessary medical records can be automatically retrieved. For example, if a doctor enters "Yamada Taro, 45 years old, male, lower back pain" into the terminal, this information will automatically retrieve detailed medical records from the EHR system.

[0221] The device then packages the entered data and retrieved medical records and sends them to a server over a secure, encrypted channel, preserving patient privacy and data integrity.

[0222] Information analysis and referral generation

[0223] The server analyzes the received data and generates a referral letter using natural language processing (NLP) technology. Specifically, the necessary information is input into the AI ​​model, which then drafts an appropriate referral letter based on the patient's symptoms and treatment details. For example, based on the information "Yamada Taro, 45 years old, chronic back pain, currently undergoing rehabilitation," it generates a sentence such as "Yamada Taro suffers from chronic back pain and is currently undergoing rehabilitation and drug treatment. A referral to an orthopedic surgeon for further detailed examination is required."

[0224] Formatting consistency and error checking

[0225] The generated letter of introduction is then adapted to a unified format by the server. A template engine is used to embed the generated text into a standard document. An error-checking program is then launched to detect typos and missing information. For example, the sentence "Taro Yamada suffers from chronic back pain" is automatically checked for typos and missing information.

[0226] Use of emotion engine

[0227] Next, when the user (doctor or medical staff) checks the referral letter generated on the device, the emotion engine analyzes the user's voice and facial expressions to understand their emotional state, such as stress or fatigue. If the emotion engine detects a situation where an error is likely to occur, such as when the user is overly tired, it automatically displays a warning. The content and display format of the referral letter are also adjusted to allow the user to work in a relaxed state.

[0228] Checking and correcting the letter of introduction

[0229] After receiving feedback from the emotion engine, the user can review the referral generated on their device and manually correct it if necessary, for example by adding additional information such as "An additional consultation with an orthopedic surgeon is recommended."

[0230] Send and save referrals

[0231] Finally, the terminal electronically sends the final referral letter to the designated hospital or medical institution via the server. The transmission is done via email or a medical information exchange system. At the same time, the server stores a copy of the generated referral letter in a database for future reference or reuse. For example, the terminal sends the referral letter to the email address of an orthopedic department, and the server stores the referral letter in a database.

[0232] In this way, the system of the present invention streamlines the referral process, ensuring accuracy and consistency of information while optimizing the process by taking into account the user's emotional state, thereby improving the quality of patient care and supporting the operational efficiency of medical institutions.

[0233] The processing flow will be explained below.

[0234] Step 1:

[0235] The user (doctor or medical staff) uses the terminal to enter basic patient information, such as name, age, sex, address, contact information, medical history, current diagnosis information, and details of symptoms.

[0236] Step 2:

[0237] Based on the information entered, the device will automatically retrieve medical records in conjunction with the electronic health record system (EHR), using an API to obtain the necessary medical data.

[0238] Step 3:

[0239] The terminal packages the entered patient information and the acquired medical records into a single data packet, formats the data, and prepares the packet for transmission to the server.

[0240] Step 4:

[0241] The device encrypts the data packet and sends it to the server over a secure channel, calling an API to send the data.

[0242] Step 5:

[0243] The server analyzes the received data packets and generates a referral letter using natural language processing (NLP) technology. The information is input into an AI model, which automatically generates a letter that corresponds to the patient's symptoms and treatment details.

[0244] Step 6:

[0245] The server adapts the generated letters of introduction to a uniform format and uses a template engine to embed the generated text into a template document.

[0246] Step 7:

[0247] The server runs an error check program to detect typos, incorrect information, missing information, etc. in the generated letter of introduction, thereby confirming the accuracy of the information.

[0248] Step 8:

[0249] The user (doctor or medical staff) checks the referral letter generated on the terminal, checks the contents of the referral letter on the screen, and manually corrects them if necessary.

[0250] Step 9:

[0251] While the user is checking the referral letter, the device runs an emotion engine that analyzes the user's voice and facial expressions to detect emotional states such as stress and fatigue.

[0252] Step 10:

[0253] If the emotion engine detects stress or fatigue, the device will display a warning to the user, such as a message saying, "Take a break to reduce stress."

[0254] Step 11:

[0255] Based on feedback from the emotion engine, the device will adjust the content and presentation of the referral letter according to the user's emotional state, for example, changing it to a simpler, more understandable format for a user who is overly tired.

[0256] Step 12:

[0257] The terminal then reviews the revised letter of introduction again and converts the final version into a printable format, such as PDF, and sends it to the printer.

[0258] Step 13:

[0259] The terminal then electronically sends the final referral letter to the designated hospital or medical institution via the server, either by email or via a medical information exchange system.

[0260] Step 14:

[0261] The server stores a copy of the generated referral in its database, where it is stored for future reference and reuse.

[0262] In this way, not only is the referral process more efficient, but the task is optimized taking into account the user's emotional state, improving the quality of patient care and helping medical institutions work more efficiently.

[0263] Example 2

[0264] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0265] With conventional referral letter creation systems, when creating referral letters using basic patient information and medical records, it was difficult to ensure the accuracy and consistency of the information, and if the user was tired or stressed, there was a high possibility of typos or incomplete information.In addition, because the referral letter creation process was done manually, there were problems such as reduced work efficiency and a heavy burden on medical staff.

[0266] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for inputting basic information and medical records of a patient; means for transmitting the basic information and medical records of the patient to an information system; means for analyzing the received information and generating a referral letter using natural language processing technology; means for adapting the generated referral letter to a unified format and performing error checking; means for analyzing the emotional state of the user and providing feedback; means for checking and correcting the referral letter on a terminal; and means for electronically transmitting and storing the final referral letter to the medical institution. This improves the efficiency of the automatic referral letter generation process and enables optimization of the work taking into account the emotional state of the user while maintaining the accuracy and consistency of the information.

[0267] "Basic patient information" refers to basic personal information about the patient, such as the patient's name, age, sex, address, contact information, and chief complaint.

[0268] "Medical records" refers to all records related to medical treatment at medical institutions, such as a patient's medical history, test results, prescription history, surgical history, allergy information, and treatment plans.

[0269] "Information systems" refer to computer systems, such as electronic health records (EHRs) and healthcare information systems (HISs), that digitally manage and operate patient medical records and basic information.

[0270] "Natural language processing technology" refers to technology that enables computers to understand, analyze, and generate human language. Examples include text analysis, speech recognition, and language generation.

[0271] A "referral document" is an official document containing the information necessary for a patient to be referred to another medical institution or specialist.

[0272] A "template engine" is a software tool for embedding dynamic data into boilerplate documents, and includes libraries such as Jinja2 and Handlebars.

[0273] An "error checking program" is a software program that automatically detects typographical errors, grammatical errors, missing information, etc. in a document.

[0274] The "emotion engine" is an analytical engine that analyzes voice, facial expressions, movements, etc. to evaluate the user's emotional state (stress, fatigue, relaxation, etc.).

[0275] The "means for providing feedback" is a means for reporting the status to the user based on the data obtained from the emotion engine and presenting appropriate countermeasures or warnings.

[0276] "Device" means a computing device, including a PC or tablet, on which a User enters information and / or reviews or modifies the generated referral document.

[0277] "Electronic transmission means" refers to a means for securely transmitting the generated referral document to the designated medical institution via email or a medical information exchange system.

[0278] The "means for storing" refers to a means for storing the generated introduction document in a storage system such as a database for future reference or reuse.

[0279] The present invention combines a system for streamlining the automatic creation of referral letters with an emotion engine that recognizes the user's emotions. This system supports the process of inputting and analyzing basic patient information and medical records, generating and sending referral letters, and further adjusts the process by taking the user's emotional state into consideration. Specific embodiments of this system are described below.

[0280] Entering and submitting patient data

[0281] First, the user (doctor or medical staff) enters the patient's basic information and medical records into a dedicated terminal. This terminal is linked to the electronic health record system (EHR) and can automatically retrieve the necessary medical records. For example, if a doctor enters "Yamada Taro, 45 years old, male, lower back pain" into the terminal, this information will automatically retrieve detailed medical records from the EHR system.

[0282] The terminal packages the entered data and the acquired medical records and sends them to the server over a secure channel (e.g., SSL / TLS protocol) after encrypting the data, thereby preserving patient privacy and data integrity.

[0283] Information analysis and referral generation

[0284] The server analyzes the received data and generates a referral letter using natural language processing (NLP) technology. Specifically, it inputs the necessary information into a generative AI model (e.g., GPT-3 (registered trademark)), which then drafts an appropriate referral letter based on the patient's symptoms and treatment details. For example, based on the information "Yamada Taro, 45 years old, chronic back pain, currently undergoing rehabilitation," it generates a sentence such as "Yamada Taro suffers from chronic back pain and is currently undergoing rehabilitation and drug treatment. He needs to be referred to an orthopedic surgeon for further detailed examination."

[0285] Formatting consistency and error checking

[0286] The generated letter of introduction is then adapted to a unified format by the server. Specifically, a template engine (e.g., Jinja2) is used to embed the generated text into a standard document. An error-checking program is then launched to detect typos and missing information. For example, the sentence "Yamada Taro suffers from chronic back pain" is automatically checked for typos and missing information.

[0287] Use of emotion engine

[0288] Next, when the user (doctor or medical staff) checks the referral letter generated on the device, the emotion engine analyzes the user's voice and facial expressions to understand their emotional state, such as stress or fatigue. If the emotion engine detects a situation where an error is likely to occur, such as when the user is overly tired, it automatically displays a warning. The content and display format of the referral letter are also adjusted to allow the user to work in a relaxed state.

[0289] Checking and correcting the letter of introduction

[0290] After receiving feedback from the emotion engine, the user can review the referral generated on their device and manually correct it if necessary, for example by adding additional information such as "An additional consultation with an orthopedic surgeon is recommended."

[0291] Send and save referrals

[0292] Finally, the terminal electronically sends the final referral letter to the designated hospital or medical institution via the server. This can be done via email or a healthcare information exchange (HIE). At the same time, the server stores a copy of the generated referral letter in a database for future reference or reuse. For example, the terminal sends the referral letter to the orthopedic department's email address, and the server stores it in the database.

[0293] Specific examples

[0294] Here is an example of a prompt that can be fed into a generative AI model to automatically generate a referral letter:

[0295] "Yamada Taro, 45 years old, male, chronic lower back pain, currently undergoing rehabilitation. Referred to orthopedic clinic."

[0296] For this input, the resulting letter of introduction might look like this:

[0297] "Yamada Taro suffers from chronic back pain and is currently undergoing rehabilitation and medication. He will need to be referred to an orthopedic surgeon for further detailed examination."

[0298] In this way, the system of the present invention streamlines the referral process, ensuring accuracy and consistency of information while optimizing the process by taking into account the user's emotional state, thereby improving the quality of patient care and supporting the operational efficiency of medical institutions.

[0299] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0300] Step 1: Enter patient demographics and medical records

[0301] The user (doctor or medical staff) enters the patient's basic information (name, age, sex, symptoms, etc.) into a dedicated terminal. The terminal uses this information to retrieve detailed medical records from the electronic health record system (EHR). Specifically, when the user enters "Yamada Taro, 45 years old, male, lower back pain" into the terminal, the "medical record for lower back pain" is automatically retrieved from the EHR based on this information. The entered basic information and medical records are compiled on the terminal.

[0302] Input: Patient's basic information ("Taro Yamada, 45 years old, male, lower back pain")

[0303] Output: Patient basic information and medical record ("Medical record of low back pain")

[0304] Step 2: Packaging and sending the data

[0305] The terminal compiles the entered basic information and the acquired medical records into a data package. The packaged data is then encrypted and sent to the server. The SSL / TLS protocol is generally used for encryption. Specifically, the terminal compiles data in a format such as "Yamada Taro, 45 years old, male, lower back pain, medical record for lower back pain," encrypts it, and sends it to the server.

[0306] Input: Patient demographics and medical records

[0307] Output: Encrypted data package

[0308] Step 3: Analyze the data

[0309] The server decrypts the received encrypted data package and analyzes the data. Natural language processing (NLP) technology is used for the analysis. Specifically, the server analyzes the information "Yamada Taro, 45 years old, suffering from chronic back pain, currently undergoing rehabilitation" and prepares to send the necessary information to the generative AI model.

[0310] Input: Encrypted data package

[0311] Output: Parsed patient information

[0312] Step 4: Generate introductory documentation

[0313] Based on the analyzed data, the server inputs a prompt into a generative AI model (e.g., GPT-3) to generate an introductory document. Specifically, the server inputs the following prompt into the generative AI model:

[0314] "Yamada Taro, 45 years old, suffering from chronic lower back pain, currently undergoing rehabilitation. Referred to an orthopedic surgeon."

[0315] The referral letter obtained from the generative AI model read something like, "Taro Yamada suffers from chronic lower back pain and is currently undergoing rehabilitation and medication. He needs to be referred to an orthopedic surgeon for further detailed examination."

[0316] Input: Parsed patient information

[0317] Output: Generated introductory document

[0318] Step 5: Formatting and Error Checking

[0319] The server adapts the generated referral document to a unified format using a template engine (e.g., Jinja2). Furthermore, an error-checking program is launched to detect typos and missing information. Specifically, the server adapts the generated referral document to a "standard referral template" and checks for errors using a grammar checker.

[0320] Input: Generated referral document

[0321] Output: Introduction document and error report in a unified format

[0322] Step 6: Analysis and feedback by the emotion engine

[0323] When a user checks the referral letter generated on the device, the emotion engine installed on the device analyzes the user's voice and facial expressions. The emotion engine grasps the user's emotional state, such as stress and fatigue, and displays a warning if excessive fatigue is detected. Specifically, while the user is checking the referral letter, the emotion engine analyzes stress and fatigue in real time and displays a message such as "Please relax and check" as necessary.

[0324] Input: User's voice and facial expressions

[0325] Output: Feedback based on emotional state

[0326] Step 7: Review and correct the referral letter

[0327] The user can review the generated referral letter on the device and manually correct it if necessary. Specifically, the user can enter additional information, such as "An additional consultation with an orthopedic surgeon is recommended."

[0328] Input: Generated referral letter, user correction input

[0329] Output: Final letter of introduction

[0330] Step 8: Send and save your referral

[0331] The terminal then electronically sends the final referral letter to the designated medical institution via the server. This is done via email or a healthcare information exchange (HIE). At the same time, the server stores a copy of the generated referral letter in a database for future reference or reuse. Specifically, the terminal sends the referral letter to the orthopedic department's email address, and the server stores it in the database.

[0332] Input: Final version of the referral letter

[0333] Output: Referrals sent, Referrals saved

[0334] In this way, the automated referral letter generation system provides a streamlined process, maintaining accuracy of information while optimizing the process by taking into account the user's emotional state.

[0335] (Application example 2)

[0336] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0337] While conventional referral letter creation systems have successfully automated the process from patient information entry to referral letter generation, they do not take into account the user's emotional state, which means there is a risk of users entering incorrect information if they are tired or stressed. Furthermore, they require user expertise to generate accurate and consistent referral letters, and effective support is not provided. This can lead to reduced referral letter quality and work efficiency, which can affect the quality of patient care.

[0338] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting basic information and medical records of a patient; means for transmitting the basic information and medical records of the patient to the server; means for analyzing the received information and generating a referral letter; means for adapting the generated referral letter to a unified format and performing error checking; means for confirming and correcting the referral letter on a terminal; means for electronically transmitting and storing the final referral letter to the medical institution; and means for analyzing the user's emotional state using an emotion recognition engine and adjusting the work process. This enables accurate and unified referral letters to be generated even when the user is tired or stressed, thereby improving work efficiency and the quality of patient care.

[0339] "Basic patient information" refers to personal identification information such as the patient's name, age, sex, and contact information.

[0340] "Medical records" are all information related to medical care, such as a patient's medical history, diagnosis, treatment, and notes made by medical staff.

[0341] "Server" means a computer system that centrally manages and processes data, and has the function of analyzing received information and generating letters of introduction.

[0342] A "letter of referral" is a document used to refer a patient to another medical institution, and contains details of the patient's condition, treatment progress, and any necessary tests or treatments.

[0343] A "uniform format" is a standardized form used to standardize the format and wording of letters of introduction.

[0344] "Error checking" is the process of automatically detecting and correcting typos and missing information in the generated letters of introduction.

[0345] A "terminal" is a computer or smart device that a user uses to perform operations such as input, confirmation, and correction.

[0346] An "emotion recognition engine" is software that analyzes a user's emotional state (e.g., stress, fatigue, etc.) from their voice and facial expressions, and provides feedback on the results.

[0347] "Work process adjustment" refers to optimizing the user's work environment and work procedures based on the analysis results of the emotion recognition engine.

[0348] The present invention combines a system for streamlining the automatic generation of referral letters with an emotion engine that recognizes the user's emotions. This system supports the process of inputting and analyzing basic patient information and medical records, generating and sending referral letters, and adjusting the process taking into account the user's emotional state.

[0349] Entering and submitting patient data

[0350] First, the user (doctor or medical staff) enters the patient's basic information and medical records into the terminal. This terminal is linked to the electronic health record system (EHR) and can automatically retrieve the necessary medical records. For example, if a doctor enters "XX, 45 years old, male, lower back pain" into the terminal, this information automatically retrieves detailed medical records from the EHR system. Next, the terminal packages the entered data and the retrieved medical records and sends them to the server. The transmission is carried out over a secure, encrypted channel, maintaining patient privacy and data integrity.

[0351] Information analysis and referral generation

[0352] The server analyzes the received data and generates a referral letter using natural language processing (NLP) technology. For example, the AI ​​model can input information such as "X, 45 years old, chronic back pain, currently undergoing rehabilitation," and draft an appropriate referral letter based on the patient's symptoms and treatment. Specifically, it generates a sentence such as, "X suffers from chronic back pain and is currently undergoing rehabilitation and drug treatment. A referral to an orthopedic surgeon is required for further detailed examination."

[0353] Formatting consistency and error checking

[0354] The generated letter of introduction is then adapted to a unified format by the server. A template engine is used to embed the generated text into a standard document. An error-checking program is then launched to detect typos and missing information. For example, the text "Mr. / Ms. X suffers from chronic back pain" is automatically checked for typos and missing information.

[0355] Use of emotion engine

[0356] Next, when the user (doctor or medical staff) checks the referral letter generated on the device, the emotion engine analyzes the user's voice and facial expressions to understand their emotional state, such as stress or fatigue. If the emotion engine detects a situation where an error is likely to occur, such as when the user is overly tired, it automatically displays a warning. The content and display format of the referral letter are also adjusted to allow the user to work in a relaxed state.

[0357] Checking and correcting the letter of introduction

[0358] After receiving feedback from the emotion engine, the user can review the referral generated on their device and manually correct it if necessary, for example, by adding additional information such as "An additional consultation with an orthopedic surgeon is recommended."

[0359] Send and save referrals

[0360] Finally, the terminal electronically sends the final referral letter to the designated hospital or medical institution via the server. The transmission is done via email or a medical information exchange system. At the same time, the server stores a copy of the generated referral letter in a database for future reference or reuse. For example, the terminal sends the referral letter to the email address of an orthopedic department, and the server stores the referral letter in a database.

[0361] Hardware and software used

[0362] Hardware:

[0363] Terminal (computer operated by the user)

[0364] Server (a computer system that centrally manages and processes data)

[0365] software:

[0366] Electronic Health Record System (EHR)

[0367] Emotion Recognition Engine (EmotionAnalyzer)

[0368] Natural Language Processing (NLP)

[0369] Template Engine

[0370] Error Checking Program

[0371] Examples of specific examples and prompts

[0372] For example, suppose a worker is wearing smart glasses. The prompt is, "Emotion Identification: Enter a facial image to analyze the stress and fatigue levels." This prompt allows the worker to enter the work data appropriately, and the emotion recognition engine can analyze the user's emotional state.

[0373] Specific prompt examples:

[0374] "Enter facial image data and analyze stress and fatigue levels. The model used is EmotionAnalyzer."

[0375] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0376] Step 1: The user enters the patient's basic information and medical records into the terminal.

[0377] The user enters basic information such as the patient's name, age, sex, and medical condition, as well as medical records, into the terminal. This input information is linked to the electronic health record system (EHR), and detailed medical records are automatically obtained. The input data is temporarily stored within the terminal.

[0378] Step 2: The device sends the patient's basic information and medical records to the server.

[0379] The terminal sends the entered basic patient information and medical records to the server via a secure encrypted channel. The input data is encrypted, packetized, and sent to the server.

[0380] Step 3: The server analyzes the received information and generates a referral.

[0381] The server analyzes the received patient's basic information and medical records. Using natural language processing (NLP) technology within the server, this information is input into an AI model, which then drafts an appropriate referral letter based on the patient's symptoms and treatment. For example, a referral letter can be generated using information such as "XX, 45 years old, chronic back pain, currently undergoing rehabilitation."

[0382] Step 4: The server adapts the generated letter of introduction to a unified format and performs error checking.

[0383] The generated letters of introduction are then adapted to a unified format by the server. A template engine is used to embed the generated text into a standardized format. An error-checking program is also initiated to automatically detect typos and missing information and identify areas that need correction.

[0384] Step 5: Analyze the user's emotional state using an emotion recognition engine and adjust the work process

[0385] The user checks the referral letter generated on the device, and the emotion recognition engine analyzes the user's voice and facial expressions to understand their emotional state, such as stress or fatigue. For example, the system may display feedback such as, "High stress or fatigue has been detected. A break is recommended."

[0386] Step 6: User reviews the referral and makes any necessary corrections

[0387] After receiving feedback from the emotion recognition engine, the user can review the generated letter of introduction on their device, manually correct it if necessary, enter additional information or corrections, and finally confirm the letter of introduction.

[0388] Step 7: The terminal electronically sends the final referral letter to the medical institution via the server and stores it.

[0389] Finally, the terminal electronically sends the final referral letter to the designated hospital or medical institution via the server. The transmission is done via email or a medical information exchange system, and the server also simultaneously stores a copy of the generated referral letter in a database. For example, the terminal sends the referral letter to the email address of an orthopedic department, and the server stores the referral letter in a database.

[0390] 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.

[0391] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0392] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0393] [Second embodiment]

[0394] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0395] 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.

[0396] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0397] 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.

[0398] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0399] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0400] 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.

[0401] 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.

[0402] 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 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.

[0403] 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.

[0404] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0405] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0406] The present invention is a system for efficiently creating referral letters automatically, and supports the process of inputting and analyzing basic patient information and medical records, and generating and sending referral letters. Specific embodiments of this system are described below.

[0407] Entering and submitting patient data

[0408] First, the user (doctor or medical staff) enters the patient's basic information and medical records into a dedicated terminal. This terminal is linked to the electronic health record system (EHR), and the necessary medical records can be automatically retrieved. For example, if a doctor enters "Yamada Taro, 45 years old, male, lower back pain" into the terminal, this information will automatically retrieve detailed medical records from the EHR system.

[0409] The device then packages the entered data and retrieved medical records and sends them to a server over a secure, encrypted channel, preserving patient privacy and data integrity.

[0410] Information analysis and referral generation

[0411] The server analyzes the received data and generates a referral letter using natural language processing (NLP) technology. Specifically, the server inputs the necessary information into the AI ​​model and drafts an appropriate referral letter based on the patient's symptoms and treatment details. For example, based on the information "Yamada Taro, 45 years old, chronic back pain, currently undergoing rehabilitation," it generates a sentence such as "Yamada Taro suffers from chronic back pain and is currently undergoing rehabilitation and drug treatment. A referral to an orthopedic surgeon for further detailed examination is required."

[0412] Formatting consistency and error checking

[0413] The generated letter of introduction is then adapted to a unified format by the server. A template engine is used to embed the generated text into a standard document. An error-checking program is then launched to detect typos and missing information. For example, the sentence "Yamada Taro suffers from chronic back pain" is automatically checked for typos and missing information.

[0414] Checking and correcting the letter of introduction

[0415] Next, the user (doctor or medical staff) checks the generated referral letter on the terminal. They check the contents of the referral letter on the screen and manually correct it if necessary. For example, they can enter additional information such as "An additional consultation with an orthopedic surgeon is recommended."

[0416] Send and save referrals

[0417] Finally, the terminal electronically sends the final referral letter to the designated hospital or medical institution via the server. The transmission is done via email or a medical information exchange system. At the same time, the server stores a copy of the generated referral letter in a database for future reference or reuse. For example, the terminal sends the referral letter to the email address of an orthopedic department, and the server stores the referral letter in a database.

[0418] In this way, the system of the present invention streamlines the referral process and ensures accuracy and consistency of information, improving the quality of patient care and helping medical institutions operate more efficiently.

[0419] The processing flow will be explained below.

[0420] Step 1:

[0421] The user (doctor or medical staff) uses the terminal to enter basic patient information, such as name, age, sex, address, contact information, medical history, current diagnosis information, and details of symptoms.

[0422] Step 2:

[0423] Based on the entered data, the device connects to the electronic health record system (EHR) to retrieve the patient's medical records, and calls the API to obtain the necessary information.

[0424] Step 3:

[0425] The terminal packages the entered basic patient information and the acquired medical records into a single packet, reformatting the data as necessary.

[0426] Step 4:

[0427] The device encrypts the packaged data and sends it over a secure channel to the server, using an API to handle the transmission.

[0428] Step 5:

[0429] The server analyzes the received data and generates a draft referral letter using natural language processing (NLP) technology. Specifically, the necessary information is input into an AI model, which automatically generates a letter appropriate to the patient's condition.

[0430] Step 6:

[0431] The server adapts the generated letter of introduction to a unified format and uses a template engine to embed the generated text into a template document.

[0432] Step 7:

[0433] The server starts an error check program to automatically detect typos, incorrect information, and missing information in the generated letter of introduction.

[0434] Step 8:

[0435] The user (doctor or medical staff) checks the referral letter generated on the terminal, confirms the contents on the screen, and manually corrects them if necessary.

[0436] Step 9:

[0437] The terminal converts the modified referral into a printable format, for example, PDF format, and sends it to a printer.

[0438] Step 10:

[0439] The terminal electronically transmits the final version of the referral letter to the designated hospital or medical institution via the server, via email or a medical information exchange system.

[0440] Step 11:

[0441] The server stores a copy of the generated referral in its database for future reference and reuse.

[0442] Example 1

[0443] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0444] In today's medical field, creating referral letters is a time-consuming, labor-intensive process that is prone to errors. Information can also be inconsistent and inaccurate, potentially affecting appropriate patient care. Furthermore, ensuring the security of referral letter storage and transmission is also an issue.

[0445] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0446] In this invention, the server includes a means for inputting basic information and medical records of a patient, a means for transmitting the basic information and medical records of the patient to the server, a means for analyzing the received information and generating a referral letter using natural language processing technology, and a means for adapting the generated referral letter to a unified format and performing error checking, thereby enabling automatic generation of referral letters, improved accuracy and consistency, and secure storage and transmission of data.

[0447] "Patient basic information" refers to basic personal information such as the patient's name, age, gender, and symptoms.

[0448] "Medical records" refer to detailed medical records, such as a patient's medical history, current treatment, medications, and rehabilitation status.

[0449] "Server" refers to a central processing unit for analyzing patient information, generating referral letters, and storing and transmitting data.

[0450] "Terminal" refers to an electronic device used by doctors and medical staff to enter patient basic information and medical records, and to review and correct referral letters.

[0451] "Natural language processing technology" refers to technology that enables computers to understand and process human language, including text analysis and automatic generation.

[0452] A "generative AI model" is an artificial intelligence model trained for a specific task, in this case the automated generation of letters of introduction.

[0453] "Unified format" refers to a standard document format that ensures that referral documents are output in a consistent format.

[0454] "Error checking" refers to the process of detecting and correcting typographical errors and missing information in the generated referral letters.

[0455] An "electronic medical record system" refers to a system that electronically manages and stores patient medical records.

[0456] "Encryption" refers to a technology that converts information using a specific algorithm to protect data and prevent unauthorized access by third parties.

[0457] A "secure path" refers to a communication path through which data is sent and received securely, using encryption and authentication technologies.

[0458] This invention is a system for making referral letters more efficient by utilizing basic patient information and medical records. Specific embodiments for implementing this system are described below.

[0459] Entering and submitting patient data

[0460] First, the user (doctor or medical staff) uses a dedicated terminal to input basic information and medical records of the patient, including details such as the patient's name, age, gender, and current symptoms.

[0461] As a specific example, a user enters "Yamada Taro, 45 years old, male, lower back pain" into the terminal. This input information is linked to the electronic medical record system (EHR), and more detailed medical records are automatically retrieved.

[0462] The device then packages the entered data and retrieved medical records, encrypts them using a secure protocol, and transmits them to a server, ensuring data integrity and privacy.

[0463] Information analysis and referral generation

[0464] The server analyzes the received data and generates a referral letter using natural language processing (NLP) technology. Specifically, it inputs the necessary information as prompt sentences into the generative AI model and drafts an appropriate referral letter.

[0465] For example, the server might use the following prompt:

[0466] Patient demographics:

[0467] Name: Yamada Taro

[0468] Age: 45

[0469] Gender: Male

[0470] Symptom: Lower back pain

[0471] Medical Records:

[0472] Under rehabilitation and drug treatment

[0473] Generate a testimonial:

[0474] Based on this prompt, the AI ​​model generates the following sentence: "Taro Yamada suffers from chronic lower back pain and is currently undergoing rehabilitation and medication. He needs to be referred to an orthopedic surgeon for further detailed examination."

[0475] Formatting consistency and error checking

[0476] The server uses a template engine to adapt the generated letters of introduction to a unified format, ensuring that they are output in a consistent format. It also runs an error-checking program to detect typos and missing information and suggest corrections.

[0477] Checking and correcting the letter of introduction

[0478] The user (doctor or medical staff) can review the referral letter generated on the terminal. They can check the contents on the screen and manually make corrections if necessary. For example, they can manually enter additional information such as "Additional consultation with an orthopedic surgeon is recommended."

[0479] Send and save referrals

[0480] The terminal then electronically transmits the final version of the referral letter to the designated hospital or medical institution via a server, using email or a medical information exchange system as the transmission method.

[0481] The server stores a copy of the referral in a database for future reference or reuse. For example, the device sends the referral to the orthopedic department's email address, and the server stores the referral in a database.

[0482] In this way, the system of the present invention streamlines the referral process, ensures accuracy and consistency of information, improves the quality of patient care, and helps medical institutions operate more efficiently.

[0483] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0484] Step 1: Enter patient demographics and medical records

[0485] The user (doctor or medical staff) uses a dedicated terminal to input basic information and medical records of the patient. In this input step, details such as the patient's name, age, gender, and current symptoms are entered.

[0486] As a specific operation, the user inputs "Yamada Taro, 45 years old, male, lower back pain" into the terminal. This is the input.

[0487] Step 2: Automated patient data capture and packaging

[0488] The terminal will then link with the electronic health record system (EHR) based on the basic information entered, and automatically retrieve the patient's detailed medical records. Specifically, the terminal will query the EHR system for records for "Yamada Taro." This will retrieve detailed medical records for "under rehabilitation and drug treatment."

[0489] Input: Basic information entered by the user

[0490] Output: Data package containing detailed medical records

[0491] Step 3: Send patient data

[0492] The device packages the acquired data, encrypts it, and sends it to the server. Specifically, the device sends it using a secure protocol.

[0493] Input: Data package containing detailed medical records

[0494] Output: Sending encrypted data

[0495] Step 4: Analyze the data and generate a draft letter of introduction

[0496] The server analyzes the received data and uses natural language processing (NLP) techniques to generate appropriate prompts, which are then fed into a generative AI model, which then drafts a referral letter based on the prompts.

[0497] Example prompt sentence:

[0498] Patient demographics:

[0499] Name: Yamada Taro

[0500] Age: 45

[0501] Gender: Male

[0502] Symptom: Lower back pain

[0503] Medical Records:

[0504] Under rehabilitation and drug treatment

[0505] Generate a testimonial:

[0506] Input: Detailed medical records

[0507] Output: Draft letter of introduction

[0508] Step 5: Formatting and Error Checking

[0509] The server uses a template engine to adapt the generated letters of introduction to a unified format, and also runs an error-checking program to detect typos and missing information.

[0510] Input: Draft letter of introduction

[0511] Output: Error checked referral letter

[0512] Step 6: Review and correct the referral letter

[0513] The user (doctor or medical staff) checks the referral generated on the device and manually corrects it if necessary. Specifically, the user enters information about additional consultations or treatments. For example, the user adds information such as "An additional consultation with an orthopedic surgeon is recommended."

[0514] Input: Error-checked referral letter

[0515] Output: Revised version of the referral letter

[0516] Step 7: Send and save your referral

[0517] The terminal electronically sends the final referral letter to the designated hospital or medical institution via the server. Specifically, the terminal sends the letter via email or a medical information exchange system. At the same time, the server stores a copy of the generated referral letter in a database.

[0518] Input: Revised version of the referral letter

[0519] Output: Referrals sent and referrals saved

[0520] These processing steps streamline the referral process and ensure accuracy and consistency of information.

[0521] (Application example 1)

[0522] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0523] The current process for creating referrals and industrial product inspection reports requires a lot of manual input and correction, which is time-consuming and labor-intensive. It is also prone to errors and it is difficult to maintain consistency of information. In the industrial sector, in particular, there is a demand for fast and accurate production of inspection reports, and a system as efficient as the creation of referrals is required.

[0524] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0525] In this invention, the server includes means for inputting basic patient information and medical records, means for transmitting the basic patient information and medical records to the server, means for analyzing the received information and generating a referral letter, means for adapting the generated referral letter to a unified format and performing error checks, means for checking and correcting the referral letter on a terminal, means for electronically transmitting the final referral letter to a medical institution and storing it, means for inputting inspection data of industrial products and transmitting it to the server, means for analyzing the received inspection data of industrial products and generating an inspection report, means for adapting the generated inspection report to a unified format and performing error checks, means for checking and correcting the inspection report on a terminal, and means for electronically transmitting the final inspection report to a relevant department and storing it, thereby enabling efficient and accurate generation of referral letters and inspection reports in medical institutions and industrial fields.

[0526] "Basic patient information" refers to basic personal information necessary to identify a patient, such as the patient's name, age, gender, and contact information.

[0527] "Medical records" refers to medical-related information such as details of examinations, prescriptions, treatment history, and test results conducted for patients at medical institutions.

[0528] "Server" means a central processing unit for receiving, analyzing, storing, and transmitting data over a network.

[0529] A "referral letter" is an official document created to refer a patient to another medical institution or specialist.

[0530] A "uniform format" is a standard for organizing documents and data into a specific standard format.

[0531] "Error checking" is the process of verifying the accuracy of documents or data and detecting typographical errors or incomplete information.

[0532] A "terminal" is a device for inputting, displaying, and modifying data. Examples include personal computers and tablets.

[0533] "Industrial product inspection data" refers to data on the quality and performance of inspected items collected at factories and manufacturing sites.

[0534] "Inspection report" means an official document summarizing the results of an inspection and including conclusions regarding the quality and performance of the inspected item.

[0535] "Related departments" are internal departments related to the inspection results and quality control of industrial products.

[0536] These definitions provide clearer explanations of key terms found in the claims and facilitate a better understanding of the invention.

[0537] The present invention is a system for efficiently creating letters of introduction and inspection reports for industrial products. Specific embodiments of this system will be described below.

[0538] First, the user (doctor or medical staff) enters the patient's basic information and medical records into a dedicated terminal. This terminal is linked to the electronic health record system (EHR) and can automatically retrieve the necessary medical records. For example, if a doctor enters "Patient A, 45 years old, male, lower back pain" into the terminal, this information will automatically retrieve detailed medical records from the EHR system.

[0539] The device then packages the entered data and retrieved medical records and sends them to a server over a secure, encrypted channel, preserving patient privacy and data integrity.

[0540] The server analyzes the received data and generates a referral letter using natural language processing (NLP) technology. Specifically, the server inputs the necessary information into the AI ​​model and drafts an appropriate referral letter based on the patient's symptoms and treatment details. For example, based on the information "Patient A, 45 years old, chronic lower back pain, undergoing rehabilitation," it generates a sentence such as "Patient A suffers from chronic lower back pain and is currently undergoing rehabilitation and drug treatment. A referral to an orthopedic surgeon is required for further detailed examination."

[0541] The generated referral letter is adapted to a unified format by the server. A template engine is used to embed the generated text into a standard document. An error-checking program is then launched to detect typos and missing information. For example, the sentence "Patient A suffers from chronic lower back pain" is automatically checked for typos and missing information.

[0542] Next, the user (doctor or medical staff) checks the generated referral letter on the terminal. They check the contents of the referral letter on the screen and manually correct it if necessary. For example, they can enter additional information such as "An additional consultation with an orthopedic surgeon is recommended."

[0543] Finally, the terminal electronically sends the final referral letter to the designated medical institution via the server. The transmission is done via email or a medical information exchange system. At the same time, the server stores a copy of the generated referral letter in a database for future reference or reuse. For example, the terminal sends the referral letter to the email address of the orthopedic department, and the server stores the referral letter in the database.

[0544] A similar process can also be used to automatically generate inspection reports using inspection data from industrial products. Inspectors use their smartphones to input inspection data and send it to a server. The server analyzes the received data and generates an appropriate inspection report. The generated inspection report is then adapted to a unified format and checked for errors. The inspector reviews the report and makes any necessary corrections. Finally, the report is electronically sent to the relevant department and stored.

[0545] As a specific example, if an inspector enters data for item ID "12345" and records it as "Passed," the server can analyze this data and generate an inspection report such as "Inspection results performed by inspector A, item ID: 12345, result: Passed."

[0546] Examples of prompts to input to a generative AI model include:

[0547] Inspection Data:

[0548] Inspector: Inspector A

[0549] Item ID: 12345

[0550] Result: Passed

[0551] Based on this, generate an inspection report in the following format:

[0552] ----------------------

[0553] Inspection Report:

[0554] Inspector: Inspector A

[0555] Item ID: 12345

[0556] Test result: Passed

[0557] ----------------------

[0558] In this way, the system of the present invention streamlines the process of creating referral letters and test reports, ensuring accuracy and consistency of information, thereby helping to improve operational efficiency and quality in medical institutions and industrial fields.

[0559] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0560] Step 1: User enters basic information and medical records

[0561] The user (doctor or medical staff) uses the terminal to input basic information and medical records about the patient. For example, the information input in this step might be "Patient A, 45 years old, male, with lower back pain." The terminal is linked to the electronic medical record system (EHR), and automatically obtains detailed medical records based on the input information. The input data is temporarily stored in the terminal.

[0562] Step 2: The device sends the data to the server

[0563] The terminal compiles the entered basic patient information and the acquired medical records, packages them, and sends them to the server. The transmission is carried out via a secure, encrypted communication channel. The input data is encrypted before being sent to the server. This ensures patient privacy and data integrity.

[0564] Step 3: The server analyzes the data and generates a referral.

[0565] The server analyzes the received data and generates a referral letter using natural language processing (NLP) technology. Based on the input basic information and medical records, the AI ​​model generates an appropriate draft referral letter. For example, from the data "Patient A, 45 years old, chronic back pain, undergoing rehabilitation," a referral letter may be generated that reads, "Patient A suffers from chronic back pain and is currently undergoing rehabilitation and drug treatment. He or she will need to be referred to an orthopedic surgeon for further detailed examination." The generated referral letter is stored on the server.

[0566] Step 4: The server standardizes the format of the letters of introduction and checks for errors

[0567] The server uses a template engine to adapt the generated letter of introduction to a standardized format. The generated text is converted into a standard document format. An error check program is then run to detect typos and missing information in the letter of introduction. Once the error check is complete, the letter of introduction is saved back to the server.

[0568] Step 5: User confirms and modifies the referral letter

[0569] The user (doctor or medical staff) uses the terminal to check the generated referral letter. They check the details of the referral letter displayed on the screen and manually correct it if necessary. For example, they can enter additional information such as "An orthopedic surgeon recommends an additional consultation." The corrected referral letter is temporarily saved on the terminal.

[0570] Step 6: The device sends and saves the final version of the introduction letter

[0571] After confirmation and correction, the final referral letter is sent electronically to the designated medical institution via the server. The transmission route is email or a medical information exchange system. At the same time, the server stores a copy of the generated and sent referral letter in a database for future reference and reuse.

[0572] Step 7: Inspectors enter the inspection data for industrial products

[0573] Inspectors use their smartphones to input inspection data for industrial products. For example, the item ID is "12345" and the inspection result is "Passed." The input data is temporarily saved on the smartphone.

[0574] Step 8: The smartphone sends the data to the server

[0575] The smartphone packages the entered test data and sends it to the server via a secure, encrypted communication channel. The entered data is sent to the server in encrypted form and stored there.

[0576] Step 9: The server analyzes the inspection data and generates an inspection report.

[0577] The server analyzes the received inspection data and generates an inspection report. Based on the input inspection data, the AI ​​model generates an appropriate draft inspection report. For example, based on the data item ID "12345" and inspection result "Passed," an inspection report such as "Inspection results performed by inspector A, item ID: 12345, result: Passed" is generated. The generated report is saved on the server.

[0578] Step 10: The server unifies the format of the inspection report and checks for errors.

[0579] The server uses a template engine to adapt the generated inspection report to a standardized format. The generated text is converted into a standard document format. An error check program is then run to detect typographical errors and missing information in the inspection report. Once the error check is complete, the report is saved back to the server.

[0580] Step 11: Inspector reviews and corrects the inspection report

[0581] The inspector uses a smartphone to check the generated inspection report. He / she carefully reviews the contents of the inspection report displayed on the screen and manually corrects them as necessary. For example, he / she enters additional information such as "additional quality checks are required." The corrected inspection report is temporarily saved on the smartphone.

[0582] Step 12: Your smartphone sends and saves the final inspection report

[0583] After confirmation and corrections are completed, the final inspection report is sent electronically to the relevant department via the smartphone server, via email or the internal information exchange system. At the same time, the server stores a copy of the generated and sent inspection report in a database for future reference and reuse.

[0584] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0585] The present invention combines a system for streamlining the automatic creation of referral letters with an emotion engine that recognizes the user's emotions. This system supports the process of inputting and analyzing basic patient information and medical records, generating and sending referral letters, and further adjusts the process by taking the user's emotional state into consideration. Specific embodiments of this system are described below.

[0586] Entering and submitting patient data

[0587] First, the user (doctor or medical staff) enters the patient's basic information and medical records into a dedicated terminal. This terminal is linked to the electronic health record system (EHR), and the necessary medical records can be automatically retrieved. For example, if a doctor enters "Yamada Taro, 45 years old, male, lower back pain" into the terminal, this information will automatically retrieve detailed medical records from the EHR system.

[0588] The device then packages the entered data and retrieved medical records and sends them to a server over a secure, encrypted channel, preserving patient privacy and data integrity.

[0589] Information analysis and referral generation

[0590] The server analyzes the received data and generates a referral letter using natural language processing (NLP) technology. Specifically, the necessary information is input into the AI ​​model, which then drafts an appropriate referral letter based on the patient's symptoms and treatment details. For example, based on the information "Yamada Taro, 45 years old, chronic back pain, currently undergoing rehabilitation," it generates a sentence such as "Yamada Taro suffers from chronic back pain and is currently undergoing rehabilitation and drug treatment. A referral to an orthopedic surgeon for further detailed examination is required."

[0591] Formatting consistency and error checking

[0592] The generated letter of introduction is then adapted to a unified format by the server. A template engine is used to embed the generated text into a standard document. An error-checking program is then launched to detect typos and missing information. For example, the sentence "Taro Yamada suffers from chronic back pain" is automatically checked for typos and missing information.

[0593] Use of emotion engine

[0594] Next, when the user (doctor or medical staff) checks the referral letter generated on the device, the emotion engine analyzes the user's voice and facial expressions to understand their emotional state, such as stress or fatigue. If the emotion engine detects a situation where an error is likely to occur, such as when the user is overly tired, it automatically displays a warning. The content and display format of the referral letter are also adjusted to allow the user to work in a relaxed state.

[0595] Checking and correcting the letter of introduction

[0596] After receiving feedback from the emotion engine, the user can review the referral generated on their device and manually correct it if necessary, for example by adding additional information such as "An additional consultation with an orthopedic surgeon is recommended."

[0597] Send and save referrals

[0598] Finally, the terminal electronically sends the final referral letter to the designated hospital or medical institution via the server. The transmission is done via email or a medical information exchange system. At the same time, the server stores a copy of the generated referral letter in a database for future reference or reuse. For example, the terminal sends the referral letter to the email address of an orthopedic department, and the server stores the referral letter in a database.

[0599] In this way, the system of the present invention streamlines the referral process, ensuring accuracy and consistency of information while optimizing the process by taking into account the user's emotional state, thereby improving the quality of patient care and supporting the operational efficiency of medical institutions.

[0600] The processing flow will be explained below.

[0601] Step 1:

[0602] The user (doctor or medical staff) uses the terminal to enter basic patient information, such as name, age, sex, address, contact information, medical history, current diagnosis information, and details of symptoms.

[0603] Step 2:

[0604] Based on the information entered, the device will automatically retrieve medical records in conjunction with the electronic health record system (EHR), using an API to obtain the necessary medical data.

[0605] Step 3:

[0606] The terminal packages the entered patient information and the acquired medical records into a single data packet, formats the data, and prepares the packet for transmission to the server.

[0607] Step 4:

[0608] The device encrypts the data packet and sends it to the server over a secure channel, calling an API to send the data.

[0609] Step 5:

[0610] The server analyzes the received data packets and generates a referral letter using natural language processing (NLP) technology. The information is input into an AI model, which automatically generates a letter that corresponds to the patient's symptoms and treatment details.

[0611] Step 6:

[0612] The server adapts the generated letters of introduction to a uniform format and uses a template engine to embed the generated text into a template document.

[0613] Step 7:

[0614] The server runs an error check program to detect typos, incorrect information, missing information, etc. in the generated letter of introduction, thereby confirming the accuracy of the information.

[0615] Step 8:

[0616] The user (doctor or medical staff) checks the referral letter generated on the terminal, checks the contents of the referral letter on the screen, and manually corrects them if necessary.

[0617] Step 9:

[0618] While the user is checking the referral letter, the device runs an emotion engine that analyzes the user's voice and facial expressions to detect emotional states such as stress and fatigue.

[0619] Step 10:

[0620] If the emotion engine detects stress or fatigue, the device will display a warning to the user, such as a message saying, "Take a break to reduce stress."

[0621] Step 11:

[0622] Based on feedback from the emotion engine, the device will adjust the content and presentation of the referral letter according to the user's emotional state, for example, changing it to a simpler, more understandable format for a user who is overly tired.

[0623] Step 12:

[0624] The terminal then reviews the revised letter of introduction again and converts the final version into a printable format, such as PDF, and sends it to the printer.

[0625] Step 13:

[0626] The terminal then electronically sends the final referral letter to the designated hospital or medical institution via the server, either by email or via a medical information exchange system.

[0627] Step 14:

[0628] The server stores a copy of the generated referral in its database, where it is stored for future reference and reuse.

[0629] In this way, not only is the referral process more efficient, but the task is optimized taking into account the user's emotional state, improving the quality of patient care and helping medical institutions work more efficiently.

[0630] Example 2

[0631] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0632] With conventional referral letter creation systems, when creating referral letters using basic patient information and medical records, it was difficult to ensure the accuracy and consistency of the information, and if the user was tired or stressed, there was a high possibility of typos or incomplete information.In addition, because the referral letter creation process was done manually, there were problems such as reduced work efficiency and a heavy burden on medical staff.

[0633] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for inputting basic information and medical records of a patient; means for transmitting the basic information and medical records of the patient to an information system; means for analyzing the received information and generating a referral letter using natural language processing technology; means for adapting the generated referral letter to a unified format and performing error checking; means for analyzing the emotional state of the user and providing feedback; means for checking and correcting the referral letter on a terminal; and means for electronically transmitting and storing the final referral letter to the medical institution. This improves the efficiency of the automatic referral letter generation process and enables optimization of the work taking into account the emotional state of the user while maintaining the accuracy and consistency of the information.

[0634] "Basic patient information" refers to basic personal information about the patient, such as the patient's name, age, sex, address, contact information, and chief complaint.

[0635] "Medical records" refers to all records related to medical treatment at medical institutions, such as a patient's medical history, test results, prescription history, surgical history, allergy information, and treatment plans.

[0636] "Information systems" refer to computer systems, such as electronic health records (EHRs) and healthcare information systems (HISs), that digitally manage and operate patient medical records and basic information.

[0637] "Natural language processing technology" refers to technology that enables computers to understand, analyze, and generate human language. Examples include text analysis, speech recognition, and language generation.

[0638] A "referral document" is an official document containing the information necessary for a patient to be referred to another medical institution or specialist.

[0639] A "template engine" is a software tool for embedding dynamic data into boilerplate documents, and includes libraries such as Jinja2 and Handlebars.

[0640] An "error checking program" is a software program that automatically detects typographical errors, grammatical errors, missing information, etc. in a document.

[0641] The "emotion engine" is an analytical engine that analyzes voice, facial expressions, movements, etc. to evaluate the user's emotional state (stress, fatigue, relaxation, etc.).

[0642] The "means for providing feedback" is a means for reporting the status to the user based on the data obtained from the emotion engine and presenting appropriate countermeasures or warnings.

[0643] "Device" means a computing device, including a PC or tablet, on which a User enters information and / or reviews or modifies the generated referral document.

[0644] "Electronic transmission means" refers to a means for securely transmitting the generated referral document to the designated medical institution via email or a medical information exchange system.

[0645] The "means for storing" refers to a means for storing the generated introduction document in a storage system such as a database for future reference or reuse.

[0646] The present invention combines a system for streamlining the automatic creation of referral letters with an emotion engine that recognizes the user's emotions. This system supports the process of inputting and analyzing basic patient information and medical records, generating and sending referral letters, and further adjusts the process by taking the user's emotional state into consideration. Specific embodiments of this system are described below.

[0647] Entering and submitting patient data

[0648] First, the user (doctor or medical staff) enters the patient's basic information and medical records into a dedicated terminal. This terminal is linked to the electronic health record system (EHR) and can automatically retrieve the necessary medical records. For example, if a doctor enters "Yamada Taro, 45 years old, male, lower back pain" into the terminal, this information will automatically retrieve detailed medical records from the EHR system.

[0649] The terminal packages the entered data and the acquired medical records and sends them to the server over a secure channel (e.g., SSL / TLS protocol) after encrypting the data, thereby preserving patient privacy and data integrity.

[0650] Information analysis and referral generation

[0651] The server analyzes the received data and generates a referral letter using natural language processing (NLP) technology. Specifically, the necessary information is input into a generative AI model (e.g., GPT-3), which then drafts an appropriate referral letter based on the patient's symptoms and treatment details. For example, based on the information "Yamada Taro, 45 years old, chronic back pain, currently undergoing rehabilitation," it generates a sentence such as "Yamada Taro suffers from chronic back pain and is currently undergoing rehabilitation and drug treatment. He needs to be referred to an orthopedic surgeon for further detailed examination."

[0652] Formatting consistency and error checking

[0653] The generated letter of introduction is then adapted to a unified format by the server. Specifically, a template engine (e.g., Jinja2) is used to embed the generated text into a standard document. An error-checking program is then launched to detect typos and missing information. For example, the sentence "Yamada Taro suffers from chronic back pain" is automatically checked for typos and missing information.

[0654] Use of emotion engine

[0655] Next, when the user (doctor or medical staff) checks the referral letter generated on the device, the emotion engine analyzes the user's voice and facial expressions to understand their emotional state, such as stress or fatigue. If the emotion engine detects a situation where an error is likely to occur, such as when the user is overly tired, it automatically displays a warning. The content and display format of the referral letter are also adjusted to allow the user to work in a relaxed state.

[0656] Checking and correcting the letter of introduction

[0657] After receiving feedback from the emotion engine, the user can review the referral generated on their device and manually correct it if necessary, for example by adding additional information such as "An additional consultation with an orthopedic surgeon is recommended."

[0658] Send and save referrals

[0659] Finally, the terminal electronically sends the final referral letter to the designated hospital or medical institution via the server. This can be done via email or a healthcare information exchange (HIE). At the same time, the server stores a copy of the generated referral letter in a database for future reference or reuse. For example, the terminal sends the referral letter to the orthopedic department's email address, and the server stores it in the database.

[0660] Specific examples

[0661] Here is an example of a prompt that can be fed into a generative AI model to automatically generate a referral letter:

[0662] "Yamada Taro, 45 years old, male, chronic lower back pain, currently undergoing rehabilitation. Referred to orthopedic clinic."

[0663] For this input, the resulting letter of introduction might look like this:

[0664] "Yamada Taro suffers from chronic back pain and is currently undergoing rehabilitation and medication. He will need to be referred to an orthopedic surgeon for further detailed examination."

[0665] In this way, the system of the present invention streamlines the referral process, ensuring accuracy and consistency of information while optimizing the process by taking into account the user's emotional state, thereby improving the quality of patient care and supporting the operational efficiency of medical institutions.

[0666] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0667] Step 1: Enter patient demographics and medical records

[0668] The user (doctor or medical staff) enters the patient's basic information (name, age, sex, symptoms, etc.) into a dedicated terminal. The terminal uses this information to retrieve detailed medical records from the electronic health record system (EHR). Specifically, when the user enters "Yamada Taro, 45 years old, male, lower back pain" into the terminal, the "medical record for lower back pain" is automatically retrieved from the EHR based on this information. The entered basic information and medical records are compiled on the terminal.

[0669] Input: Patient's basic information ("Taro Yamada, 45 years old, male, lower back pain")

[0670] Output: Patient basic information and medical record ("Medical record of low back pain")

[0671] Step 2: Packaging and sending the data

[0672] The terminal compiles the entered basic information and the acquired medical records into a data package. The packaged data is then encrypted and sent to the server. The SSL / TLS protocol is generally used for encryption. Specifically, the terminal compiles data in a format such as "Yamada Taro, 45 years old, male, lower back pain, medical record for lower back pain," encrypts it, and sends it to the server.

[0673] Input: Patient demographics and medical records

[0674] Output: Encrypted data package

[0675] Step 3: Analyze the data

[0676] The server decrypts the received encrypted data package and analyzes the data. Natural language processing (NLP) technology is used for the analysis. Specifically, the server analyzes the information "Yamada Taro, 45 years old, suffering from chronic back pain, currently undergoing rehabilitation" and prepares to send the necessary information to the generative AI model.

[0677] Input: Encrypted data package

[0678] Output: Parsed patient information

[0679] Step 4: Generate introductory documentation

[0680] Based on the analyzed data, the server inputs a prompt into a generative AI model (e.g., GPT-3) to generate an introductory document. Specifically, the server inputs the following prompt into the generative AI model:

[0681] "Yamada Taro, 45 years old, suffering from chronic lower back pain, currently undergoing rehabilitation. Referred to an orthopedic surgeon."

[0682] The referral letter obtained from the generative AI model read something like, "Taro Yamada suffers from chronic lower back pain and is currently undergoing rehabilitation and medication. He needs to be referred to an orthopedic surgeon for further detailed examination."

[0683] Input: Parsed patient information

[0684] Output: Generated introductory document

[0685] Step 5: Formatting and Error Checking

[0686] The server adapts the generated referral document to a unified format using a template engine (e.g., Jinja2). Furthermore, an error-checking program is launched to detect typos and missing information. Specifically, the server adapts the generated referral document to a "standard referral template" and checks for errors using a grammar checker.

[0687] Input: Generated referral document

[0688] Output: Introduction document and error report in a unified format

[0689] Step 6: Analysis and feedback by the emotion engine

[0690] When a user checks the referral letter generated on the device, the emotion engine installed on the device analyzes the user's voice and facial expressions. The emotion engine grasps the user's emotional state, such as stress and fatigue, and displays a warning if excessive fatigue is detected. Specifically, while the user is checking the referral letter, the emotion engine analyzes stress and fatigue in real time and displays a message such as "Please relax and check" as necessary.

[0691] Input: User's voice and facial expressions

[0692] Output: Feedback based on emotional state

[0693] Step 7: Review and correct the referral letter

[0694] The user can review the generated referral letter on the device and manually correct it if necessary. Specifically, the user can enter additional information, such as "An additional consultation with an orthopedic surgeon is recommended."

[0695] Input: Generated referral letter, user correction input

[0696] Output: Final letter of introduction

[0697] Step 8: Send and save your referral

[0698] The terminal then electronically sends the final referral letter to the designated medical institution via the server. This is done via email or a healthcare information exchange (HIE). At the same time, the server stores a copy of the generated referral letter in a database for future reference or reuse. Specifically, the terminal sends the referral letter to the orthopedic department's email address, and the server stores it in the database.

[0699] Input: Final version of the referral letter

[0700] Output: Referrals sent, Referrals saved

[0701] In this way, the automated referral letter generation system provides a streamlined process, maintaining accuracy of information while optimizing the process by taking into account the user's emotional state.

[0702] (Application example 2)

[0703] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0704] While conventional referral letter creation systems have successfully automated the process from patient information entry to referral letter generation, they do not take into account the user's emotional state, which means there is a risk of users entering incorrect information if they are tired or stressed. Furthermore, they require user expertise to generate accurate and consistent referral letters, and effective support is not provided. This can lead to reduced referral letter quality and work efficiency, which can affect the quality of patient care.

[0705] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting basic information and medical records of a patient; means for transmitting the basic information and medical records of the patient to the server; means for analyzing the received information and generating a referral letter; means for adapting the generated referral letter to a unified format and performing error checking; means for confirming and correcting the referral letter on a terminal; means for electronically transmitting and storing the final referral letter to the medical institution; and means for analyzing the user's emotional state using an emotion recognition engine and adjusting the work process. This enables accurate and unified referral letters to be generated even when the user is tired or stressed, thereby improving work efficiency and the quality of patient care.

[0706] "Basic patient information" refers to personal identification information such as the patient's name, age, sex, and contact information.

[0707] "Medical records" are all information related to medical care, such as a patient's medical history, diagnosis, treatment, and notes made by medical staff.

[0708] "Server" means a computer system that centrally manages and processes data, and has the function of analyzing received information and generating letters of introduction.

[0709] A "letter of referral" is a document used to refer a patient to another medical institution, and contains details of the patient's condition, treatment progress, and any necessary tests or treatments.

[0710] A "uniform format" is a standardized form used to standardize the format and wording of letters of introduction.

[0711] "Error checking" is the process of automatically detecting and correcting typos and missing information in the generated letters of introduction.

[0712] A "terminal" is a computer or smart device that a user uses to perform operations such as input, confirmation, and correction.

[0713] An "emotion recognition engine" is software that analyzes a user's emotional state (e.g., stress, fatigue, etc.) from their voice and facial expressions, and provides feedback on the results.

[0714] "Work process adjustment" refers to optimizing the user's work environment and work procedures based on the analysis results of the emotion recognition engine.

[0715] The present invention combines a system for streamlining the automatic generation of referral letters with an emotion engine that recognizes the user's emotions. This system supports the process of inputting and analyzing basic patient information and medical records, generating and sending referral letters, and adjusting the process taking into account the user's emotional state.

[0716] Entering and submitting patient data

[0717] First, the user (doctor or medical staff) enters the patient's basic information and medical records into the terminal. This terminal is linked to the electronic health record system (EHR) and can automatically retrieve the necessary medical records. For example, if a doctor enters "XX, 45 years old, male, lower back pain" into the terminal, this information automatically retrieves detailed medical records from the EHR system. Next, the terminal packages the entered data and the retrieved medical records and sends them to the server. The transmission is carried out over a secure, encrypted channel, maintaining patient privacy and data integrity.

[0718] Information analysis and referral generation

[0719] The server analyzes the received data and generates a referral letter using natural language processing (NLP) technology. For example, the AI ​​model can input information such as "X, 45 years old, chronic back pain, currently undergoing rehabilitation," and draft an appropriate referral letter based on the patient's symptoms and treatment. Specifically, it generates a sentence such as, "X suffers from chronic back pain and is currently undergoing rehabilitation and drug treatment. A referral to an orthopedic surgeon is required for further detailed examination."

[0720] Formatting consistency and error checking

[0721] The generated letter of introduction is then adapted to a unified format by the server. A template engine is used to embed the generated text into a standard document. An error-checking program is then launched to detect typos and missing information. For example, the text "Mr. / Ms. X suffers from chronic back pain" is automatically checked for typos and missing information.

[0722] Use of emotion engine

[0723] Next, when the user (doctor or medical staff) checks the referral letter generated on the device, the emotion engine analyzes the user's voice and facial expressions to understand their emotional state, such as stress or fatigue. If the emotion engine detects a situation where an error is likely to occur, such as when the user is overly tired, it automatically displays a warning. The content and display format of the referral letter are also adjusted to allow the user to work in a relaxed state.

[0724] Checking and correcting the letter of introduction

[0725] After receiving feedback from the emotion engine, the user can review the referral generated on their device and manually correct it if necessary, for example, by adding additional information such as "An additional consultation with an orthopedic surgeon is recommended."

[0726] Send and save referrals

[0727] Finally, the terminal electronically sends the final referral letter to the designated hospital or medical institution via the server. The transmission is done via email or a medical information exchange system. At the same time, the server stores a copy of the generated referral letter in a database for future reference or reuse. For example, the terminal sends the referral letter to the email address of an orthopedic department, and the server stores the referral letter in a database.

[0728] Hardware and software used

[0729] Hardware:

[0730] Terminal (computer operated by the user)

[0731] Server (a computer system that centrally manages and processes data)

[0732] software:

[0733] Electronic Health Record System (EHR)

[0734] Emotion Recognition Engine (EmotionAnalyzer)

[0735] Natural Language Processing (NLP)

[0736] Template Engine

[0737] Error Checking Program

[0738] Examples of specific examples and prompts

[0739] For example, suppose a worker is wearing smart glasses. The prompt is, "Emotion Identification: Enter a facial image to analyze the stress and fatigue levels." This prompt allows the worker to enter the work data appropriately, and the emotion recognition engine can analyze the user's emotional state.

[0740] Specific prompt examples:

[0741] "Enter facial image data and analyze stress and fatigue levels. The model used is EmotionAnalyzer."

[0742] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0743] Step 1: The user enters the patient's basic information and medical records into the terminal.

[0744] The user enters basic information such as the patient's name, age, sex, and medical condition, as well as medical records, into the terminal. This input information is linked to the electronic health record system (EHR), and detailed medical records are automatically obtained. The input data is temporarily stored within the terminal.

[0745] Step 2: The device sends the patient's basic information and medical records to the server.

[0746] The terminal sends the entered basic patient information and medical records to the server via a secure encrypted channel. The input data is encrypted, packetized, and sent to the server.

[0747] Step 3: The server analyzes the received information and generates a referral.

[0748] The server analyzes the received patient's basic information and medical records. Using natural language processing (NLP) technology within the server, this information is input into an AI model, which then drafts an appropriate referral letter based on the patient's symptoms and treatment. For example, a referral letter can be generated using information such as "XX, 45 years old, chronic back pain, currently undergoing rehabilitation."

[0749] Step 4: The server adapts the generated letter of introduction to a unified format and performs error checking.

[0750] The generated letters of introduction are then adapted to a unified format by the server. A template engine is used to embed the generated text into a standardized format. An error-checking program is also initiated to automatically detect typos and missing information and identify areas that need correction.

[0751] Step 5: Analyze the user's emotional state using an emotion recognition engine and adjust the work process

[0752] The user checks the referral letter generated on the device, and the emotion recognition engine analyzes the user's voice and facial expressions to understand their emotional state, such as stress or fatigue. For example, the system may display feedback such as, "High stress or fatigue has been detected. A break is recommended."

[0753] Step 6: User reviews the referral and makes any necessary corrections

[0754] After receiving feedback from the emotion recognition engine, the user can review the generated letter of introduction on their device, manually correct it if necessary, enter additional information or corrections, and finally confirm the letter of introduction.

[0755] Step 7: The terminal electronically sends the final referral letter to the medical institution via the server and stores it.

[0756] Finally, the terminal electronically sends the final referral letter to the designated hospital or medical institution via the server. The transmission is done via email or a medical information exchange system, and the server also simultaneously stores a copy of the generated referral letter in a database. For example, the terminal sends the referral letter to the email address of an orthopedic department, and the server stores the referral letter in a database.

[0757] 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.

[0758] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0759] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0760] [Third embodiment]

[0761] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0762] 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.

[0763] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0764] 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.

[0765] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0766] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0767] 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.

[0768] 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.

[0769] 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 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.

[0770] 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.

[0771] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0772] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0773] The present invention is a system for efficiently creating referral letters automatically, and supports the process of inputting and analyzing basic patient information and medical records, and generating and sending referral letters. Specific embodiments of this system are described below.

[0774] Entering and submitting patient data

[0775] First, the user (doctor or medical staff) enters the patient's basic information and medical records into a dedicated terminal. This terminal is linked to the electronic health record system (EHR), and the necessary medical records can be automatically retrieved. For example, if a doctor enters "Yamada Taro, 45 years old, male, lower back pain" into the terminal, this information will automatically retrieve detailed medical records from the EHR system.

[0776] The device then packages the entered data and retrieved medical records and sends them to a server over a secure, encrypted channel, preserving patient privacy and data integrity.

[0777] Information analysis and referral generation

[0778] The server analyzes the received data and generates a referral letter using natural language processing (NLP) technology. Specifically, the server inputs the necessary information into the AI ​​model and drafts an appropriate referral letter based on the patient's symptoms and treatment details. For example, based on the information "Yamada Taro, 45 years old, chronic back pain, currently undergoing rehabilitation," it generates a sentence such as "Yamada Taro suffers from chronic back pain and is currently undergoing rehabilitation and drug treatment. A referral to an orthopedic surgeon for further detailed examination is required."

[0779] Formatting consistency and error checking

[0780] The generated letter of introduction is then adapted to a unified format by the server. A template engine is used to embed the generated text into a standard document. An error-checking program is then launched to detect typos and missing information. For example, the sentence "Yamada Taro suffers from chronic back pain" is automatically checked for typos and missing information.

[0781] Checking and correcting the letter of introduction

[0782] Next, the user (doctor or medical staff) checks the generated referral letter on the terminal. They check the contents of the referral letter on the screen and manually correct it if necessary. For example, they can enter additional information such as "An additional consultation with an orthopedic surgeon is recommended."

[0783] Send and save referrals

[0784] Finally, the terminal electronically sends the final referral letter to the designated hospital or medical institution via the server. The transmission is done via email or a medical information exchange system. At the same time, the server stores a copy of the generated referral letter in a database for future reference or reuse. For example, the terminal sends the referral letter to the email address of an orthopedic department, and the server stores the referral letter in a database.

[0785] In this way, the system of the present invention streamlines the referral process and ensures accuracy and consistency of information, improving the quality of patient care and helping medical institutions operate more efficiently.

[0786] The processing flow will be explained below.

[0787] Step 1:

[0788] The user (doctor or medical staff) uses the terminal to enter basic patient information, such as name, age, sex, address, contact information, medical history, current diagnosis information, and details of symptoms.

[0789] Step 2:

[0790] Based on the entered data, the device connects to the electronic health record system (EHR) to retrieve the patient's medical records, and calls the API to obtain the necessary information.

[0791] Step 3:

[0792] The terminal packages the entered basic patient information and the acquired medical records into a single packet, reformatting the data as necessary.

[0793] Step 4:

[0794] The device encrypts the packaged data and sends it over a secure channel to the server, using an API to handle the transmission.

[0795] Step 5:

[0796] The server analyzes the received data and generates a draft referral letter using natural language processing (NLP) technology. Specifically, the necessary information is input into an AI model, which automatically generates a letter appropriate to the patient's condition.

[0797] Step 6:

[0798] The server adapts the generated letter of introduction to a unified format and uses a template engine to embed the generated text into a template document.

[0799] Step 7:

[0800] The server starts an error check program to automatically detect typos, incorrect information, and missing information in the generated letter of introduction.

[0801] Step 8:

[0802] The user (doctor or medical staff) checks the referral letter generated on the terminal, confirms the contents on the screen, and manually corrects them if necessary.

[0803] Step 9:

[0804] The terminal converts the modified referral into a printable format, for example, PDF format, and sends it to a printer.

[0805] Step 10:

[0806] The terminal electronically transmits the final version of the referral letter to the designated hospital or medical institution via the server, via email or a medical information exchange system.

[0807] Step 11:

[0808] The server stores a copy of the generated referral in its database for future reference and reuse.

[0809] Example 1

[0810] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0811] In today's medical field, creating referral letters is a time-consuming, labor-intensive process that is prone to errors. Information can also be inconsistent and inaccurate, potentially affecting appropriate patient care. Furthermore, ensuring the security of referral letter storage and transmission is also an issue.

[0812] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0813] In this invention, the server includes a means for inputting basic information and medical records of a patient, a means for transmitting the basic information and medical records of the patient to the server, a means for analyzing the received information and generating a referral letter using natural language processing technology, and a means for adapting the generated referral letter to a unified format and performing error checking, thereby enabling automatic generation of referral letters, improved accuracy and consistency, and secure storage and transmission of data.

[0814] "Patient basic information" refers to basic personal information such as the patient's name, age, gender, and symptoms.

[0815] "Medical records" refer to detailed medical records, such as a patient's medical history, current treatment, medications, and rehabilitation status.

[0816] "Server" refers to a central processing unit for analyzing patient information, generating referral letters, and storing and transmitting data.

[0817] "Terminal" refers to an electronic device used by doctors and medical staff to enter patient basic information and medical records, and to review and correct referral letters.

[0818] "Natural language processing technology" refers to technology that enables computers to understand and process human language, including text analysis and automatic generation.

[0819] A "generative AI model" is an artificial intelligence model trained for a specific task, in this case the automated generation of letters of introduction.

[0820] "Unified format" refers to a standard document format that ensures that referral documents are output in a consistent format.

[0821] "Error checking" refers to the process of detecting and correcting typographical errors and missing information in the generated referral letters.

[0822] An "electronic medical record system" refers to a system that electronically manages and stores patient medical records.

[0823] "Encryption" refers to a technology that converts information using a specific algorithm to protect data and prevent unauthorized access by third parties.

[0824] A "secure path" refers to a communication path through which data is sent and received securely, using encryption and authentication technologies.

[0825] This invention is a system for making referral letters more efficient by utilizing basic patient information and medical records. Specific embodiments for implementing this system are described below.

[0826] Entering and submitting patient data

[0827] First, the user (doctor or medical staff) uses a dedicated terminal to input basic information and medical records of the patient, including details such as the patient's name, age, gender, and current symptoms.

[0828] As a specific example, a user enters "Yamada Taro, 45 years old, male, lower back pain" into the terminal. This input information is linked to the electronic medical record system (EHR), and more detailed medical records are automatically retrieved.

[0829] The device then packages the entered data and retrieved medical records, encrypts them using a secure protocol, and transmits them to a server, ensuring data integrity and privacy.

[0830] Information analysis and referral generation

[0831] The server analyzes the received data and generates a referral letter using natural language processing (NLP) technology. Specifically, it inputs the necessary information as prompt sentences into the generative AI model and drafts an appropriate referral letter.

[0832] For example, the server might use the following prompt:

[0833] Patient demographics:

[0834] Name: Yamada Taro

[0835] Age: 45

[0836] Gender: Male

[0837] Symptom: Lower back pain

[0838] Medical Records:

[0839] Under rehabilitation and drug treatment

[0840] Generate a testimonial:

[0841] Based on this prompt, the AI ​​model generates the following sentence: "Taro Yamada suffers from chronic lower back pain and is currently undergoing rehabilitation and medication. He needs to be referred to an orthopedic surgeon for further detailed examination."

[0842] Formatting consistency and error checking

[0843] The server uses a template engine to adapt the generated letters of introduction to a unified format, ensuring that they are output in a consistent format. It also runs an error-checking program to detect typos and missing information and suggest corrections.

[0844] Checking and correcting the letter of introduction

[0845] The user (doctor or medical staff) can review the referral letter generated on the terminal. They can check the contents on the screen and manually make corrections if necessary. For example, they can manually enter additional information such as "Additional consultation with an orthopedic surgeon is recommended."

[0846] Send and save referrals

[0847] The terminal then electronically transmits the final version of the referral letter to the designated hospital or medical institution via a server, using email or a medical information exchange system as the transmission method.

[0848] The server stores a copy of the referral in a database for future reference or reuse. For example, the device sends the referral to the orthopedic department's email address, and the server stores the referral in a database.

[0849] In this way, the system of the present invention streamlines the referral process, ensures accuracy and consistency of information, improves the quality of patient care, and helps medical institutions operate more efficiently.

[0850] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0851] Step 1: Enter patient demographics and medical records

[0852] The user (doctor or medical staff) uses a dedicated terminal to input basic information and medical records of the patient. In this input step, details such as the patient's name, age, gender, and current symptoms are entered.

[0853] As a specific operation, the user inputs "Yamada Taro, 45 years old, male, lower back pain" into the terminal. This is the input.

[0854] Step 2: Automated patient data capture and packaging

[0855] The terminal will then link with the electronic health record system (EHR) based on the basic information entered, and automatically retrieve the patient's detailed medical records. Specifically, the terminal will query the EHR system for records for "Yamada Taro." This will retrieve detailed medical records for "under rehabilitation and drug treatment."

[0856] Input: Basic information entered by the user

[0857] Output: Data package containing detailed medical records

[0858] Step 3: Send patient data

[0859] The device packages the acquired data, encrypts it, and sends it to the server. Specifically, the device sends it using a secure protocol.

[0860] Input: Data package containing detailed medical records

[0861] Output: Sending encrypted data

[0862] Step 4: Analyze the data and generate a draft letter of introduction

[0863] The server analyzes the received data and uses natural language processing (NLP) techniques to generate appropriate prompts, which are then fed into a generative AI model, which then drafts a referral letter based on the prompts.

[0864] Example prompt sentence:

[0865] Patient demographics:

[0866] Name: Yamada Taro

[0867] Age: 45

[0868] Gender: Male

[0869] Symptom: Lower back pain

[0870] Medical Records:

[0871] Under rehabilitation and drug treatment

[0872] Generate a testimonial:

[0873] Input: Detailed medical records

[0874] Output: Draft letter of introduction

[0875] Step 5: Formatting and Error Checking

[0876] The server uses a template engine to adapt the generated letters of introduction to a unified format, and also runs an error-checking program to detect typos and missing information.

[0877] Input: Draft letter of introduction

[0878] Output: Error checked referral letter

[0879] Step 6: Review and correct the referral letter

[0880] The user (doctor or medical staff) checks the referral generated on the device and manually corrects it if necessary. Specifically, the user enters information about additional consultations or treatments. For example, the user adds information such as "An additional consultation with an orthopedic surgeon is recommended."

[0881] Input: Error-checked referral letter

[0882] Output: Revised version of the referral letter

[0883] Step 7: Send and save your referral

[0884] The terminal electronically sends the final referral letter to the designated hospital or medical institution via the server. Specifically, the terminal sends the letter via email or a medical information exchange system. At the same time, the server stores a copy of the generated referral letter in a database.

[0885] Input: Revised version of the referral letter

[0886] Output: Referrals sent and referrals saved

[0887] These processing steps streamline the referral process and ensure accuracy and consistency of information.

[0888] (Application example 1)

[0889] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0890] The current process for creating referrals and industrial product inspection reports requires a lot of manual input and correction, which is time-consuming and labor-intensive. It is also prone to errors and it is difficult to maintain consistency of information. In the industrial sector, in particular, there is a demand for fast and accurate production of inspection reports, and a system as efficient as the creation of referrals is required.

[0891] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0892] In this invention, the server includes means for inputting basic patient information and medical records, means for transmitting the basic patient information and medical records to the server, means for analyzing the received information and generating a referral letter, means for adapting the generated referral letter to a unified format and performing error checks, means for checking and correcting the referral letter on a terminal, means for electronically transmitting the final referral letter to a medical institution and storing it, means for inputting inspection data of industrial products and transmitting it to the server, means for analyzing the received inspection data of industrial products and generating an inspection report, means for adapting the generated inspection report to a unified format and performing error checks, means for checking and correcting the inspection report on a terminal, and means for electronically transmitting the final inspection report to a relevant department and storing it, thereby enabling efficient and accurate generation of referral letters and inspection reports in medical institutions and industrial fields.

[0893] "Basic patient information" refers to basic personal information necessary to identify a patient, such as the patient's name, age, gender, and contact information.

[0894] "Medical records" refers to medical-related information such as details of examinations, prescriptions, treatment history, and test results conducted for patients at medical institutions.

[0895] "Server" means a central processing unit for receiving, analyzing, storing, and transmitting data over a network.

[0896] A "referral letter" is an official document created to refer a patient to another medical institution or specialist.

[0897] A "uniform format" is a standard for organizing documents and data into a specific standard format.

[0898] "Error checking" is the process of verifying the accuracy of documents or data and detecting typographical errors or incomplete information.

[0899] A "terminal" is a device for inputting, displaying, and modifying data. Examples include personal computers and tablets.

[0900] "Industrial product inspection data" refers to data on the quality and performance of inspected items collected at factories and manufacturing sites.

[0901] "Inspection report" means an official document summarizing the results of an inspection and including conclusions regarding the quality and performance of the inspected item.

[0902] "Related departments" are internal departments related to the inspection results and quality control of industrial products.

[0903] These definitions provide clearer explanations of key terms found in the claims and facilitate a better understanding of the invention.

[0904] The present invention is a system for efficiently creating letters of introduction and inspection reports for industrial products. Specific embodiments of this system will be described below.

[0905] First, the user (doctor or medical staff) enters the patient's basic information and medical records into a dedicated terminal. This terminal is linked to the electronic health record system (EHR) and can automatically retrieve the necessary medical records. For example, if a doctor enters "Patient A, 45 years old, male, lower back pain" into the terminal, this information will automatically retrieve detailed medical records from the EHR system.

[0906] The device then packages the entered data and retrieved medical records and sends them to a server over a secure, encrypted channel, preserving patient privacy and data integrity.

[0907] The server analyzes the received data and generates a referral letter using natural language processing (NLP) technology. Specifically, the server inputs the necessary information into the AI ​​model and drafts an appropriate referral letter based on the patient's symptoms and treatment details. For example, based on the information "Patient A, 45 years old, chronic lower back pain, undergoing rehabilitation," it generates a sentence such as "Patient A suffers from chronic lower back pain and is currently undergoing rehabilitation and drug treatment. A referral to an orthopedic surgeon is required for further detailed examination."

[0908] The generated referral letter is adapted to a unified format by the server. A template engine is used to embed the generated text into a standard document. An error-checking program is then launched to detect typos and missing information. For example, the sentence "Patient A suffers from chronic lower back pain" is automatically checked for typos and missing information.

[0909] Next, the user (doctor or medical staff) checks the generated referral letter on the terminal. They check the contents of the referral letter on the screen and manually correct it if necessary. For example, they can enter additional information such as "An additional consultation with an orthopedic surgeon is recommended."

[0910] Finally, the terminal electronically sends the final referral letter to the designated medical institution via the server. The transmission is done via email or a medical information exchange system. At the same time, the server stores a copy of the generated referral letter in a database for future reference or reuse. For example, the terminal sends the referral letter to the email address of the orthopedic department, and the server stores the referral letter in the database.

[0911] A similar process can also be used to automatically generate inspection reports using inspection data from industrial products. Inspectors use their smartphones to input inspection data and send it to a server. The server analyzes the received data and generates an appropriate inspection report. The generated inspection report is then adapted to a unified format and checked for errors. The inspector reviews the report and makes any necessary corrections. Finally, the report is electronically sent to the relevant department and stored.

[0912] As a specific example, if an inspector enters data for item ID "12345" and records it as "Passed," the server can analyze this data and generate an inspection report such as "Inspection results performed by inspector A, item ID: 12345, result: Passed."

[0913] Examples of prompts to input to a generative AI model include:

[0914] Inspection Data:

[0915] Inspector: Inspector A

[0916] Item ID: 12345

[0917] Result: Passed

[0918] Based on this, generate an inspection report in the following format:

[0919] ----------------------

[0920] Inspection Report:

[0921] Inspector: Inspector A

[0922] Item ID: 12345

[0923] Test result: Passed

[0924] ----------------------

[0925] In this way, the system of the present invention streamlines the process of creating referral letters and test reports, ensuring accuracy and consistency of information, thereby helping to improve operational efficiency and quality in medical institutions and industrial fields.

[0926] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0927] Step 1: User enters basic information and medical records

[0928] The user (doctor or medical staff) uses the terminal to input basic information and medical records about the patient. For example, the information input in this step might be "Patient A, 45 years old, male, with lower back pain." The terminal is linked to the electronic medical record system (EHR), and automatically obtains detailed medical records based on the input information. The input data is temporarily stored in the terminal.

[0929] Step 2: The device sends the data to the server

[0930] The terminal compiles the entered basic patient information and the acquired medical records, packages them, and sends them to the server. The transmission is carried out via a secure, encrypted communication channel. The input data is encrypted before being sent to the server. This ensures patient privacy and data integrity.

[0931] Step 3: The server analyzes the data and generates a referral.

[0932] The server analyzes the received data and generates a referral letter using natural language processing (NLP) technology. Based on the input basic information and medical records, the AI ​​model generates an appropriate draft referral letter. For example, from the data "Patient A, 45 years old, chronic back pain, undergoing rehabilitation," a referral letter may be generated that reads, "Patient A suffers from chronic back pain and is currently undergoing rehabilitation and drug treatment. He or she will need to be referred to an orthopedic surgeon for further detailed examination." The generated referral letter is stored on the server.

[0933] Step 4: The server standardizes the format of the letters of introduction and checks for errors

[0934] The server uses a template engine to adapt the generated letter of introduction to a standardized format. The generated text is converted into a standard document format. An error check program is then run to detect typos and missing information in the letter of introduction. Once the error check is complete, the letter of introduction is saved back to the server.

[0935] Step 5: User confirms and modifies the referral letter

[0936] The user (doctor or medical staff) uses the terminal to check the generated referral letter. They check the details of the referral letter displayed on the screen and manually correct it if necessary. For example, they can enter additional information such as "An orthopedic surgeon recommends an additional consultation." The corrected referral letter is temporarily saved on the terminal.

[0937] Step 6: The device sends and saves the final version of the introduction letter

[0938] After confirmation and correction, the final referral letter is sent electronically to the designated medical institution via the server. The transmission route is email or a medical information exchange system. At the same time, the server stores a copy of the generated and sent referral letter in a database for future reference and reuse.

[0939] Step 7: Inspectors enter the inspection data for industrial products

[0940] Inspectors use their smartphones to input inspection data for industrial products. For example, the item ID is "12345" and the inspection result is "Passed." The input data is temporarily saved on the smartphone.

[0941] Step 8: The smartphone sends the data to the server

[0942] The smartphone packages the entered test data and sends it to the server via a secure, encrypted communication channel. The entered data is sent to the server in encrypted form and stored there.

[0943] Step 9: The server analyzes the inspection data and generates an inspection report.

[0944] The server analyzes the received inspection data and generates an inspection report. Based on the input inspection data, the AI ​​model generates an appropriate draft inspection report. For example, based on the data item ID "12345" and inspection result "Passed," an inspection report such as "Inspection results performed by inspector A, item ID: 12345, result: Passed" is generated. The generated report is saved on the server.

[0945] Step 10: The server unifies the format of the inspection report and checks for errors.

[0946] The server uses a template engine to adapt the generated inspection report to a standardized format. The generated text is converted into a standard document format. An error check program is then run to detect typographical errors and missing information in the inspection report. Once the error check is complete, the report is saved back to the server.

[0947] Step 11: Inspector reviews and corrects the inspection report

[0948] The inspector uses a smartphone to check the generated inspection report. He / she carefully reviews the contents of the inspection report displayed on the screen and manually corrects them as necessary. For example, he / she enters additional information such as "additional quality checks are required." The corrected inspection report is temporarily saved on the smartphone.

[0949] Step 12: Your smartphone sends and saves the final inspection report

[0950] After confirmation and corrections are completed, the final inspection report is sent electronically to the relevant department via the smartphone server, via email or the internal information exchange system. At the same time, the server stores a copy of the generated and sent inspection report in a database for future reference and reuse.

[0951] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0952] The present invention combines a system for streamlining the automatic creation of referral letters with an emotion engine that recognizes the user's emotions. This system supports the process of inputting and analyzing basic patient information and medical records, generating and sending referral letters, and further adjusts the process by taking the user's emotional state into consideration. Specific embodiments of this system are described below.

[0953] Entering and submitting patient data

[0954] First, the user (doctor or medical staff) enters the patient's basic information and medical records into a dedicated terminal. This terminal is linked to the electronic health record system (EHR), and the necessary medical records can be automatically retrieved. For example, if a doctor enters "Yamada Taro, 45 years old, male, lower back pain" into the terminal, this information will automatically retrieve detailed medical records from the EHR system.

[0955] The device then packages the entered data and retrieved medical records and sends them to a server over a secure, encrypted channel, preserving patient privacy and data integrity.

[0956] Information analysis and referral generation

[0957] The server analyzes the received data and generates a referral letter using natural language processing (NLP) technology. Specifically, the necessary information is input into the AI ​​model, which then drafts an appropriate referral letter based on the patient's symptoms and treatment details. For example, based on the information "Yamada Taro, 45 years old, chronic back pain, currently undergoing rehabilitation," it generates a sentence such as "Yamada Taro suffers from chronic back pain and is currently undergoing rehabilitation and drug treatment. A referral to an orthopedic surgeon for further detailed examination is required."

[0958] Formatting consistency and error checking

[0959] The generated letter of introduction is then adapted to a unified format by the server. A template engine is used to embed the generated text into a standard document. An error-checking program is then launched to detect typos and missing information. For example, the sentence "Taro Yamada suffers from chronic back pain" is automatically checked for typos and missing information.

[0960] Use of emotion engine

[0961] Next, when the user (doctor or medical staff) checks the referral letter generated on the device, the emotion engine analyzes the user's voice and facial expressions to understand their emotional state, such as stress or fatigue. If the emotion engine detects a situation where an error is likely to occur, such as when the user is overly tired, it automatically displays a warning. The content and display format of the referral letter are also adjusted to allow the user to work in a relaxed state.

[0962] Checking and correcting the letter of introduction

[0963] After receiving feedback from the emotion engine, the user can review the referral generated on their device and manually correct it if necessary, for example by adding additional information such as "An additional consultation with an orthopedic surgeon is recommended."

[0964] Send and save referrals

[0965] Finally, the terminal electronically sends the final referral letter to the designated hospital or medical institution via the server. The transmission is done via email or a medical information exchange system. At the same time, the server stores a copy of the generated referral letter in a database for future reference or reuse. For example, the terminal sends the referral letter to the email address of an orthopedic department, and the server stores the referral letter in a database.

[0966] In this way, the system of the present invention streamlines the referral process, ensuring accuracy and consistency of information while optimizing the process by taking into account the user's emotional state, thereby improving the quality of patient care and supporting the operational efficiency of medical institutions.

[0967] The processing flow will be explained below.

[0968] Step 1:

[0969] The user (doctor or medical staff) uses the terminal to enter basic patient information, such as name, age, sex, address, contact information, medical history, current diagnosis information, and details of symptoms.

[0970] Step 2:

[0971] Based on the information entered, the device will automatically retrieve medical records in conjunction with the electronic health record system (EHR), using an API to obtain the necessary medical data.

[0972] Step 3:

[0973] The terminal packages the entered patient information and the acquired medical records into a single data packet, formats the data, and prepares the packet for transmission to the server.

[0974] Step 4:

[0975] The device encrypts the data packet and sends it to the server over a secure channel, calling an API to send the data.

[0976] Step 5:

[0977] The server analyzes the received data packets and generates a referral letter using natural language processing (NLP) technology. The information is input into an AI model, which automatically generates a letter that corresponds to the patient's symptoms and treatment details.

[0978] Step 6:

[0979] The server adapts the generated letters of introduction to a uniform format and uses a template engine to embed the generated text into a template document.

[0980] Step 7:

[0981] The server runs an error check program to detect typos, incorrect information, missing information, etc. in the generated letter of introduction, thereby confirming the accuracy of the information.

[0982] Step 8:

[0983] The user (doctor or medical staff) checks the referral letter generated on the terminal, checks the contents of the referral letter on the screen, and manually corrects them if necessary.

[0984] Step 9:

[0985] While the user is checking the referral letter, the device runs an emotion engine that analyzes the user's voice and facial expressions to detect emotional states such as stress and fatigue.

[0986] Step 10:

[0987] If the emotion engine detects stress or fatigue, the device will display a warning to the user, such as a message saying, "Take a break to reduce stress."

[0988] Step 11:

[0989] Based on feedback from the emotion engine, the device will adjust the content and presentation of the referral letter according to the user's emotional state, for example, changing it to a simpler, more understandable format for a user who is overly tired.

[0990] Step 12:

[0991] The terminal then reviews the revised letter of introduction again and converts the final version into a printable format, such as PDF, and sends it to the printer.

[0992] Step 13:

[0993] The terminal then electronically sends the final referral letter to the designated hospital or medical institution via the server, either by email or via a medical information exchange system.

[0994] Step 14:

[0995] The server stores a copy of the generated referral in its database, where it is stored for future reference and reuse.

[0996] In this way, not only is the referral process more efficient, but the task is optimized taking into account the user's emotional state, improving the quality of patient care and helping medical institutions work more efficiently.

[0997] Example 2

[0998] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0999] With conventional referral letter creation systems, when creating referral letters using basic patient information and medical records, it was difficult to ensure the accuracy and consistency of the information, and if the user was tired or stressed, there was a high possibility of typos or incomplete information.In addition, because the referral letter creation process was done manually, there were problems such as reduced work efficiency and a heavy burden on medical staff.

[1000] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for inputting basic information and medical records of a patient; means for transmitting the basic information and medical records of the patient to an information system; means for analyzing the received information and generating a referral letter using natural language processing technology; means for adapting the generated referral letter to a unified format and performing error checking; means for analyzing the emotional state of the user and providing feedback; means for checking and correcting the referral letter on a terminal; and means for electronically transmitting and storing the final referral letter to the medical institution. This improves the efficiency of the automatic referral letter generation process and enables optimization of the work taking into account the emotional state of the user while maintaining the accuracy and consistency of the information.

[1001] "Basic patient information" refers to basic personal information about the patient, such as the patient's name, age, sex, address, contact information, and chief complaint.

[1002] "Medical records" refers to all records related to medical treatment at medical institutions, such as a patient's medical history, test results, prescription history, surgical history, allergy information, and treatment plans.

[1003] "Information systems" refer to computer systems, such as electronic health records (EHRs) and healthcare information systems (HISs), that digitally manage and operate patient medical records and basic information.

[1004] "Natural language processing technology" refers to technology that enables computers to understand, analyze, and generate human language. Examples include text analysis, speech recognition, and language generation.

[1005] A "referral document" is an official document containing the information necessary for a patient to be referred to another medical institution or specialist.

[1006] A "template engine" is a software tool for embedding dynamic data into boilerplate documents, and includes libraries such as Jinja2 and Handlebars.

[1007] An "error checking program" is a software program that automatically detects typographical errors, grammatical errors, missing information, etc. in a document.

[1008] The "emotion engine" is an analytical engine that analyzes voice, facial expressions, movements, etc. to evaluate the user's emotional state (stress, fatigue, relaxation, etc.).

[1009] The "means for providing feedback" is a means for reporting the status to the user based on the data obtained from the emotion engine and presenting appropriate countermeasures or warnings.

[1010] "Device" means a computing device, including a PC or tablet, on which a User enters information and / or reviews or modifies the generated referral document.

[1011] "Electronic transmission means" refers to a means for securely transmitting the generated referral document to the designated medical institution via email or a medical information exchange system.

[1012] The "means for storing" refers to a means for storing the generated introduction document in a storage system such as a database for future reference or reuse.

[1013] The present invention combines a system for streamlining the automatic creation of referral letters with an emotion engine that recognizes the user's emotions. This system supports the process of inputting and analyzing basic patient information and medical records, generating and sending referral letters, and further adjusts the process by taking the user's emotional state into consideration. Specific embodiments of this system are described below.

[1014] Entering and submitting patient data

[1015] First, the user (doctor or medical staff) enters the patient's basic information and medical records into a dedicated terminal. This terminal is linked to the electronic health record system (EHR) and can automatically retrieve the necessary medical records. For example, if a doctor enters "Yamada Taro, 45 years old, male, lower back pain" into the terminal, this information will automatically retrieve detailed medical records from the EHR system.

[1016] The terminal packages the entered data and the acquired medical records and sends them to the server over a secure channel (e.g., SSL / TLS protocol) after encrypting the data, thereby preserving patient privacy and data integrity.

[1017] Information analysis and referral generation

[1018] The server analyzes the received data and generates a referral letter using natural language processing (NLP) technology. Specifically, the necessary information is input into a generative AI model (e.g., GPT-3), which then drafts an appropriate referral letter based on the patient's symptoms and treatment details. For example, based on the information "Yamada Taro, 45 years old, chronic back pain, currently undergoing rehabilitation," it generates a sentence such as "Yamada Taro suffers from chronic back pain and is currently undergoing rehabilitation and drug treatment. He needs to be referred to an orthopedic surgeon for further detailed examination."

[1019] Formatting consistency and error checking

[1020] The generated letter of introduction is then adapted to a unified format by the server. Specifically, a template engine (e.g., Jinja2) is used to embed the generated text into a standard document. An error-checking program is then launched to detect typos and missing information. For example, the sentence "Yamada Taro suffers from chronic back pain" is automatically checked for typos and missing information.

[1021] Use of emotion engine

[1022] Next, when the user (doctor or medical staff) checks the referral letter generated on the device, the emotion engine analyzes the user's voice and facial expressions to understand their emotional state, such as stress or fatigue. If the emotion engine detects a situation where an error is likely to occur, such as when the user is overly tired, it automatically displays a warning. The content and display format of the referral letter are also adjusted to allow the user to work in a relaxed state.

[1023] Checking and correcting the letter of introduction

[1024] After receiving feedback from the emotion engine, the user can review the referral generated on their device and manually correct it if necessary, for example by adding additional information such as "An additional consultation with an orthopedic surgeon is recommended."

[1025] Send and save referrals

[1026] Finally, the terminal electronically sends the final referral letter to the designated hospital or medical institution via the server. This can be done via email or a healthcare information exchange (HIE). At the same time, the server stores a copy of the generated referral letter in a database for future reference or reuse. For example, the terminal sends the referral letter to the orthopedic department's email address, and the server stores it in the database.

[1027] Specific examples

[1028] Here is an example of a prompt that can be fed into a generative AI model to automatically generate a referral letter:

[1029] "Yamada Taro, 45 years old, male, chronic lower back pain, currently undergoing rehabilitation. Referred to orthopedic clinic."

[1030] For this input, the resulting letter of introduction might look like this:

[1031] "Yamada Taro suffers from chronic back pain and is currently undergoing rehabilitation and medication. He will need to be referred to an orthopedic surgeon for further detailed examination."

[1032] In this way, the system of the present invention streamlines the referral process, ensuring accuracy and consistency of information while optimizing the process by taking into account the user's emotional state, thereby improving the quality of patient care and supporting the operational efficiency of medical institutions.

[1033] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1034] Step 1: Enter patient demographics and medical records

[1035] The user (doctor or medical staff) enters the patient's basic information (name, age, sex, symptoms, etc.) into a dedicated terminal. The terminal uses this information to retrieve detailed medical records from the electronic health record system (EHR). Specifically, when the user enters "Yamada Taro, 45 years old, male, lower back pain" into the terminal, the "medical record for lower back pain" is automatically retrieved from the EHR based on this information. The entered basic information and medical records are compiled on the terminal.

[1036] Input: Patient's basic information ("Taro Yamada, 45 years old, male, lower back pain")

[1037] Output: Patient basic information and medical record ("Medical record of low back pain")

[1038] Step 2: Packaging and sending the data

[1039] The terminal compiles the entered basic information and the acquired medical records into a data package. The packaged data is then encrypted and sent to the server. The SSL / TLS protocol is generally used for encryption. Specifically, the terminal compiles data in a format such as "Yamada Taro, 45 years old, male, lower back pain, medical record for lower back pain," encrypts it, and sends it to the server.

[1040] Input: Patient demographics and medical records

[1041] Output: Encrypted data package

[1042] Step 3: Analyze the data

[1043] The server decrypts the received encrypted data package and analyzes the data. Natural language processing (NLP) technology is used for the analysis. Specifically, the server analyzes the information "Yamada Taro, 45 years old, suffering from chronic back pain, currently undergoing rehabilitation" and prepares to send the necessary information to the generative AI model.

[1044] Input: Encrypted data package

[1045] Output: Parsed patient information

[1046] Step 4: Generate introductory documentation

[1047] Based on the analyzed data, the server inputs a prompt into a generative AI model (e.g., GPT-3) to generate an introductory document. Specifically, the server inputs the following prompt into the generative AI model:

[1048] "Yamada Taro, 45 years old, suffering from chronic lower back pain, currently undergoing rehabilitation. Referred to an orthopedic surgeon."

[1049] The referral letter obtained from the generative AI model read something like, "Taro Yamada suffers from chronic lower back pain and is currently undergoing rehabilitation and medication. He needs to be referred to an orthopedic surgeon for further detailed examination."

[1050] Input: Parsed patient information

[1051] Output: Generated introductory document

[1052] Step 5: Formatting and Error Checking

[1053] The server adapts the generated referral document to a unified format using a template engine (e.g., Jinja2). Furthermore, an error-checking program is launched to detect typos and missing information. Specifically, the server adapts the generated referral document to a "standard referral template" and checks for errors using a grammar checker.

[1054] Input: Generated referral document

[1055] Output: Introduction document and error report in a unified format

[1056] Step 6: Analysis and feedback by the emotion engine

[1057] When a user checks the referral letter generated on the device, the emotion engine installed on the device analyzes the user's voice and facial expressions. The emotion engine grasps the user's emotional state, such as stress and fatigue, and displays a warning if excessive fatigue is detected. Specifically, while the user is checking the referral letter, the emotion engine analyzes stress and fatigue in real time and displays a message such as "Please relax and check" as necessary.

[1058] Input: User's voice and facial expressions

[1059] Output: Feedback based on emotional state

[1060] Step 7: Review and correct the referral letter

[1061] The user can review the generated referral letter on the device and manually correct it if necessary. Specifically, the user can enter additional information, such as "An additional consultation with an orthopedic surgeon is recommended."

[1062] Input: Generated referral letter, user correction input

[1063] Output: Final letter of introduction

[1064] Step 8: Send and save your referral

[1065] The terminal then electronically sends the final referral letter to the designated medical institution via the server. This is done via email or a healthcare information exchange (HIE). At the same time, the server stores a copy of the generated referral letter in a database for future reference or reuse. Specifically, the terminal sends the referral letter to the orthopedic department's email address, and the server stores it in the database.

[1066] Input: Final version of the referral letter

[1067] Output: Referrals sent, Referrals saved

[1068] In this way, the automated referral letter generation system provides a streamlined process, maintaining accuracy of information while optimizing the process by taking into account the user's emotional state.

[1069] (Application example 2)

[1070] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1071] While conventional referral letter creation systems have successfully automated the process from patient information entry to referral letter generation, they do not take into account the user's emotional state, which means there is a risk of users entering incorrect information if they are tired or stressed. Furthermore, they require user expertise to generate accurate and consistent referral letters, and effective support is not provided. This can lead to reduced referral letter quality and work efficiency, which can affect the quality of patient care.

[1072] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting basic information and medical records of a patient; means for transmitting the basic information and medical records of the patient to the server; means for analyzing the received information and generating a referral letter; means for adapting the generated referral letter to a unified format and performing error checking; means for confirming and correcting the referral letter on a terminal; means for electronically transmitting and storing the final referral letter to the medical institution; and means for analyzing the user's emotional state using an emotion recognition engine and adjusting the work process. This enables accurate and unified referral letters to be generated even when the user is tired or stressed, thereby improving work efficiency and the quality of patient care.

[1073] "Basic patient information" refers to personal identification information such as the patient's name, age, sex, and contact information.

[1074] "Medical records" are all information related to medical care, such as a patient's medical history, diagnosis, treatment, and notes made by medical staff.

[1075] "Server" means a computer system that centrally manages and processes data, and has the function of analyzing received information and generating letters of introduction.

[1076] A "letter of referral" is a document used to refer a patient to another medical institution, and contains details of the patient's condition, treatment progress, and any necessary tests or treatments.

[1077] A "uniform format" is a standardized form used to standardize the format and wording of letters of introduction.

[1078] "Error checking" is the process of automatically detecting and correcting typos and missing information in the generated letters of introduction.

[1079] A "terminal" is a computer or smart device that a user uses to perform operations such as input, confirmation, and correction.

[1080] An "emotion recognition engine" is software that analyzes a user's emotional state (e.g., stress, fatigue, etc.) from their voice and facial expressions, and provides feedback on the results.

[1081] "Work process adjustment" refers to optimizing the user's work environment and work procedures based on the analysis results of the emotion recognition engine.

[1082] The present invention combines a system for streamlining the automatic generation of referral letters with an emotion engine that recognizes the user's emotions. This system supports the process of inputting and analyzing basic patient information and medical records, generating and sending referral letters, and adjusting the process taking into account the user's emotional state.

[1083] Entering and submitting patient data

[1084] First, the user (doctor or medical staff) enters the patient's basic information and medical records into the terminal. This terminal is linked to the electronic health record system (EHR) and can automatically retrieve the necessary medical records. For example, if a doctor enters "XX, 45 years old, male, lower back pain" into the terminal, this information automatically retrieves detailed medical records from the EHR system. Next, the terminal packages the entered data and the retrieved medical records and sends them to the server. The transmission is carried out over a secure, encrypted channel, maintaining patient privacy and data integrity.

[1085] Information analysis and referral generation

[1086] The server analyzes the received data and generates a referral letter using natural language processing (NLP) technology. For example, the AI ​​model can input information such as "X, 45 years old, chronic back pain, currently undergoing rehabilitation," and draft an appropriate referral letter based on the patient's symptoms and treatment. Specifically, it generates a sentence such as, "X suffers from chronic back pain and is currently undergoing rehabilitation and drug treatment. A referral to an orthopedic surgeon is required for further detailed examination."

[1087] Formatting consistency and error checking

[1088] The generated letter of introduction is then adapted to a unified format by the server. A template engine is used to embed the generated text into a standard document. An error-checking program is then launched to detect typos and missing information. For example, the text "Mr. / Ms. X suffers from chronic back pain" is automatically checked for typos and missing information.

[1089] Use of emotion engine

[1090] Next, when the user (doctor or medical staff) checks the referral letter generated on the device, the emotion engine analyzes the user's voice and facial expressions to understand their emotional state, such as stress or fatigue. If the emotion engine detects a situation where an error is likely to occur, such as when the user is overly tired, it automatically displays a warning. The content and display format of the referral letter are also adjusted to allow the user to work in a relaxed state.

[1091] Checking and correcting the letter of introduction

[1092] After receiving feedback from the emotion engine, the user can review the referral generated on their device and manually correct it if necessary, for example, by adding additional information such as "An additional consultation with an orthopedic surgeon is recommended."

[1093] Send and save referrals

[1094] Finally, the terminal electronically sends the final referral letter to the designated hospital or medical institution via the server. The transmission is done via email or a medical information exchange system. At the same time, the server stores a copy of the generated referral letter in a database for future reference or reuse. For example, the terminal sends the referral letter to the email address of an orthopedic department, and the server stores the referral letter in a database.

[1095] Hardware and software used

[1096] Hardware:

[1097] Terminal (computer operated by the user)

[1098] Server (a computer system that centrally manages and processes data)

[1099] software:

[1100] Electronic Health Record System (EHR)

[1101] Emotion Recognition Engine (EmotionAnalyzer)

[1102] Natural Language Processing (NLP)

[1103] Template Engine

[1104] Error Checking Program

[1105] Examples of specific examples and prompts

[1106] For example, suppose a worker is wearing smart glasses. The prompt is, "Emotion Identification: Enter a facial image to analyze the stress and fatigue levels." This prompt allows the worker to enter the work data appropriately, and the emotion recognition engine can analyze the user's emotional state.

[1107] Specific prompt examples:

[1108] "Enter facial image data and analyze stress and fatigue levels. The model used is EmotionAnalyzer."

[1109] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1110] Step 1: The user enters the patient's basic information and medical records into the terminal.

[1111] The user enters basic information such as the patient's name, age, sex, and medical condition, as well as medical records, into the terminal. This input information is linked to the electronic health record system (EHR), and detailed medical records are automatically obtained. The input data is temporarily stored within the terminal.

[1112] Step 2: The device sends the patient's basic information and medical records to the server.

[1113] The terminal sends the entered basic patient information and medical records to the server via a secure encrypted channel. The input data is encrypted, packetized, and sent to the server.

[1114] Step 3: The server analyzes the received information and generates a referral.

[1115] The server analyzes the received patient's basic information and medical records. Using natural language processing (NLP) technology within the server, this information is input into an AI model, which then drafts an appropriate referral letter based on the patient's symptoms and treatment. For example, a referral letter can be generated using information such as "XX, 45 years old, chronic back pain, currently undergoing rehabilitation."

[1116] Step 4: The server adapts the generated letter of introduction to a unified format and performs error checking.

[1117] The generated letters of introduction are then adapted to a unified format by the server. A template engine is used to embed the generated text into a standardized format. An error-checking program is also initiated to automatically detect typos and missing information and identify areas that need correction.

[1118] Step 5: Analyze the user's emotional state using an emotion recognition engine and adjust the work process

[1119] The user checks the referral letter generated on the device, and the emotion recognition engine analyzes the user's voice and facial expressions to understand their emotional state, such as stress or fatigue. For example, the system may display feedback such as, "High stress or fatigue has been detected. A break is recommended."

[1120] Step 6: User reviews the referral and makes any necessary corrections

[1121] After receiving feedback from the emotion recognition engine, the user can review the generated letter of introduction on their device, manually correct it if necessary, enter additional information or corrections, and finally confirm the letter of introduction.

[1122] Step 7: The terminal electronically sends the final referral letter to the medical institution via the server and stores it.

[1123] Finally, the terminal electronically sends the final referral letter to the designated hospital or medical institution via the server. The transmission is done via email or a medical information exchange system, and the server also simultaneously stores a copy of the generated referral letter in a database. For example, the terminal sends the referral letter to the email address of an orthopedic department, and the server stores the referral letter in a database.

[1124] 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.

[1125] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1126] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1127] [Fourth embodiment]

[1128] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1129] 7, a 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.

[1130] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[1131] 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.

[1132] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1133] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1134] 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.

[1135] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1136] 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.

[1137] 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 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.

[1138] 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.

[1139] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1140] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1141] The present invention is a system for efficiently creating referral letters automatically, and supports the process of inputting and analyzing basic patient information and medical records, and generating and sending referral letters. Specific embodiments of this system are described below.

[1142] Entering and submitting patient data

[1143] First, the user (doctor or medical staff) enters the patient's basic information and medical records into a dedicated terminal. This terminal is linked to the electronic health record system (EHR), and the necessary medical records can be automatically retrieved. For example, if a doctor enters "Yamada Taro, 45 years old, male, lower back pain" into the terminal, this information will automatically retrieve detailed medical records from the EHR system.

[1144] The device then packages the entered data and retrieved medical records and sends them to a server over a secure, encrypted channel, preserving patient privacy and data integrity.

[1145] Information analysis and referral generation

[1146] The server analyzes the received data and generates a referral letter using natural language processing (NLP) technology. Specifically, the server inputs the necessary information into the AI ​​model and drafts an appropriate referral letter based on the patient's symptoms and treatment details. For example, based on the information "Yamada Taro, 45 years old, chronic back pain, currently undergoing rehabilitation," it generates a sentence such as "Yamada Taro suffers from chronic back pain and is currently undergoing rehabilitation and drug treatment. A referral to an orthopedic surgeon for further detailed examination is required."

[1147] Formatting consistency and error checking

[1148] The generated letter of introduction is then adapted to a unified format by the server. A template engine is used to embed the generated text into a standard document. An error-checking program is then launched to detect typos and missing information. For example, the sentence "Yamada Taro suffers from chronic back pain" is automatically checked for typos and missing information.

[1149] Checking and correcting the letter of introduction

[1150] Next, the user (doctor or medical staff) checks the generated referral letter on the terminal. They check the contents of the referral letter on the screen and manually correct it if necessary. For example, they can enter additional information such as "An additional consultation with an orthopedic surgeon is recommended."

[1151] Send and save referrals

[1152] Finally, the terminal electronically sends the final referral letter to the designated hospital or medical institution via the server. The transmission is done via email or a medical information exchange system. At the same time, the server stores a copy of the generated referral letter in a database for future reference or reuse. For example, the terminal sends the referral letter to the email address of an orthopedic department, and the server stores the referral letter in a database.

[1153] In this way, the system of the present invention streamlines the referral process and ensures accuracy and consistency of information, improving the quality of patient care and helping medical institutions operate more efficiently.

[1154] The processing flow will be explained below.

[1155] Step 1:

[1156] The user (doctor or medical staff) uses the terminal to enter basic patient information, such as name, age, sex, address, contact information, medical history, current diagnosis information, and details of symptoms.

[1157] Step 2:

[1158] Based on the entered data, the device connects to the electronic health record system (EHR) to retrieve the patient's medical records, and calls the API to obtain the necessary information.

[1159] Step 3:

[1160] The terminal packages the entered basic patient information and the acquired medical records into a single packet, reformatting the data as necessary.

[1161] Step 4:

[1162] The device encrypts the packaged data and sends it over a secure channel to the server, using an API to handle the transmission.

[1163] Step 5:

[1164] The server analyzes the received data and generates a draft referral letter using natural language processing (NLP) technology. Specifically, the necessary information is input into an AI model, which automatically generates a letter appropriate to the patient's condition.

[1165] Step 6:

[1166] The server adapts the generated letter of introduction to a unified format and uses a template engine to embed the generated text into a template document.

[1167] Step 7:

[1168] The server starts an error check program to automatically detect typos, incorrect information, and missing information in the generated letter of introduction.

[1169] Step 8:

[1170] The user (doctor or medical staff) checks the referral letter generated on the terminal, confirms the contents on the screen, and manually corrects them if necessary.

[1171] Step 9:

[1172] The terminal converts the modified referral into a printable format, for example, PDF format, and sends it to a printer.

[1173] Step 10:

[1174] The terminal electronically transmits the final version of the referral letter to the designated hospital or medical institution via the server, via email or a medical information exchange system.

[1175] Step 11:

[1176] The server stores a copy of the generated referral in its database for future reference and reuse.

[1177] Example 1

[1178] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1179] In today's medical field, creating referral letters is a time-consuming, labor-intensive process that is prone to errors. Information can also be inconsistent and inaccurate, potentially affecting appropriate patient care. Furthermore, ensuring the security of referral letter storage and transmission is also an issue.

[1180] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1181] In this invention, the server includes a means for inputting basic information and medical records of a patient, a means for transmitting the basic information and medical records of the patient to the server, a means for analyzing the received information and generating a referral letter using natural language processing technology, and a means for adapting the generated referral letter to a unified format and performing error checking, thereby enabling automatic generation of referral letters, improved accuracy and consistency, and secure storage and transmission of data.

[1182] "Patient basic information" refers to basic personal information such as the patient's name, age, gender, and symptoms.

[1183] "Medical records" refer to detailed medical records, such as a patient's medical history, current treatment, medications, and rehabilitation status.

[1184] "Server" refers to a central processing unit for analyzing patient information, generating referral letters, and storing and transmitting data.

[1185] "Terminal" refers to an electronic device used by doctors and medical staff to enter patient basic information and medical records, and to review and correct referral letters.

[1186] "Natural language processing technology" refers to technology that enables computers to understand and process human language, including text analysis and automatic generation.

[1187] A "generative AI model" is an artificial intelligence model trained for a specific task, in this case the automated generation of letters of introduction.

[1188] "Unified format" refers to a standard document format that ensures that referral documents are output in a consistent format.

[1189] "Error checking" refers to the process of detecting and correcting typographical errors and missing information in the generated referral letters.

[1190] An "electronic medical record system" refers to a system that electronically manages and stores patient medical records.

[1191] "Encryption" refers to a technology that converts information using a specific algorithm to protect data and prevent unauthorized access by third parties.

[1192] A "secure path" refers to a communication path through which data is sent and received securely, using encryption and authentication technologies.

[1193] This invention is a system for making referral letters more efficient by utilizing basic patient information and medical records. Specific embodiments for implementing this system are described below.

[1194] Entering and submitting patient data

[1195] First, the user (doctor or medical staff) uses a dedicated terminal to input basic information and medical records of the patient, including details such as the patient's name, age, gender, and current symptoms.

[1196] As a specific example, a user enters "Yamada Taro, 45 years old, male, lower back pain" into the terminal. This input information is linked to the electronic medical record system (EHR), and more detailed medical records are automatically retrieved.

[1197] The device then packages the entered data and retrieved medical records, encrypts them using a secure protocol, and transmits them to a server, ensuring data integrity and privacy.

[1198] Information analysis and referral generation

[1199] The server analyzes the received data and generates a referral letter using natural language processing (NLP) technology. Specifically, it inputs the necessary information as prompt sentences into the generative AI model and drafts an appropriate referral letter.

[1200] For example, the server might use the following prompt:

[1201] Patient demographics:

[1202] Name: Yamada Taro

[1203] Age: 45

[1204] Gender: Male

[1205] Symptom: Lower back pain

[1206] Medical Records:

[1207] Under rehabilitation and drug treatment

[1208] Generate a testimonial:

[1209] Based on this prompt, the AI ​​model generates the following sentence: "Taro Yamada suffers from chronic lower back pain and is currently undergoing rehabilitation and medication. He needs to be referred to an orthopedic surgeon for further detailed examination."

[1210] Formatting consistency and error checking

[1211] The server uses a template engine to adapt the generated letters of introduction to a unified format, ensuring that they are output in a consistent format. It also runs an error-checking program to detect typos and missing information and suggest corrections.

[1212] Checking and correcting the letter of introduction

[1213] The user (doctor or medical staff) can review the referral letter generated on the terminal. They can check the contents on the screen and manually make corrections if necessary. For example, they can manually enter additional information such as "Additional consultation with an orthopedic surgeon is recommended."

[1214] Send and save referrals

[1215] The terminal then electronically transmits the final version of the referral letter to the designated hospital or medical institution via a server, using email or a medical information exchange system as the transmission method.

[1216] The server stores a copy of the referral in a database for future reference or reuse. For example, the device sends the referral to the orthopedic department's email address, and the server stores the referral in a database.

[1217] In this way, the system of the present invention streamlines the referral process, ensures accuracy and consistency of information, improves the quality of patient care, and helps medical institutions operate more efficiently.

[1218] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1219] Step 1: Enter patient demographics and medical records

[1220] The user (doctor or medical staff) uses a dedicated terminal to input basic information and medical records of the patient. In this input step, details such as the patient's name, age, gender, and current symptoms are entered.

[1221] As a specific operation, the user inputs "Yamada Taro, 45 years old, male, lower back pain" into the terminal. This is the input.

[1222] Step 2: Automated patient data capture and packaging

[1223] The terminal will then link with the electronic health record system (EHR) based on the basic information entered, and automatically retrieve the patient's detailed medical records. Specifically, the terminal will query the EHR system for records for "Yamada Taro." This will retrieve detailed medical records for "under rehabilitation and drug treatment."

[1224] Input: Basic information entered by the user

[1225] Output: Data package containing detailed medical records

[1226] Step 3: Send patient data

[1227] The device packages the acquired data, encrypts it, and sends it to the server. Specifically, the device sends it using a secure protocol.

[1228] Input: Data package containing detailed medical records

[1229] Output: Sending encrypted data

[1230] Step 4: Analyze the data and generate a draft letter of introduction

[1231] The server analyzes the received data and uses natural language processing (NLP) techniques to generate appropriate prompts, which are then fed into a generative AI model, which then drafts a referral letter based on the prompts.

[1232] Example prompt sentence:

[1233] Patient demographics:

[1234] Name: Yamada Taro

[1235] Age: 45

[1236] Gender: Male

[1237] Symptom: Lower back pain

[1238] Medical Records:

[1239] Under rehabilitation and drug treatment

[1240] Generate a testimonial:

[1241] Input: Detailed medical records

[1242] Output: Draft letter of introduction

[1243] Step 5: Formatting and Error Checking

[1244] The server uses a template engine to adapt the generated letters of introduction to a unified format, and also runs an error-checking program to detect typos and missing information.

[1245] Input: Draft letter of introduction

[1246] Output: Error checked referral letter

[1247] Step 6: Review and correct the referral letter

[1248] The user (doctor or medical staff) checks the referral generated on the device and manually corrects it if necessary. Specifically, the user enters information about additional consultations or treatments. For example, the user adds information such as "An additional consultation with an orthopedic surgeon is recommended."

[1249] Input: Error-checked referral letter

[1250] Output: Revised version of the referral letter

[1251] Step 7: Send and save your referral

[1252] The terminal electronically sends the final referral letter to the designated hospital or medical institution via the server. Specifically, the terminal sends the letter via email or a medical information exchange system. At the same time, the server stores a copy of the generated referral letter in a database.

[1253] Input: Revised version of the referral letter

[1254] Output: Referrals sent and referrals saved

[1255] These processing steps streamline the referral process and ensure accuracy and consistency of information.

[1256] (Application example 1)

[1257] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1258] The current process for creating referrals and industrial product inspection reports requires a lot of manual input and correction, which is time-consuming and labor-intensive. It is also prone to errors and it is difficult to maintain consistency of information. In the industrial sector, in particular, there is a demand for fast and accurate production of inspection reports, and a system as efficient as the creation of referrals is required.

[1259] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1260] In this invention, the server includes means for inputting basic patient information and medical records, means for transmitting the basic patient information and medical records to the server, means for analyzing the received information and generating a referral letter, means for adapting the generated referral letter to a unified format and performing error checks, means for checking and correcting the referral letter on a terminal, means for electronically transmitting the final referral letter to a medical institution and storing it, means for inputting inspection data of industrial products and transmitting it to the server, means for analyzing the received inspection data of industrial products and generating an inspection report, means for adapting the generated inspection report to a unified format and performing error checks, means for checking and correcting the inspection report on a terminal, and means for electronically transmitting the final inspection report to a relevant department and storing it, thereby enabling efficient and accurate generation of referral letters and inspection reports in medical institutions and industrial fields.

[1261] "Basic patient information" refers to basic personal information necessary to identify a patient, such as the patient's name, age, gender, and contact information.

[1262] "Medical records" refers to medical-related information such as details of examinations, prescriptions, treatment history, and test results conducted for patients at medical institutions.

[1263] "Server" means a central processing unit for receiving, analyzing, storing, and transmitting data over a network.

[1264] A "referral letter" is an official document created to refer a patient to another medical institution or specialist.

[1265] A "uniform format" is a standard for organizing documents and data into a specific standard format.

[1266] "Error checking" is the process of verifying the accuracy of documents or data and detecting typographical errors or incomplete information.

[1267] A "terminal" is a device for inputting, displaying, and modifying data. Examples include personal computers and tablets.

[1268] "Industrial product inspection data" refers to data on the quality and performance of inspected items collected at factories and manufacturing sites.

[1269] "Inspection report" means an official document summarizing the results of an inspection and including conclusions regarding the quality and performance of the inspected item.

[1270] "Related departments" are internal departments related to the inspection results and quality control of industrial products.

[1271] These definitions provide clearer explanations of key terms found in the claims and facilitate a better understanding of the invention.

[1272] The present invention is a system for efficiently creating letters of introduction and inspection reports for industrial products. Specific embodiments of this system will be described below.

[1273] First, the user (doctor or medical staff) enters the patient's basic information and medical records into a dedicated terminal. This terminal is linked to the electronic health record system (EHR) and can automatically retrieve the necessary medical records. For example, if a doctor enters "Patient A, 45 years old, male, lower back pain" into the terminal, this information will automatically retrieve detailed medical records from the EHR system.

[1274] The device then packages the entered data and retrieved medical records and sends them to a server over a secure, encrypted channel, preserving patient privacy and data integrity.

[1275] The server analyzes the received data and generates a referral letter using natural language processing (NLP) technology. Specifically, the server inputs the necessary information into the AI ​​model and drafts an appropriate referral letter based on the patient's symptoms and treatment details. For example, based on the information "Patient A, 45 years old, chronic lower back pain, undergoing rehabilitation," it generates a sentence such as "Patient A suffers from chronic lower back pain and is currently undergoing rehabilitation and drug treatment. A referral to an orthopedic surgeon is required for further detailed examination."

[1276] The generated referral letter is adapted to a unified format by the server. A template engine is used to embed the generated text into a standard document. An error-checking program is then launched to detect typos and missing information. For example, the sentence "Patient A suffers from chronic lower back pain" is automatically checked for typos and missing information.

[1277] Next, the user (doctor or medical staff) checks the generated referral letter on the terminal. They check the contents of the referral letter on the screen and manually correct it if necessary. For example, they can enter additional information such as "An additional consultation with an orthopedic surgeon is recommended."

[1278] Finally, the terminal electronically sends the final referral letter to the designated medical institution via the server. The transmission is done via email or a medical information exchange system. At the same time, the server stores a copy of the generated referral letter in a database for future reference or reuse. For example, the terminal sends the referral letter to the email address of the orthopedic department, and the server stores the referral letter in the database.

[1279] A similar process can also be used to automatically generate inspection reports using inspection data from industrial products. Inspectors use their smartphones to input inspection data and send it to a server. The server analyzes the received data and generates an appropriate inspection report. The generated inspection report is then adapted to a unified format and checked for errors. The inspector reviews the report and makes any necessary corrections. Finally, the report is electronically sent to the relevant department and stored.

[1280] As a specific example, if an inspector enters data for item ID "12345" and records it as "Passed," the server can analyze this data and generate an inspection report such as "Inspection results performed by inspector A, item ID: 12345, result: Passed."

[1281] Examples of prompts to input to a generative AI model include:

[1282] Inspection Data:

[1283] Inspector: Inspector A

[1284] Item ID: 12345

[1285] Result: Passed

[1286] Based on this, generate an inspection report in the following format:

[1287] ----------------------

[1288] Inspection Report:

[1289] Inspector: Inspector A

[1290] Item ID: 12345

[1291] Test result: Passed

[1292] ----------------------

[1293] In this way, the system of the present invention streamlines the process of creating referral letters and test reports, ensuring accuracy and consistency of information, thereby helping to improve operational efficiency and quality in medical institutions and industrial fields.

[1294] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1295] Step 1: User enters basic information and medical records

[1296] The user (doctor or medical staff) uses the terminal to input basic information and medical records about the patient. For example, the information input in this step might be "Patient A, 45 years old, male, with lower back pain." The terminal is linked to the electronic medical record system (EHR), and automatically obtains detailed medical records based on the input information. The input data is temporarily stored in the terminal.

[1297] Step 2: The device sends the data to the server

[1298] The terminal compiles the entered basic patient information and the acquired medical records, packages them, and sends them to the server. The transmission is carried out via a secure, encrypted communication channel. The input data is encrypted before being sent to the server. This ensures patient privacy and data integrity.

[1299] Step 3: The server analyzes the data and generates a referral.

[1300] The server analyzes the received data and generates a referral letter using natural language processing (NLP) technology. Based on the input basic information and medical records, the AI ​​model generates an appropriate draft referral letter. For example, from the data "Patient A, 45 years old, chronic back pain, undergoing rehabilitation," a referral letter may be generated that reads, "Patient A suffers from chronic back pain and is currently undergoing rehabilitation and drug treatment. He or she will need to be referred to an orthopedic surgeon for further detailed examination." The generated referral letter is stored on the server.

[1301] Step 4: The server standardizes the format of the letters of introduction and checks for errors

[1302] The server uses a template engine to adapt the generated letter of introduction to a standardized format. The generated text is converted into a standard document format. An error check program is then run to detect typos and missing information in the letter of introduction. Once the error check is complete, the letter of introduction is saved back to the server.

[1303] Step 5: User confirms and modifies the referral letter

[1304] The user (doctor or medical staff) uses the terminal to check the generated referral letter. They check the details of the referral letter displayed on the screen and manually correct it if necessary. For example, they can enter additional information such as "An orthopedic surgeon recommends an additional consultation." The corrected referral letter is temporarily saved on the terminal.

[1305] Step 6: The device sends and saves the final version of the introduction letter

[1306] After confirmation and correction, the final referral letter is sent electronically to the designated medical institution via the server. The transmission route is email or a medical information exchange system. At the same time, the server stores a copy of the generated and sent referral letter in a database for future reference and reuse.

[1307] Step 7: Inspectors enter the inspection data for industrial products

[1308] Inspectors use their smartphones to input inspection data for industrial products. For example, the item ID is "12345" and the inspection result is "Passed." The input data is temporarily saved on the smartphone.

[1309] Step 8: The smartphone sends the data to the server

[1310] The smartphone packages the entered test data and sends it to the server via a secure, encrypted communication channel. The entered data is sent to the server in encrypted form and stored there.

[1311] Step 9: The server analyzes the inspection data and generates an inspection report.

[1312] The server analyzes the received inspection data and generates an inspection report. Based on the input inspection data, the AI ​​model generates an appropriate draft inspection report. For example, based on the data item ID "12345" and inspection result "Passed," an inspection report such as "Inspection results performed by inspector A, item ID: 12345, result: Passed" is generated. The generated report is saved on the server.

[1313] Step 10: The server unifies the format of the inspection report and checks for errors.

[1314] The server uses a template engine to adapt the generated inspection report to a standardized format. The generated text is converted into a standard document format. An error check program is then run to detect typographical errors and missing information in the inspection report. Once the error check is complete, the report is saved back to the server.

[1315] Step 11: Inspector reviews and corrects the inspection report

[1316] The inspector uses a smartphone to check the generated inspection report. He / she carefully reviews the contents of the inspection report displayed on the screen and manually corrects them as necessary. For example, he / she enters additional information such as "additional quality checks are required." The corrected inspection report is temporarily saved on the smartphone.

[1317] Step 12: Your smartphone sends and saves the final inspection report

[1318] After confirmation and corrections are completed, the final inspection report is sent electronically to the relevant department via the smartphone server, via email or the internal information exchange system. At the same time, the server stores a copy of the generated and sent inspection report in a database for future reference and reuse.

[1319] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1320] The present invention combines a system for streamlining the automatic creation of referral letters with an emotion engine that recognizes the user's emotions. This system supports the process of inputting and analyzing basic patient information and medical records, generating and sending referral letters, and further adjusts the process by taking the user's emotional state into consideration. Specific embodiments of this system are described below.

[1321] Entering and submitting patient data

[1322] First, the user (doctor or medical staff) enters the patient's basic information and medical records into a dedicated terminal. This terminal is linked to the electronic health record system (EHR), and the necessary medical records can be automatically retrieved. For example, if a doctor enters "Yamada Taro, 45 years old, male, lower back pain" into the terminal, this information will automatically retrieve detailed medical records from the EHR system.

[1323] The device then packages the entered data and retrieved medical records and sends them to a server over a secure, encrypted channel, preserving patient privacy and data integrity.

[1324] Information analysis and referral generation

[1325] The server analyzes the received data and generates a referral letter using natural language processing (NLP) technology. Specifically, the necessary information is input into the AI ​​model, which then drafts an appropriate referral letter based on the patient's symptoms and treatment details. For example, based on the information "Yamada Taro, 45 years old, chronic back pain, currently undergoing rehabilitation," it generates a sentence such as "Yamada Taro suffers from chronic back pain and is currently undergoing rehabilitation and drug treatment. A referral to an orthopedic surgeon for further detailed examination is required."

[1326] Formatting consistency and error checking

[1327] The generated letter of introduction is then adapted to a unified format by the server. A template engine is used to embed the generated text into a standard document. An error-checking program is then launched to detect typos and missing information. For example, the sentence "Taro Yamada suffers from chronic back pain" is automatically checked for typos and missing information.

[1328] Use of emotion engine

[1329] Next, when the user (doctor or medical staff) checks the referral letter generated on the device, the emotion engine analyzes the user's voice and facial expressions to understand their emotional state, such as stress or fatigue. If the emotion engine detects a situation where an error is likely to occur, such as when the user is overly tired, it automatically displays a warning. The content and display format of the referral letter are also adjusted to allow the user to work in a relaxed state.

[1330] Checking and correcting the letter of introduction

[1331] After receiving feedback from the emotion engine, the user can review the referral generated on their device and manually correct it if necessary, for example by adding additional information such as "An additional consultation with an orthopedic surgeon is recommended."

[1332] Send and save referrals

[1333] Finally, the terminal electronically sends the final referral letter to the designated hospital or medical institution via the server. The transmission is done via email or a medical information exchange system. At the same time, the server stores a copy of the generated referral letter in a database for future reference or reuse. For example, the terminal sends the referral letter to the email address of an orthopedic department, and the server stores the referral letter in a database.

[1334] In this way, the system of the present invention streamlines the referral process, ensuring accuracy and consistency of information while optimizing the process by taking into account the user's emotional state, thereby improving the quality of patient care and supporting the operational efficiency of medical institutions.

[1335] The processing flow will be explained below.

[1336] Step 1:

[1337] The user (doctor or medical staff) uses the terminal to enter basic patient information, such as name, age, sex, address, contact information, medical history, current diagnosis information, and details of symptoms.

[1338] Step 2:

[1339] Based on the information entered, the device will automatically retrieve medical records in conjunction with the electronic health record system (EHR), using an API to obtain the necessary medical data.

[1340] Step 3:

[1341] The terminal packages the entered patient information and the acquired medical records into a single data packet, formats the data, and prepares the packet for transmission to the server.

[1342] Step 4:

[1343] The device encrypts the data packet and sends it to the server over a secure channel, calling an API to send the data.

[1344] Step 5:

[1345] The server analyzes the received data packets and generates a referral letter using natural language processing (NLP) technology. The information is input into an AI model, which automatically generates a letter that corresponds to the patient's symptoms and treatment details.

[1346] Step 6:

[1347] The server adapts the generated letters of introduction to a uniform format and uses a template engine to embed the generated text into a template document.

[1348] Step 7:

[1349] The server runs an error check program to detect typos, incorrect information, missing information, etc. in the generated letter of introduction, thereby confirming the accuracy of the information.

[1350] Step 8:

[1351] The user (doctor or medical staff) checks the referral letter generated on the terminal, checks the contents of the referral letter on the screen, and manually corrects them if necessary.

[1352] Step 9:

[1353] While the user is checking the referral letter, the device runs an emotion engine that analyzes the user's voice and facial expressions to detect emotional states such as stress and fatigue.

[1354] Step 10:

[1355] If the emotion engine detects stress or fatigue, the device will display a warning to the user, such as a message saying, "Take a break to reduce stress."

[1356] Step 11:

[1357] Based on feedback from the emotion engine, the device will adjust the content and presentation of the referral letter according to the user's emotional state, for example, changing it to a simpler, more understandable format for a user who is overly tired.

[1358] Step 12:

[1359] The terminal then reviews the revised letter of introduction again and converts the final version into a printable format, such as PDF, and sends it to the printer.

[1360] Step 13:

[1361] The terminal then electronically sends the final referral letter to the designated hospital or medical institution via the server, either by email or via a medical information exchange system.

[1362] Step 14:

[1363] The server stores a copy of the generated referral in its database, where it is stored for future reference and reuse.

[1364] In this way, not only is the referral process more efficient, but the task is optimized taking into account the user's emotional state, improving the quality of patient care and helping medical institutions work more efficiently.

[1365] Example 2

[1366] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1367] With conventional referral letter creation systems, when creating referral letters using basic patient information and medical records, it was difficult to ensure the accuracy and consistency of the information, and if the user was tired or stressed, there was a high possibility of typos or incomplete information.In addition, because the referral letter creation process was done manually, there were problems such as reduced work efficiency and a heavy burden on medical staff.

[1368] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for inputting basic information and medical records of a patient; means for transmitting the basic information and medical records of the patient to an information system; means for analyzing the received information and generating a referral letter using natural language processing technology; means for adapting the generated referral letter to a unified format and performing error checking; means for analyzing the emotional state of the user and providing feedback; means for checking and correcting the referral letter on a terminal; and means for electronically transmitting and storing the final referral letter to the medical institution. This improves the efficiency of the automatic referral letter generation process and enables optimization of the work taking into account the emotional state of the user while maintaining the accuracy and consistency of the information.

[1369] "Basic patient information" refers to basic personal information about the patient, such as the patient's name, age, sex, address, contact information, and chief complaint.

[1370] "Medical records" refers to all records related to medical treatment at medical institutions, such as a patient's medical history, test results, prescription history, surgical history, allergy information, and treatment plans.

[1371] "Information systems" refer to computer systems, such as electronic health records (EHRs) and healthcare information systems (HISs), that digitally manage and operate patient medical records and basic information.

[1372] "Natural language processing technology" refers to technology that enables computers to understand, analyze, and generate human language. Examples include text analysis, speech recognition, and language generation.

[1373] A "referral document" is an official document containing the information necessary for a patient to be referred to another medical institution or specialist.

[1374] A "template engine" is a software tool for embedding dynamic data into boilerplate documents, and includes libraries such as Jinja2 and Handlebars.

[1375] An "error checking program" is a software program that automatically detects typographical errors, grammatical errors, missing information, etc. in a document.

[1376] The "emotion engine" is an analytical engine that analyzes voice, facial expressions, movements, etc. to evaluate the user's emotional state (stress, fatigue, relaxation, etc.).

[1377] The "means for providing feedback" is a means for reporting the status to the user based on the data obtained from the emotion engine and presenting appropriate countermeasures or warnings.

[1378] "Device" means a computing device, including a PC or tablet, on which a User enters information and / or reviews or modifies the generated referral document.

[1379] "Electronic transmission means" refers to a means for securely transmitting the generated referral document to the designated medical institution via email or a medical information exchange system.

[1380] The "means for storing" refers to a means for storing the generated introduction document in a storage system such as a database for future reference or reuse.

[1381] The present invention combines a system for streamlining the automatic creation of referral letters with an emotion engine that recognizes the user's emotions. This system supports the process of inputting and analyzing basic patient information and medical records, generating and sending referral letters, and further adjusts the process by taking the user's emotional state into consideration. Specific embodiments of this system are described below.

[1382] Entering and submitting patient data

[1383] First, the user (doctor or medical staff) enters the patient's basic information and medical records into a dedicated terminal. This terminal is linked to the electronic health record system (EHR) and can automatically retrieve the necessary medical records. For example, if a doctor enters "Yamada Taro, 45 years old, male, lower back pain" into the terminal, this information will automatically retrieve detailed medical records from the EHR system.

[1384] The terminal packages the entered data and the acquired medical records and sends them to the server over a secure channel (e.g., SSL / TLS protocol) after encrypting the data, thereby preserving patient privacy and data integrity.

[1385] Information analysis and referral generation

[1386] The server analyzes the received data and generates a referral letter using natural language processing (NLP) technology. Specifically, the necessary information is input into a generative AI model (e.g., GPT-3), which then drafts an appropriate referral letter based on the patient's symptoms and treatment details. For example, based on the information "Yamada Taro, 45 years old, chronic back pain, currently undergoing rehabilitation," it generates a sentence such as "Yamada Taro suffers from chronic back pain and is currently undergoing rehabilitation and drug treatment. He needs to be referred to an orthopedic surgeon for further detailed examination."

[1387] Formatting consistency and error checking

[1388] The generated letter of introduction is then adapted to a unified format by the server. Specifically, a template engine (e.g., Jinja2) is used to embed the generated text into a standard document. An error-checking program is then launched to detect typos and missing information. For example, the sentence "Yamada Taro suffers from chronic back pain" is automatically checked for typos and missing information.

[1389] Use of emotion engine

[1390] Next, when the user (doctor or medical staff) checks the referral letter generated on the device, the emotion engine analyzes the user's voice and facial expressions to understand their emotional state, such as stress or fatigue. If the emotion engine detects a situation where an error is likely to occur, such as when the user is overly tired, it automatically displays a warning. The content and display format of the referral letter are also adjusted to allow the user to work in a relaxed state.

[1391] Checking and correcting the letter of introduction

[1392] After receiving feedback from the emotion engine, the user can review the referral generated on their device and manually correct it if necessary, for example by adding additional information such as "An additional consultation with an orthopedic surgeon is recommended."

[1393] Send and save referrals

[1394] Finally, the terminal electronically sends the final referral letter to the designated hospital or medical institution via the server. This can be done via email or a healthcare information exchange (HIE). At the same time, the server stores a copy of the generated referral letter in a database for future reference or reuse. For example, the terminal sends the referral letter to the orthopedic department's email address, and the server stores it in the database.

[1395] Specific examples

[1396] Here is an example of a prompt that can be fed into a generative AI model to automatically generate a referral letter:

[1397] "Yamada Taro, 45 years old, male, chronic lower back pain, currently undergoing rehabilitation. Referred to orthopedic clinic."

[1398] For this input, the resulting letter of introduction might look like this:

[1399] "Yamada Taro suffers from chronic back pain and is currently undergoing rehabilitation and medication. He will need to be referred to an orthopedic surgeon for further detailed examination."

[1400] In this way, the system of the present invention streamlines the referral process, ensuring accuracy and consistency of information while optimizing the process by taking into account the user's emotional state, thereby improving the quality of patient care and supporting the operational efficiency of medical institutions.

[1401] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1402] Step 1: Enter patient demographics and medical records

[1403] The user (doctor or medical staff) enters the patient's basic information (name, age, sex, symptoms, etc.) into a dedicated terminal. The terminal uses this information to retrieve detailed medical records from the electronic health record system (EHR). Specifically, when the user enters "Yamada Taro, 45 years old, male, lower back pain" into the terminal, the "medical record for lower back pain" is automatically retrieved from the EHR based on this information. The entered basic information and medical records are compiled on the terminal.

[1404] Input: Patient's basic information ("Taro Yamada, 45 years old, male, lower back pain")

[1405] Output: Patient basic information and medical record ("Medical record of low back pain")

[1406] Step 2: Packaging and sending the data

[1407] The terminal compiles the entered basic information and the acquired medical records into a data package. The packaged data is then encrypted and sent to the server. The SSL / TLS protocol is generally used for encryption. Specifically, the terminal compiles data in a format such as "Yamada Taro, 45 years old, male, lower back pain, medical record for lower back pain," encrypts it, and sends it to the server.

[1408] Input: Patient demographics and medical records

[1409] Output: Encrypted data package

[1410] Step 3: Analyze the data

[1411] The server decrypts the received encrypted data package and analyzes the data. Natural language processing (NLP) technology is used for the analysis. Specifically, the server analyzes the information "Yamada Taro, 45 years old, suffering from chronic back pain, currently undergoing rehabilitation" and prepares to send the necessary information to the generative AI model.

[1412] Input: Encrypted data package

[1413] Output: Parsed patient information

[1414] Step 4: Generate introductory documentation

[1415] Based on the analyzed data, the server inputs a prompt into a generative AI model (e.g., GPT-3) to generate an introductory document. Specifically, the server inputs the following prompt into the generative AI model:

[1416] "Yamada Taro, 45 years old, suffering from chronic lower back pain, currently undergoing rehabilitation. Referred to an orthopedic surgeon."

[1417] The referral letter obtained from the generative AI model read something like, "Taro Yamada suffers from chronic lower back pain and is currently undergoing rehabilitation and medication. He needs to be referred to an orthopedic surgeon for further detailed examination."

[1418] Input: Parsed patient information

[1419] Output: Generated introductory document

[1420] Step 5: Formatting and Error Checking

[1421] The server adapts the generated referral document to a unified format using a template engine (e.g., Jinja2). Furthermore, an error-checking program is launched to detect typos and missing information. Specifically, the server adapts the generated referral document to a "standard referral template" and checks for errors using a grammar checker.

[1422] Input: Generated referral document

[1423] Output: Introduction document and error report in a unified format

[1424] Step 6: Analysis and feedback by the emotion engine

[1425] When a user checks the referral letter generated on the device, the emotion engine installed on the device analyzes the user's voice and facial expressions. The emotion engine grasps the user's emotional state, such as stress and fatigue, and displays a warning if excessive fatigue is detected. Specifically, while the user is checking the referral letter, the emotion engine analyzes stress and fatigue in real time and displays a message such as "Please relax and check" as necessary.

[1426] Input: User's voice and facial expressions

[1427] Output: Feedback based on emotional state

[1428] Step 7: Review and correct the referral letter

[1429] The user can review the generated referral letter on the device and manually correct it if necessary. Specifically, the user can enter additional information, such as "An additional consultation with an orthopedic surgeon is recommended."

[1430] Input: Generated referral letter, user correction input

[1431] Output: Final letter of introduction

[1432] Step 8: Send and save your referral

[1433] The terminal then electronically sends the final referral letter to the designated medical institution via the server. This is done via email or a healthcare information exchange (HIE). At the same time, the server stores a copy of the generated referral letter in a database for future reference or reuse. Specifically, the terminal sends the referral letter to the orthopedic department's email address, and the server stores it in the database.

[1434] Input: Final version of the referral letter

[1435] Output: Referrals sent, Referrals saved

[1436] In this way, the automated referral letter generation system provides a streamlined process, maintaining accuracy of information while optimizing the process by taking into account the user's emotional state.

[1437] (Application example 2)

[1438] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1439] While conventional referral letter creation systems have successfully automated the process from patient information entry to referral letter generation, they do not take into account the user's emotional state, which means there is a risk of users entering incorrect information if they are tired or stressed. Furthermore, they require user expertise to generate accurate and consistent referral letters, and effective support is not provided. This can lead to reduced referral letter quality and work efficiency, which can affect the quality of patient care.

[1440] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting basic information and medical records of a patient; means for transmitting the basic information and medical records of the patient to the server; means for analyzing the received information and generating a referral letter; means for adapting the generated referral letter to a unified format and performing error checking; means for confirming and correcting the referral letter on a terminal; means for electronically transmitting and storing the final referral letter to the medical institution; and means for analyzing the user's emotional state using an emotion recognition engine and adjusting the work process. This enables accurate and unified referral letters to be generated even when the user is tired or stressed, thereby improving work efficiency and the quality of patient care.

[1441] "Basic patient information" refers to personal identification information such as the patient's name, age, sex, and contact information.

[1442] "Medical records" are all information related to medical care, such as a patient's medical history, diagnosis, treatment, and notes made by medical staff.

[1443] "Server" means a computer system that centrally manages and processes data, and has the function of analyzing received information and generating letters of introduction.

[1444] A "letter of referral" is a document used to refer a patient to another medical institution, and contains details of the patient's condition, treatment progress, and any necessary tests or treatments.

[1445] A "uniform format" is a standardized form used to standardize the format and wording of letters of introduction.

[1446] "Error checking" is the process of automatically detecting and correcting typos and missing information in the generated letters of introduction.

[1447] A "terminal" is a computer or smart device that a user uses to perform operations such as input, confirmation, and correction.

[1448] An "emotion recognition engine" is software that analyzes a user's emotional state (e.g., stress, fatigue, etc.) from their voice and facial expressions, and provides feedback on the results.

[1449] "Work process adjustment" refers to optimizing the user's work environment and work procedures based on the analysis results of the emotion recognition engine.

[1450] The present invention combines a system for streamlining the automatic generation of referral letters with an emotion engine that recognizes the user's emotions. This system supports the process of inputting and analyzing basic patient information and medical records, generating and sending referral letters, and adjusting the process taking into account the user's emotional state.

[1451] Entering and submitting patient data

[1452] First, the user (doctor or medical staff) enters the patient's basic information and medical records into the terminal. This terminal is linked to the electronic health record system (EHR) and can automatically retrieve the necessary medical records. For example, if a doctor enters "XX, 45 years old, male, lower back pain" into the terminal, this information automatically retrieves detailed medical records from the EHR system. Next, the terminal packages the entered data and the retrieved medical records and sends them to the server. The transmission is carried out over a secure, encrypted channel, maintaining patient privacy and data integrity.

[1453] Information analysis and referral generation

[1454] The server analyzes the received data and generates a referral letter using natural language processing (NLP) technology. For example, the AI ​​model can input information such as "X, 45 years old, chronic back pain, currently undergoing rehabilitation," and draft an appropriate referral letter based on the patient's symptoms and treatment. Specifically, it generates a sentence such as, "X suffers from chronic back pain and is currently undergoing rehabilitation and drug treatment. A referral to an orthopedic surgeon is required for further detailed examination."

[1455] Formatting consistency and error checking

[1456] The generated letter of introduction is then adapted to a unified format by the server. A template engine is used to embed the generated text into a standard document. An error-checking program is then launched to detect typos and missing information. For example, the text "Mr. / Ms. X suffers from chronic back pain" is automatically checked for typos and missing information.

[1457] Use of emotion engine

[1458] Next, when the user (doctor or medical staff) checks the referral letter generated on the device, the emotion engine analyzes the user's voice and facial expressions to understand their emotional state, such as stress or fatigue. If the emotion engine detects a situation where an error is likely to occur, such as when the user is overly tired, it automatically displays a warning. The content and display format of the referral letter are also adjusted to allow the user to work in a relaxed state.

[1459] Checking and correcting the letter of introduction

[1460] After receiving feedback from the emotion engine, the user can review the referral generated on their device and manually correct it if necessary, for example, by adding additional information such as "An additional consultation with an orthopedic surgeon is recommended."

[1461] Send and save referrals

[1462] Finally, the terminal electronically sends the final referral letter to the designated hospital or medical institution via the server. The transmission is done via email or a medical information exchange system. At the same time, the server stores a copy of the generated referral letter in a database for future reference or reuse. For example, the terminal sends the referral letter to the email address of an orthopedic department, and the server stores the referral letter in a database.

[1463] Hardware and software used

[1464] Hardware:

[1465] Terminal (computer operated by the user)

[1466] Server (a computer system that centrally manages and processes data)

[1467] software:

[1468] Electronic Health Record System (EHR)

[1469] Emotion Recognition Engine (EmotionAnalyzer)

[1470] Natural Language Processing (NLP)

[1471] Template Engine

[1472] Error Checking Program

[1473] Examples of specific examples and prompts

[1474] For example, suppose a worker is wearing smart glasses. The prompt is, "Emotion Identification: Enter a facial image to analyze the stress and fatigue levels." This prompt allows the worker to enter the work data appropriately, and the emotion recognition engine can analyze the user's emotional state.

[1475] Specific prompt examples:

[1476] "Enter facial image data and analyze stress and fatigue levels. The model used is EmotionAnalyzer."

[1477] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1478] Step 1: The user enters the patient's basic information and medical records into the terminal.

[1479] The user enters basic information such as the patient's name, age, sex, and medical condition, as well as medical records, into the terminal. This input information is linked to the electronic health record system (EHR), and detailed medical records are automatically obtained. The input data is temporarily stored within the terminal.

[1480] Step 2: The device sends the patient's basic information and medical records to the server.

[1481] The terminal sends the entered basic patient information and medical records to the server via a secure encrypted channel. The input data is encrypted, packetized, and sent to the server.

[1482] Step 3: The server analyzes the received information and generates a referral.

[1483] The server analyzes the received patient's basic information and medical records. Using natural language processing (NLP) technology within the server, this information is input into an AI model, which then drafts an appropriate referral letter based on the patient's symptoms and treatment. For example, a referral letter can be generated using information such as "XX, 45 years old, chronic back pain, currently undergoing rehabilitation."

[1484] Step 4: The server adapts the generated letter of introduction to a unified format and performs error checking.

[1485] The generated letters of introduction are then adapted to a unified format by the server. A template engine is used to embed the generated text into a standardized format. An error-checking program is also initiated to automatically detect typos and missing information and identify areas that need correction.

[1486] Step 5: Analyze the user's emotional state using an emotion recognition engine and adjust the work process

[1487] The user checks the referral letter generated on the device, and the emotion recognition engine analyzes the user's voice and facial expressions to understand their emotional state, such as stress or fatigue. For example, the system may display feedback such as, "High stress or fatigue has been detected. A break is recommended."

[1488] Step 6: User reviews the referral and makes any necessary corrections

[1489] After receiving feedback from the emotion recognition engine, the user can review the generated letter of introduction on their device, manually correct it if necessary, enter additional information or corrections, and finally confirm the letter of introduction.

[1490] Step 7: The terminal electronically sends the final referral letter to the medical institution via the server and stores it.

[1491] Finally, the terminal electronically sends the final referral letter to the designated hospital or medical institution via the server. The transmission is done via email or a medical information exchange system, and the server also simultaneously stores a copy of the generated referral letter in a database. For example, the terminal sends the referral letter to the email address of an orthopedic department, and the server stores the referral letter in a database.

[1492] 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.

[1493] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1494] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1495] 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.

[1496] FIG. 9 is a diagram illustrating 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 actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect 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.

[1497] 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.

[1498] 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).

[1499] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1500] 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."

[1501] 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.

[1502] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1503] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1504] 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.

[1505] 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.

[1506] 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.

[1507] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[1508] The hardware resource that executes the specific processing 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 processing may be a single processor.

[1509] 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.

[1510] 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.

[1511] 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.

[1512] 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.

[1513] The following is further disclosed regarding the above embodiment.

[1514] (Claim 1)

[1515] a means of inputting patient demographics and medical records;

[1516] means for transmitting the basic information and medical records of the patient to a server;

[1517] means for analyzing the received information and generating a letter of introduction;

[1518] means for adapting the generated letters of introduction to a unified format and for error checking;

[1519] means for checking and correcting the letter of introduction on a terminal;

[1520] A means to electronically transmit and store the final referral to the healthcare provider;

[1521] A system including:

[1522] (Claim 2)

[1523] 10. The system of claim 1, which interfaces with an electronic medical record system to capture patient information.

[1524] (Claim 3)

[1525] 10. The system of claim 1, further comprising means for encrypting and transmitting the generated referral letter.

[1526] "Example 1"

[1527] (Claim 1)

[1528] a means of inputting patient demographics and medical records;

[1529] means for transmitting the patient's basic information and medical records to a server;

[1530] means for analyzing the received information and generating a letter of introduction using natural language processing technology;

[1531] means for adapting the generated letters of introduction to a unified format and for error checking;

[1532] means for checking and correcting the letter of introduction on a terminal;

[1533] A means to electronically transmit and store the final referral to the healthcare provider;

[1534] a means for linking with an electronic medical record system to automatically retrieve patient information;

[1535] a means for encrypting and transmitting the generated letter of introduction;

[1536] A system including:

[1537] (Claim 2)

[1538] 10. The system of claim 1, wherein communication occurs over an encrypted, secure channel.

[1539] (Claim 3)

[1540] 10. The system of claim 1, including a generative AI model for generating referral letters using natural language processing techniques.

[1541] "Application Example 1"

[1542] (Claim 1)

[1543] a means of inputting patient demographics and medical records;

[1544] means for transmitting the basic information and medical records of the patient to a server;

[1545] means for analyzing the received information and generating a letter of introduction;

[1546] means for adapting the generated letters of introduction to a unified format and for error checking;

[1547] means for checking and correcting the letter of introduction on a terminal;...

Claims

1. a means of inputting patient demographics and medical records; means for transmitting the basic information and medical records of the patient to a server; means for analyzing the received information and generating a letter of introduction; means for adapting the generated letters of introduction to a unified format and for error checking; means for checking and correcting the letter of introduction on a terminal; A means to electronically transmit and store the final referral to the healthcare provider; A system including:

2. 10. The system of claim 1, which interfaces with an electronic medical record system to capture patient information.

3. 10. The system of claim 1, further comprising means for encrypting and transmitting the generated referral.

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