System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving medical information comprising personal information; means for automatically anonymizing the received medical information based on Personal Information Protection Law; and means for storing the anonymized medical information in a secure database.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] In today's big data era, there is a demand for the use of medical information, but the Personal Information Protection Act imposes strict restrictions on the handling of medical information such as medical history. This has hindered the use of big data in the medical industry. This has restricted medical and pharmaceutical research, such as the development of effective treatments and early detection of disease. Furthermore, there are practical issues, such as the need for advanced specialized knowledge to perform specialized data analysis, which takes a lot of time and effort. [Means for solving the problem]

[0005] This invention provides a system for anonymizing medical information, including personal information, and safely collecting, analyzing, and viewing that anonymized medical information. Specifically, the proposed system includes a means for receiving medical information, including personal information, a means for automatically anonymizing the received medical information in accordance with the Personal Information Protection Act, and a means for storing the anonymized medical information in a secure database. The system also includes a means for searching for anonymized medical information, a means for analyzing the searched medical information, a means for displaying the analysis results, a means for generating answers to questions based on the analysis, and a means for storing a history of questions and answers. This system enables the safe use of medical information and promotes medical and pharmaceutical research.

[0006] "Medical information" refers to any information relating to an individual's health, including patient records, medical history, diagnosis, treatment, medication, and test results.

[0007] The Personal Information Protection Act is a Japanese law that sets basic rules for handling personal information and protects individuals' right to privacy.

[0008] "Anonymization" refers to processing or deleting part of data so that it is no longer possible to identify a specific individual.

[0009] "Means for receiving" refers to the function for acquiring information from the outside and inputting it into a state that can be processed within the system.

[0010] "Means for anonymization" refers to a function that automatically processes received medical information to prevent individuals from being identified.

[0011] "Storage means" refers to the functionality for safely and efficiently storing processed data.

[0012] A "database" is a collection of information that stores data in a structured format and can be efficiently searched, managed, and updated.

[0013] "Search means" refers to a function for extracting necessary information from stored data based on specific conditions.

[0014] "Means of analysis" refers to functions that derive useful knowledge by performing statistical analysis, pattern recognition, correlation analysis, etc. based on the acquired data.

[0015] "Means for displaying" refers to a function for visually showing analysis results and search results to users.

[0016] "Means for generating answers to questions" refers to a function for automatically creating appropriate answers based on questions entered by users.

[0017] "Means for storing question and answer history" refers to the functionality for keeping a record of all questions and answers that take place within the system. [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] An embodiment of the present invention is configured as follows. This system is composed of two main AI tools that anonymize medical information and efficiently collect, analyze, and display that data. This allows for smooth utilization of medical information while complying with the Personal Information Protection Act.

[0040] System configuration

[0041] 1. Server

[0042] It is responsible for receiving medical information and anonymizing it.

[0043] The data received is anonymized and stored in a secure database.

[0044] 2. Terminal

[0045] It will be used by medical professionals and researchers and will be responsible for searching and analyzing anonymized data.

[0046] Visually display the analysis results and generate answers to questions.

[0047] Program processing overview and specific examples

[0048] Receiving and de-identifying medical information

[0049] The user (a terminal at a medical institution) inputs the patient's medical record information and medication information.

[0050] The terminal transmits the input medical information to the server.

[0051] The server stores the received medical information in temporary storage.

[0052] The server analyzes the data structure and identifies which parts correspond to personal information.

[0053] The server uses an AI algorithm to anonymize the data in accordance with the Personal Information Protection Act.

[0054] For example, suppose a patient's medical record information contains the data "Taro Tanaka, Tokyo, Shinjuku Ward, born April 5, 1965." In this case, the server processes it into the format "XX Tanaka, XX Ward, Tokyo, born April XX, 1965."

[0055] The server stores the de-identified medical information in a secure database.

[0056] Data exploration and analysis

[0057] Users (researchers and medical professionals) enter search criteria for anonymized medical information from their terminal.

[0058] The terminal sends a search request to the server.

[0059] The server extracts anonymously processed medical information that matches the search criteria from the database and returns it to the terminal.

[0060] The device analyzes the received data using AI tools.

[0061] For example, in response to a request to "analyze the medication data of diabetic patients," the server extracts the relevant data and generates analysis results on the terminal showing "the correlation between medication frequency and blood glucose levels in adult male diabetic patients."

[0062] The terminal visually displays the analysis results to the user as graphs and tables.

[0063] The user enters further questions based on the analysis results.

[0064] The device uses AI to generate answers to questions and display them to the user.

[0065] For example, if you ask a question such as "Regarding the development of new drugs that are effective in treating diabetes," the AI ​​will provide an appropriate answer based on relevant information.

[0066] Question and answer history management

[0067] The device securely stores a history of the user's questions and answers for future reference.

[0068] The above is an embodiment of the present invention. Based on this system, anonymization of medical information and its efficient utilization can be realized, contributing to the advancement of medical and pharmaceutical sciences.

[0069] The processing flow will be explained below.

[0070] Receiving and de-identifying medical information

[0071] Step 1:

[0072] The user (a terminal at a medical institution) inputs the patient's medical record information and medication information into the electronic medical record system.

[0073] Step 2:

[0074] The device sends the entered medical information to the server, where the data is encrypted using a secure communication protocol (e.g., HTTPS).

[0075] Step 3:

[0076] The server stores the received medical information in temporary storage.

[0077] Step 4:

[0078] The server analyzes the data structure of the medical information and automatically identifies data that needs to be anonymized in accordance with the Personal Information Protection Act (e.g., name, address, date of birth).

[0079] Step 5:

[0080] The server uses an AI algorithm to anonymize the received medical information. Specifically, the server processes personal information as follows:

[0081] Convert names to initials (e.g., Tanaka Taro → Tanaka T)

[0082] Remove or generalize address details (e.g., Shinjuku Ward to Shinjuku District)

[0083] Convert date of birth to age, or hide part of the date (e.g., born April 5, 1965 → born April XX, 1965)

[0084] Step 6:

[0085] The server stores the de-identified medical information in a secure database, which is also managed in a secure environment to prevent inappropriate access.

[0086] Data exploration and analysis

[0087] Step 1:

[0088] Users (researchers and medical professionals) enter search criteria for anonymized medical information on their terminal and execute a search request.

[0089] Step 2:

[0090] The terminal transmits the search request input by the user to the server.

[0091] Step 3:

[0092] The server extracts anonymously processed medical information that matches the search criteria from the database.

[0093] Step 4:

[0094] The server transmits the extracted data to the terminal.

[0095] Step 5:

[0096] The device then analyzes the anonymized medical information it receives using AI tools. Specifically, it performs the following analysis:

[0097] Statistical analysis (e.g., calculating the mean, median, and standard deviation)

[0098] Pattern recognition (e.g., disease trend analysis)

[0099] Correlation analysis (e.g., assessing the correlation between medication frequency and treatment effect)

[0100] Step 6:

[0101] The terminal visually displays the analysis results to the user as charts and graphs.

[0102] Question and answer generation and history management

[0103] Step 1:

[0104] The user inputs a follow-up question into the terminal based on the analysis results.

[0105] Step 2:

[0106] The device passes the input question to an AI model, which generates an appropriate answer.

[0107] Step 3:

[0108] The terminal displays the generated answer to the user.

[0109] Step 4:

[0110] The terminal stores a history of user questions and generated answers in a database.

[0111] These are the specific processing steps of the program, which will enable the anonymization of medical information and its efficient collection, search, and analysis.

[0112] Example 1

[0113] 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."

[0114] In conventional medical information management systems, anonymization processing, required by the Personal Information Protection Act, was largely manual and lacked efficiency. Searching and analyzing anonymized data also placed a heavy burden on users, making it difficult to respond quickly. Furthermore, the lack of a question-answering system based on analysis results meant that researchers and medical professionals needed time and effort to quickly extract useful information from the data they obtained. A system that solves these issues and allows for efficient and safe use of medical information is needed.

[0115] 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.

[0116] In this invention, the server includes a means for receiving medical information including personal information, a means for automatically anonymizing the received medical information in accordance with the Personal Information Protection Act, a means for storing the anonymized medical information in a secure database, a means for analyzing the data structure using an artificial intelligence algorithm during the anonymization process to identify personal identification information, and a means for providing storage for temporarily storing the anonymized data before storing it in the database, thereby enabling efficient anonymization and secure storage of medical information.

[0117] Furthermore, the server includes a means for searching the anonymized medical information, a means for analyzing the searched medical information using an artificial intelligence tool, and a means for visually displaying the analysis results, thereby enabling a user to easily search and analyze data and obtain visual results.

[0118] In addition, the server includes a means for generating answers to questions based on the analysis, a means for storing a history of questions and answers, and a means for providing appropriate answers to questions in natural language from users using the generative AI model, thereby enabling users to obtain interactive question and answer responses based on the analysis results and efficiently collect necessary information.

[0119] "Medical information including personal information" refers to medical data such as a patient's diagnosis, prescription details, and treatment history, including information that can be used to identify an individual.

[0120] "Means for receiving" refers to a combination of hardware and software that allows a server or a terminal to acquire data from the outside.

[0121] "Anonymization methods" are algorithms or processes that remove or transform personally identifiable information from received data so that it cannot be used to identify an individual.

[0122] "Means of storing data in a secure database" means a database equipped with encryption technology and access control mechanisms to protect data from unauthorized access or leakage.

[0123] "Artificial intelligence algorithms" are advanced algorithms, including machine learning and deep learning models, for data analysis and pattern recognition.

[0124] "Means for analyzing data structure" refers to a process that automatically analyzes data formats and fields to identify personally identifiable information.

[0125] "Temporary storage" refers to a memory or disk device that temporarily stores received data during processing.

[0126] A "searching means" is a query processing function for extracting data that matches specific criteria from a database.

[0127] "Artificial intelligence tools" refers to a group of software tools for efficiently analyzing large amounts of data, specifically including machine learning and statistical analysis libraries.

[0128] "Visual display means" refers to tools and technologies for presenting data analysis results to users in a visually easy-to-understand format, such as graphs or tables.

[0129] "Means for generating answers to questions based on analysis" refers to generative AI models and natural language processing technologies that answer users' questions based on analysis results and related information.

[0130] The "means for storing question and answer history" is a data management system for storing user-asked questions and their answers for future reference.

[0131] A "generative AI model" is an artificial intelligence model specially trained for text generation and question answering, and uses natural language processing to generate high-performance responses.

[0132] The present invention is a system for anonymizing medical information and efficiently collecting, analyzing, and displaying that data. This system is designed to facilitate the use of medical information while complying with the Personal Information Protection Act. Specific embodiments of the system are described in detail below.

[0133] System configuration

[0134] This system is mainly composed of two main components: a server and a terminal.

[0135] server

[0136] The server receives the medical information, anonymizes the personal information, and stores it in a secure database. The server uses the following technologies:

[0137] Receiving method: The server receives medical information from the medical institution's terminal using a secure communication protocol such as HTTPS.

[0138] De-identification methods: AI algorithms are used to analyze data structures to identify personally identifiable information, such as a patient's name, address, or date of birth, which is then de-identified.

[0139] Means of storage in a secure database: Anonymized data is stored in a secure database using encryption techniques (e.g., AES encryption).

[0140] Specifically, if the server receives the data "Taro Tanaka, Tokyo, Shinjuku Ward, born April 5, 1965," it processes it into the form "Tanaka XX, Tokyo, XX Ward, born April XX, 1965" and stores it in an anonymized database.

[0141] Terminal

[0142] The device will be used by medical professionals and researchers to search and analyze anonymized data. The device will use the following technologies:

[0143] Searching method: A UI is provided for the user to input search criteria, and the criteria are sent to the server.

[0144] Analysis using artificial intelligence tools: The received data is analyzed using AI tools such as TensorFlow and PyTorch.

[0145] Visual display: Display the analysis results as graphs and tables using Matplotlib or Tableau.

[0146] For example, if a user requests to "analyze the medication data of diabetic patients," the server extracts the relevant data and generates analysis results on the terminal that show the "correlation between medication frequency and blood glucose levels in adult male diabetic patients."

[0147] The device also uses generative AI models (e.g., GPT-3) to provide appropriate answers to questions posed by users in natural language. For example, if a user asks, "Regarding the development of new drugs effective in treating diabetes," the AI ​​will provide an appropriate answer based on relevant information.

[0148] Specific examples

[0149] Below is a concrete example of how the system works.

[0150] 1. The user enters the patient's medical record information into the terminal: "Taro Tanaka, Shinjuku Ward, Tokyo, born April 5, 1965, diabetes, prescribed metformin 500 mg."

[0151] 2. The device sends this information to the server.

[0152] 3. The server anonymizes the received information and adds it to a stored database.

[0153] 4. The user types "Search for medication data for diabetes patients" into the terminal.

[0154] 5. The server extracts the relevant data from the database and returns it to the terminal.

[0155] 6. The device analyzes the received data and generates a graph showing the correlation between the frequency of metformin use and blood glucose levels among diabetic patients.

[0156] 7. A user asks, "What about the development of new drugs that are effective in treating diabetes?"

[0157] 8. The device's AI generates an answer based on relevant research data and displays it to the user.

[0158] Prompt Sentence Examples

[0159] Below is an example of a prompt that a user can enter into a generative AI model.

[0160] "Please enter the medical record information for Taro Tanaka, born April 5, 1965, in Shinjuku Ward, Tokyo."

[0161] "Please set the medication data of diabetic patients as search criteria."

[0162] "Please provide specific information regarding the development of new drugs that are effective in treating diabetes."

[0163] The above is an embodiment of the present invention. This system enables efficient anonymization, search, analysis, and interactive question-and-answering of medical information, and is expected to contribute to the advancement of medical and pharmaceutical sciences.

[0164] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0165] Step 1:

[0166] The user inputs the patient's medical record information and medication information into the terminal.

[0167] Input: Patient's medical record information (e.g., "Taro Tanaka, Tokyo, Shinjuku Ward, born April 5, 1965, diabetes, prescribed metformin 500 mg")

[0168] Output: The entered medical information is saved on the device.

[0169] Specific operation: The user enters patient information on the input screen of the terminal, and the terminal temporarily stores the information.

[0170] Step 2:

[0171] The terminal transmits the input medical information to the server.

[0172] Input: Medical information stored on the device

[0173] Output: Medical information transferred to the server

[0174] Specific operation: The device sends the entered data to the server using a secure communication protocol (e.g., HTTPS).

[0175] Step 3:

[0176] The server stores the received medical information in temporary storage.

[0177] Input: Medical information transferred to the server

[0178] Output: Medical information saved in temporary storage

[0179] What happens: The server receives the data and immediately stores it in temporary storage (e.g., RAM or a fast SSD).

[0180] Step 4:

[0181] The server analyzes the data structure and identifies which parts correspond to personal information.

[0182] Input: Medical information stored in temporary storage

[0183] Output: List of identified personal information

[0184] How it works: The server uses AI algorithms (e.g., natural language processing tools) to analyze each field of data and identify personal information such as name, address, and date of birth.

[0185] Step 5:

[0186] The server uses an AI algorithm to anonymize data in accordance with the Personal Information Protection Act.

[0187] Input: List of identified personal information and medical information

[0188] Output: De-identified medical information

[0189] Specific operation: The server anonymizes the identified personal information by replacing it with a random string of characters, and processes "Taro Tanaka, Shinjuku Ward, Tokyo, born April 5, 1965" into "XX Tanaka, XX Ward, Tokyo, born April XX, 1965."

[0190] Step 6:

[0191] The server stores the de-identified medical information in a secure database.

[0192] Input: De-identified medical information

[0193] Output: De-identified medical information stored in a database

[0194] What it does: The server uses encryption technology (e.g., AES encryption) to store the anonymized data in a secure database.

[0195] Step 7:

[0196] The user inputs search conditions for anonymously processed medical information from the terminal.

[0197] Input: User search criteria (e.g., "Search for medication data for diabetes patients")

[0198] Output: Search terms entered on the terminal

[0199] Specific operation: The user enters search criteria into the search screen on the device, and the device prepares to send the criteria to the server.

[0200] Step 8:

[0201] The device sends a search request to the server.

[0202] Input: Search criteria entered into the terminal

[0203] Output: The search request forwarded to the server

[0204] Specific operation: The terminal sends search criteria to the server, and the server generates a database query based on the criteria.

[0205] Step 9:

[0206] The server extracts anonymously processed medical information that matches the search criteria from the database and returns it to the terminal.

[0207] Input: Search criteria transferred to the server

[0208] Output: Extracted and anonymized medical information

[0209] Specific operation: The server executes a database query, extracts anonymized data that matches the search criteria, and returns it to the device.

[0210] Step 10:

[0211] The data received by the device is analyzed using AI tools.

[0212] Input: Anonymously processed medical information received from the server

[0213] Output: Analysis results (e.g., "Correlation between medication frequency and blood glucose levels in adult male diabetic patients")

[0214] How it works: The device uses AI tools such as TensorFlow and PyTorch to analyze the incoming data and extract relevant patterns and trends.

[0215] Step 11:

[0216] The device visually displays the analysis results to the user as graphs and tables.

[0217] Input: Results analyzed by AI tools

[0218] Output: Visualized analysis results (graphs and tables)

[0219] Specific operation: The terminal uses Matplotlib and Tableau to visually display the analysis results and provide them in a format that is easy for the user to understand.

[0220] Step 12:

[0221] The user enters further questions based on the analysis results.

[0222] Input: Additional questions from the user (e.g., "About the development of new drugs that are effective in treating diabetes")

[0223] Output: Additional questions typed into the terminal

[0224] Specific operation: The user enters an additional question into the terminal, and the terminal sends the question to the AI ​​model.

[0225] Step 13:

[0226] The device uses AI to generate answers to questions and display them to the user.

[0227] Input: Additional questions entered into the terminal

[0228] Output: The generated answer

[0229] Specific operation: The device uses a generative AI model (e.g., GPT-3) to generate appropriate answers based on follow-up questions and display them to the user.

[0230] Step 14:

[0231] The device securely stores a history of the user's questions and answers for future reference.

[0232] Input: User question and answer data

[0233] Output: A history of saved questions and answers

[0234] What it does: The device saves all of the user's questions and answers in secure storage for later access.

[0235] The above are the specific processing steps of this system. The data input and output at each step have been clearly stated, and the specific operation has been explained.

[0236] (Application example 1)

[0237] 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."

[0238] In modern factories, IoT sensors are used to collect large amounts of production data. However, this data often contains confidential and personal information and cannot be shared without restriction. Furthermore, to effectively utilize the collected data and maximize production efficiency, it is necessary to analyze and display the information while maintaining security. However, such systems are not currently widespread, posing a major challenge for factory managers.

[0239] 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.

[0240] In this invention, the server includes means for receiving medical information including personal information, means for automatically anonymizing the received medical information in accordance with the Personal Information Protection Act, means for storing the anonymized medical information in a secure database, means for receiving production data collected in a factory, means for anonymizing the received production data, and means for analyzing the anonymized production data and generating information for optimizing production, thereby enabling the secure sharing and analysis of production data.

[0241] 1. "Medical information" refers to data relating to an individual's health condition and treatment, such as a patient's medical record and medication information.

[0242] 2. "Personal information" means information that can identify a specific individual, including name, address, date of birth, etc.

[0243] 3. "Anonymization" is the process of processing data so that specific individuals or confidential information cannot be identified.

[0244] 4. A "database" is a collection of data designed to efficiently manage, search, and update large amounts of data.

[0245] 5. "Server" means a computer system that receives, processes, stores, and distributes data.

[0246] 6. "Receiving means" means a method or device for receiving data from an external source.

[0247] 7. A "search tool" is a method or device for locating specific information within a database.

[0248] 8. "Analytical tools" are methods or devices for analyzing data and extracting useful information.

[0249] 9. "Display means" means a method or device for visually presenting analysis results or other information to a user.

[0250] 10. "In-factory production data" refers to information collected during the production process of a factory, including machine performance, product quality, production volume, etc.

[0251] 11. An "IoT sensor" is a sensor that is connected to the Internet and collects and transmits data on the environment, machine status, etc.

[0252] 12. "Production efficiency" is the ratio of the quantity or quality of goods produced using specific resources (time, labor, etc.).

[0253] An embodiment of the present invention is configured as follows. This system is composed of an AI tool for anonymizing medical information and in-factory production data, and for efficiently collecting, analyzing, and displaying that data. This allows for the effective use of data to proceed smoothly while protecting personal and confidential information.

[0254] System configuration

[0255] 1. Server

[0256] We are responsible for receiving medical information and in-factory production data, including personal information, and anonymizing it.

[0257] The data received is anonymized and stored in a secure database.

[0258] 2. Terminal

[0259] It will be used by medical professionals, researchers, and factory managers and will be responsible for searching and analyzing anonymized data.

[0260] Visually display the analysis results and generate answers to questions.

[0261] Program processing overview and specific examples

[0262] Receiving and anonymizing medical information and in-factory production data

[0263] The user (at a medical institution or factory terminal) inputs medical information and production data.

[0264] The terminal transmits the input data to the server.

[0265] The server stores the received data in temporary storage.

[0266] The server analyzes the data structure and identifies which parts contain personal or confidential information.

[0267] The server uses an AI algorithm to anonymize the data in accordance with the Personal Information Protection Act.

[0268] For example, if the medical information contains the data "Taro Tanaka, Shinjuku Ward, Tokyo, born April 5, 1965," the server will process it into the form "Tanaka XX, XX Ward, Tokyo, born April XX, 1965."

[0269] If the factory production data contains data such as "M123, 2023-01-01", the server will process it into a format such as "M1XX, 2023-XX-XX".

[0270] The server stores the anonymized data in a secure database.

[0271] Data exploration and analysis

[0272] Users (researchers, medical professionals, factory managers) enter search criteria for anonymized data from their terminals.

[0273] The terminal sends a search request to the server.

[0274] The server extracts anonymously processed data that matches the search criteria from the database and returns it to the terminal.

[0275] The device analyzes the received data using AI tools.

[0276] For example, in the case of medical information, in response to a request to "analyze the medication data of diabetic patients," the server extracts the relevant data and generates analysis results on the terminal that show "the correlation between medication frequency and blood glucose levels in adult male diabetic patients."

[0277] When it comes to in-factory production data, in response to a request to "identify machines with high production efficiency," the server extracts the relevant data and generates analysis results on the terminal that show the "correlation between production volume and downtime."

[0278] The terminal visually displays the analysis results to the user as graphs and tables.

[0279] Question and answer history management

[0280] The device securely stores a history of the user's questions and answers for future reference.

[0281] The above is an embodiment of the present invention. This system enables the anonymization and efficient use of medical information and in-factory production data, contributing to the advancement of medical research and improved production efficiency.

[0282] Examples of concrete examples and prompts

[0283] A specific example is a procedure to anonymize specific columns (e.g., "Machine ID" and "Production Date") containing production data collected in a factory, and then extract data where the production count exceeds a certain standard (e.g., "Production Count > 150"). Below is an example of a prompt sentence to be input to a generative AI model based on this specific example.

[0284] Prompt Sentence Examples

[0285] Write a Python program to anonymize specific columns (column names: MachineID, ProductionDate) containing production data collected in the factory, and then extract data where the production count exceeds a certain threshold. To anonymize, replace all numbers with 'X', and use the extraction condition 'ProductionCount > 150'.

[0286]

[0287] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0288] Step 1:

[0289] The user (at a medical institution or factory terminal) inputs medical information and production data.

[0290] Input: User-entered medical information (e.g., "Taro Tanaka, Shinjuku Ward, Tokyo, born April 5, 1965") or production data (e.g., "M123, 2023-01-01").

[0291] Output: Data entry completed on terminal.

[0292] Step 2:

[0293] The terminal transmits the input data to the server.

[0294] Input: Entered medical information or production data.

[0295] Output: The data sent to the server.

[0296] Step 3:

[0297] The server stores the received data in temporary storage.

[0298] Input: The data received by the server.

[0299] Output: Data saved in temporary storage.

[0300] Step 4:

[0301] The server analyzes the data structure and identifies which parts contain personal or confidential information.

[0302] Input: Data stored in temporary storage.

[0303] Output: Data with identified personal or sensitive information.

[0304] Step 5:

[0305] The server uses an AI algorithm to anonymize the data in accordance with the Personal Information Protection Act.

[0306] Input: Data that identifies personal or sensitive information.

[0307] Data processing: Identified personal information and confidential information is anonymized (e.g., "Taro Tanaka, Shinjuku Ward, Tokyo, born April 5, 1965" to "Tanaka XX, XX Ward, Tokyo, born April XX, 1965"; "M123, 2023-01-01" to "M1XX, 2023-XX-XX").

[0308] Output: Anonymized data.

[0309] Step 6:

[0310] The server stores the anonymized data in a secure database.

[0311] Input: Anonymized data.

[0312] Output: Data stored in a secure database.

[0313] Step 7:

[0314] Users (researchers, medical professionals, factory managers) enter search criteria for anonymized data from their terminals.

[0315] Input: Search criteria for anonymized data (e.g., "medication data for diabetes patients" or "machines with high production efficiency").

[0316] Output: Search criteria entered on the terminal.

[0317] Step 8:

[0318] The terminal sends a search request to the server.

[0319] Input: Search criteria.

[0320] Output: The search request sent to the server.

[0321] Step 9:

[0322] The server extracts anonymously processed data that matches the search criteria from the database and returns it to the terminal.

[0323] Input: A request based on search criteria.

[0324] Data processing: Searching and extracting anonymized data from databases (e.g., extracting "medication data of diabetes patients").

[0325] Output: The extracted anonymously processed data is returned to the terminal.

[0326] Step 10:

[0327] The device analyzes the received data using AI tools.

[0328] Input: Anonymously processed data returned from the server.

[0329] Data calculation: Data analysis using AI tools (e.g., analyzing the correlation between medication data and blood glucose levels).

[0330] Output: Analysis results.

[0331] Step 11:

[0332] The terminal visually displays the analysis results to the user as graphs and tables.

[0333] Input: Analysis results.

[0334] Output: Visually displayed graphs and tables.

[0335] Step 12:

[0336] If the user enters further questions based on the analysis results, a similar process is repeated.

[0337] Input: Further user questions.

[0338] Output: Start of a new search and analysis process.

[0339] 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.

[0340] An embodiment of the present invention is configured as follows. This system combines medical information anonymization, data search and analysis, and an emotion engine that recognizes user emotions. This makes it possible to achieve a more advanced user experience while complying with the Personal Information Protection Act.

[0341] System configuration

[0342] 1. Server

[0343] Receive medical information and de-identify it.

[0344] The data received is anonymized and stored in a secure database.

[0345] Data from the emotion engine will also be stored anonymously and used for future improvements.

[0346] 2. Terminal

[0347] Used by healthcare professionals and researchers to search and analyze anonymized data and perform emotion recognition.

[0348] The analysis results are displayed and information is provided according to the user's emotions.

[0349] Generate answers to questions and store a history of questions and answers.

[0350] 3. Emotion Engine

[0351] Recognizes user emotions and provides appropriate feedback and information.

[0352] Sentiment data is stored anonymously to help improve future interactions.

[0353] Program processing overview and specific examples

[0354] Receiving and de-identifying medical information

[0355] The user (a terminal at a medical institution) inputs the patient's medical record information and medication information into the electronic medical record system.

[0356] The device sends the entered medical information to the server, where the data is encrypted using a secure communication protocol (e.g., HTTPS).

[0357] The server stores the received medical information in temporary storage.

[0358] The server analyzes the data structure of the medical information and automatically identifies data that needs to be anonymized in accordance with the Personal Information Protection Act (e.g., name, address, date of birth).

[0359] The server uses an AI algorithm to anonymize the medical information it receives. For example, suppose a patient's medical record contains the data "Taro Tanaka, Shinjuku Ward, Tokyo, born April 5, 1965." In this case, the server processes it into the form "XX Tanaka, XX Ward, Tokyo, born April XX, 1965."

[0360] The server stores the de-identified medical information in a secure database, which is maintained in a secure environment to prevent inappropriate access.

[0361] Data exploration and analysis

[0362] Users (researchers and medical professionals) enter search criteria for anonymized medical information on their terminal and execute a search request.

[0363] The terminal sends a search request to the server.

[0364] The server extracts anonymously processed medical information that matches the search criteria from the database.

[0365] The server transmits the extracted data to the terminal.

[0366] The device then analyzes the received data using AI tools. Specifically, it performs statistical analysis, pattern recognition, correlation analysis, etc. For example, in response to a request to "analyze the medication data of diabetic patients," the server extracts the relevant data and generates an analysis result on the device that shows "the correlation between medication frequency and blood glucose levels in adult male diabetic patients."

[0367] The terminal visually displays the analysis results to the user as charts and graphs.

[0368] Use of emotion engine

[0369] While the user is viewing the analysis results, the emotion engine analyzes the user's facial expressions, tone of voice, etc. to recognize their emotions.

[0370] The device adjusts the information and presentation method according to the user's emotions. For example, if the user is feeling anxious, the device will present more polite and reassuring information.

[0371] The server stores the emotional data anonymously and uses it to improve future interactions.

[0372] Question and answer generation and history management

[0373] The user inputs a follow-up question into the terminal based on the analysis results.

[0374] The device passes the input question to an AI model, which generates an appropriate answer.

[0375] The device displays the generated answer to the user. The emotion engine is also used when generating answers, allowing the answer to be generated while taking the user's emotions into consideration.

[0376] The terminal stores a history of user questions and generated answers in a database.

[0377] The above is an embodiment of the present invention. This system enables anonymization of medical information, efficient data search and analysis, and an improved user experience, contributing to the advancement of medical science, pharmacy, and healthcare.

[0378] The processing flow will be explained below.

[0379] Receiving and de-identifying medical information

[0380] Step 1:

[0381] The user (a terminal at a medical institution) inputs the patient's medical record information and medication information into the electronic medical record system.

[0382] Step 2:

[0383] The device sends the entered medical information to the server, where the data is encrypted using a secure communication protocol (e.g., HTTPS).

[0384] Step 3:

[0385] The server stores the received medical information in temporary storage.

[0386] Step 4:

[0387] The server analyzes the data structure of the medical information and automatically identifies data that needs to be anonymized in accordance with the Personal Information Protection Act (e.g., name, address, date of birth).

[0388] Step 5:

[0389] The server uses an AI algorithm to anonymize the received medical information. Specifically, the server processes personal information as follows:

[0390] Convert names to initials (e.g., Tanaka Taro → Tanaka T)

[0391] Remove or generalize address details (e.g., Shinjuku Ward to Shinjuku District)

[0392] Convert date of birth to age, or hide part of the date (e.g., born April 5, 1965 → born April XX, 1965)

[0393] Step 6:

[0394] The server stores the de-identified medical information in a secure database, which is maintained in a secure environment to prevent inappropriate access.

[0395] ---

[0396] Data exploration and analysis

[0397] Step 1:

[0398] Users (researchers and medical professionals) enter search criteria for anonymized medical information on their terminal and execute a search request.

[0399] Step 2:

[0400] The terminal sends a search request to the server.

[0401] Step 3:

[0402] The server extracts anonymously processed medical information that matches the search criteria from the database.

[0403] Step 4:

[0404] The server transmits the extracted data to the terminal.

[0405] Step 5:

[0406] The device then analyzes the anonymized medical information it receives using AI tools. Specifically, it performs the following analysis:

[0407] Statistical analysis (e.g., calculating the mean, median, and standard deviation)

[0408] Pattern recognition (e.g., disease trend analysis)

[0409] Correlation analysis (e.g., assessing the correlation between medication frequency and treatment effect)

[0410] Step 6:

[0411] The terminal visually displays the analysis results to the user as charts and graphs.

[0412] ---

[0413] Use of emotion engine

[0414] Step 1:

[0415] While the user is viewing the analysis results, the emotion engine analyzes the user's facial expressions and tone of voice in real time to recognize the user's emotions.

[0416] Step 2:

[0417] The emotion engine adjusts the information displayed and the way it is presented based on the emotions it recognizes. For example, if the user is feeling anxious, the emotion engine will instruct the device to present information in a polite manner that gives a sense of security.

[0418] Step 3:

[0419] The terminal displays the tailored information to the user, improving the user's experience.

[0420] Step 4:

[0421] The server anonymously stores the emotion data obtained by the emotion engine, which can be used to improve the system in the future.

[0422] ---

[0423] Question and answer generation and history management

[0424] Step 1:

[0425] The user inputs a follow-up question into the terminal based on the analysis results.

[0426] Step 2:

[0427] The device passes the input question to the AI ​​model, which generates an appropriate answer based on the question content and the user's emotions.

[0428] Step 3:

[0429] The device displays the generated answers to the user, which are adjusted by the emotion engine and presented in a way that takes the user's emotions into consideration.

[0430] Step 4:

[0431] The terminal stores a history of user questions and generated answers in a database.

[0432] These are the specific processing steps of the program. The introduction of the emotion engine not only enables medical and pharmaceutical analysis, but also improves the user experience.

[0433] Example 2

[0434] 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."

[0435] Modern medical information contains a high degree of personal information, so it is necessary to appropriately protect it while making effective use of it. There is also a need for a system that allows medical professionals and researchers to efficiently search and analyze anonymized medical data and support decision-making. Furthermore, to improve the user experience, there is a need for technology that recognizes users' emotions and provides appropriate feedback. Since no system exists that integrates these features, the objective of this invention is to solve this problem.

[0436] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0437] In this invention, the server includes a means for receiving medical information including personal information, a means for automatically anonymizing the received medical information based on personal information protection standards, a means for storing the anonymized medical information in a secure database, a sentiment analysis means for recognizing the user's emotions and providing appropriate feedback and information, and a means for anonymously storing the sentiment data and using it to improve future interactions. This makes it possible to effectively utilize medical data while appropriately protecting personal information and providing an even more advanced user experience.

[0438] "Personal information" is information that can be used to identify a specific individual.

[0439] "Medical information" refers to data including a patient's health condition, diagnosis results, treatment details, medication information, and the like.

[0440] "Anonymization" is the process of processing data so that it cannot identify a specific individual.

[0441] A "database" is a collection of data that is systematically organized and used for efficient search and management.

[0442] "Emotion analysis" is a technology that detects and analyzes a user's emotions from facial expressions, tone of voice, etc.

[0443] "Emotion data" is data that records the user's emotional state.

[0444] A "generative AI model" is a machine learning algorithm that automatically generates optimal results based on input data.

[0445] "Search criteria" refers to the criteria or filters set to extract the desired data from a database.

[0446] "Statistical analysis" is a method of analyzing the statistical characteristics of collected data to reveal trends and relationships.

[0447] "Pattern recognition" is the art of detecting and classifying specific patterns in data.

[0448] "AI tools" refer to software and programs that use artificial intelligence technology.

[0449] This invention is a system that combines medical information anonymization, data search and analysis, and an emotion engine that recognizes user emotions. This makes it possible to achieve a more advanced user experience while complying with the Personal Information Protection Act. The detailed configuration and operation of this system are described below.

[0450] System configuration

[0451] 1. Server

[0452] The medical information including personal information is received from the terminal used by the user. At this time, the medical information includes the patient's medical record information and medication information.

[0453] The server automatically anonymizes the received medical information based on personal information protection standards. For example, data such as "Taro Tanaka, Shinjuku Ward, Tokyo, born April 5, 1965" is converted to "XX Tanaka, XX Ward, Tokyo, born April XX, 1965."

[0454] The server stores de-identified medical information in a secure database, which is managed in a secure, access-controlled environment.

[0455] The server also includes an emotion engine that recognizes the user's emotions and stores the user's emotion data anonymously to improve future interactions.

[0456] 2. Terminal

[0457] It is used by users (healthcare professionals and researchers) to enter search criteria for anonymized medical information.

[0458] The device sends a search request to the server and analyzes the anonymized medical information received from the server using AI tools, specifically statistical analysis, pattern recognition, correlation analysis, etc.

[0459] The terminal visually displays the analysis results to the user as charts and graphs.

[0460] The device uses the generative AI model to generate answers to follow-up questions based on the analysis results, displays them to the user, and saves a history of questions and answers.

[0461] 3. Emotion Engine

[0462] The emotion engine analyzes the user's facial expressions and tone of voice to recognize emotions.

[0463] The device adjusts the information and presentation method to suit the user's emotions. For example, if the user is feeling anxious, the device will present more attentive and reassuring information.

[0464] The emotion engine stores emotional data anonymously to help improve future interactions.

[0465] Specific hardware or software names used

[0466] Server: A high-performance data processing server (any brand or model)

[0467] Device: PC or tablet used by medical professionals and researchers

[0468] Secure communication protocol: HTTPS

[0469] AI algorithms: K-anonymity, L-diversity, etc.

[0470] AI tools: Machine learning software (e.g., TensorFlow, PyTorch)

[0471] Examples of concrete examples and prompts

[0472] Specific examples

[0473] 1. Receipt and de-identification of medical information

[0474] A user enters "Taro Tanaka, Shinjuku-ku, Tokyo, born April 5, 1965" into a hospital's electronic medical record system.

[0475] The device sends this data to a server, which then anonymizes the information and converts it into "Tanaka XX, XX Ward, Tokyo, born April XX, 1965."

[0476] The server stores the anonymized data in a secure database.

[0477] 2. Data retrieval and analysis

[0478] The user inputs the search criteria "I want to analyze the medication data of diabetic patients" into the terminal.

[0479] The server extracts the relevant anonymous data from the database and sends it to the device.

[0480] The device uses AI tools to analyze the correlation between the frequency of medication use and blood sugar levels in diabetic patients and displays the results to the user as a chart.

[0481] 3. Use of Emotion Engine

[0482] When a user views the analysis results, the emotion engine detects the user's anxiety.

[0483] The device adjusts the information presentation to give a sense of security.

[0484] The server stores the emotional data anonymously and uses it to improve interactions.

[0485] Prompt Sentence Examples

[0486] 1. "What is the procedure for de-identifying patient records?"

[0487] 2. "Show me a detailed analysis of medication patterns for diabetes patients."

[0488] 3. "How can I optimize user interactions using an emotion engine?"

[0489] The above is an embodiment of the present invention. This system makes it possible to anonymize medical information, efficiently search and analyze data, and provide an advanced user experience.

[0490] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0491] Program processing flow

[0492] Receiving and de-identifying medical information

[0493] Step 1:

[0494] A user inputs a patient's medical record information and medication information into the electronic medical record system. For example, the user inputs information such as "Taro Tanaka, Shinjuku Ward, Tokyo, born April 5, 1965."

[0495] Input: Patient medical record information

[0496] Output: Medical information stored in the electronic medical record system

[0497] Step 2:

[0498] The device sends the entered medical information to the server as encrypted data using a secure communication protocol (HTTPS).

[0499] Input: Medical information stored in the electronic medical record system

[0500] Output: Encrypted medical information sent to the server

[0501] Step 3:

[0502] The server stores the received medical information in a dedicated temporary storage, which is a secure area with controlled access.

[0503] Input: Encrypted medical information

[0504] Output: Medical information stored in temporary storage

[0505] Step 4:

[0506] The server analyzes the received medical information and identifies data that needs to be anonymized (such as name, address, date of birth, etc.) based on personal information protection standards.

[0507] Input: Medical information in temporary storage

[0508] Output: List of data that needs to be anonymized

[0509] Step 5:

[0510] The server uses an AI algorithm to anonymize medical information. For example, data such as "Taro Tanaka, Shinjuku Ward, Tokyo, born April 5, 1965" is converted to "XX Tanaka, XX Ward, Tokyo, born April XX, 1965."

[0511] Input: List of data that needs to be anonymized, medical information in temporary storage

[0512] Output: De-identified medical information

[0513] Step 6:

[0514] The server stores the de-identified medical information in a secure database that is protected from unauthorized access.

[0515] Input: De-identified medical information

[0516] Output: De-identified medical information stored in a database

[0517] Data exploration and analysis

[0518] Step 1:

[0519] A user enters specific search criteria on a device and executes a search request for anonymized medical information. For example, "I want to analyze medication data for diabetes patients."

[0520] Input: Search criteria

[0521] Output: Search request

[0522] Step 2:

[0523] The terminal sends a search request to the server.

[0524] Input: Search request

[0525] Output: Search request sent to the server

[0526] Step 3:

[0527] The server extracts anonymized medical information that matches the search criteria from the database.

[0528] Input: Search request

[0529] Output: Extracted and anonymized medical information

[0530] Step 4:

[0531] The server transmits the extracted medical information to the terminal.

[0532] Input: Extracted and de-identified medical information

[0533] Output: Medical information sent to the device

[0534] Step 5:

[0535] The device then analyzes the received medical information using AI tools, such as statistical analysis, pattern recognition, and correlation analysis. For example, it can show the correlation between a diabetic patient's medication frequency and blood sugar levels.

[0536] Input: Extracted and de-identified medical information

[0537] Output: Analysis results

[0538] Step 6:

[0539] The terminal visually displays the analysis results to the user as charts and graphs.

[0540] Input: Analysis results

[0541] Output: Visually displayed results

[0542] Use of emotion engine

[0543] Step 1:

[0544] When a user views the analysis results, the emotion engine analyzes the user's facial expressions and tone of voice to recognize their emotions.

[0545] Input: User facial expressions and tone of voice

[0546] Output: Recognized emotion data

[0547] Step 2:

[0548] The device adjusts the information and presentation method to suit the user's emotions. For example, if the user is feeling anxious, the device will present more attentive and reassuring information.

[0549] Input: Recognized emotion data

[0550] Output: Tailored information presentation

[0551] Step 3:

[0552] The server stores the emotional data anonymously and uses it to improve future interactions.

[0553] Input: Recognized emotion data

[0554] Output: Saved emotion data

[0555] Question and answer generation and history management

[0556] Step 1:

[0557] The user types a follow-up question into the terminal based on the analysis results, for example, "What are some other successful diabetes treatments?"

[0558] Input: Additional Question

[0559] Output: Question typed into the terminal

[0560] Step 2:

[0561] The device sends the input question to a generative AI model that generates an appropriate answer, also using an emotion engine.

[0562] Input: Additional Question

[0563] Output: The generated answer

[0564] Step 3:

[0565] The terminal displays the generated answer to the user.

[0566] Input: Generated Answer

[0567] Output: The answer displayed to the user

[0568] Step 4:

[0569] The terminal stores a history of questions and generated answers in a database.

[0570] Input: Question and generated answer

[0571] Output: Question and answer history stored in a database

[0572] The above are the specific processing steps for implementing the present invention. This system enables anonymization of medical information, efficient data search and analysis, and an advanced user experience.

[0573] (Application example 2)

[0574] 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."

[0575] In modern medical and research institutions, the importance of protecting personal information is increasing, and there is a need to handle medical information safely. However, there is a lack of systems that can not only simply anonymize information but also provide appropriate information based on the user's emotions. In addition, there is a need for systems that are easier for medical professionals and researchers to use by combining efficient search and analysis of massive amounts of medical data with user emotion recognition.

[0576] The identification process by the identification 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 receiving data including personal information, means for automatically anonymizing the received information in accordance with security regulations, means for storing the anonymized data in a secure storage area, means for utilizing an emotion engine that recognizes the user's emotions, and means for generating interactive presentation information tailored to the user. This makes it possible to dynamically adjust the presentation method of search and analysis results according to the user's emotions while ensuring the protection of personal information.

[0577] "Personal information" means information that can identify a specific individual, or information that can be easily matched with other information to identify a specific individual.

[0578] "Data" is a collection of information that can be recorded, stored, or transmitted in various formats.

[0579] "Receiving" is the act of receiving a transmitted signal or data.

[0580] "Security regulations" are laws and regulations that stipulate the protection and safe handling of information.

[0581] "Anonymization" means processing personal information so that a specific individual cannot be identified.

[0582] A "storage area" refers to a location or environment where data can be stored, including physical disks and cloud storage.

[0583] An "emotion engine" is an artificial intelligence system for recognizing, analyzing, and applying user emotions.

[0584] "Interactive" refers to a state or system that allows for interaction with a user.

[0585] "Presented information" refers to information or data that is displayed to the user.

[0586] "Secure storage" means storing data in a manner that takes security measures to prevent unauthorized access from outside and information leaks.

[0587] "Search" is the act of investigating and searching a database or storage to find specific data.

[0588] "Analysis" is the act of examining and evaluating data to extract meaningful information and knowledge.

[0589] "Dynamic adjustment" means changing settings and display methods in real time in response to changing situations and conditions.

[0590] This invention is a system for efficiently handling anonymized medical data and improving the user experience by recognizing user emotions. The main components of the system and their respective processes are described below.

[0591] System configuration

[0592] server

[0593] The server has the following features:

[0594] Receiving means: The server has a means for receiving data including personal information sent from medical institutions and research institutions. At this time, the data is encrypted and received using a secure communication protocol (e.g., HTTPS).

[0595] Anonymization method: Received data is automatically anonymized in accordance with the Personal Information Protection Act. AI algorithms are used to appropriately process personally identifiable information. For example, patient names, addresses, and dates of birth are anonymized by redacting or encoding them.

[0596] Storage method: Anonymized data is stored in a secure storage area that is highly secured to prevent unauthorized access from outside.

[0597] Terminal

[0598] The devices will be used by medical professionals and researchers and will have the following features:

[0599] Search method: Search the database for anonymized medical information. Users can enter search criteria and quickly extract relevant data.

[0600] Analysis method: The searched data is analyzed using AI tools, including statistical analysis, pattern recognition, and correlation analysis, to provide the user with the information they need.

[0601] Display: The analysis results are displayed visually, using graphs and charts to provide easy-to-understand results and enable users to make decisions quickly.

[0602] Emotion engine: Analyzes the user's facial expressions and tone of voice to recognize their emotions, and dynamically adjusts the content displayed and interactions accordingly.

[0603] Technology used

[0604] Hardware:

[0605] Smartphone

[0606] Head-mounted display (HMD)

[0607] Webcam (image capture device for emotion recognition)

[0608] software:

[0609] Programs using Python

[0610] Requests library (data retrieval)

[0611] Scikit-learn (data analysis)

[0612] OpenCV (Image capture for emotion recognition)

[0613] EmotionRecognizer (custom emotion recognition model, e.g., built with TensorFlow / Keras)

[0614] Specific examples

[0615] When a healthcare professional is using a smartphone app to analyze the data of a diabetic patient, if the user looks anxious while looking at the analysis results on the screen, the emotion engine will recognize that emotion and display a prompt message like the one below.

[0616] Prompt Sentence Examples

[0617] The patient's condition is stable. Based on the data so far, the progress of the treatment is good enough and there is no need to worry. Please refer to the graph for detailed analysis results.

[0618] This system allows medical professionals and researchers to efficiently search and analyze data while protecting personal information, and also provides appropriate information based on the user's emotions.

[0619] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0620] Step 1:

[0621] The server receives medical data, including personal information, sent from medical institutions and research institutions. At this time, the data is encrypted using a secure communication protocol (e.g., HTTPS) and received. The input is the data sent from the medical institution, and the output is the raw data stored on the server.

[0622] Step 2:

[0623] The server anonymizes the medical data it receives. It uses an AI algorithm to process personally identifiable information (e.g., name, address, date of birth) by redacting or encoding it. The input is raw medical data, and the output is anonymized medical data.

[0624] Step 3:

[0625] The server stores the anonymized medical data in a secure storage area. This storage area is highly secure to prevent unauthorized access from outside. The input is the anonymized medical data, and the output is the anonymized data stored in the secure storage area.

[0626] Step 4:

[0627] A user searches for anonymized medical data using a device. The user enters search criteria into the device, and the device sends the search request to a server. The server extracts anonymized data that matches the criteria from a database. The input is the search criteria entered by the user, and the output is anonymized medical data that matches the criteria.

[0628] Step 5:

[0629] The device analyzes the retrieved medical data using AI tools, performing statistical analysis, pattern recognition, correlation analysis, and other techniques to extract the necessary information. The input is the retrieved anonymized medical data, and the output is the analysis results.

[0630] Step 6:

[0631] The terminal visually displays the analysis results, using graphs and charts to provide easy-to-understand results. The input is the analysis results, and the output is the analysis results displayed on the user's screen.

[0632] Step 7:

[0633] The device's emotion engine analyzes the user's facial expressions and tone of voice to recognize emotions. The camera and microphone capture the user's facial images and voice, which are then analyzed by an emotion recognition model. The input is the user's facial images and voice, and the output is the recognized user emotion.

[0634] Step 8:

[0635] The device dynamically adjusts the display content and interaction based on the user's recognized emotions. For example, if the user feels anxious, it displays more polite prompts. The input is the user's emotions and the analysis results, and the output is the dynamically adjusted information presentation.

[0636] Step 9:

[0637] If the user enters a follow-up question based on the analysis results, the device generates an answer to that question. An appropriate answer is automatically generated using an AI model. The input is the user's question, and the output is the generated answer.

[0638] Step 10:

[0639] The device securely stores the history of generated answers and the user's questions. The stored history can be used for future analysis and service improvement. The input is the history of questions and answers, and the output is the securely stored history data.

[0640] 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.

[0641] 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.

[0642] 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.

[0643] [Second embodiment]

[0644] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0645] 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.

[0646] 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).

[0647] 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.

[0648] 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.

[0649] 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).

[0650] 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.

[0651] 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.

[0652] 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.

[0653] 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.

[0654] 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.

[0655] 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."

[0656] An embodiment of the present invention is configured as follows. This system is composed of two main AI tools that anonymize medical information and efficiently collect, analyze, and display that data. This allows for smooth utilization of medical information while complying with the Personal Information Protection Act.

[0657] System configuration

[0658] 1. Server

[0659] It is responsible for receiving medical information and anonymizing it.

[0660] The data received is anonymized and stored in a secure database.

[0661] 2. Terminal

[0662] It will be used by medical professionals and researchers and will be responsible for searching and analyzing anonymized data.

[0663] Visually display the analysis results and generate answers to questions.

[0664] Program processing overview and specific examples

[0665] Receiving and de-identifying medical information

[0666] The user (a terminal at a medical institution) inputs the patient's medical record information and medication information.

[0667] The terminal transmits the input medical information to the server.

[0668] The server stores the received medical information in temporary storage.

[0669] The server analyzes the data structure and identifies which parts correspond to personal information.

[0670] The server uses an AI algorithm to anonymize the data in accordance with the Personal Information Protection Act.

[0671] For example, suppose a patient's medical record information contains the data "Taro Tanaka, Tokyo, Shinjuku Ward, born April 5, 1965." In this case, the server processes it into the format "XX Tanaka, XX Ward, Tokyo, born April XX, 1965."

[0672] The server stores the de-identified medical information in a secure database.

[0673] Data exploration and analysis

[0674] Users (researchers and medical professionals) enter search criteria for anonymized medical information from their terminal.

[0675] The terminal sends a search request to the server.

[0676] The server extracts anonymously processed medical information that matches the search criteria from the database and returns it to the terminal.

[0677] The device analyzes the received data using AI tools.

[0678] For example, in response to a request to "analyze the medication data of diabetic patients," the server extracts the relevant data and generates analysis results on the terminal showing "the correlation between medication frequency and blood glucose levels in adult male diabetic patients."

[0679] The terminal visually displays the analysis results to the user as graphs and tables.

[0680] The user enters further questions based on the analysis results.

[0681] The device uses AI to generate answers to questions and display them to the user.

[0682] For example, if you ask a question such as "Regarding the development of new drugs that are effective in treating diabetes," the AI ​​will provide an appropriate answer based on relevant information.

[0683] Question and answer history management

[0684] The device securely stores a history of the user's questions and answers for future reference.

[0685] The above is an embodiment of the present invention. Based on this system, anonymization of medical information and its efficient utilization can be realized, contributing to the advancement of medical and pharmaceutical sciences.

[0686] The processing flow will be explained below.

[0687] Receiving and de-identifying medical information

[0688] Step 1:

[0689] The user (a terminal at a medical institution) inputs the patient's medical record information and medication information into the electronic medical record system.

[0690] Step 2:

[0691] The device sends the entered medical information to the server, where the data is encrypted using a secure communication protocol (e.g., HTTPS).

[0692] Step 3:

[0693] The server stores the received medical information in temporary storage.

[0694] Step 4:

[0695] The server analyzes the data structure of the medical information and automatically identifies data that needs to be anonymized in accordance with the Personal Information Protection Act (e.g., name, address, date of birth).

[0696] Step 5:

[0697] The server uses an AI algorithm to anonymize the received medical information. Specifically, the server processes personal information as follows:

[0698] Convert names to initials (e.g., Tanaka Taro → Tanaka T)

[0699] Remove or generalize address details (e.g., Shinjuku Ward to Shinjuku District)

[0700] Convert date of birth to age, or hide part of the date (e.g., born April 5, 1965 → born April XX, 1965)

[0701] Step 6:

[0702] The server stores the de-identified medical information in a secure database, which is also managed in a secure environment to prevent inappropriate access.

[0703] Data exploration and analysis

[0704] Step 1:

[0705] Users (researchers and medical professionals) enter search criteria for anonymized medical information on their terminal and execute a search request.

[0706] Step 2:

[0707] The terminal transmits the search request input by the user to the server.

[0708] Step 3:

[0709] The server extracts anonymously processed medical information that matches the search criteria from the database.

[0710] Step 4:

[0711] The server transmits the extracted data to the terminal.

[0712] Step 5:

[0713] The device then analyzes the anonymized medical information it receives using AI tools. Specifically, it performs the following analysis:

[0714] Statistical analysis (e.g., calculating the mean, median, and standard deviation)

[0715] Pattern recognition (e.g., disease trend analysis)

[0716] Correlation analysis (e.g., assessing the correlation between medication frequency and treatment effect)

[0717] Step 6:

[0718] The terminal visually displays the analysis results to the user as charts and graphs.

[0719] Question and answer generation and history management

[0720] Step 1:

[0721] The user inputs a follow-up question into the terminal based on the analysis results.

[0722] Step 2:

[0723] The device passes the input question to an AI model, which generates an appropriate answer.

[0724] Step 3:

[0725] The terminal displays the generated answer to the user.

[0726] Step 4:

[0727] The terminal stores a history of user questions and generated answers in a database.

[0728] These are the specific processing steps of the program, which will enable the anonymization of medical information and its efficient collection, search, and analysis.

[0729] Example 1

[0730] 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."

[0731] In conventional medical information management systems, anonymization processing, required by the Personal Information Protection Act, was largely manual and lacked efficiency. Searching and analyzing anonymized data also placed a heavy burden on users, making it difficult to respond quickly. Furthermore, the lack of a question-answering system based on analysis results meant that researchers and medical professionals needed time and effort to quickly extract useful information from the data they obtained. A system that solves these issues and allows for efficient and safe use of medical information is needed.

[0732] 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.

[0733] In this invention, the server includes a means for receiving medical information including personal information, a means for automatically anonymizing the received medical information in accordance with the Personal Information Protection Act, a means for storing the anonymized medical information in a secure database, a means for analyzing the data structure using an artificial intelligence algorithm during the anonymization process to identify personal identification information, and a means for providing storage for temporarily storing the anonymized data before storing it in the database, thereby enabling efficient anonymization and secure storage of medical information.

[0734] Furthermore, the server includes a means for searching the anonymized medical information, a means for analyzing the searched medical information using an artificial intelligence tool, and a means for visually displaying the analysis results, thereby enabling a user to easily search and analyze data and obtain visual results.

[0735] In addition, the server includes a means for generating answers to questions based on the analysis, a means for storing a history of questions and answers, and a means for providing appropriate answers to questions in natural language from users using the generative AI model, thereby enabling users to obtain interactive question and answer responses based on the analysis results and efficiently collect necessary information.

[0736] "Medical information including personal information" refers to medical data such as a patient's diagnosis, prescription details, and treatment history, including information that can be used to identify an individual.

[0737] "Means for receiving" refers to a combination of hardware and software that allows a server or a terminal to acquire data from the outside.

[0738] "Anonymization methods" are algorithms or processes that remove or transform personally identifiable information from received data so that it cannot be used to identify an individual.

[0739] "Means of storing data in a secure database" means a database equipped with encryption technology and access control mechanisms to protect data from unauthorized access or leakage.

[0740] "Artificial intelligence algorithms" are advanced algorithms, including machine learning and deep learning models, for data analysis and pattern recognition.

[0741] "Means for analyzing data structure" refers to a process that automatically analyzes data formats and fields to identify personally identifiable information.

[0742] "Temporary storage" refers to a memory or disk device that temporarily stores received data during processing.

[0743] A "searching means" is a query processing function for extracting data that matches specific criteria from a database.

[0744] "Artificial intelligence tools" refers to a group of software tools for efficiently analyzing large amounts of data, specifically including machine learning and statistical analysis libraries.

[0745] "Visual display means" refers to tools and technologies for presenting data analysis results to users in a visually easy-to-understand format, such as graphs or tables.

[0746] "Means for generating answers to questions based on analysis" refers to generative AI models and natural language processing technologies that answer users' questions based on analysis results and related information.

[0747] The "means for storing question and answer history" is a data management system for storing user-asked questions and their answers for future reference.

[0748] A "generative AI model" is an artificial intelligence model specially trained for text generation and question answering, and uses natural language processing to generate high-performance responses.

[0749] The present invention is a system for anonymizing medical information and efficiently collecting, analyzing, and displaying that data. This system is designed to facilitate the use of medical information while complying with the Personal Information Protection Act. Specific embodiments of the system are described in detail below.

[0750] System configuration

[0751] This system is mainly composed of two main components: a server and a terminal.

[0752] server

[0753] The server receives the medical information, anonymizes the personal information, and stores it in a secure database. The server uses the following technologies:

[0754] Receiving method: The server receives medical information from the medical institution's terminal using a secure communication protocol such as HTTPS.

[0755] De-identification methods: AI algorithms are used to analyze data structures to identify personally identifiable information, such as a patient's name, address, or date of birth, which is then de-identified.

[0756] Means of storage in a secure database: Anonymized data is stored in a secure database using encryption techniques (e.g., AES encryption).

[0757] Specifically, if the server receives the data "Taro Tanaka, Tokyo, Shinjuku Ward, born April 5, 1965," it processes it into the form "Tanaka XX, Tokyo, XX Ward, born April XX, 1965" and stores it in an anonymized database.

[0758] Terminal

[0759] The device will be used by medical professionals and researchers to search and analyze anonymized data. The device will use the following technologies:

[0760] Searching method: A UI is provided for the user to input search criteria, and the criteria are sent to the server.

[0761] Analysis using artificial intelligence tools: The received data is analyzed using AI tools such as TensorFlow and PyTorch.

[0762] Visual display: Display the analysis results as graphs and tables using Matplotlib or Tableau.

[0763] For example, if a user requests to "analyze the medication data of diabetic patients," the server extracts the relevant data and generates analysis results on the terminal that show the "correlation between medication frequency and blood glucose levels in adult male diabetic patients."

[0764] The device also uses generative AI models (e.g., GPT-3) to provide appropriate answers to questions posed by users in natural language. For example, if a user asks, "Regarding the development of new drugs effective in treating diabetes," the AI ​​will provide an appropriate answer based on relevant information.

[0765] Specific examples

[0766] Below is a concrete example of how the system works.

[0767] 1. The user enters the patient's medical record information into the terminal: "Taro Tanaka, Shinjuku Ward, Tokyo, born April 5, 1965, diabetes, prescribed metformin 500 mg."

[0768] 2. The device sends this information to the server.

[0769] 3. The server anonymizes the received information and adds it to a stored database.

[0770] 4. The user types "Search for medication data for diabetes patients" into the terminal.

[0771] 5. The server extracts the relevant data from the database and returns it to the terminal.

[0772] 6. The device analyzes the received data and generates a graph showing the correlation between the frequency of metformin use and blood glucose levels among diabetic patients.

[0773] 7. A user asks, "What about the development of new drugs that are effective in treating diabetes?"

[0774] 8. The device's AI generates an answer based on relevant research data and displays it to the user.

[0775] Prompt Sentence Examples

[0776] Below is an example of a prompt that a user can enter into a generative AI model.

[0777] "Please enter the medical record information for Taro Tanaka, born April 5, 1965, in Shinjuku Ward, Tokyo."

[0778] "Please set the medication data of diabetic patients as search criteria."

[0779] "Please provide specific information regarding the development of new drugs that are effective in treating diabetes."

[0780] The above is an embodiment of the present invention. This system enables efficient anonymization, search, analysis, and interactive question-and-answering of medical information, and is expected to contribute to the advancement of medical and pharmaceutical sciences.

[0781] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0782] Step 1:

[0783] The user inputs the patient's medical record information and medication information into the terminal.

[0784] Input: Patient's medical record information (e.g., "Taro Tanaka, Tokyo, Shinjuku Ward, born April 5, 1965, diabetes, prescribed metformin 500 mg")

[0785] Output: The entered medical information is saved on the device.

[0786] Specific operation: The user enters patient information on the input screen of the terminal, and the terminal temporarily stores the information.

[0787] Step 2:

[0788] The terminal transmits the input medical information to the server.

[0789] Input: Medical information stored on the device

[0790] Output: Medical information transferred to the server

[0791] Specific operation: The device sends the entered data to the server using a secure communication protocol (e.g., HTTPS).

[0792] Step 3:

[0793] The server stores the received medical information in temporary storage.

[0794] Input: Medical information transferred to the server

[0795] Output: Medical information saved in temporary storage

[0796] What happens: The server receives the data and immediately stores it in temporary storage (e.g., RAM or a fast SSD).

[0797] Step 4:

[0798] The server analyzes the data structure and identifies which parts correspond to personal information.

[0799] Input: Medical information stored in temporary storage

[0800] Output: List of identified personal information

[0801] How it works: The server uses AI algorithms (e.g., natural language processing tools) to analyze each field of data and identify personal information such as name, address, and date of birth.

[0802] Step 5:

[0803] The server uses an AI algorithm to anonymize data in accordance with the Personal Information Protection Act.

[0804] Input: List of identified personal information and medical information

[0805] Output: De-identified medical information

[0806] Specific operation: The server anonymizes the identified personal information by replacing it with a random string of characters, and processes "Taro Tanaka, Shinjuku Ward, Tokyo, born April 5, 1965" into "XX Tanaka, XX Ward, Tokyo, born April XX, 1965."

[0807] Step 6:

[0808] The server stores the de-identified medical information in a secure database.

[0809] Input: De-identified medical information

[0810] Output: De-identified medical information stored in a database

[0811] What it does: The server uses encryption technology (e.g., AES encryption) to store the anonymized data in a secure database.

[0812] Step 7:

[0813] The user inputs search conditions for anonymously processed medical information from the terminal.

[0814] Input: User search criteria (e.g., "Search for medication data for diabetes patients")

[0815] Output: Search terms entered on the terminal

[0816] Specific operation: The user enters search criteria into the search screen on the device, and the device prepares to send the criteria to the server.

[0817] Step 8:

[0818] The device sends a search request to the server.

[0819] Input: Search criteria entered into the terminal

[0820] Output: The search request forwarded to the server

[0821] Specific operation: The terminal sends search criteria to the server, and the server generates a database query based on the criteria.

[0822] Step 9:

[0823] The server extracts anonymously processed medical information that matches the search criteria from the database and returns it to the terminal.

[0824] Input: Search criteria transferred to the server

[0825] Output: Extracted and anonymized medical information

[0826] Specific operation: The server executes a database query, extracts anonymized data that matches the search criteria, and returns it to the device.

[0827] Step 10:

[0828] The data received by the device is analyzed using AI tools.

[0829] Input: Anonymously processed medical information received from the server

[0830] Output: Analysis results (e.g., "Correlation between medication frequency and blood glucose levels in adult male diabetic patients")

[0831] How it works: The device uses AI tools such as TensorFlow and PyTorch to analyze the incoming data and extract relevant patterns and trends.

[0832] Step 11:

[0833] The device visually displays the analysis results to the user as graphs and tables.

[0834] Input: Results analyzed by AI tools

[0835] Output: Visualized analysis results (graphs and tables)

[0836] Specific operation: The terminal uses Matplotlib and Tableau to visually display the analysis results and provide them in a format that is easy for the user to understand.

[0837] Step 12:

[0838] The user enters further questions based on the analysis results.

[0839] Input: Additional questions from the user (e.g., "About the development of new drugs that are effective in treating diabetes")

[0840] Output: Additional questions typed into the terminal

[0841] Specific operation: The user enters an additional question into the terminal, and the terminal sends the question to the AI ​​model.

[0842] Step 13:

[0843] The device uses AI to generate answers to questions and display them to the user.

[0844] Input: Additional questions entered into the terminal

[0845] Output: The generated answer

[0846] Specific operation: The device uses a generative AI model (e.g., GPT-3) to generate appropriate answers based on follow-up questions and display them to the user.

[0847] Step 14:

[0848] The device securely stores a history of the user's questions and answers for future reference.

[0849] Input: User question and answer data

[0850] Output: A history of saved questions and answers

[0851] What it does: The device saves all of the user's questions and answers in secure storage for later access.

[0852] The above are the specific processing steps of this system. The data input and output at each step have been clearly stated, and the specific operation has been explained.

[0853] (Application example 1)

[0854] 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."

[0855] In modern factories, IoT sensors are used to collect large amounts of production data. However, this data often contains confidential and personal information and cannot be shared without restriction. Furthermore, to effectively utilize the collected data and maximize production efficiency, it is necessary to analyze and display the information while maintaining security. However, such systems are not currently widespread, posing a major challenge for factory managers.

[0856] 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.

[0857] In this invention, the server includes means for receiving medical information including personal information, means for automatically anonymizing the received medical information in accordance with the Personal Information Protection Act, means for storing the anonymized medical information in a secure database, means for receiving production data collected in a factory, means for anonymizing the received production data, and means for analyzing the anonymized production data and generating information for optimizing production, thereby enabling the secure sharing and analysis of production data.

[0858] 1. "Medical information" refers to data relating to an individual's health condition and treatment, such as a patient's medical record and medication information.

[0859] 2. "Personal information" means information that can identify a specific individual, including name, address, date of birth, etc.

[0860] 3. "Anonymization" is the process of processing data so that specific individuals or confidential information cannot be identified.

[0861] 4. A "database" is a collection of data designed to efficiently manage, search, and update large amounts of data.

[0862] 5. "Server" means a computer system that receives, processes, stores, and distributes data.

[0863] 6. "Receiving means" means a method or device for receiving data from an external source.

[0864] 7. A "search tool" is a method or device for locating specific information within a database.

[0865] 8. "Analytical tools" are methods or devices for analyzing data and extracting useful information.

[0866] 9. "Display means" means a method or device for visually presenting analysis results or other information to a user.

[0867] 10. "In-factory production data" refers to information collected during the production process of a factory, including machine performance, product quality, production volume, etc.

[0868] 11. An "IoT sensor" is a sensor that is connected to the Internet and collects and transmits data on the environment, machine status, etc.

[0869] 12. "Production efficiency" is the ratio of the quantity or quality of goods produced using specific resources (time, labor, etc.).

[0870] An embodiment of the present invention is configured as follows. This system is composed of an AI tool for anonymizing medical information and in-factory production data, and for efficiently collecting, analyzing, and displaying that data. This allows for the effective use of data to proceed smoothly while protecting personal and confidential information.

[0871] System configuration

[0872] 1. Server

[0873] We are responsible for receiving medical information and in-factory production data, including personal information, and anonymizing it.

[0874] The data received is anonymized and stored in a secure database.

[0875] 2. Terminal

[0876] It will be used by medical professionals, researchers, and factory managers and will be responsible for searching and analyzing anonymized data.

[0877] Visually display the analysis results and generate answers to questions.

[0878] Program processing overview and specific examples

[0879] Receiving and anonymizing medical information and in-factory production data

[0880] The user (at a medical institution or factory terminal) inputs medical information and production data.

[0881] The terminal transmits the input data to the server.

[0882] The server stores the received data in temporary storage.

[0883] The server analyzes the data structure and identifies which parts contain personal or confidential information.

[0884] The server uses an AI algorithm to anonymize the data in accordance with the Personal Information Protection Act.

[0885] For example, if the medical information contains the data "Taro Tanaka, Shinjuku Ward, Tokyo, born April 5, 1965," the server will process it into the form "Tanaka XX, XX Ward, Tokyo, born April XX, 1965."

[0886] If the factory production data contains data such as "M123, 2023-01-01", the server will process it into a format such as "M1XX, 2023-XX-XX".

[0887] The server stores the anonymized data in a secure database.

[0888] Data exploration and analysis

[0889] Users (researchers, medical professionals, factory managers) enter search criteria for anonymized data from their terminals.

[0890] The terminal sends a search request to the server.

[0891] The server extracts anonymously processed data that matches the search criteria from the database and returns it to the terminal.

[0892] The device analyzes the received data using AI tools.

[0893] For example, in the case of medical information, in response to a request to "analyze the medication data of diabetic patients," the server extracts the relevant data and generates analysis results on the terminal that show "the correlation between medication frequency and blood glucose levels in adult male diabetic patients."

[0894] When it comes to in-factory production data, in response to a request to "identify machines with high production efficiency," the server extracts the relevant data and generates analysis results on the terminal that show the "correlation between production volume and downtime."

[0895] The terminal visually displays the analysis results to the user as graphs and tables.

[0896] Question and answer history management

[0897] The device securely stores a history of the user's questions and answers for future reference.

[0898] The above is an embodiment of the present invention. This system enables the anonymization and efficient use of medical information and in-factory production data, contributing to the advancement of medical research and improved production efficiency.

[0899] Examples of concrete examples and prompts

[0900] A specific example is a procedure to anonymize specific columns (e.g., "Machine ID" and "Production Date") containing production data collected in a factory, and then extract data where the production count exceeds a certain standard (e.g., "Production Count > 150"). Below is an example of a prompt sentence to be input to a generative AI model based on this specific example.

[0901] Prompt Sentence Examples

[0902] Write a Python program to anonymize specific columns (column names: MachineID, ProductionDate) containing production data collected in the factory, and then extract data where the production count exceeds a certain threshold. To anonymize, replace all numbers with 'X', and use the extraction condition 'ProductionCount > 150'.

[0903]

[0904] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0905] Step 1:

[0906] The user (at a medical institution or factory terminal) inputs medical information and production data.

[0907] Input: User-entered medical information (e.g., "Taro Tanaka, Shinjuku Ward, Tokyo, born April 5, 1965") or production data (e.g., "M123, 2023-01-01").

[0908] Output: Data entry completed on terminal.

[0909] Step 2:

[0910] The terminal transmits the input data to the server.

[0911] Input: Entered medical information or production data.

[0912] Output: The data sent to the server.

[0913] Step 3:

[0914] The server stores the received data in temporary storage.

[0915] Input: The data received by the server.

[0916] Output: Data saved in temporary storage.

[0917] Step 4:

[0918] The server analyzes the data structure and identifies which parts contain personal or confidential information.

[0919] Input: Data stored in temporary storage.

[0920] Output: Data with identified personal or sensitive information.

[0921] Step 5:

[0922] The server uses an AI algorithm to anonymize the data in accordance with the Personal Information Protection Act.

[0923] Input: Data that identifies personal or sensitive information.

[0924] Data processing: Identified personal information and confidential information is anonymized (e.g., "Taro Tanaka, Shinjuku Ward, Tokyo, born April 5, 1965" to "Tanaka XX, XX Ward, Tokyo, born April XX, 1965"; "M123, 2023-01-01" to "M1XX, 2023-XX-XX").

[0925] Output: Anonymized data.

[0926] Step 6:

[0927] The server stores the anonymized data in a secure database.

[0928] Input: Anonymized data.

[0929] Output: Data stored in a secure database.

[0930] Step 7:

[0931] Users (researchers, medical professionals, factory managers) enter search criteria for anonymized data from their terminals.

[0932] Input: Search criteria for anonymized data (e.g., "medication data for diabetes patients" or "machines with high production efficiency").

[0933] Output: Search criteria entered on the terminal.

[0934] Step 8:

[0935] The terminal sends a search request to the server.

[0936] Input: Search criteria.

[0937] Output: The search request sent to the server.

[0938] Step 9:

[0939] The server extracts anonymously processed data that matches the search criteria from the database and returns it to the terminal.

[0940] Input: A request based on search criteria.

[0941] Data processing: Searching and extracting anonymized data from databases (e.g., extracting "medication data of diabetes patients").

[0942] Output: The extracted anonymously processed data is returned to the terminal.

[0943] Step 10:

[0944] The device analyzes the received data using AI tools.

[0945] Input: Anonymously processed data returned from the server.

[0946] Data calculation: Data analysis using AI tools (e.g., analyzing the correlation between medication data and blood glucose levels).

[0947] Output: Analysis results.

[0948] Step 11:

[0949] The terminal visually displays the analysis results to the user as graphs and tables.

[0950] Input: Analysis results.

[0951] Output: Visually displayed graphs and tables.

[0952] Step 12:

[0953] If the user enters further questions based on the analysis results, a similar process is repeated.

[0954] Input: Further user questions.

[0955] Output: Start of a new search and analysis process.

[0956] 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.

[0957] An embodiment of the present invention is configured as follows. This system combines medical information anonymization, data search and analysis, and an emotion engine that recognizes user emotions. This makes it possible to achieve a more advanced user experience while complying with the Personal Information Protection Act.

[0958] System configuration

[0959] 1. Server

[0960] Receive medical information and de-identify it.

[0961] The data received is anonymized and stored in a secure database.

[0962] Data from the emotion engine will also be stored anonymously and used for future improvements.

[0963] 2. Terminal

[0964] Used by healthcare professionals and researchers to search and analyze anonymized data and perform emotion recognition.

[0965] The analysis results are displayed and information is provided according to the user's emotions.

[0966] Generate answers to questions and store a history of questions and answers.

[0967] 3. Emotion Engine

[0968] Recognizes user emotions and provides appropriate feedback and information.

[0969] Sentiment data is stored anonymously to help improve future interactions.

[0970] Program processing overview and specific examples

[0971] Receiving and de-identifying medical information

[0972] The user (a terminal at a medical institution) inputs the patient's medical record information and medication information into the electronic medical record system.

[0973] The device sends the entered medical information to the server, where the data is encrypted using a secure communication protocol (e.g., HTTPS).

[0974] The server stores the received medical information in temporary storage.

[0975] The server analyzes the data structure of the medical information and automatically identifies data that needs to be anonymized in accordance with the Personal Information Protection Act (e.g., name, address, date of birth).

[0976] The server uses an AI algorithm to anonymize the medical information it receives. For example, suppose a patient's medical record contains the data "Taro Tanaka, Shinjuku Ward, Tokyo, born April 5, 1965." In this case, the server processes it into the form "XX Tanaka, XX Ward, Tokyo, born April XX, 1965."

[0977] The server stores the de-identified medical information in a secure database, which is maintained in a secure environment to prevent inappropriate access.

[0978] Data exploration and analysis

[0979] Users (researchers and medical professionals) enter search criteria for anonymized medical information on their terminal and execute a search request.

[0980] The terminal sends a search request to the server.

[0981] The server extracts anonymously processed medical information that matches the search criteria from the database.

[0982] The server transmits the extracted data to the terminal.

[0983] The device then analyzes the received data using AI tools. Specifically, it performs statistical analysis, pattern recognition, correlation analysis, etc. For example, in response to a request to "analyze the medication data of diabetic patients," the server extracts the relevant data and generates an analysis result on the device that shows "the correlation between medication frequency and blood glucose levels in adult male diabetic patients."

[0984] The terminal visually displays the analysis results to the user as charts and graphs.

[0985] Use of emotion engine

[0986] While the user is viewing the analysis results, the emotion engine analyzes the user's facial expressions, tone of voice, etc. to recognize their emotions.

[0987] The device adjusts the information and presentation method according to the user's emotions. For example, if the user is feeling anxious, the device will present more polite and reassuring information.

[0988] The server stores the emotional data anonymously and uses it to improve future interactions.

[0989] Question and answer generation and history management

[0990] The user inputs a follow-up question into the terminal based on the analysis results.

[0991] The device passes the input question to an AI model, which generates an appropriate answer.

[0992] The device displays the generated answer to the user. The emotion engine is also used when generating answers, allowing the answer to be generated while taking the user's emotions into consideration.

[0993] The terminal stores a history of user questions and generated answers in a database.

[0994] The above is an embodiment of the present invention. This system enables anonymization of medical information, efficient data search and analysis, and an improved user experience, contributing to the advancement of medical science, pharmacy, and healthcare.

[0995] The processing flow will be explained below.

[0996] Receiving and de-identifying medical information

[0997] Step 1:

[0998] The user (a terminal at a medical institution) inputs the patient's medical record information and medication information into the electronic medical record system.

[0999] Step 2:

[1000] The device sends the entered medical information to the server, where the data is encrypted using a secure communication protocol (e.g., HTTPS).

[1001] Step 3:

[1002] The server stores the received medical information in temporary storage.

[1003] Step 4:

[1004] The server analyzes the data structure of the medical information and automatically identifies data that needs to be anonymized in accordance with the Personal Information Protection Act (e.g., name, address, date of birth).

[1005] Step 5:

[1006] The server uses an AI algorithm to anonymize the received medical information. Specifically, the server processes personal information as follows:

[1007] Convert names to initials (e.g., Tanaka Taro → Tanaka T)

[1008] Remove or generalize address details (e.g., Shinjuku Ward to Shinjuku District)

[1009] Convert date of birth to age, or hide part of the date (e.g., born April 5, 1965 → born April XX, 1965)

[1010] Step 6:

[1011] The server stores the de-identified medical information in a secure database, which is maintained in a secure environment to prevent inappropriate access.

[1012] ---

[1013] Data exploration and analysis

[1014] Step 1:

[1015] Users (researchers and medical professionals) enter search criteria for anonymized medical information on their terminal and execute a search request.

[1016] Step 2:

[1017] The terminal sends a search request to the server.

[1018] Step 3:

[1019] The server extracts anonymously processed medical information that matches the search criteria from the database.

[1020] Step 4:

[1021] The server transmits the extracted data to the terminal.

[1022] Step 5:

[1023] The device then analyzes the anonymized medical information it receives using AI tools. Specifically, it performs the following analysis:

[1024] Statistical analysis (e.g., calculating the mean, median, and standard deviation)

[1025] Pattern recognition (e.g., disease trend analysis)

[1026] Correlation analysis (e.g., assessing the correlation between medication frequency and treatment effect)

[1027] Step 6:

[1028] The terminal visually displays the analysis results to the user as charts and graphs.

[1029] ---

[1030] Use of emotion engine

[1031] Step 1:

[1032] While the user is viewing the analysis results, the emotion engine analyzes the user's facial expressions and tone of voice in real time to recognize the user's emotions.

[1033] Step 2:

[1034] The emotion engine adjusts the information displayed and the way it is presented based on the emotions it recognizes. For example, if the user is feeling anxious, the emotion engine will instruct the device to present information in a polite manner that gives a sense of security.

[1035] Step 3:

[1036] The terminal displays the tailored information to the user, improving the user's experience.

[1037] Step 4:

[1038] The server anonymously stores the emotion data obtained by the emotion engine, which can be used to improve the system in the future.

[1039] ---

[1040] Question and answer generation and history management

[1041] Step 1:

[1042] The user inputs a follow-up question into the terminal based on the analysis results.

[1043] Step 2:

[1044] The device passes the input question to the AI ​​model, which generates an appropriate answer based on the question content and the user's emotions.

[1045] Step 3:

[1046] The device displays the generated answers to the user, which are adjusted by the emotion engine and presented in a way that takes the user's emotions into consideration.

[1047] Step 4:

[1048] The terminal stores a history of user questions and generated answers in a database.

[1049] These are the specific processing steps of the program. The introduction of the emotion engine not only enables medical and pharmaceutical analysis, but also improves the user experience.

[1050] Example 2

[1051] 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."

[1052] Modern medical information contains a high degree of personal information, so it is necessary to appropriately protect it while making effective use of it. There is also a need for a system that allows medical professionals and researchers to efficiently search and analyze anonymized medical data and support decision-making. Furthermore, to improve the user experience, there is a need for technology that recognizes users' emotions and provides appropriate feedback. Since no system exists that integrates these features, the objective of this invention is to solve this problem.

[1053] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1054] In this invention, the server includes a means for receiving medical information including personal information, a means for automatically anonymizing the received medical information based on personal information protection standards, a means for storing the anonymized medical information in a secure database, a sentiment analysis means for recognizing the user's emotions and providing appropriate feedback and information, and a means for anonymously storing the sentiment data and using it to improve future interactions. This makes it possible to effectively utilize medical data while appropriately protecting personal information and providing an even more advanced user experience.

[1055] "Personal information" is information that can be used to identify a specific individual.

[1056] "Medical information" refers to data including a patient's health condition, diagnosis results, treatment details, medication information, and the like.

[1057] "Anonymization" is the process of processing data so that it cannot identify a specific individual.

[1058] A "database" is a collection of data that is systematically organized and used for efficient search and management.

[1059] "Emotion analysis" is a technology that detects and analyzes a user's emotions from facial expressions, tone of voice, etc.

[1060] "Emotion data" is data that records the user's emotional state.

[1061] A "generative AI model" is a machine learning algorithm that automatically generates optimal results based on input data.

[1062] "Search criteria" refers to the criteria or filters set to extract the desired data from a database.

[1063] "Statistical analysis" is a method of analyzing the statistical characteristics of collected data to reveal trends and relationships.

[1064] "Pattern recognition" is the art of detecting and classifying specific patterns in data.

[1065] "AI tools" refer to software and programs that use artificial intelligence technology.

[1066] This invention is a system that combines medical information anonymization, data search and analysis, and an emotion engine that recognizes user emotions. This makes it possible to achieve a more advanced user experience while complying with the Personal Information Protection Act. The detailed configuration and operation of this system are described below.

[1067] System configuration

[1068] 1. Server

[1069] The medical information including personal information is received from the terminal used by the user. At this time, the medical information includes the patient's medical record information and medication information.

[1070] The server automatically anonymizes the received medical information based on personal information protection standards. For example, data such as "Taro Tanaka, Shinjuku Ward, Tokyo, born April 5, 1965" is converted to "XX Tanaka, XX Ward, Tokyo, born April XX, 1965."

[1071] The server stores de-identified medical information in a secure database, which is managed in a secure, access-controlled environment.

[1072] The server also includes an emotion engine that recognizes the user's emotions and stores the user's emotion data anonymously to improve future interactions.

[1073] 2. Terminal

[1074] It is used by users (healthcare professionals and researchers) to enter search criteria for anonymized medical information.

[1075] The device sends a search request to the server and analyzes the anonymized medical information received from the server using AI tools, specifically statistical analysis, pattern recognition, correlation analysis, etc.

[1076] The terminal visually displays the analysis results to the user as charts and graphs.

[1077] The device uses the generative AI model to generate answers to follow-up questions based on the analysis results, displays them to the user, and saves a history of questions and answers.

[1078] 3. Emotion Engine

[1079] The emotion engine analyzes the user's facial expressions and tone of voice to recognize emotions.

[1080] The device adjusts the information and presentation method to suit the user's emotions. For example, if the user is feeling anxious, the device will present more attentive and reassuring information.

[1081] The emotion engine stores emotional data anonymously to help improve future interactions.

[1082] Specific hardware or software names used

[1083] Server: A high-performance data processing server (any brand or model)

[1084] Device: PC or tablet used by medical professionals and researchers

[1085] Secure communication protocol: HTTPS

[1086] AI algorithms: K-anonymity, L-diversity, etc.

[1087] AI tools: Machine learning software (e.g., TensorFlow, PyTorch)

[1088] Examples of concrete examples and prompts

[1089] Specific examples

[1090] 1. Receipt and de-identification of medical information

[1091] A user enters "Taro Tanaka, Shinjuku-ku, Tokyo, born April 5, 1965" into a hospital's electronic medical record system.

[1092] The device sends this data to a server, which then anonymizes the information and converts it into "Tanaka XX, XX Ward, Tokyo, born April XX, 1965."

[1093] The server stores the anonymized data in a secure database.

[1094] 2. Data retrieval and analysis

[1095] The user inputs the search criteria "I want to analyze the medication data of diabetic patients" into the terminal.

[1096] The server extracts the relevant anonymous data from the database and sends it to the device.

[1097] The device uses AI tools to analyze the correlation between the frequency of medication use and blood sugar levels in diabetic patients and displays the results to the user as a chart.

[1098] 3. Use of Emotion Engine

[1099] When a user views the analysis results, the emotion engine detects the user's anxiety.

[1100] The device adjusts the information presentation to give a sense of security.

[1101] The server stores the emotional data anonymously and uses it to improve interactions.

[1102] Prompt Sentence Examples

[1103] 1. "What is the procedure for de-identifying patient records?"

[1104] 2. "Show me a detailed analysis of medication patterns for diabetes patients."

[1105] 3. "How can I optimize user interactions using an emotion engine?"

[1106] The above is an embodiment of the present invention. This system makes it possible to anonymize medical information, efficiently search and analyze data, and provide an advanced user experience.

[1107] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1108] Program processing flow

[1109] Receiving and de-identifying medical information

[1110] Step 1:

[1111] A user inputs a patient's medical record information and medication information into the electronic medical record system. For example, the user inputs information such as "Taro Tanaka, Shinjuku Ward, Tokyo, born April 5, 1965."

[1112] Input: Patient medical record information

[1113] Output: Medical information stored in the electronic medical record system

[1114] Step 2:

[1115] The device sends the entered medical information to the server as encrypted data using a secure communication protocol (HTTPS).

[1116] Input: Medical information stored in the electronic medical record system

[1117] Output: Encrypted medical information sent to the server

[1118] Step 3:

[1119] The server stores the received medical information in a dedicated temporary storage, which is a secure area with controlled access.

[1120] Input: Encrypted medical information

[1121] Output: Medical information stored in temporary storage

[1122] Step 4:

[1123] The server analyzes the received medical information and identifies data that needs to be anonymized (such as name, address, date of birth, etc.) based on personal information protection standards.

[1124] Input: Medical information in temporary storage

[1125] Output: List of data that needs to be anonymized

[1126] Step 5:

[1127] The server uses an AI algorithm to anonymize medical information. For example, data such as "Taro Tanaka, Shinjuku Ward, Tokyo, born April 5, 1965" is converted to "XX Tanaka, XX Ward, Tokyo, born April XX, 1965."

[1128] Input: List of data that needs to be anonymized, medical information in temporary storage

[1129] Output: De-identified medical information

[1130] Step 6:

[1131] The server stores the de-identified medical information in a secure database that is protected from unauthorized access.

[1132] Input: De-identified medical information

[1133] Output: De-identified medical information stored in a database

[1134] Data exploration and analysis

[1135] Step 1:

[1136] A user enters specific search criteria on a device and executes a search request for anonymized medical information. For example, "I want to analyze medication data for diabetes patients."

[1137] Input: Search criteria

[1138] Output: Search request

[1139] Step 2:

[1140] The terminal sends a search request to the server.

[1141] Input: Search request

[1142] Output: Search request sent to the server

[1143] Step 3:

[1144] The server extracts anonymized medical information that matches the search criteria from the database.

[1145] Input: Search request

[1146] Output: Extracted and anonymized medical information

[1147] Step 4:

[1148] The server transmits the extracted medical information to the terminal.

[1149] Input: Extracted and de-identified medical information

[1150] Output: Medical information sent to the device

[1151] Step 5:

[1152] The device then analyzes the received medical information using AI tools, such as statistical analysis, pattern recognition, and correlation analysis. For example, it can show the correlation between a diabetic patient's medication frequency and blood sugar levels.

[1153] Input: Extracted and de-identified medical information

[1154] Output: Analysis results

[1155] Step 6:

[1156] The terminal visually displays the analysis results to the user as charts and graphs.

[1157] Input: Analysis results

[1158] Output: Visually displayed results

[1159] Use of emotion engine

[1160] Step 1:

[1161] When a user views the analysis results, the emotion engine analyzes the user's facial expressions and tone of voice to recognize their emotions.

[1162] Input: User facial expressions and tone of voice

[1163] Output: Recognized emotion data

[1164] Step 2:

[1165] The device adjusts the information and presentation method to suit the user's emotions. For example, if the user is feeling anxious, the device will present more attentive and reassuring information.

[1166] Input: Recognized emotion data

[1167] Output: Tailored information presentation

[1168] Step 3:

[1169] The server stores the emotional data anonymously and uses it to improve future interactions.

[1170] Input: Recognized emotion data

[1171] Output: Saved emotion data

[1172] Question and answer generation and history management

[1173] Step 1:

[1174] The user types a follow-up question into the terminal based on the analysis results, for example, "What are some other successful diabetes treatments?"

[1175] Input: Additional Question

[1176] Output: Question typed into the terminal

[1177] Step 2:

[1178] The device sends the input question to a generative AI model that generates an appropriate answer, also using an emotion engine.

[1179] Input: Additional Question

[1180] Output: The generated answer

[1181] Step 3:

[1182] The terminal displays the generated answer to the user.

[1183] Input: Generated Answer

[1184] Output: The answer displayed to the user

[1185] Step 4:

[1186] The terminal stores a history of questions and generated answers in a database.

[1187] Input: Question and generated answer

[1188] Output: Question and answer history stored in a database

[1189] The above are the specific processing steps for implementing the present invention. This system enables anonymization of medical information, efficient data search and analysis, and an advanced user experience.

[1190] (Application example 2)

[1191] 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."

[1192] In modern medical and research institutions, the importance of protecting personal information is increasing, and there is a need to handle medical information safely. However, there is a lack of systems that can not only simply anonymize information but also provide appropriate information based on the user's emotions. In addition, there is a need for systems that are easier for medical professionals and researchers to use by combining efficient search and analysis of massive amounts of medical data with user emotion recognition.

[1193] The identification process by the identification 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 receiving data including personal information, means for automatically anonymizing the received information in accordance with security regulations, means for storing the anonymized data in a secure storage area, means for utilizing an emotion engine that recognizes the user's emotions, and means for generating interactive presentation information tailored to the user. This makes it possible to dynamically adjust the presentation method of search and analysis results according to the user's emotions while ensuring the protection of personal information.

[1194] "Personal information" means information that can identify a specific individual, or information that can be easily matched with other information to identify a specific individual.

[1195] "Data" is a collection of information that can be recorded, stored, or transmitted in various formats.

[1196] "Receiving" is the act of receiving a transmitted signal or data.

[1197] "Security regulations" are laws and regulations that stipulate the protection and safe handling of information.

[1198] "Anonymization" means processing personal information so that a specific individual cannot be identified.

[1199] A "storage area" refers to a location or environment where data can be stored, including physical disks and cloud storage.

[1200] An "emotion engine" is an artificial intelligence system for recognizing, analyzing, and applying user emotions.

[1201] "Interactive" refers to a state or system that allows for interaction with a user.

[1202] "Presented information" refers to information or data that is displayed to the user.

[1203] "Secure storage" means storing data in a manner that takes security measures to prevent unauthorized access from outside and information leaks.

[1204] "Search" is the act of investigating and searching a database or storage to find specific data.

[1205] "Analysis" is the act of examining and evaluating data to extract meaningful information and knowledge.

[1206] "Dynamic adjustment" means changing settings and display methods in real time in response to changing situations and conditions.

[1207] This invention is a system for efficiently handling anonymized medical data and improving the user experience by recognizing user emotions. The main components of the system and their respective processes are described below.

[1208] System configuration

[1209] server

[1210] The server has the following features:

[1211] Receiving means: The server has a means for receiving data including personal information sent from medical institutions and research institutions. At this time, the data is encrypted and received using a secure communication protocol (e.g., HTTPS).

[1212] Anonymization method: Received data is automatically anonymized in accordance with the Personal Information Protection Act. AI algorithms are used to appropriately process personally identifiable information. For example, patient names, addresses, and dates of birth are anonymized by redacting or encoding them.

[1213] Storage method: Anonymized data is stored in a secure storage area that is highly secured to prevent unauthorized access from outside.

[1214] Terminal

[1215] The devices will be used by medical professionals and researchers and will have the following features:

[1216] Search method: Search the database for anonymized medical information. Users can enter search criteria and quickly extract relevant data.

[1217] Analysis method: The searched data is analyzed using AI tools, including statistical analysis, pattern recognition, and correlation analysis, to provide the user with the information they need.

[1218] Display: The analysis results are displayed visually, using graphs and charts to provide easy-to-understand results and enable users to make decisions quickly.

[1219] Emotion engine: Analyzes the user's facial expressions and tone of voice to recognize their emotions, and dynamically adjusts the content displayed and interactions accordingly.

[1220] Technology used

[1221] Hardware:

[1222] Smartphone

[1223] Head-mounted display (HMD)

[1224] Webcam (image capture device for emotion recognition)

[1225] software:

[1226] Programs using Python

[1227] Requests library (data retrieval)

[1228] Scikit-learn (data analysis)

[1229] OpenCV (Image capture for emotion recognition)

[1230] EmotionRecognizer (custom emotion recognition model, e.g., built with TensorFlow / Keras)

[1231] Specific examples

[1232] When a healthcare professional is using a smartphone app to analyze the data of a diabetic patient, if the user looks anxious while looking at the analysis results on the screen, the emotion engine will recognize that emotion and display a prompt message like the one below.

[1233] Prompt Sentence Examples

[1234] The patient's condition is stable. Based on the data so far, the progress of the treatment is good enough and there is no need to worry. Please refer to the graph for detailed analysis results.

[1235] This system allows medical professionals and researchers to efficiently search and analyze data while protecting personal information, and also provides appropriate information based on the user's emotions.

[1236] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1237] Step 1:

[1238] The server receives medical data, including personal information, sent from medical institutions and research institutions. At this time, the data is encrypted using a secure communication protocol (e.g., HTTPS) and received. The input is the data sent from the medical institution, and the output is the raw data stored on the server.

[1239] Step 2:

[1240] The server anonymizes the medical data it receives. It uses an AI algorithm to process personally identifiable information (e.g., name, address, date of birth) by redacting or encoding it. The input is raw medical data, and the output is anonymized medical data.

[1241] Step 3:

[1242] The server stores the anonymized medical data in a secure storage area. This storage area is highly secure to prevent unauthorized access from outside. The input is the anonymized medical data, and the output is the anonymized data stored in the secure storage area.

[1243] Step 4:

[1244] A user searches for anonymized medical data using a device. The user enters search criteria into the device, and the device sends the search request to a server. The server extracts anonymized data that matches the criteria from a database. The input is the search criteria entered by the user, and the output is anonymized medical data that matches the criteria.

[1245] Step 5:

[1246] The device analyzes the retrieved medical data using AI tools, performing statistical analysis, pattern recognition, correlation analysis, and other techniques to extract the necessary information. The input is the retrieved anonymized medical data, and the output is the analysis results.

[1247] Step 6:

[1248] The terminal visually displays the analysis results, using graphs and charts to provide easy-to-understand results. The input is the analysis results, and the output is the analysis results displayed on the user's screen.

[1249] Step 7:

[1250] The device's emotion engine analyzes the user's facial expressions and tone of voice to recognize emotions. The camera and microphone capture the user's facial images and voice, which are then analyzed by an emotion recognition model. The input is the user's facial images and voice, and the output is the recognized user emotion.

[1251] Step 8:

[1252] The device dynamically adjusts the display content and interaction based on the user's recognized emotions. For example, if the user feels anxious, it displays more polite prompts. The input is the user's emotions and the analysis results, and the output is the dynamically adjusted information presentation.

[1253] Step 9:

[1254] If the user enters a follow-up question based on the analysis results, the device generates an answer to that question. An appropriate answer is automatically generated using an AI model. The input is the user's question, and the output is the generated answer.

[1255] Step 10:

[1256] The device securely stores the history of generated answers and the user's questions. The stored history can be used for future analysis and service improvement. The input is the history of questions and answers, and the output is the securely stored history data.

[1257] 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.

[1258] 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.

[1259] 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.

[1260] [Third embodiment]

[1261] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1262] 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.

[1263] 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).

[1264] 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.

[1265] 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.

[1266] 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).

[1267] 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.

[1268] 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.

[1269] 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.

[1270] 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.

[1271] 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.

[1272] 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."

[1273] An embodiment of the present invention is configured as follows. This system is composed of two main AI tools that anonymize medical information and efficiently collect, analyze, and display that data. This allows for smooth utilization of medical information while complying with the Personal Information Protection Act.

[1274] System configuration

[1275] 1. Server

[1276] It is responsible for receiving medical information and anonymizing it.

[1277] The data received is anonymized and stored in a secure database.

[1278] 2. Terminal

[1279] It will be used by medical professionals and researchers and will be responsible for searching and analyzing anonymized data.

[1280] Visually display the analysis results and generate answers to questions.

[1281] Program processing overview and specific examples

[1282] Receiving and de-identifying medical information

[1283] The user (a terminal at a medical institution) inputs the patient's medical record information and medication information.

[1284] The terminal transmits the input medical information to the server.

[1285] The server stores the received medical information in temporary storage.

[1286] The server analyzes the data structure and identifies which parts correspond to personal information.

[1287] The server uses an AI algorithm to anonymize the data in accordance with the Personal Information Protection Act.

[1288] For example, suppose a patient's medical record information contains the data "Taro Tanaka, Tokyo, Shinjuku Ward, born April 5, 1965." In this case, the server processes it into the format "XX Tanaka, XX Ward, Tokyo, born April XX, 1965."

[1289] The server stores the de-identified medical information in a secure database.

[1290] Data exploration and analysis

[1291] Users (researchers and medical professionals) enter search criteria for anonymized medical information from their terminal.

[1292] The terminal sends a search request to the server.

[1293] The server extracts anonymously processed medical information that matches the search criteria from the database and returns it to the terminal.

[1294] The device analyzes the received data using AI tools.

[1295] For example, in response to a request to "analyze the medication data of diabetic patients," the server extracts the relevant data and generates analysis results on the terminal showing "the correlation between medication frequency and blood glucose levels in adult male diabetic patients."

[1296] The terminal visually displays the analysis results to the user as graphs and tables.

[1297] The user enters further questions based on the analysis results.

[1298] The device uses AI to generate answers to questions and display them to the user.

[1299] For example, if you ask a question such as "Regarding the development of new drugs that are effective in treating diabetes," the AI ​​will provide an appropriate answer based on relevant information.

[1300] Question and answer history management

[1301] The device securely stores a history of the user's questions and answers for future reference.

[1302] The above is an embodiment of the present invention. Based on this system, anonymization of medical information and its efficient utilization can be realized, contributing to the advancement of medical and pharmaceutical sciences.

[1303] The processing flow will be explained below.

[1304] Receiving and de-identifying medical information

[1305] Step 1:

[1306] The user (a terminal at a medical institution) inputs the patient's medical record information and medication information into the electronic medical record system.

[1307] Step 2:

[1308] The device sends the entered medical information to the server, where the data is encrypted using a secure communication protocol (e.g., HTTPS).

[1309] Step 3:

[1310] The server stores the received medical information in temporary storage.

[1311] Step 4:

[1312] The server analyzes the data structure of the medical information and automatically identifies data that needs to be anonymized in accordance with the Personal Information Protection Act (e.g., name, address, date of birth).

[1313] Step 5:

[1314] The server uses an AI algorithm to anonymize the received medical information. Specifically, the server processes personal information as follows:

[1315] Convert names to initials (e.g., Tanaka Taro → Tanaka T)

[1316] Remove or generalize address details (e.g., Shinjuku Ward to Shinjuku District)

[1317] Convert date of birth to age, or hide part of the date (e.g., born April 5, 1965 → born April XX, 1965)

[1318] Step 6:

[1319] The server stores the de-identified medical information in a secure database, which is also managed in a secure environment to prevent inappropriate access.

[1320] Data exploration and analysis

[1321] Step 1:

[1322] Users (researchers and medical professionals) enter search criteria for anonymized medical information on their terminal and execute a search request.

[1323] Step 2:

[1324] The terminal transmits the search request input by the user to the server.

[1325] Step 3:

[1326] The server extracts anonymously processed medical information that matches the search criteria from the database.

[1327] Step 4:

[1328] The server transmits the extracted data to the terminal.

[1329] Step 5:

[1330] The device then analyzes the anonymized medical information it receives using AI tools. Specifically, it performs the following analysis:

[1331] Statistical analysis (e.g., calculating the mean, median, and standard deviation)

[1332] Pattern recognition (e.g., disease trend analysis)

[1333] Correlation analysis (e.g., assessing the correlation between medication frequency and treatment effect)

[1334] Step 6:

[1335] The terminal visually displays the analysis results to the user as charts and graphs.

[1336] Question and answer generation and history management

[1337] Step 1:

[1338] The user inputs a follow-up question into the terminal based on the analysis results.

[1339] Step 2:

[1340] The device passes the input question to an AI model, which generates an appropriate answer.

[1341] Step 3:

[1342] The terminal displays the generated answer to the user.

[1343] Step 4:

[1344] The terminal stores a history of user questions and generated answers in a database.

[1345] These are the specific processing steps of the program, which will enable the anonymization of medical information and its efficient collection, search, and analysis.

[1346] Example 1

[1347] 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."

[1348] In conventional medical information management systems, anonymization processing, required by the Personal Information Protection Act, was largely manual and lacked efficiency. Searching and analyzing anonymized data also placed a heavy burden on users, making it difficult to respond quickly. Furthermore, the lack of a question-answering system based on analysis results meant that researchers and medical professionals needed time and effort to quickly extract useful information from the data they obtained. A system that solves these issues and allows for efficient and safe use of medical information is needed.

[1349] 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.

[1350] In this invention, the server includes a means for receiving medical information including personal information, a means for automatically anonymizing the received medical information in accordance with the Personal Information Protection Act, a means for storing the anonymized medical information in a secure database, a means for analyzing the data structure using an artificial intelligence algorithm during the anonymization process to identify personal identification information, and a means for providing storage for temporarily storing the anonymized data before storing it in the database, thereby enabling efficient anonymization and secure storage of medical information.

[1351] Furthermore, the server includes a means for searching the anonymized medical information, a means for analyzing the searched medical information using an artificial intelligence tool, and a means for visually displaying the analysis results, thereby enabling a user to easily search and analyze data and obtain visual results.

[1352] In addition, the server includes a means for generating answers to questions based on the analysis, a means for storing a history of questions and answers, and a means for providing appropriate answers to questions in natural language from users using the generative AI model, thereby enabling users to obtain interactive question and answer responses based on the analysis results and efficiently collect necessary information.

[1353] "Medical information including personal information" refers to medical data such as a patient's diagnosis, prescription details, and treatment history, including information that can be used to identify an individual.

[1354] "Means for receiving" refers to a combination of hardware and software that allows a server or a terminal to acquire data from the outside.

[1355] "Anonymization methods" are algorithms or processes that remove or transform personally identifiable information from received data so that it cannot be used to identify an individual.

[1356] "Means of storing data in a secure database" means a database equipped with encryption technology and access control mechanisms to protect data from unauthorized access or leakage.

[1357] "Artificial intelligence algorithms" are advanced algorithms, including machine learning and deep learning models, for data analysis and pattern recognition.

[1358] "Means for analyzing data structure" refers to a process that automatically analyzes data formats and fields to identify personally identifiable information.

[1359] "Temporary storage" refers to a memory or disk device that temporarily stores received data during processing.

[1360] A "searching means" is a query processing function for extracting data that matches specific criteria from a database.

[1361] "Artificial intelligence tools" refers to a group of software tools for efficiently analyzing large amounts of data, specifically including machine learning and statistical analysis libraries.

[1362] "Visual display means" refers to tools and technologies for presenting data analysis results to users in a visually easy-to-understand format, such as graphs or tables.

[1363] "Means for generating answers to questions based on analysis" refers to generative AI models and natural language processing technologies that answer users' questions based on analysis results and related information.

[1364] The "means for storing question and answer history" is a data management system for storing user-asked questions and their answers for future reference.

[1365] A "generative AI model" is an artificial intelligence model specially trained for text generation and question answering, and uses natural language processing to generate high-performance responses.

[1366] The present invention is a system for anonymizing medical information and efficiently collecting, analyzing, and displaying that data. This system is designed to facilitate the use of medical information while complying with the Personal Information Protection Act. Specific embodiments of the system are described in detail below.

[1367] System configuration

[1368] This system is mainly composed of two main components: a server and a terminal.

[1369] server

[1370] The server receives the medical information, anonymizes the personal information, and stores it in a secure database. The server uses the following technologies:

[1371] Receiving method: The server receives medical information from the medical institution's terminal using a secure communication protocol such as HTTPS.

[1372] De-identification methods: AI algorithms are used to analyze data structures to identify personally identifiable information, such as a patient's name, address, or date of birth, which is then de-identified.

[1373] Means of storage in a secure database: Anonymized data is stored in a secure database using encryption techniques (e.g., AES encryption).

[1374] Specifically, if the server receives the data "Taro Tanaka, Tokyo, Shinjuku Ward, born April 5, 1965," it processes it into the form "Tanaka XX, Tokyo, XX Ward, born April XX, 1965" and stores it in an anonymized database.

[1375] Terminal

[1376] The device will be used by medical professionals and researchers to search and analyze anonymized data. The device will use the following technologies:

[1377] Searching method: A UI is provided for the user to input search criteria, and the criteria are sent to the server.

[1378] Analysis using artificial intelligence tools: The received data is analyzed using AI tools such as TensorFlow and PyTorch.

[1379] Visual display: Display the analysis results as graphs and tables using Matplotlib or Tableau.

[1380] For example, if a user requests to "analyze the medication data of diabetic patients," the server extracts the relevant data and generates analysis results on the terminal that show the "correlation between medication frequency and blood glucose levels in adult male diabetic patients."

[1381] The device also uses generative AI models (e.g., GPT-3) to provide appropriate answers to questions posed by users in natural language. For example, if a user asks, "Regarding the development of new drugs effective in treating diabetes," the AI ​​will provide an appropriate answer based on relevant information.

[1382] Specific examples

[1383] Below is a concrete example of how the system works.

[1384] 1. The user enters the patient's medical record information into the terminal: "Taro Tanaka, Shinjuku Ward, Tokyo, born April 5, 1965, diabetes, prescribed metformin 500 mg."

[1385] 2. The device sends this information to the server.

[1386] 3. The server anonymizes the received information and adds it to a stored database.

[1387] 4. The user types "Search for medication data for diabetes patients" into the terminal.

[1388] 5. The server extracts the relevant data from the database and returns it to the terminal.

[1389] 6. The device analyzes the received data and generates a graph showing the correlation between the frequency of metformin use and blood glucose levels among diabetic patients.

[1390] 7. A user asks, "What about the development of new drugs that are effective in treating diabetes?"

[1391] 8. The device's AI generates an answer based on relevant research data and displays it to the user.

[1392] Prompt Sentence Examples

[1393] Below is an example of a prompt that a user can enter into a generative AI model.

[1394] "Please enter the medical record information for Taro Tanaka, born April 5, 1965, in Shinjuku Ward, Tokyo."

[1395] "Please set the medication data of diabetic patients as search criteria."

[1396] "Please provide specific information regarding the development of new drugs that are effective in treating diabetes."

[1397] The above is an embodiment of the present invention. This system enables efficient anonymization, search, analysis, and interactive question-and-answering of medical information, and is expected to contribute to the advancement of medical and pharmaceutical sciences.

[1398] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1399] Step 1:

[1400] The user inputs the patient's medical record information and medication information into the terminal.

[1401] Input: Patient's medical record information (e.g., "Taro Tanaka, Tokyo, Shinjuku Ward, born April 5, 1965, diabetes, prescribed metformin 500 mg")

[1402] Output: The entered medical information is saved on the device.

[1403] Specific operation: The user enters patient information on the input screen of the terminal, and the terminal temporarily stores the information.

[1404] Step 2:

[1405] The terminal transmits the input medical information to the server.

[1406] Input: Medical information stored on the device

[1407] Output: Medical information transferred to the server

[1408] Specific operation: The device sends the entered data to the server using a secure communication protocol (e.g., HTTPS).

[1409] Step 3:

[1410] The server stores the received medical information in temporary storage.

[1411] Input: Medical information transferred to the server

[1412] Output: Medical information saved in temporary storage

[1413] What happens: The server receives the data and immediately stores it in temporary storage (e.g., RAM or a fast SSD).

[1414] Step 4:

[1415] The server analyzes the data structure and identifies which parts correspond to personal information.

[1416] Input: Medical information stored in temporary storage

[1417] Output: List of identified personal information

[1418] How it works: The server uses AI algorithms (e.g., natural language processing tools) to analyze each field of data and identify personal information such as name, address, and date of birth.

[1419] Step 5:

[1420] The server uses an AI algorithm to anonymize data in accordance with the Personal Information Protection Act.

[1421] Input: List of identified personal information and medical information

[1422] Output: De-identified medical information

[1423] Specific operation: The server anonymizes the identified personal information by replacing it with a random string of characters, and processes "Taro Tanaka, Shinjuku Ward, Tokyo, born April 5, 1965" into "XX Tanaka, XX Ward, Tokyo, born April XX, 1965."

[1424] Step 6:

[1425] The server stores the de-identified medical information in a secure database.

[1426] Input: De-identified medical information

[1427] Output: De-identified medical information stored in a database

[1428] What it does: The server uses encryption technology (e.g., AES encryption) to store the anonymized data in a secure database.

[1429] Step 7:

[1430] The user inputs search conditions for anonymously processed medical information from the terminal.

[1431] Input: User search criteria (e.g., "Search for medication data for diabetes patients")

[1432] Output: Search terms entered on the terminal

[1433] Specific operation: The user enters search criteria into the search screen on the device, and the device prepares to send the criteria to the server.

[1434] Step 8:

[1435] The device sends a search request to the server.

[1436] Input: Search criteria entered into the terminal

[1437] Output: The search request forwarded to the server

[1438] Specific operation: The terminal sends search criteria to the server, and the server generates a database query based on the criteria.

[1439] Step 9:

[1440] The server extracts anonymously processed medical information that matches the search criteria from the database and returns it to the terminal.

[1441] Input: Search criteria transferred to the server

[1442] Output: Extracted and anonymized medical information

[1443] Specific operation: The server executes a database query, extracts anonymized data that matches the search criteria, and returns it to the device.

[1444] Step 10:

[1445] The data received by the device is analyzed using AI tools.

[1446] Input: Anonymously processed medical information received from the server

[1447] Output: Analysis results (e.g., "Correlation between medication frequency and blood glucose levels in adult male diabetic patients")

[1448] How it works: The device uses AI tools such as TensorFlow and PyTorch to analyze the incoming data and extract relevant patterns and trends.

[1449] Step 11:

[1450] The device visually displays the analysis results to the user as graphs and tables.

[1451] Input: Results analyzed by AI tools

[1452] Output: Visualized analysis results (graphs and tables)

[1453] Specific operation: The terminal uses Matplotlib and Tableau to visually display the analysis results and provide them in a format that is easy for the user to understand.

[1454] Step 12:

[1455] The user enters further questions based on the analysis results.

[1456] Input: Additional questions from the user (e.g., "About the development of new drugs that are effective in treating diabetes")

[1457] Output: Additional questions typed into the terminal

[1458] Specific operation: The user enters an additional question into the terminal, and the terminal sends the question to the AI ​​model.

[1459] Step 13:

[1460] The device uses AI to generate answers to questions and display them to the user.

[1461] Input: Additional questions entered into the terminal

[1462] Output: The generated answer

[1463] Specific operation: The device uses a generative AI model (e.g., GPT-3) to generate appropriate answers based on follow-up questions and display them to the user.

[1464] Step 14:

[1465] The device securely stores a history of the user's questions and answers for future reference.

[1466] Input: User question and answer data

[1467] Output: A history of saved questions and answers

[1468] What it does: The device saves all of the user's questions and answers in secure storage for later access.

[1469] The above are the specific processing steps of this system. The data input and output at each step have been clearly stated, and the specific operation has been explained.

[1470] (Application example 1)

[1471] 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."

[1472] In modern factories, IoT sensors are used to collect large amounts of production data. However, this data often contains confidential and personal information and cannot be shared without restriction. Furthermore, to effectively utilize the collected data and maximize production efficiency, it is necessary to analyze and display the information while maintaining security. However, such systems are not currently widespread, posing a major challenge for factory managers.

[1473] 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.

[1474] In this invention, the server includes means for receiving medical information including personal information, means for automatically anonymizing the received medical information in accordance with the Personal Information Protection Act, means for storing the anonymized medical information in a secure database, means for receiving production data collected in a factory, means for anonymizing the received production data, and means for analyzing the anonymized production data and generating information for optimizing production, thereby enabling the secure sharing and analysis of production data.

[1475] 1. "Medical information" refers to data relating to an individual's health condition and treatment, such as a patient's medical record and medication information.

[1476] 2. "Personal information" means information that can identify a specific individual, including name, address, date of birth, etc.

[1477] 3. "Anonymization" is the process of processing data so that specific individuals or confidential information cannot be identified.

[1478] 4. A "database" is a collection of data designed to efficiently manage, search, and update large amounts of data.

[1479] 5. "Server" means a computer system that receives, processes, stores, and distributes data.

[1480] 6. "Receiving means" means a method or device for receiving data from an external source.

[1481] 7. A "search tool" is a method or device for locating specific information within a database.

[1482] 8. "Analytical tools" are methods or devices for analyzing data and extracting useful information.

[1483] 9. "Display means" means a method or device for visually presenting analysis results or other information to a user.

[1484] 10. "In-factory production data" refers to information collected during the production process of a factory, including machine performance, product quality, production volume, etc.

[1485] 11. An "IoT sensor" is a sensor that is connected to the Internet and collects and transmits data on the environment, machine status, etc.

[1486] 12. "Production efficiency" is the ratio of the quantity or quality of goods produced using specific resources (time, labor, etc.).

[1487] An embodiment of the present invention is configured as follows. This system is composed of an AI tool for anonymizing medical information and in-factory production data, and for efficiently collecting, analyzing, and displaying that data. This allows for the effective use of data to proceed smoothly while protecting personal and confidential information.

[1488] System configuration

[1489] 1. Server

[1490] We are responsible for receiving medical information and in-factory production data, including personal information, and anonymizing it.

[1491] The data received is anonymized and stored in a secure database.

[1492] 2. Terminal

[1493] It will be used by medical professionals, researchers, and factory managers and will be responsible for searching and analyzing anonymized data.

[1494] Visually display the analysis results and generate answers to questions.

[1495] Program processing overview and specific examples

[1496] Receiving and anonymizing medical information and in-factory production data

[1497] The user (at a medical institution or factory terminal) inputs medical information and production data.

[1498] The terminal transmits the input data to the server.

[1499] The server stores the received data in temporary storage.

[1500] The server analyzes the data structure and identifies which parts contain personal or confidential information.

[1501] The server uses an AI algorithm to anonymize the data in accordance with the Personal Information Protection Act.

[1502] For example, if the medical information contains the data "Taro Tanaka, Shinjuku Ward, Tokyo, born April 5, 1965," the server will process it into the form "Tanaka XX, XX Ward, Tokyo, born April XX, 1965."

[1503] If the factory production data contains data such as "M123, 2023-01-01", the server will process it into a format such as "M1XX, 2023-XX-XX".

[1504] The server stores the anonymized data in a secure database.

[1505] Data exploration and analysis

[1506] Users (researchers, medical professionals, factory managers) enter search criteria for anonymized data from their terminals.

[1507] The terminal sends a search request to the server.

[1508] The server extracts anonymously processed data that matches the search criteria from the database and returns it to the terminal.

[1509] The device analyzes the received data using AI tools.

[1510] For example, in the case of medical information, in response to a request to "analyze the medication data of diabetic patients," the server extracts the relevant data and generates analysis results on the terminal that show "the correlation between medication frequency and blood glucose levels in adult male diabetic patients."

[1511] When it comes to in-factory production data, in response to a request to "identify machines with high production efficiency," the server extracts the relevant data and generates analysis results on the terminal that show the "correlation between production volume and downtime."

[1512] The terminal visually displays the analysis results to the user as graphs and tables.

[1513] Question and answer history management

[1514] The device securely stores a history of the user's questions and answers for future reference.

[1515] The above is an embodiment of the present invention. This system enables the anonymization and efficient use of medical information and in-factory production data, contributing to the advancement of medical research and improved production efficiency.

[1516] Examples of concrete examples and prompts

[1517] A specific example is a procedure to anonymize specific columns (e.g., "Machine ID" and "Production Date") containing production data collected in a factory, and then extract data where the production count exceeds a certain standard (e.g., "Production Count > 150"). Below is an example of a prompt sentence to be input to a generative AI model based on this specific example.

[1518] Prompt Sentence Examples

[1519] Write a Python program to anonymize specific columns (column names: MachineID, ProductionDate) containing production data collected in the factory, and then extract data where the production count exceeds a certain threshold. To anonymize, replace all numbers with 'X', and use the extraction condition 'ProductionCount > 150'.

[1520]

[1521] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1522] Step 1:

[1523] The user (at a medical institution or factory terminal) inputs medical information and production data.

[1524] Input: User-entered medical information (e.g., "Taro Tanaka, Shinjuku Ward, Tokyo, born April 5, 1965") or production data (e.g., "M123, 2023-01-01").

[1525] Output: Data entry completed on terminal.

[1526] Step 2:

[1527] The terminal transmits the input data to the server.

[1528] Input: Entered medical information or production data.

[1529] Output: The data sent to the server.

[1530] Step 3:

[1531] The server stores the received data in temporary storage.

[1532] Input: The data received by the server.

[1533] Output: Data saved in temporary storage.

[1534] Step 4:

[1535] The server analyzes the data structure and identifies which parts contain personal or confidential information.

[1536] Input: Data stored in temporary storage.

[1537] Output: Data with identified personal or sensitive information.

[1538] Step 5:

[1539] The server uses an AI algorithm to anonymize the data in accordance with the Personal Information Protection Act.

[1540] Input: Data that identifies personal or sensitive information.

[1541] Data processing: Identified personal information and confidential information is anonymized (e.g., "Taro Tanaka, Shinjuku Ward, Tokyo, born April 5, 1965" to "Tanaka XX, XX Ward, Tokyo, born April XX, 1965"; "M123, 2023-01-01" to "M1XX, 2023-XX-XX").

[1542] Output: Anonymized data.

[1543] Step 6:

[1544] The server stores the anonymized data in a secure database.

[1545] Input: Anonymized data.

[1546] Output: Data stored in a secure database.

[1547] Step 7:

[1548] Users (researchers, medical professionals, factory managers) enter search criteria for anonymized data from their terminals.

[1549] Input: Search criteria for anonymized data (e.g., "medication data for diabetes patients" or "machines with high production efficiency").

[1550] Output: Search criteria entered on the terminal.

[1551] Step 8:

[1552] The terminal sends a search request to the server.

[1553] Input: Search criteria.

[1554] Output: The search request sent to the server.

[1555] Step 9:

[1556] The server extracts anonymously processed data that matches the search criteria from the database and returns it to the terminal.

[1557] Input: A request based on search criteria.

[1558] Data processing: Searching and extracting anonymized data from databases (e.g., extracting "medication data of diabetes patients").

[1559] Output: The extracted anonymously processed data is returned to the terminal.

[1560] Step 10:

[1561] The device analyzes the received data using AI tools.

[1562] Input: Anonymously processed data returned from the server.

[1563] Data calculation: Data analysis using AI tools (e.g., analyzing the correlation between medication data and blood glucose levels).

[1564] Output: Analysis results.

[1565] Step 11:

[1566] The terminal visually displays the analysis results to the user as graphs and tables.

[1567] Input: Analysis results.

[1568] Output: Visually displayed graphs and tables.

[1569] Step 12:

[1570] If the user enters further questions based on the analysis results, a similar process is repeated.

[1571] Input: Further user questions.

[1572] Output: Start of a new search and analysis process.

[1573] 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.

[1574] An embodiment of the present invention is configured as follows. This system combines medical information anonymization, data search and analysis, and an emotion engine that recognizes user emotions. This makes it possible to achieve a more advanced user experience while complying with the Personal Information Protection Act.

[1575] System configuration

[1576] 1. Server

[1577] Receive medical information and de-identify it.

[1578] The data received is anonymized and stored in a secure database.

[1579] Data from the emotion engine will also be stored anonymously and used for future improvements.

[1580] 2. Terminal

[1581] Used by healthcare professionals and researchers to search and analyze anonymized data and perform emotion recognition.

[1582] The analysis results are displayed and information is provided according to the user's emotions.

[1583] Generate answers to questions and store a history of questions and answers.

[1584] 3. Emotion Engine

[1585] Recognizes user emotions and provides appropriate feedback and information.

[1586] Sentiment data is stored anonymously to help improve future interactions.

[1587] Program processing overview and specific examples

[1588] Receiving and de-identifying medical information

[1589] The user (a terminal at a medical institution) inputs the patient's medical record information and medication information into the electronic medical record system.

[1590] The device sends the entered medical information to the server, where the data is encrypted using a secure communication protocol (e.g., HTTPS).

[1591] The server stores the received medical information in temporary storage.

[1592] The server analyzes the data structure of the medical information and automatically identifies data that needs to be anonymized in accordance with the Personal Information Protection Act (e.g., name, address, date of birth).

[1593] The server uses an AI algorithm to anonymize the medical information it receives. For example, suppose a patient's medical record contains the data "Taro Tanaka, Shinjuku Ward, Tokyo, born April 5, 1965." In this case, the server processes it into the form "XX Tanaka, XX Ward, Tokyo, born April XX, 1965."

[1594] The server stores the de-identified medical information in a secure database, which is maintained in a secure environment to prevent inappropriate access.

[1595] Data exploration and analysis

[1596] Users (researchers and medical professionals) enter search criteria for anonymized medical information on their terminal and execute a search request.

[1597] The terminal sends a search request to the server.

[1598] The server extracts anonymously processed medical information that matches the search criteria from the database.

[1599] The server transmits the extracted data to the terminal.

[1600] The device then analyzes the received data using AI tools. Specifically, it performs statistical analysis, pattern recognition, correlation analysis, etc. For example, in response to a request to "analyze the medication data of diabetic patients," the server extracts the relevant data and generates an analysis result on the device that shows "the correlation between medication frequency and blood glucose levels in adult male diabetic patients."

[1601] The terminal visually displays the analysis results to the user as charts and graphs.

[1602] Use of emotion engine

[1603] While the user is viewing the analysis results, the emotion engine analyzes the user's facial expressions, tone of voice, etc. to recognize their emotions.

[1604] The device adjusts the information and presentation method according to the user's emotions. For example, if the user is feeling anxious, the device will present more polite and reassuring information.

[1605] The server stores the emotional data anonymously and uses it to improve future interactions.

[1606] Question and answer generation and history management

[1607] The user inputs a follow-up question into the terminal based on the analysis results.

[1608] The device passes the input question to an AI model, which generates an appropriate answer.

[1609] The device displays the generated answer to the user. The emotion engine is also used when generating answers, allowing the answer to be generated while taking the user's emotions into consideration.

[1610] The terminal stores a history of user questions and generated answers in a database.

[1611] The above is an embodiment of the present invention. This system enables anonymization of medical information, efficient data search and analysis, and an improved user experience, contributing to the advancement of medical science, pharmacy, and healthcare.

[1612] The processing flow will be explained below.

[1613] Receiving and de-identifying medical information

[1614] Step 1:

[1615] The user (a terminal at a medical institution) inputs the patient's medical record information and medication information into the electronic medical record system.

[1616] Step 2:

[1617] The device sends the entered medical information to the server, where the data is encrypted using a secure communication protocol (e.g., HTTPS).

[1618] Step 3:

[1619] The server stores the received medical information in temporary storage.

[1620] Step 4:

[1621] The server analyzes the data structure of the medical information and automatically identifies data that needs to be anonymized in accordance with the Personal Information Protection Act (e.g., name, address, date of birth).

[1622] Step 5:

[1623] The server uses an AI algorithm to anonymize the received medical information. Specifically, the server processes personal information as follows:

[1624] Convert names to initials (e.g., Tanaka Taro → Tanaka T)

[1625] Remove or generalize address details (e.g., Shinjuku Ward to Shinjuku District)

[1626] Convert date of birth to age, or hide part of the date (e.g., born April 5, 1965 → born April XX, 1965)

[1627] Step 6:

[1628] The server stores the de-identified medical information in a secure database, which is maintained in a secure environment to prevent inappropriate access.

[1629] ---

[1630] Data exploration and analysis

[1631] Step 1:

[1632] Users (researchers and medical professionals) enter search criteria for anonymized medical information on their terminal and execute a search request.

[1633] Step 2:

[1634] The terminal sends a search request to the server.

[1635] Step 3:

[1636] The server extracts anonymously processed medical information that matches the search criteria from the database.

[1637] Step 4:

[1638] The server transmits the extracted data to the terminal.

[1639] Step 5:

[1640] The device then analyzes the anonymized medical information it receives using AI tools. Specifically, it performs the following analysis:

[1641] Statistical analysis (e.g., calculating the mean, median, and standard deviation)

[1642] Pattern recognition (e.g., disease trend analysis)

[1643] Correlation analysis (e.g., assessing the correlation between medication frequency and treatment effect)

[1644] Step 6:

[1645] The terminal visually displays the analysis results to the user as charts and graphs.

[1646] ---

[1647] Use of emotion engine

[1648] Step 1:

[1649] While the user is viewing the analysis results, the emotion engine analyzes the user's facial expressions and tone of voice in real time to recognize the user's emotions.

[1650] Step 2:

[1651] The emotion engine adjusts the information displayed and the way it is presented based on the emotions it recognizes. For example, if the user is feeling anxious, the emotion engine will instruct the device to present information in a polite manner that gives a sense of security.

[1652] Step 3:

[1653] The terminal displays the tailored information to the user, improving the user's experience.

[1654] Step 4:

[1655] The server anonymously stores the emotion data obtained by the emotion engine, which can be used to improve the system in the future.

[1656] ---

[1657] Question and answer generation and history management

[1658] Step 1:

[1659] The user inputs a follow-up question into the terminal based on the analysis results.

[1660] Step 2:

[1661] The device passes the input question to the AI ​​model, which generates an appropriate answer based on the question content and the user's emotions.

[1662] Step 3:

[1663] The device displays the generated answers to the user, which are adjusted by the emotion engine and presented in a way that takes the user's emotions into consideration.

[1664] Step 4:

[1665] The terminal stores a history of user questions and generated answers in a database.

[1666] These are the specific processing steps of the program. The introduction of the emotion engine not only enables medical and pharmaceutical analysis, but also improves the user experience.

[1667] Example 2

[1668] 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."

[1669] Modern medical information contains a high degree of personal information, so it is necessary to appropriately protect it while making effective use of it. There is also a need for a system that allows medical professionals and researchers to efficiently search and analyze anonymized medical data and support decision-making. Furthermore, to improve the user experience, there is a need for technology that recognizes users' emotions and provides appropriate feedback. Since no system exists that integrates these features, the objective of this invention is to solve this problem.

[1670] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1671] In this invention, the server includes a means for receiving medical information including personal information, a means for automatically anonymizing the received medical information based on personal information protection standards, a means for storing the anonymized medical information in a secure database, a sentiment analysis means for recognizing the user's emotions and providing appropriate feedback and information, and a means for anonymously storing the sentiment data and using it to improve future interactions. This makes it possible to effectively utilize medical data while appropriately protecting personal information and providing an even more advanced user experience.

[1672] "Personal information" is information that can be used to identify a specific individual.

[1673] "Medical information" refers to data including a patient's health condition, diagnosis results, treatment details, medication information, and the like.

[1674] "Anonymization" is the process of processing data so that it cannot identify a specific individual.

[1675] A "database" is a collection of data that is systematically organized and used for efficient search and management.

[1676] "Emotion analysis" is a technology that detects and analyzes a user's emotions from facial expressions, tone of voice, etc.

[1677] "Emotion data" is data that records the user's emotional state.

[1678] A "generative AI model" is a machine learning algorithm that automatically generates optimal results based on input data.

[1679] "Search criteria" refers to the criteria or filters set to extract the desired data from a database.

[1680] "Statistical analysis" is a method of analyzing the statistical characteristics of collected data to reveal trends and relationships.

[1681] "Pattern recognition" is the art of detecting and classifying specific patterns in data.

[1682] "AI tools" refer to software and programs that use artificial intelligence technology.

[1683] This invention is a system that combines medical information anonymization, data search and analysis, and an emotion engine that recognizes user emotions. This makes it possible to achieve a more advanced user experience while complying with the Personal Information Protection Act. The detailed configuration and operation of this system are described below.

[1684] System configuration

[1685] 1. Server

[1686] The medical information including personal information is received from the terminal used by the user. At this time, the medical information includes the patient's medical record information and medication information.

[1687] The server automatically anonymizes the received medical information based on personal information protection standards. For example, data such as "Taro Tanaka, Shinjuku Ward, Tokyo, born April 5, 1965" is converted to "XX Tanaka, XX Ward, Tokyo, born April XX, 1965."

[1688] The server stores de-identified medical information in a secure database, which is managed in a secure, access-controlled environment.

[1689] The server also includes an emotion engine that recognizes the user's emotions and stores the user's emotion data anonymously to improve future interactions.

[1690] 2. Terminal

[1691] It is used by users (healthcare professionals and researchers) to enter search criteria for anonymized medical information.

[1692] The device sends a search request to the server and analyzes the anonymized medical information received from the server using AI tools, specifically statistical analysis, pattern recognition, correlation analysis, etc.

[1693] The terminal visually displays the analysis results to the user as charts and graphs.

[1694] The device uses the generative AI model to generate answers to follow-up questions based on the analysis results, displays them to the user, and saves a history of questions and answers.

[1695] 3. Emotion Engine

[1696] The emotion engine analyzes the user's facial expressions and tone of voice to recognize emotions.

[1697] The device adjusts the information and presentation method to suit the user's emotions. For example, if the user is feeling anxious, the device will present more attentive and reassuring information.

[1698] The emotion engine stores emotional data anonymously to help improve future interactions.

[1699] Specific hardware or software names used

[1700] Server: A high-performance data processing server (any brand or model)

[1701] Device: PC or tablet used by medical professionals and researchers

[1702] Secure communication protocol: HTTPS

[1703] AI algorithms: K-anonymity, L-diversity, etc.

[1704] AI tools: Machine learning software (e.g., TensorFlow, PyTorch)

[1705] Examples of concrete examples and prompts

[1706] Specific examples

[1707] 1. Receipt and de-identification of medical information

[1708] A user enters "Taro Tanaka, Shinjuku-ku, Tokyo, born April 5, 1965" into a hospital's electronic medical record system.

[1709] The device sends this data to a server, which then anonymizes the information and converts it into "Tanaka XX, XX Ward, Tokyo, born April XX, 1965."

[1710] The server stores the anonymized data in a secure database.

[1711] 2. Data retrieval and analysis

[1712] The user inputs the search criteria "I want to analyze the medication data of diabetic patients" into the terminal.

[1713] The server extracts the relevant anonymous data from the database and sends it to the device.

[1714] The device uses AI tools to analyze the correlation between the frequency of medication use and blood sugar levels in diabetic patients and displays the results to the user as a chart.

[1715] 3. Use of Emotion Engine

[1716] When a user views the analysis results, the emotion engine detects the user's anxiety.

[1717] The device adjusts the information presentation to give a sense of security.

[1718] The server stores the emotional data anonymously and uses it to improve interactions.

[1719] Prompt Sentence Examples

[1720] 1. "What is the procedure for de-identifying patient records?"

[1721] 2. "Show me a detailed analysis of medication patterns for diabetes patients."

[1722] 3. "How can I optimize user interactions using an emotion engine?"

[1723] The above is an embodiment of the present invention. This system makes it possible to anonymize medical information, efficiently search and analyze data, and provide an advanced user experience.

[1724] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1725] Program processing flow

[1726] Receiving and de-identifying medical information

[1727] Step 1:

[1728] A user inputs a patient's medical record information and medication information into the electronic medical record system. For example, the user inputs information such as "Taro Tanaka, Shinjuku Ward, Tokyo, born April 5, 1965."

[1729] Input: Patient medical record information

[1730] Output: Medical information stored in the electronic medical record system

[1731] Step 2:

[1732] The device sends the entered medical information to the server as encrypted data using a secure communication protocol (HTTPS).

[1733] Input: Medical information stored in the electronic medical record system

[1734] Output: Encrypted medical information sent to the server

[1735] Step 3:

[1736] The server stores the received medical information in a dedicated temporary storage, which is a secure area with controlled access.

[1737] Input: Encrypted medical information

[1738] Output: Medical information stored in temporary storage

[1739] Step 4:

[1740] The server analyzes the received medical information and identifies data that needs to be anonymized (such as name, address, date of birth, etc.) based on personal information protection standards.

[1741] Input: Medical information in temporary storage

[1742] Output: List of data that needs to be anonymized

[1743] Step 5:

[1744] The server uses an AI algorithm to anonymize medical information. For example, data such as "Taro Tanaka, Shinjuku Ward, Tokyo, born April 5, 1965" is converted to "XX Tanaka, XX Ward, Tokyo, born April XX, 1965."

[1745] Input: List of data that needs to be anonymized, medical information in temporary storage

[1746] Output: De-identified medical information

[1747] Step 6:

[1748] The server stores the de-identified medical information in a secure database that is protected from unauthorized access.

[1749] Input: De-identified medical information

[1750] Output: De-identified medical information stored in a database

[1751] Data exploration and analysis

[1752] Step 1:

[1753] A user enters specific search criteria on a device and executes a search request for anonymized medical information. For example, "I want to analyze medication data for diabetes patients."

[1754] Input: Search criteria

[1755] Output: Search request

[1756] Step 2:

[1757] The terminal sends a search request to the server.

[1758] Input: Search request

[1759] Output: Search request sent to the server

[1760] Step 3:

[1761] The server extracts anonymized medical information that matches the search criteria from the database.

[1762] Input: Search request

[1763] Output: Extracted and anonymized medical information

[1764] Step 4:

[1765] The server transmits the extracted medical information to the terminal.

[1766] Input: Extracted and de-identified medical information

[1767] Output: Medical information sent to the device

[1768] Step 5:

[1769] The device then analyzes the received medical information using AI tools, such as statistical analysis, pattern recognition, and correlation analysis. For example, it can show the correlation between a diabetic patient's medication frequency and blood sugar levels.

[1770] Input: Extracted and de-identified medical information

[1771] Output: Analysis results

[1772] Step 6:

[1773] The terminal visually displays the analysis results to the user as charts and graphs.

[1774] Input: Analysis results

[1775] Output: Visually displayed results

[1776] Use of emotion engine

[1777] Step 1:

[1778] When a user views the analysis results, the emotion engine analyzes the user's facial expressions and tone of voice to recognize their emotions.

[1779] Input: User facial expressions and tone of voice

[1780] Output: Recognized emotion data

[1781] Step 2:

[1782] The device adjusts the information and presentation method to suit the user's emotions. For example, if the user is feeling anxious, the device will present more attentive and reassuring information.

[1783] Input: Recognized emotion data

[1784] Output: Tailored information presentation

[1785] Step 3:

[1786] The server stores the emotional data anonymously and uses it to improve future interactions.

[1787] Input: Recognized emotion data

[1788] Output: Saved emotion data

[1789] Question and answer generation and history management

[1790] Step 1:

[1791] The user types a follow-up question into the terminal based on the analysis results, for example, "What are some other successful diabetes treatments?"

[1792] Input: Additional Question

[1793] Output: Question typed into the terminal

[1794] Step 2:

[1795] The device sends the input question to a generative AI model that generates an appropriate answer, also using an emotion engine.

[1796] Input: Additional Question

[1797] Output: The generated answer

[1798] Step 3:

[1799] The terminal displays the generated answer to the user.

[1800] Input: Generated Answer

[1801] Output: The answer displayed to the user

[1802] Step 4:

[1803] The terminal stores a history of questions and generated answers in a database.

[1804] Input: Question and generated answer

[1805] Output: Question and answer history stored in a database

[1806] The above are the specific processing steps for implementing the present invention. This system enables anonymization of medical information, efficient data search and analysis, and an advanced user experience.

[1807] (Application example 2)

[1808] 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."

[1809] In modern medical and research institutions, the importance of protecting personal information is increasing, and there is a need to handle medical information safely. However, there is a lack of systems that can not only simply anonymize information but also provide appropriate information based on the user's emotions. In addition, there is a need for systems that are easier for medical professionals and researchers to use by combining efficient search and analysis of massive amounts of medical data with user emotion recognition.

[1810] The identification process by the identification 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 receiving data including personal information, means for automatically anonymizing the received information in accordance with security regulations, means for storing the anonymized data in a secure storage area, means for utilizing an emotion engine that recognizes the user's emotions, and means for generating interactive presentation information tailored to the user. This makes it possible to dynamically adjust the presentation method of search and analysis results according to the user's emotions while ensuring the protection of personal information.

[1811] "Personal information" means information that can identify a specific individual, or information that can be easily matched with other information to identify a specific individual.

[1812] "Data" is a collection of information that can be recorded, stored, or transmitted in various formats.

[1813] "Receiving" is the act of receiving a transmitted signal or data.

[1814] "Security regulations" are laws and regulations that stipulate the protection and safe handling of information.

[1815] "Anonymization" means processing personal information so that a specific individual cannot be identified.

[1816] A "storage area" refers to a location or environment where data can be stored, including physical disks and cloud storage.

[1817] An "emotion engine" is an artificial intelligence system for recognizing, analyzing, and applying user emotions.

[1818] "Interactive" refers to a state or system that allows for interaction with a user.

[1819] "Presented information" refers to information or data that is displayed to the user.

[1820] "Secure storage" means storing data in a manner that takes security measures to prevent unauthorized access from outside and information leaks.

[1821] "Search" is the act of investigating and searching a database or storage to find specific data.

[1822] "Analysis" is the act of examining and evaluating data to extract meaningful information and knowledge.

[1823] "Dynamic adjustment" means changing settings and display methods in real time in response to changing situations and conditions.

[1824] This invention is a system for efficiently handling anonymized medical data and improving the user experience by recognizing user emotions. The main components of the system and their respective processes are described below.

[1825] System configuration

[1826] server

[1827] The server has the following features:

[1828] Receiving means: The server has a means for receiving data including personal information sent from medical institutions and research institutions. At this time, the data is encrypted and received using a secure communication protocol (e.g., HTTPS).

[1829] Anonymization method: Received data is automatically anonymized in accordance with the Personal Information Protection Act. AI algorithms are used to appropriately process personally identifiable information. For example, patient names, addresses, and dates of birth are anonymized by redacting or encoding them.

[1830] Storage method: Anonymized data is stored in a secure storage area that is highly secured to prevent unauthorized access from outside.

[1831] Terminal

[1832] The devices will be used by medical professionals and researchers and will have the following features:

[1833] Search method: Search the database for anonymized medical information. Users can enter search criteria and quickly extract relevant data.

[1834] Analysis method: The searched data is analyzed using AI tools, including statistical analysis, pattern recognition, and correlation analysis, to provide the user with the information they need.

[1835] Display: The analysis results are displayed visually, using graphs and charts to provide easy-to-understand results and enable users to make decisions quickly.

[1836] Emotion engine: Analyzes the user's facial expressions and tone of voice to recognize their emotions, and dynamically adjusts the content displayed and interactions accordingly.

[1837] Technology used

[1838] Hardware:

[1839] Smartphone

[1840] Head-mounted display (HMD)

[1841] Webcam (image capture device for emotion recognition)

[1842] software:

[1843] Programs using Python

[1844] Requests library (data retrieval)

[1845] Scikit-learn (data analysis)

[1846] OpenCV (Image capture for emotion recognition)

[1847] EmotionRecognizer (custom emotion recognition model, e.g., built with TensorFlow / Keras)

[1848] Specific examples

[1849] When a healthcare professional is using a smartphone app to analyze the data of a diabetic patient, if the user looks anxious while looking at the analysis results on the screen, the emotion engine will recognize that emotion and display a prompt message like the one below.

[1850] Prompt Sentence Examples

[1851] The patient's condition is stable. Based on the data so far, the progress of the treatment is good enough and there is no need to worry. Please refer to the graph for detailed analysis results.

[1852] This system allows medical professionals and researchers to efficiently search and analyze data while protecting personal information, and also provides appropriate information based on the user's emotions.

[1853] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1854] Step 1:

[1855] The server receives medical data, including personal information, sent from medical institutions and research institutions. At this time, the data is encrypted using a secure communication protocol (e.g., HTTPS) and received. The input is the data sent from the medical institution, and the output is the raw data stored on the server.

[1856] Step 2:

[1857] The server anonymizes the medical data it receives. It uses an AI algorithm to process personally identifiable information (e.g., name, address, date of birth) by redacting or encoding it. The input is raw medical data, and the output is anonymized medical data.

[1858] Step 3:

[1859] The server stores the anonymized medical data in a secure storage area. This storage area is highly secure to prevent unauthorized access from outside. The input is the anonymized medical data, and the output is the anonymized data stored in the secure storage area.

[1860] Step 4:

[1861] A user searches for anonymized medical data using a device. The user enters search criteria into the device, and the device sends the search request to a server. The server extracts anonymized data that matches the criteria from a database. The input is the search criteria entered by the user, and the output is anonymized medical data that matches the criteria.

[1862] Step 5:

[1863] The device analyzes the retrieved medical data using AI tools, performing statistical analysis, pattern recognition, correlation analysis, and other techniques to extract the necessary information. The input is the retrieved anonymized medical data, and the output is the analysis results.

[1864] Step 6:

[1865] The terminal visually displays the analysis results, using graphs and charts to provide easy-to-understand results. The input is the analysis results, and the output is the analysis results displayed on the user's screen.

[1866] Step 7:

[1867] The device's emotion engine analyzes the user's facial expressions and tone of voice to recognize emotions. The camera and microphone capture the user's facial images and voice, which are then analyzed by an emotion recognition model. The input is the user's facial images and voice, and the output is the recognized user emotion.

[1868] Step 8:

[1869] The device dynamically adjusts the display content and interaction based on the user's recognized emotions. For example, if the user feels anxious, it displays more polite prompts. The input is the user's emotions and the analysis results, and the output is the dynamically adjusted information presentation.

[1870] Step 9:

[1871] If the user enters a follow-up question based on the analysis results, the device generates an answer to that question. An appropriate answer is automatically generated using an AI model. The input is the user's question, and the output is the generated answer.

[1872] Step 10:

[1873] The device securely stores the history of generated answers and the user's questions. The stored history can be used for future analysis and service improvement. The input is the history of questions and answers, and the output is the securely stored history data.

[1874] 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.

[1875] 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.

[1876] 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.

[1877] [Fourth embodiment]

[1878] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1879] 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.

[1880] 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).

[1881] 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.

[1882] 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.

[1883] 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).

[1884] 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.

[1885] 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.

[1886] 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.

[1887] 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.

[1888] 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.

[1889] 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.

[1890] 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."

[1891] An embodiment of the present invention is configured as follows. This system is composed of two main AI tools that anonymize medical information and efficiently collect, analyze, and display that data. This allows for smooth utilization of medical information while complying with the Personal Information Protection Act.

[1892] System configuration

[1893] 1. Server

[1894] It is responsible for receiving medical information and anonymizing it.

[1895] The data received is anonymized and stored in a secure database.

[1896] 2. Terminal

[1897] It will be used by medical professionals and researchers and will be responsible for searching and analyzing anonymized data.

[1898] Visually display the analysis results and generate answers to questions.

[1899] Program processing overview and specific examples

[1900] Receiving and de-identifying medical information

[1901] The user (a terminal at a medical institution) inputs the patient's medical record information and medication information.

[1902] The terminal transmits the input medical information to the server.

[1903] The server stores the received medical information in temporary storage.

[1904] The server analyzes the data structure and identifies which parts correspond to personal information.

[1905] The server uses an AI algorithm to anonymize the data in accordance with the Personal Information Protection Act.

[1906] For example, suppose a patient's medical record information contains the data "Taro Tanaka, Tokyo, Shinjuku Ward, born April 5, 1965." In this case, the server processes it into the format "XX Tanaka, XX Ward, Tokyo, born April XX, 1965."

[1907] The server stores the de-identified medical information in a secure database.

[1908] Data exploration and analysis

[1909] Users (researchers and medical professionals) enter search criteria for anonymized medical information from their terminal.

[1910] The terminal sends a search request to the server.

[1911] The server extracts anonymously processed medical information that matches the search criteria from the database and returns it to the terminal.

[1912] The device analyzes the received data using AI tools.

[1913] For example, in response to a request to "analyze the medication data of diabetic patients," the server extracts the relevant data and generates analysis results on the terminal showing "the correlation between medication frequency and blood glucose levels in adult male diabetic patients."

[1914] The terminal visually displays the analysis results to the user as graphs and tables.

[1915] The user enters further questions based on the analysis results.

[1916] The device uses AI to generate answers to questions and display them to the user.

[1917] For example, if you ask a question such as "Regarding the development of new drugs that are effective in treating diabetes," the AI ​​will provide an appropriate answer based on relevant information.

[1918] Question and answer history management

[1919] The device securely stores a history of the user's questions and answers for future reference.

[1920] The above is an embodiment of the present invention. Based on this system, anonymization of medical information and its efficient utilization can be realized, contributing to the advancement of medical and pharmaceutical sciences.

[1921] The processing flow will be explained below.

[1922] Receiving and de-identifying medical information

[1923] Step 1:

[1924] The user (a terminal at a medical institution) inputs the patient's medical record information and medication information into the electronic medical record system.

[1925] Step 2:

[1926] The device sends the entered medical information to the server, where the data is encrypted using a secure communication protocol (e.g., HTTPS).

[1927] Step 3:

[1928] The server stores the received medical information in temporary storage.

[1929] Step 4:

[1930] The server analyzes the data structure of the medical information and automatically identifies data that needs to be anonymized in accordance with the Personal Information Protection Act (e.g., name, address, date of birth).

[1931] Step 5:

[1932] The server uses an AI algorithm to anonymize the received medical information. Specifically, the server processes personal information as follows:

[1933] Convert names to initials (e.g., Tanaka Taro → Tanaka T)

[1934] Remove or generalize address details (e.g., Shinjuku Ward to Shinjuku District)

[1935] Convert date of birth to age, or hide part of the date (e.g., born April 5, 1965 → born April XX, 1965)

[1936] Step 6:

[1937] The server stores the de-identified medical information in a secure database, which is also managed in a secure environment to prevent inappropriate access.

[1938] Data exploration and analysis

[1939] Step 1:

[1940] Users (researchers and medical professionals) enter search criteria for anonymized medical information on their terminal and execute a search request.

[1941] Step 2:

[1942] The terminal transmits the search request input by the user to the server.

[1943] Step 3:

[1944] The server extracts anonymously processed medical information that matches the search criteria from the database.

[1945] Step 4:

[1946] The server transmits the extracted data to the terminal.

[1947] Step 5:

[1948] The device then analyzes the anonymized medical information it receives using AI tools. Specifically, it performs the following analysis:

[1949] Statistical analysis (e.g., calculating the mean, median, and standard deviation)

[1950] Pattern recognition (e.g., disease trend analysis)

[1951] Correlation analysis (e.g., assessing the correlation between medication frequency and treatment effect)

[1952] Step 6:

[1953] The terminal visually displays the analysis results to the user as charts and graphs.

[1954] Question and answer generation and history management

[1955] Step 1:

[1956] The user inputs a follow-up question into the terminal based on the analysis results.

[1957] Step 2:

[1958] The device passes the input question to an AI model, which generates an appropriate answer.

[1959] Step 3:

[1960] The terminal displays the generated answer to the user.

[1961] Step 4:

[1962] The terminal stores a history of user questions and generated answers in a database.

[1963] These are the specific processing steps of the program, which will enable the anonymization of medical information and its efficient collection, search, and analysis.

[1964] Example 1

[1965] 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."

[1966] In conventional medical information management systems, anonymization processing, required by the Personal Information Protection Act, was largely manual and lacked efficiency. Searching and analyzing anonymized data also placed a heavy burden on users, making it difficult to respond quickly. Furthermore, the lack of a question-answering system based on analysis results meant that researchers and medical professionals needed time and effort to quickly extract useful information from the data they obtained. A system that solves these issues and allows for efficient and safe use of medical information is needed.

[1967] 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.

[1968] In this invention, the server includes a means for receiving medical information including personal information, a means for automatically anonymizing the received medical information in accordance with the Personal Information Protection Act, a means for storing the anonymized medical information in a secure database, a means for analyzing the data structure using an artificial intelligence algorithm during the anonymization process to identify personal identification information, and a means for providing storage for temporarily storing the anonymized data before storing it in the database, thereby enabling efficient anonymization and secure storage of medical information.

[1969] Furthermore, the server includes a means for searching the anonymized medical information, a means for analyzing the searched medical information using an artificial intelligence tool, and a means for visually displaying the analysis results, thereby enabling a user to easily search and analyze data and obtain visual results.

[1970] In addition, the server includes a means for generating answers to questions based on the analysis, a means for storing a history of questions and answers, and a means for providing appropriate answers to questions in natural language from users using the generative AI model, thereby enabling users to obtain interactive question and answer responses based on the analysis results and efficiently collect necessary information.

[1971] "Medical information including personal information" refers to medical data such as a patient's diagnosis, prescription details, and treatment history, including information that can be used to identify an individual.

[1972] "Means for receiving" refers to a combination of hardware and software that allows a server or a terminal to acquire data from the outside.

[1973] "Anonymization methods" are algorithms or processes that remove or transform personally identifiable information from received data so that it cannot be used to identify an individual.

[1974] "Means of storing data in a secure database" means a database equipped with encryption technology and access control mechanisms to protect data from unauthorized access or leakage.

[1975] "Artificial intelligence algorithms" are advanced algorithms, including machine learning and deep learning models, for data analysis and pattern recognition.

[1976] "Means for analyzing data structure" refers to a process that automatically analyzes data formats and fields to identify personally identifiable information.

[1977] "Temporary storage" refers to a memory or disk device that temporarily stores received data during processing.

[1978] A "searching means" is a query processing function for extracting data that matches specific criteria from a database.

[1979] "Artificial intelligence tools" refers to a group of software tools for efficiently analyzing large amounts of data, specifically including machine learning and statistical analysis libraries.

[1980] "Visual display means" refers to tools and technologies for presenting data analysis results to users in a visually easy-to-understand format, such as graphs or tables.

[1981] "Means for generating answers to questions based on analysis" refers to generative AI models and natural language processing technologies that answer users' questions based on analysis results and related information.

[1982] The "means for storing question and answer history" is a data management system for storing user-asked questions and their answers for future reference.

[1983] A "generative AI model" is an artificial intelligence model specially trained for text generation and question answering, and uses natural language processing to generate high-performance responses.

[1984] The present invention is a system for anonymizing medical information and efficiently collecting, analyzing, and displaying that data. This system is designed to facilitate the use of medical information while complying with the Personal Information Protection Act. Specific embodiments of the system are described in detail below.

[1985] System configuration

[1986] This system is mainly composed of two main components: a server and a terminal.

[1987] server

[1988] The server receives the medical information, anonymizes the personal information, and stores it in a secure database. The server uses the following technologies:

[1989] Receiving method: The server receives medical information from the medical institution's terminal using a secure communication protocol such as HTTPS.

[1990] De-identification methods: AI algorithms are used to analyze data structures to identify personally identifiable information, such as a patient's name, address, or date of birth, which is then de-identified.

[1991] Means of storage in a secure database: Anonymized data is stored in a secure database using encryption techniques (e.g., AES encryption).

[1992] Specifically, if the server receives the data "Taro Tanaka, Tokyo, Shinjuku Ward, born April 5, 1965," it processes it into the form "Tanaka XX, Tokyo, XX Ward, born April XX, 1965" and stores it in an anonymized database.

[1993] Terminal

[1994] The device will be used by medical professionals and researchers to search and analyze anonymized data. The device will use the following technologies:

[1995] Searching method: A UI is provided for the user to input search criteria, and the criteria are sent to the server.

[1996] Analysis using artificial intelligence tools: The received data is analyzed using AI tools such as TensorFlow and PyTorch.

[1997] Visual display: Display the analysis results as graphs and tables using Matplotlib or Tableau.

[1998] For example, if a user requests to "analyze the medication data of diabetic patients," the server extracts the relevant data and generates analysis results on the terminal that show the "correlation between medication frequency and blood glucose levels in adult male diabetic patients."

[1999] The device also uses generative AI models (e.g., GPT-3) to provide appropriate answers to questions posed by users in natural language. For example, if a user asks, "Regarding the development of new drugs effective in treating diabetes," the AI ​​will provide an appropriate answer based on relevant information.

[2000] Specific examples

[2001] Below is a concrete example of how the system works.

[2002] 1. The user enters the patient's medical record information into the terminal: "Taro Tanaka, Shinjuku Ward, Tokyo, born April 5, 1965, diabetes, prescribed metformin 500 mg."

[2003] 2. The device sends this information to the server.

[2004] 3. The server anonymizes the received information and adds it to a stored database.

[2005] 4. The user types "Search for medication data for diabetes patients" into the terminal.

[2006] 5. The server extracts the relevant data from the database and returns it to the terminal.

[2007] 6. The device analyzes the received data and generates a graph showing the correlation between the frequency of metformin use and blood glucose levels among diabetic patients.

[2008] 7. A user asks, "What about the development of new drugs that are effective in treating diabetes?"

[2009] 8. The device's AI generates an answer based on relevant research data and displays it to the user.

[2010] Prompt Sentence Examples

[2011] Below is an example of a prompt that a user can enter into a generative AI model.

[2012] "Please enter the medical record information for Taro Tanaka, born April 5, 1965, in Shinjuku Ward, Tokyo."

[2013] "Please set the medication data of diabetic patients as search criteria."

[2014] "Please provide specific information regarding the development of new drugs that are effective in treating diabetes."

[2015] The above is an embodiment of the present invention. This system enables efficient anonymization, search, analysis, and interactive question-and-answering of medical information, and is expected to contribute to the advancement of medical and pharmaceutical sciences.

[2016] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2017] Step 1:

[2018] The user inputs the patient's medical record information and medication information into the terminal.

[2019] Input: Patient's medical record information (e.g., "Taro Tanaka, Tokyo, Shinjuku Ward, born April 5, 1965, diabetes, prescribed metformin 500 mg")

[2020] Output: The entered medical information is saved on the device.

[2021] Specific operation: The user enters patient information on the input screen of the terminal, and the terminal temporarily stores the information.

[2022] Step 2:

[2023] The terminal transmits the input medical information to the server.

[2024] Input: Medical information stored on the device

[2025] Output: Medical information transferred to the server

[2026] Specific operation: The device sends the entered data to the server using a secure communication protocol (e.g., HTTPS).

[2027] Step 3:

[2028] The server stores the received medical information in temporary storage.

[2029] Input: Medical information transferred to the server

[2030] Output: Medical information saved in temporary storage

[2031] What happens: The server receives the data and immediately stores it in temporary storage (e.g., RAM or a fast SSD).

[2032] Step 4:

[2033] The server analyzes the data structure and identifies which parts correspond to personal information.

[2034] Input: Medical information stored in temporary storage

[2035] Output: List of identified personal information

[2036] How it works: The server uses AI algorithms (e.g., natural language processing tools) to analyze each field of data and identify personal information such as name, address, and date of birth.

[2037] Step 5:

[2038] The server uses an AI algorithm to anonymize data in accordance with the Personal Information Protection Act.

[2039] Input: List of identified personal information and medical information

[2040] Output: De-identified medical information

[2041] Specific operation: The server anonymizes the identified personal information by replacing it with a random string of characters, and processes "Taro Tanaka, Shinjuku Ward, Tokyo, born April 5, 1965" into "XX Tanaka, XX Ward, Tokyo, born April XX, 1965."

[2042] Step 6:

[2043] The server stores the de-identified medical information in a secure database.

[2044] Input: De-identified medical information

[2045] Output: De-identified medical information stored in a database

[2046] What it does: The server uses encryption technology (e.g., AES encryption) to store the anonymized data in a secure database.

[2047] Step 7:

[2048] The user inputs search conditions for anonymously processed medical information from the terminal.

[2049] Input: User search criteria (e.g., "Search for medication data for diabetes patients")

[2050] Output: Search terms entered on the terminal

[2051] Specific operation: The user enters search criteria into the search screen on the device, and the device prepares to send the criteria to the server.

[2052] Step 8:

[2053] The device sends a search request to the server.

[2054] Input: Search criteria entered into the terminal

[2055] Output: The search request forwarded to the server

[2056] Specific operation: The terminal sends search criteria to the server, and the server generates a database query based on the criteria.

[2057] Step 9:

[2058] The server extracts anonymously processed medical information that matches the search criteria from the database and returns it to the terminal.

[2059] Input: Search criteria transferred to the server

[2060] Output: Extracted and anonymized medical information

[2061] Specific operation: The server executes a database query, extracts anonymized data that matches the search criteria, and returns it to the device.

[2062] Step 10:

[2063] The data received by the device is analyzed using AI tools.

[2064] Input: Anonymously processed medical information received from the server

[2065] Output: Analysis results (e.g., "Correlation between medication frequency and blood glucose levels in adult male diabetic patients")

[2066] How it works: The device uses AI tools such as TensorFlow and PyTorch to analyze the incoming data and extract relevant patterns and trends.

[2067] Step 11:

[2068] The device visually displays the analysis results to the user as graphs and tables.

[2069] Input: Results analyzed by AI tools

[2070] Output: Visualized analysis results (graphs and tables)

[2071] Specific operation: The terminal uses Matplotlib and Tableau to visually display the analysis results and provide them in a format that is easy for the user to understand.

[2072] Step 12:

[2073] The user enters further questions based on the analysis results.

[2074] Input: Additional questions from the user (e.g., "About the development of new drugs that are effective in treating diabetes")

[2075] Output: Additional questions typed into the terminal

[2076] Specific operation: The user enters an additional question into the terminal, and the terminal sends the question to the AI ​​model.

[2077] Step 13:

[2078] The device uses AI to generate answers to questions and display them to the user.

[2079] Input: Additional questions entered into the terminal

[2080] Output: The generated answer

[2081] Specific operation: The device uses a generative AI model (e.g., GPT-3) to generate appropriate answers based on follow-up questions and display them to the user.

[2082] Step 14:

[2083] The device securely stores a history of the user's questions and answers for future reference.

[2084] Input: User question and answer data

[2085] Output: A history of saved questions and answers

[2086] What it does: The device saves all of the user's questions and answers in secure storage for later access.

[2087] The above are the specific processing steps of this system. The data input and output at each step have been clearly stated, and the specific operation has been explained.

[2088] (Application example 1)

[2089] 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."

[2090] In modern factories, IoT sensors are used to collect large amounts of production data. However, this data often contains confidential and personal information and cannot be shared without restriction. Furthermore, to effectively utilize the collected data and maximize production efficiency, it is necessary to analyze and display the information while maintaining security. However, such systems are not currently widespread, posing a major challenge for factory managers.

[2091] 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.

[2092] In this invention, the server includes means for receiving medical information including personal information, means for automatically anonymizing the received medical information in accordance with the Personal Information Protection Act, means for storing the anonymized medical information in a secure database, means for receiving production data collected in a factory, means for anonymizing the received production data, and means for analyzing the anonymized production data and generating information for optimizing production, thereby enabling the secure sharing and analysis of production data.

[2093] 1. "Medical information" refers to data relating to an individual's health condition and treatment, such as a patient's medical record and medication information.

[2094] 2. "Personal information" means information that can identify a specific individual, including name, address, date of birth, etc.

[2095] 3. "Anonymization" is the process of processing data so that specific individuals or confidential information cannot be identified.

[2096] 4. A "database" is a collection of data designed to efficiently manage, search, and update large amounts of data.

[2097] 5. "Server" means a computer system that receives, processes, stores, and distributes data.

[2098] 6. "Receiving means" means a method or device for receiving data from an external source.

[2099] 7. A "search tool" is a method or device for locating specific information within a database.

[2100] 8. "Analytical tools" are methods or devices for analyzing data and extracting useful information.

[2101] 9. "Display means" means a method or device for visually presenting analysis results or other information to a user.

[2102] 10. "In-factory production data" refers to information collected during the production process of a factory, including machine performance, product quality, production volume, etc.

[2103] 11. An "IoT sensor" is a sensor that is connected to the Internet and collects and transmits data on the environment, machine status, etc.

[2104] 12. "Production efficiency" is the ratio of the quantity or quality of goods produced using specific resources (time, labor, etc.).

[2105] An embodiment of the present invention is configured as follows. This system is composed of an AI tool for anonymizing medical information and in-factory production data, and for efficiently collecting, analyzing, and displaying that data. This allows for the effective use of data to proceed smoothly while protecting personal and confidential information.

[2106] System configuration

[2107] 1. Server

[2108] We are responsible for receiving medical information and in-factory production data, including personal information, and anonymizing it.

[2109] The data received is anonymized and stored in a secure database.

[2110] 2. Terminal

[2111] It will be used by medical professionals, researchers, and factory managers and will be responsible for searching and analyzing anonymized data.

[2112] Visually display the analysis results and generate answers to questions.

[2113] Program processing overview and specific examples

[2114] Receiving and anonymizing medical information and in-factory production data

[2115] The user (at a medical institution or factory terminal) inputs medical information and production data.

[2116] The terminal transmits the input data to the server.

[2117] The server stores the received data in temporary storage.

[2118] The server analyzes the data structure and identifies which parts contain personal or confidential information.

[2119] The server uses an AI algorithm to anonymize the data in accordance with the Personal Information Protection Act.

[2120] For example, if the medical information contains the data "Taro Tanaka, Shinjuku Ward, Tokyo, born April 5, 1965," the server will process it into the form "Tanaka XX, XX Ward, Tokyo, born April XX, 1965."

[2121] If the factory production data contains data such as "M123, 2023-01-01", the server will process it into a format such as "M1XX, 2023-XX-XX".

[2122] The server stores the anonymized data in a secure database.

[2123] Data exploration and analysis

[2124] Users (researchers, medical professionals, factory managers) enter search criteria for anonymized data from their terminals.

[2125] The terminal sends a search request to the server.

[2126] The server extracts anonymously processed data that matches the search criteria from the database and returns it to the terminal.

[2127] The device analyzes the received data using AI tools.

[2128] For example, in the case of medical information, in response to a request to "analyze the medication data of diabetic patients," the server extracts the relevant data and generates analysis results on the terminal that show "the correlation between medication frequency and blood glucose levels in adult male diabetic patients."

[2129] When it comes to in-factory production data, in response to a request to "identify machines with high production efficiency," the server extracts the relevant data and generates analysis results on the terminal that show the "correlation between production volume and downtime."

[2130] The terminal visually displays the analysis results to the user as graphs and tables.

[2131] Question and answer history management

[2132] The device securely stores a history of the user's questions and answers for future reference.

[2133] The above is an embodiment of the present invention. This system enables the anonymization and efficient use of medical information and in-factory production data, contributing to the advancement of medical research and improved production efficiency.

[2134] Examples of concrete examples and prompts

[2135] A specific example is a procedure to anonymize specific columns (e.g., "Machine ID" and "Production Date") containing production data collected in a factory, and then extract data where the production count exceeds a certain standard (e.g., "Production Count > 150"). Below is an example of a prompt sentence to be input to a generative AI model based on this specific example.

[2136] Prompt Sentence Examples

[2137] Write a Python program to anonymize specific columns (column names: MachineID, ProductionDate) containing production data collected in the factory, and then extract data where the production count exceeds a certain threshold. To anonymize, replace all numbers with 'X', and use the extraction condition 'ProductionCount > 150'.

[2138]

[2139] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2140] Step 1:

[2141] The user (at a medical institution or factory terminal) inputs medical information and production data.

[2142] Input: User-entered medical information (e.g., "Taro Tanaka, Shinjuku Ward, Tokyo, born April 5, 1965") or production data (e.g., "M123, 2023-01-01").

[2143] Output: Data entry completed on terminal.

[2144] Step 2:

[2145] The terminal transmits the input data to the server.

[2146] Input: Entered medical information or production data.

[2147] Output: The data sent to the server.

[2148] Step 3:

[2149] The server stores the received data in temporary storage.

[2150] Input: The data received by the server.

[2151] Output: Data saved in temporary storage.

[2152] Step 4:

[2153] The server analyzes the data structure and identifies which parts contain personal or confidential information.

[2154] Input: Data stored in temporary storage.

[2155] Output: Data with identified personal or sensitive information.

[2156] Step 5:

[2157] The server uses an AI algorithm to anonymize the data in accordance with the Personal Information Protection Act.

[2158] Input: Data that identifies personal or sensitive information.

[2159] Data processing: Identified personal information and confidential information is anonymized (e.g., "Taro Tanaka, Shinjuku Ward, Tokyo, born April 5, 1965" to "Tanaka XX, XX Ward, Tokyo, born April XX, 1965"; "M123, 2023-01-01" to "M1XX, 2023-XX-XX").

[2160] Output: Anonymized data.

[2161] Step 6:

[2162] The server stores the anonymized data in a secure database.

[2163] Input: Anonymized data.

[2164] Output: Data stored in a secure database.

[2165] Step 7:

[2166] Users (researchers, medical professionals, factory managers) enter search criteria for anonymized data from their terminals.

[2167] Input: Search criteria for anonymized data (e.g., "medication data for diabetes patients" or "machines with high production efficiency").

[2168] Output: Search criteria entered on the terminal.

[2169] Step 8:

[2170] The terminal sends a search request to the server.

[2171] Input: Search criteria.

[2172] Output: The search request sent to the server.

[2173] Step 9:

[2174] The server extracts anonymously processed data that matches the search criteria from the database and returns it to the terminal.

[2175] Input: A request based on search criteria.

[2176] Data processing: Searching and extracting anonymized data from databases (e.g., extracting "medication data of diabetes patients").

[2177] Output: The extracted anonymously processed data is returned to the terminal.

[2178] Step 10:

[2179] The device analyzes the received data using AI tools.

[2180] Input: Anonymously processed data returned from the server.

[2181] Data calculation: Data analysis using AI tools (e.g., analyzing the correlation between medication data and blood glucose levels).

[2182] Output: Analysis results.

[2183] Step 11:

[2184] The terminal visually displays the analysis results to the user as graphs and tables.

[2185] Input: Analysis results.

[2186] Output: Visually displayed graphs and tables.

[2187] Step 12:

[2188] If the user enters further questions based on the analysis results, a similar process is repeated.

[2189] Input: Further user questions.

[2190] Output: Start of a new search and analysis process.

[2191] 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.

[2192] An embodiment of the present invention is configured as follows. This system combines medical information anonymization, data search and analysis, and an emotion engine that recognizes user emotions. This makes it possible to achieve a more advanced user experience while complying with the Personal Information Protection Act.

[2193] System configuration

[2194] 1. Server

[2195] Receive medical information and de-identify it.

[2196] The data received is anonymized and stored in a secure database.

[2197] Data from the emotion engine will also be stored anonymously and used for future improvements.

[2198] 2. Terminal

[2199] Used by healthcare professionals and researchers to search and analyze anonymized data and perform emotion recognition.

[2200] The analysis results are displayed and information is provided according to the user's emotions.

[2201] Generate answers to questions and store a history of questions and answers.

[2202] 3. Emotion Engine

[2203] Recognizes user emotions and provides appropriate feedback and information.

[2204] Sentiment data is stored anonymously to help improve future interactions.

[2205] Program processing overview and specific examples

[2206] Receiving and de-identifying medical information

[2207] The user (a terminal at a medical institution) inputs the patient's medical record information and medication information into the electronic medical record system.

[2208] The device sends the entered medical information to the server, where the data is encrypted using a secure communication protocol (e.g., HTTPS).

[2209] The server stores the received medical information in temporary storage.

[2210] The server analyzes the data structure of the medical information and automatically identifies data that needs to be anonymized in accordance with the Personal Information Protection Act (e.g., name, address, date of birth).

[2211] The server uses an AI algorithm to anonymize the medical information it receives. For example, suppose a patient's medical record contains the data "Taro Tanaka, Shinjuku Ward, Tokyo, born April 5, 1965." In this case, the server processes it into the form "XX Tanaka, XX Ward, Tokyo, born April XX, 1965."

[2212] The server stores the de-identified medical information in a secure database, which is maintained in a secure environment to prevent inappropriate access.

[2213] Data exploration and analysis

[2214] Users (researchers and medical professionals) enter search criteria for anonymized medical information on their terminal and execute a search request.

[2215] The terminal sends a search request to the server.

[2216] The server extracts anonymously processed medical information that matches the search criteria from the database.

[2217] The server transmits the extracted data to the terminal.

[2218] The device then analyzes the received data using AI tools. Specifically, it performs statistical analysis, pattern recognition, correlation analysis, etc. For example, in response to a request to "analyze the medication data of diabetic patients," the server extracts the relevant data and generates an analysis result on the device that shows "the correlation between medication frequency and blood glucose levels in adult male diabetic patients."

[2219] The terminal visually displays the analysis results to the user as charts and graphs.

[2220] Use of emotion engine

[2221] While the user is viewing the analysis results, the emotion engine analyzes the user's facial expressions, tone of voice, etc. to recognize their emotions.

[2222] The device adjusts the information and presentation method according to the user's emotions. For example, if the user is feeling anxious, the device will present more polite and reassuring information.

[2223] The server stores the emotional data anonymously and uses it to improve future interactions.

[2224] Question and answer generation and history management

[2225] The user inputs a follow-up question into the terminal based on the analysis results.

[2226] The device passes the input question to an AI model, which generates an appropriate answer.

[2227] The device displays the generated answer to the user. The emotion engine is also used when generating answers, allowing the answer to be generated while taking the user's emotions into consideration.

[2228] The terminal stores a history of user questions and generated answers in a database.

[2229] The above is an embodiment of the present invention. This system enables anonymization of medical information, efficient data search and analysis, and an improved user experience, contributing to the advancement of medical science, pharmacy, and healthcare.

[2230] The processing flow will be explained below.

[2231] Receiving and de-identifying medical information

[2232] Step 1:

[2233] The user (a terminal at a medical institution) inputs the patient's medical record information and medication information into the electronic medical record system.

[2234] Step 2:

[2235] The device sends the entered medical information to the server, where the data is encrypted using a secure communication protocol (e.g., HTTPS).

[2236] Step 3:

[2237] The server stores the received medical information in temporary storage.

[2238] Step 4:

[2239] The server analyzes the data structure of the medical information and automatically identifies data that needs to be anonymized in accordance with the Personal Information Protection Act (e.g., name, address, date of birth).

[2240] Step 5:

[2241] The server uses an AI algorithm to anonymize the received medical information. Specifically, the server processes personal information as follows:

[2242] Convert names to initials (e.g., Tanaka Taro → Tanaka T)

[2243] Remove or generalize address details (e.g., Shinjuku Ward to Shinjuku District)

[2244] Convert date of birth to age, or hide part of the date (e.g., born April 5, 1965 → born April XX, 1965)

[2245] Step 6:

[2246] The server stores the de-identified medical information in a secure database, which is maintained in a secure environment to prevent inappropriate access.

[2247] ---

[2248] Data exploration and analysis

[2249] Step 1:

[2250] Users (researchers and medical professionals) enter search criteria for anonymized medical information on their terminal and execute a search request.

[2251] Step 2:

[2252] The terminal sends a search request to the server.

[2253] Step 3:

[2254] The server extracts anonymously processed medical information that matches the search criteria from the database.

[2255] Step 4:

[2256] The server transmits the extracted data to the terminal.

[2257] Step 5:

[2258] The device then analyzes the anonymized medical information it receives using AI tools. Specifically, it performs the following analysis:

[2259] Statistical analysis (e.g., calculating the mean, median, and standard deviation)

[2260] Pattern recognition (e.g., disease trend analysis)

[2261] Correlation analysis (e.g., assessing the correlation between medication frequency and treatment effect)

[2262] Step 6:

[2263] The terminal visually displays the analysis results to the user as charts and graphs.

[2264] ---

[2265] Use of emotion engine

[2266] Step 1:

[2267] While the user is viewing the analysis results, the emotion engine analyzes the user's facial expressions and tone of voice in real time to recognize the user's emotions.

[2268] Step 2:

[2269] The emotion engine adjusts the information displayed and the way it is presented based on the emotions it recognizes. For example, if the user is feeling anxious, the emotion engine will instruct the device to present information in a polite manner that gives a sense of security.

[2270] Step 3:

[2271] The terminal displays the tailored information to the user, improving the user's experience.

[2272] Step 4:

[2273] The server anonymously stores the emotion data obtained by the emotion engine, which can be used to improve the system in the future.

[2274] ---

[2275] Question and answer generation and history management

[2276] Step 1:

[2277] The user inputs a follow-up question into the terminal based on the analysis results.

[2278] Step 2:

[2279] The device passes the input question to the AI ​​model, which generates an appropriate answer based on the question content and the user's emotions.

[2280] Step 3:

[2281] The device displays the generated answers to the user, which are adjusted by the emotion engine and presented in a way that takes the user's emotions into consideration.

[2282] Step 4:

[2283] The terminal stores a history of user questions and generated answers in a database.

[2284] These are the specific processing steps of the program. The introduction of the emotion engine not only enables medical and pharmaceutical analysis, but also improves the user experience.

[2285] Example 2

[2286] 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."

[2287] Modern medical information contains a high degree of personal information, so it is necessary to appropriately protect it while making effective use of it. There is also a need for a system that allows medical professionals and researchers to efficiently search and analyze anonymized medical data and support decision-making. Furthermore, to improve the user experience, there is a need for technology that recognizes users' emotions and provides appropriate feedback. Since no system exists that integrates these features, the objective of this invention is to solve this problem.

[2288] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2289] In this invention, the server includes a means for receiving medical information including personal information, a means for automatically anonymizing the received medical information based on personal information protection standards, a means for storing the anonymized medical information in a secure database, a sentiment analysis means for recognizing the user's emotions and providing appropriate feedback and information, and a means for anonymously storing the sentiment data and using it to improve future interactions. This makes it possible to effectively utilize medical data while appropriately protecting personal information and providing an even more advanced user experience.

[2290] "Personal information" is information that can be used to identify a specific individual.

[2291] "Medical information" refers to data including a patient's health condition, diagnosis results, treatment details, medication information, and the like.

[2292] "Anonymization" is the process of processing data so that it cannot identify a specific individual.

[2293] A "database" is a collection of data that is systematically organized and used for efficient search and management.

[2294] "Emotion analysis" is a technology that detects and analyzes a user's emotions from facial expressions, tone of voice, etc.

[2295] "Emotion data" is data that records the user's emotional state.

[2296] A "generative AI model" is a machine learning algorithm that automatically generates optimal results based on input data.

[2297] "Search criteria" refers to the criteria or filters set to extract the desired data from a database.

[2298] "Statistical analysis" is a method of analyzing the statistical characteristics of collected data to reveal trends and relationships.

[2299] "Pattern recognition" is the art of detecting and classifying specific patterns in data.

[2300] "AI tools" refer to software and programs that use artificial intelligence technology.

[2301] This invention is a system that combines medical information anonymization, data search and analysis, and an emotion engine that recognizes user emotions. This makes it possible to achieve a more advanced user experience while complying with the Personal Information Protection Act. The detailed configuration and operation of this system are described below.

[2302] System configuration

[2303] 1. Server

[2304] The medical information including personal information is received from the terminal used by the user. At this time, the medical information includes the patient's medical record information and medication information.

[2305] The server automatically anonymizes the received medical information based on personal information protection standards. For example, data such as "Taro Tanaka, Shinjuku Ward, Tokyo, born April 5, 1965" is converted to "XX Tanaka, XX Ward, Tokyo, born April XX, 1965."

[2306] The server stores de-identified medical information in a secure database, which is managed in a secure, access-controlled environment.

[2307] The server also includes an emotion engine that recognizes the user's emotions and stores the user's emotion data anonymously to improve future interactions.

[2308] 2. Terminal

[2309] It is used by users (healthcare professionals and researchers) to enter search criteria for anonymized medical information.

[2310] The device sends a search request to the server and analyzes the anonymized medical information received from the server using AI tools, specifically statistical analysis, pattern recognition, correlation analysis, etc.

[2311] The terminal visually displays the analysis results to the user as charts and graphs.

[2312] The device uses the generative AI model to generate answers to follow-up questions based on the analysis results, displays them to the user, and saves a history of questions and answers.

[2313] 3. Emotion Engine

[2314] The emotion engine analyzes the user's facial expressions and tone of voice to recognize emotions.

[2315] The device adjusts the information and presentation method to suit the user's emotions. For example, if the user is feeling anxious, the device will present more attentive and reassuring information.

[2316] The emotion engine stores emotional data anonymously to help improve future interactions.

[2317] Specific hardware or software names used

[2318] Server: A high-performance data processing server (any brand or model)

[2319] Device: PC or tablet used by medical professionals and researchers

[2320] Secure communication protocol: HTTPS

[2321] AI algorithms: K-anonymity, L-diversity, etc.

[2322] AI tools: Machine learning software (e.g., TensorFlow, PyTorch)

[2323] Examples of concrete examples and prompts

[2324] Specific examples

[2325] 1. Receipt and de-identification of medical information

[2326] A user enters "Taro Tanaka, Shinjuku-ku, Tokyo, born April 5, 1965" into a hospital's electronic medical record system.

[2327] The device sends this data to a server, which then anonymizes the information and converts it into "Tanaka XX, XX Ward, Tokyo, born April XX, 1965."

[2328] The server stores the anonymized data in a secure database.

[2329] 2. Data retrieval and analysis

[2330] The user inputs the search criteria "I want to analyze the medication data of diabetic patients" into the terminal.

[2331] The server extracts the relevant anonymous data from the database and sends it to the device.

[2332] The device uses AI tools to analyze the correlation between the frequency of medication use and blood sugar levels in diabetic patients and displays the results to the user as a chart.

[2333] 3. Use of Emotion Engine

[2334] When a user views the analysis results, the emotion engine detects the user's anxiety.

[2335] The device adjusts the information presentation to give a sense of security.

[2336] The server stores the emotional data anonymously and uses it to improve interactions.

[2337] Prompt Sentence Examples

[2338] 1. "What is the procedure for de-identifying patient records?"

[2339] 2. "Show me a detailed analysis of medication patterns for diabetes patients."

[2340] 3. "How can I optimize user interactions using an emotion engine?"

[2341] The above is an embodiment of the present invention. This system makes it possible to anonymize medical information, efficiently search and analyze data, and provide an advanced user experience.

[2342] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2343] Program processing flow

[2344] Receiving and de-identifying medical information

[2345] Step 1:

[2346] A user inputs a patient's medical record information and medication information into the electronic medical record system. For example, the user inputs information such as "Taro Tanaka, Shinjuku Ward, Tokyo, born April 5, 1965."

[2347] Input: Patient medical record information

[2348] Output: Medical information stored in the electronic medical record system

[2349] Step 2:

[2350] The device sends the entered medical information to the server as encrypted data using a secure communication protocol (HTTPS).

[2351] Input: Medical information stored in the electronic medical record system

[2352] Output: Encrypted medical information sent to the server

[2353] Step 3:

[2354] The server stores the received medical information in a dedicated temporary storage, which is a secure area with controlled access.

[2355] Input: Encrypted medical information

[2356] Output: Medical information stored in temporary storage

[2357] Step 4:

[2358] The server analyzes the received medical information and identifies data that needs to be anonymized (such as name, address, date of birth, etc.) based on personal information protection standards.

[2359] Input: Medical information in temporary storage

[2360] Output: List of data that needs to be anonymized

[2361] Step 5:

[2362] The server uses an AI algorithm to anonymize medical information. For example, data such as "Taro Tanaka, Shinjuku Ward, Tokyo, born April 5, 1965" is converted to "XX Tanaka, XX Ward, Tokyo, born April XX, 1965."

[2363] Input: List of data that needs to be anonymized, medical information in temporary storage

[2364] Output: De-identified medical information

[2365] Step 6:

[2366] The server stores the de-identified medical information in a secure database that is protected from unauthorized access.

[2367] Input: De-identified medical information

[2368] Output: De-identified medical information stored in a database

[2369] Data exploration and analysis

[2370] Step 1:

[2371] A user enters specific search criteria on a device and executes a search request for anonymized medical information. For example, "I want to analyze medication data for diabetes patients."

[2372] Input: Search criteria

[2373] Output: Search request

[2374] Step 2:

[2375] The terminal sends a search request to the server.

[2376] Input: Search request

[2377] Output: Search request sent to the server

[2378] Step 3:

[2379] The server extracts anonymized medical information that matches the search criteria from the database.

[2380] Input: Search request

[2381] Output: Extracted and anonymized medical information

[2382] Step 4:

[2383] The server transmits the extracted medical information to the terminal.

[2384] Input: Extracted and de-identified medical information

[2385] Output: Medical information sent to the device

[2386] Step 5:

[2387] The device then analyzes the received medical information using AI tools, such as statistical analysis, pattern recognition, and correlation analysis. For example, it can show the correlation between a diabetic patient's medication frequency and blood sugar levels.

[2388] Input: Extracted and de-identified medical information

[2389] Output: Analysis results

[2390] Step 6:

[2391] The terminal visually displays the analysis results to the user as charts and graphs.

[2392] Input: Analysis results

[2393] Output: Visually displayed results

[2394] Use of emotion engine

[2395] Step 1:

[2396] When a user views the analysis results, the emotion engine analyzes the user's facial expressions and tone of voice to recognize their emotions.

[2397] Input: User facial expressions and tone of voice

[2398] Output: Recognized emotion data

[2399] Step 2:

[2400] The device adjusts the information and presentation method to suit the user's emotions. For example, if the user is feeling anxious, the device will present more attentive and reassuring information.

[2401] Input: Recognized emotion data

[2402] Output: Tailored information presentation

[2403] Step 3:

[2404] The server stores the emotional data anonymously and uses it to improve future interactions.

[2405] Input: Recognized emotion data

[2406] Output: Saved emotion data

[2407] Question and answer generation and history management

[2408] Step 1:

[2409] The user types a follow-up question into the terminal based on the analysis results, for example, "What are some other successful diabetes treatments?"

[2410] Input: Additional Question

[2411] Output: Question typed into the terminal

[2412] Step 2:

[2413] The device sends the input question to a generative AI model that generates an appropriate answer, also using an emotion engine.

[2414] Input: Additional Question

[2415] Output: The generated answer

[2416] Step 3:

[2417] The terminal displays the generated answer to the user.

[2418] Input: Generated Answer

[2419] Output: The answer displayed to the user

[2420] Step 4:

[2421] The terminal stores a history of questions and generated answers in a database.

[2422] Input: Question and generated answer

[2423] Output: Question and answer history stored in a database

[2424] The above are the specific processing steps for implementing the present invention. This system enables anonymization of medical information, efficient data search and analysis, and an advanced user experience.

[2425] (Application example 2)

[2426] 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."

[2427] In modern medical and research institutions, the importance of protecting personal information is increasing, and there is a need to handle medical information safely. However, there is a lack of systems that can not only simply anonymize information but also provide appropriate information based on the user's emotions. In addition, there is a need for systems that are easier for medical professionals and researchers to use by combining efficient search and analysis of massive amounts of medical data with user emotion recognition.

[2428] The identification process by the identification 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 receiving data including personal information, means for automatically anonymizing the received information in accordance with security regulations, means for storing the anonymized data in a secure storage area, means for utilizing an emotion engine that recognizes the user's emotions, and means for generating interactive presentation information tailored to the user. This makes it possible to dynamically adjust the presentation method of search and analysis results according to the user's emotions while ensuring the protection of personal information.

[2429] "Personal information" means information that can identify a specific individual, or information that can be easily matched with other information to identify a specific individual.

[2430] "Data" is a collection of information that can be recorded, stored, or transmitted in various formats.

[2431] "Receiving" is the act of receiving a transmitted signal or data.

[2432] "Security regulations" are laws and regulations that stipulate the protection and safe handling of information.

[2433] "Anonymization" means processing personal information so that a specific individual cannot be identified.

[2434] A "storage area" refers to a location or environment where data can be stored, including physical disks and cloud storage.

[2435] An "emotion engine" is an artificial intelligence system for recognizing, analyzing, and applying user emotions.

[2436] "Interactive" refers to a state or system that allows for interaction with a user.

[2437] "Presented information" refers to information or data that is displayed to the user.

[2438] "Secure storage" means storing data in a manner that takes security measures to prevent unauthorized access from outside and information leaks.

[2439] "Search" is the act of investigating and searching a database or storage to find specific data.

[2440] "Analysis" is the act of examining and evaluating data to extract meaningful information and knowledge.

[2441] "Dynamic adjustment" means changing settings and display methods in real time in response to changing situations and conditions.

[2442] This invention is a system for efficiently handling anonymized medical data and improving the user experience by recognizing user emotions. The main components of the system and their respective processes are described below.

[2443] System configuration

[2444] server

[2445] The server has the following features:

[2446] Receiving means: The server has a means for receiving data including personal information sent from medical institutions and research institutions. At this time, the data is encrypted and received using a secure communication protocol (e.g., HTTPS).

[2447] Anonymization method: Received data is automatically anonymized in accordance with the Personal Information Protection Act. AI algorithms are used to appropriately process personally identifiable information. For example, patient names, addresses, and dates of birth are anonymized by redacting or encoding them.

[2448] Storage method: Anonymized data is stored in a secure storage area that is highly secured to prevent unauthorized access from outside.

[2449] Terminal

[2450] The devices will be used by medical professionals and researchers and will have the following features:

[2451] Search method: Search the database for anonymized medical information. Users can enter search criteria and quickly extract relevant data.

[2452] Analysis method: The searched data is analyzed using AI tools, including statistical analysis, pattern recognition, and correlation analysis, to provide the user with the information they need.

[2453] Display: The analysis results are displayed visually, using graphs and charts to provide easy-to-understand results and enable users to make decisions quickly.

[2454] Emotion engine: Analyzes the user's facial expressions and tone of voice to recognize their emotions, and dynamically adjusts the content displayed and interactions accordingly.

[2455] Technology used

[2456] Hardware:

[2457] Smartphone

[2458] Head-mounted display (HMD)

[2459] Webcam (image capture device for emotion recognition)

[2460] software:

[2461] Programs using Python

[2462] Requests library (data retrieval)

[2463] Scikit-learn (data analysis)

[2464] OpenCV (Image capture for emotion recognition)

[2465] EmotionRecognizer (custom emotion recognition model, e.g., built with TensorFlow / Keras)

[2466] Specific examples

[2467] When a healthcare professional is using a smartphone app to analyze the data of a diabetic patient, if the user looks anxious while looking at the analysis results on the screen, the emotion engine will recognize that emotion and display a prompt message like the one below.

[2468] Prompt Sentence Examples

[2469] The patient's condition is stable. Based on the data so far, the progress of the treatment is good enough and there is no need to worry. Please refer to the graph for detailed analysis results.

[2470] This system allows medical professionals and researchers to efficiently search and analyze data while protecting personal information, and also provides appropriate information based on the user's emotions.

[2471] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2472] Step 1:

[2473] The server receives medical data, including personal information, sent from medical institutions and research institutions. At this time, the data is encrypted using a secure communication protocol (e.g., HTTPS) and received. The input is the data sent from the medical institution, and the output is the raw data stored on the server.

[2474] Step 2:

[2475] The server anonymizes the medical data it receives. It uses an AI algorithm to process personally identifiable information (e.g., name, address, date of birth) by redacting or encoding it. The input is raw medical data, and the output is anonymized medical data.

[2476] Step 3:

[2477] The server stores the anonymized medical data in a secure storage area. This storage area is highly secure to prevent unauthorized access from outside. The input is the anonymized medical data, and the output is the anonymized data stored in the secure storage area.

[2478] Step 4:

[2479] A user searches for anonymized m...

Claims

1. a means for receiving medical information, including personal information; A means of automatically anonymizing received medical information in accordance with the Personal Information Protection Act; a means of storing de-identified medical information in a secure database; A system including:

2. 10. The system of claim 1, A means of searching de-identified medical information; means for analyzing the retrieved medical information; a means for displaying the analysis results; A system including:

3. 3. The system of claim 2, a means for generating answers to the analysis-based questions; a means for storing a history of questions and answers; A system including:

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