system

Abstract

We provide the system. [Solution] A means of receiving patient medical images, A means for analyzing the aforementioned medical images and extracting anatomical features, A means for automatically generating an implant design based on the aforementioned anatomical features, A means for manufacturing the aforementioned designed implant using a three-dimensional printing machine, A means for notifying the user of the progress information regarding the implant design, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventional medical implants are provided in standardized shapes and sizes and do not fit all patients. Therefore, when they do not fit, reoperation may be required, which increases the burden on patients. Also, the efficiency of design and manufacturing is an issue, and there is a need to supply optimized medical implants quickly. It is required to solve these problems and efficiently provide medical implants suitable for each patient.

Means for Solving the Problems

[0005] This invention relates to a system capable of analyzing a patient's medical images and extracting anatomical features from those images. This system incorporates artificial intelligence that automatically designs implants based on the obtained anatomical features. Furthermore, it manufactures implants using a 3D printer and provides feedback through post-manufacturing quality evaluation to help improve the design process. This enables the efficient supply of patient-optimized medical implants, thereby improving surgical success rates and patient satisfaction.

[0006] "Patient" refers to a person who is the recipient of medical information or treatment.

[0007] "Medical images" refer to image information that visualizes the internal structure of the human body, obtained using devices such as CT scans and MRIs.

[0008] "Anatomical features" refer to information that describes the structural characteristics of a patient's body parts, including bone shape, density, and soft tissue arrangement.

[0009] An "implant" refers to an artificial object embedded in the human body, a medical device used to assist or replace the function of bones, teeth, joints, etc.

[0010] "Automatically generating designs" refers to the process by which computer programs or artificial intelligence create design drawings that meet specific specifications and conditions based on user input, without any manual work.

[0011] A "three-dimensional printer" refers to a device that physically constructs three-dimensional objects by layering materials based on digital data.

[0012] "Artificial intelligence" refers to a technology or system that enables computers to mimic human intellectual work and make autonomous decisions through machine learning and data analysis.

[0013] "Feedback" refers to the process or information used to evaluate the output of a particular process or system and to return that information for future improvements or adjustments based on that evaluation. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a processor with a reference number (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), etc.

[0018] In the following embodiments, a RAM (Random Access Memory) with a reference number is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a storage with a reference number is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the 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.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0028] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention is a system for efficiently designing and manufacturing medical implants tailored to the individual needs of patients. The main components of the system include a server, terminals, and users.

[0036] Server Functions

[0037] The server receives patient CT scans and MRI data sent from medical institutions and securely stores it in a database. This data forms the basis for analyzing the patient's anatomical features. An AI agent within the server analyzes this data and extracts anatomical features using image analysis techniques. Based on these analysis results, the server generates a patient-specific 3D model.

[0038] Next, the server designs the optimal implant for the patient based on the generated 3D model. This design process utilizes artificial intelligence that has learned from past successes and failures to suggest materials and shapes. The designed implant is then converted into a 3D printable format.

[0039] Furthermore, the server connects with the 3D printer and sends the design data to the printer to begin manufacturing the actual implant. During the manufacturing process, the server monitors the printer's operation in real time and issues a rapid alert if any problems occur.

[0040] Device functions

[0041] The terminal is part of the system at the medical institution that collects medical images. The terminal transmits patient CT scans and MRI data, along with their identification information, to the server. Furthermore, when manufactured implants are shipped, the terminal receives notifications from the server and manages the arrival of the implants.

[0042] User involvement

[0043] The user is a healthcare worker responsible for receiving and quality-checking implants. The user receives the manufactured implants and inspects them. If necessary, they provide feedback on the implant quality to the server. This feedback is incorporated as training data for the AI ​​and used to improve future implant design processes.

[0044] As a specific example, when manufacturing a knee implant for patient A, the terminal collects CT data of the patient's knee and sends it to the server. The server's AI agent analyzes the data and designs the optimal implant for patient A's knee. Based on this design, the implant is manufactured using a 3D printer and received by the user. This entire process allows for the rapid provision of implants tailored to individual anatomical characteristics, which is expected to improve patient satisfaction.

[0045] The following describes the processing flow.

[0046] Step 1:

[0047] The terminal acquires patient medical images from CT scanners and MRI machines and uploads this data to the server via a secure channel. After the upload, the terminal confirms the completion of data transmission and receives a notification of receipt from the server.

[0048] Step 2:

[0049] The server stores the received medical image data in a database, and the AI ​​agent performs the necessary preprocessing to analyze the data. This includes, for example, denoising the images and adjusting the resolution.

[0050] Step 3:

[0051] An AI agent on the server analyzes pre-processed images to extract the patient's anatomical features. This analysis uses machine learning algorithms to identify features such as bone shape and density.

[0052] Step 4:

[0053] The server generates a digital 3D model of the implant, tailored to the patient, based on the extracted anatomical features. This model is created using an automated design algorithm and verified to meet specific requirements.

[0054] Step 5:

[0055] The server converts the designed implant model into a format that can be manufactured by a 3D printer and sends the data to the 3D printer. Here, the server places the data in a queue of print jobs and monitors the manufacturing process.

[0056] Step 6:

[0057] The server monitors the printing status in real time to ensure the printer is functioning correctly. If an anomaly is detected, it issues an alert and provides instructions for troubleshooting.

[0058] Step 7:

[0059] After the server confirms that manufacturing is complete, it sends the implant shipment information to the terminal. Based on this information, the terminal notifies the medical institution's logistics department of the expected arrival date of the implant.

[0060] Step 8:

[0061] The user receives the implant upon arrival at the medical institution and verifies its quality and compliance with specifications. After the inspection is complete, the user sends the results as feedback to the server, which records this information in a database for future designs.

[0062] (Example 1)

[0063] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0064] Conventional medical component design and manufacturing methods have presented challenges in quickly and precisely responding to the individual needs of each patient, leading to increased time costs and human resource requirements. Furthermore, the design phase relied heavily on human experience, making it difficult to efficiently supply medical components of consistent quality. Additionally, delays in anomaly detection during the manufacturing process increased the risk of quality defects.

[0065] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0066] In this invention, the server includes means for receiving patient medical information, means for analyzing and extracting biological characteristics, means for automatically generating medical component designs, means for optimizing the design process using machine learning algorithms, and means for monitoring the manufacturing process and detecting anomalies. This makes it possible to quickly and accurately design and manufacture medical components tailored to individual patients, and to immediately identify and address problems in the manufacturing process.

[0067] "Medical information" refers to data collected to indicate a patient's health status, and encompasses a wide range of information, including image data and diagnostic results.

[0068] "Biological characteristics" refer to data that represents the physiological or anatomical features unique to each individual patient, including, for example, bone shape and tissue density.

[0069] "Medical components" are artificial structures or devices used for the purpose of treating patients, specifically components such as implants and prostheses.

[0070] "Automated generation" refers to a process carried out by machines or programs with minimal human intervention, and specifically refers to the act of efficiently performing complex tasks using software.

[0071] "Machine learning algorithms" refer to mathematical methods and processes that enable computers to learn data patterns from experience and make predictions and decisions about the future.

[0072] "Optimization" is a technique for adjusting processes and designs to obtain the best or most favorable results for a specific purpose, and is performed to improve efficiency and performance.

[0073] "Anomaly detection" is a system function that monitors for states or patterns that deviate from normal processes, quickly identifies them, and issues warnings.

[0074] This invention is a system for efficiently designing and manufacturing medical components that meet the individual needs of each patient. Its main components include a server, terminals, and users.

[0075] The server is a powerful computer device with robust data processing capabilities that securely processes patient medical information received from healthcare institutions. Data processing utilizes domain-specific AI analysis software, particularly for image analysis and pattern recognition. This AI excels at extracting biometric features and generating highly accurate 3D models. These 3D models are then optimized using machine learning algorithms to design medical components. Advanced design support software is used to convert the data into a format suitable for 3D printing. Furthermore, the server interacts with 3D printers, monitoring the manufacturing process and issuing alerts if defects are detected.

[0076] The terminal is connected to the healthcare facility's system and is a device that collects medical information and transmits it to a server. The terminal uses a highly secure data transfer protocol to encrypt and transmit patient CT scans and MRI data. The terminal also receives manufacturing information and quality inspection results transmitted from the server and informs the user.

[0077] The user receives the manufactured medical components and performs quality assurance. The user checks the product dimensions and machining precision and provides feedback to the server. This feedback can then be used by the AI ​​in future designs, contributing to further improvements in accuracy.

[0078] As a concrete example, when manufacturing a knee joint implant for a patient, the terminal collects CT data of the patient's knee and sends it to the server. The server analyzes this data and designs the optimal implant for the patient's anatomy. Based on this data, a 3D printer manufactures the implant, and the user receives it. The server collects this feedback and uses it to improve the design for the next time. An example of a prompt message for the generated AI model could be in the form of, "Design a 3D model of the optimal knee implant using patient A's knee CT data."

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] The terminal collects patient CT scan and MRI data within the medical facility and receives associated identification information as data input. This data is converted to a specific format and sent to the server using encryption technology. An encrypted, secure data package is generated as output.

[0082] Step 2:

[0083] The server receives encrypted data sent from the terminal and decrypts it to restore the input data. Here, the data's integrity and completeness are verified, and it is stored in a secure database. As output, the data is securely stored and available for use in subsequent analysis steps.

[0084] Step 3:

[0085] An AI agent installed on the server takes securely stored medical data as input and extracts biological features using image analysis algorithms. Specifically, it uses machine learning models to analyze the characteristics of bones and tissues and outputs them as feature vectors.

[0086] Step 4:

[0087] The server receives extracted biometric data as input and constructs a patient-specific virtual model using 3D model generation software. During this process, an algorithm trained on past success and failure data is used to suggest optimal materials and shape parameters. The output is 3D printable digital design data.

[0088] Step 5:

[0089] The server sends the designed 3D model data to the 3D printer, which then receives the data as input and manufactures the actual medical component. During the manufacturing process, the server monitors the printer's status in real time and immediately issues an alert if any abnormalities are detected. The output is the completed physical medical component.

[0090] Step 6:

[0091] The user receives the manufactured medical component. The user specifically inspects the component's dimensions, shape, and surface condition to confirm that it meets quality standards. Based on this confirmation, feedback is provided to the server as input, which is then used again within the system as data for future improvements. The output consists of the quality-verified medical component and data for continuous learning.

[0092] (Application Example 1)

[0093] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0094] There is a need for efficient design and manufacturing processes for medical implants tailored to individual needs, as well as proper management of their progress and quality feedback. In particular, a challenge is enabling healthcare professionals to understand the design and manufacturing progress in real time and to provide patients with implants that are appropriate for them quickly and accurately.

[0095] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0096] In this invention, the server includes means for receiving medical images of a patient, means for analyzing the medical images to extract anatomical features, means for automatically generating an implant design based on the anatomical features, means for manufacturing the designed implant using a three-dimensional printer, and means for notifying the user of the progress of the implant design. This enables medical professionals to understand the progress of the design and manufacturing process in real time and to provide implants optimized for each patient.

[0097] "Patient medical images" refer to image data such as CT scans and MRIs taken at hospitals and medical facilities, which capture the patient's anatomical features in detail.

[0098] "Anatomical features" refer to information that describes the internal structure, shape, and location of a patient's organs, and are extracted by analyzing medical images.

[0099] "Methods for automatically generating implant designs" refer to technologies that use a computer system to design implants based on anatomical characteristics and quickly output those designs.

[0100] A "three-dimensional printing machine" is a machine that creates three-dimensional objects based on digital design data, and is used in the manufacture of implants.

[0101] "Implant design progress information" refers to information regarding the progress of the implant design and manufacturing process, which is notified to users as it progresses.

[0102] To implement this invention, collaboration between a server, a terminal, and a user is required. The server first receives the patient's medical image data transmitted from the terminal. This includes CT scans and MRI images. The received data is processed by an AI agent to analyze the anatomical characteristics of the specific patient. Through this analysis, the server generates a patient-specific three-dimensional model, and uses this model to design the implant.

[0103] During the design process, the server uses AI, learned from past surgical data and successful and unsuccessful cases, to suggest implant materials and shapes. This AI acts as computational intelligence, monitoring the design progress in real time and making design changes as needed.

[0104] Next, the server sends the data to a 3D printer to manufacture the designed implant. The 3D printer creates the physical implant based on the design. As manufacturing progresses, the server notifies the terminal of the implant design's progress, allowing the user to check the information.

[0105] As a concrete example, consider the case where a hip joint implant is manufactured for patient C. The server analyzes the CT image of the hip joint received from the terminal and designs the optimal implant. Then, it manufactures the implant using a 3D printer and provides progress information to healthcare professionals via the terminal. This allows healthcare professionals to accurately understand the preparation status of the implant and provide the patient with the best possible medical care.

[0106] Examples of prompts to input into a generative AI model:

[0107] "Please tell me how to design a custom implant based on patient C's CT images and start the manufacturing process using a 3D printer."

[0108] The hardware used included smart devices with cameras (smartphones and tablets), a cluster of servers, and a 3D printer. The software used was Python, OpenCV, and the requests library.

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] The user uses a device to capture medical images of a patient and sends them to a server. As input, the device acquires CT or MRI images of the patient, compresses them, and converts them into a digital format for transmission. As output, the medical images are sent to the server. Specifically, the device packets the captured images according to the communication protocol and uploads them to a designated endpoint on the server.

[0112] Step 2:

[0113] The server analyzes received medical images and extracts anatomical features. The input is medical image data sent from the terminal. The output is digital data containing anatomical features. An AI agent within the server analyzes the images using image processing algorithms and extracts the necessary features. This image processing uses OpenCV and leverages edge detection and region segmentation techniques.

[0114] Step 3:

[0115] The server automatically generates implant designs based on anatomical features. The input is the anatomical feature data obtained in step 2. The output is 3D printable design data. The AI ​​model selects the optimal material and shape from a historical database and converts it into a 3D design format. During this process, a Python data processing library is used to perform numerical calculations to generate the design data.

[0116] Step 4:

[0117] The server transmits the designed implant data to the 3D printer and initiates the manufacturing process. The input is the design data, and the output is the physical implant. Specifically, the server connects to the 3D printer's control system, adds the printing task to a queue, and sends execution commands. The implant is formed by layering the specified material using a continuous lamination method.

[0118] Step 5:

[0119] The server notifies the user's device of the progress of implant design and manufacturing. Input is information on the progress of the manufacturing process, and output is a message sent to the user. Specifically, the server provides the user with the latest progress in the design and manufacturing phases via email or in-app notifications. These notifications are sent in stages depending on the situation.

[0120] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0121] This invention is a system that designs and manufactures medical implants tailored to the individual needs of patients, and further improves the patient's psychological satisfaction before and after surgery by taking into account the user's emotional state. The system is mainly composed of three stakeholders: a server, a terminal, and a user, and incorporates an emotion engine within it.

[0122] Server Functions

[0123] The server uses CT scan and MRI data received from medical institutions via terminals to analyze anatomical features using an AI agent. This data analysis automatically generates a 3D model based on the patient's individual needs. The server then designs implants based on this 3D model, utilizing artificial intelligence trained on past surgical data during the design process.

[0124] Furthermore, the emotion engine integrated into the server collects and analyzes user emotional data. This information visualizes the patient's mental state during and after surgery, supporting decision-making during the surgical process.

[0125] Applications of the Emotion Engine

[0126] The emotion engine analyzes the patient's mental state based on user feedback and evaluates emotions through specific speech data and nonverbal cues. For example, if the emotion engine analyzes the patient's voice input and indicates that the patient is feeling anxious or reassured, it will provide care and surgical explanations tailored to those emotions.

[0127] Device functions

[0128] The terminal functions as an interface for healthcare users to communicate with the server. It performs operations such as uploading medical image data, receiving notifications upon implant arrival, and reviewing analysis results from the emotion engine.

[0129] User involvement

[0130] As healthcare professionals, users will understand the patient's emotional state through the emotion engine and utilize this information in post-operative care plans and explanations. Furthermore, after surgery, they will provide feedback to the server regarding the quality and design fit of the received implant, contributing to improvements in future processes.

[0131] As a concrete example, when considering surgery for patient B, the terminal receives her CT data, and the server analyzes it to design an individual implant. During this process, an emotion engine analyzes patient B's emotional state, and the server incorporates this information into the surgical plan and explanation. After inspection, the user provides appropriate follow-up, taking into account patient B's satisfaction. This entire process provides medical care that considers not only the physical aspects but also the psychological aspects.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] The terminal collects patient CT scan and MRI data from medical facility equipment and securely uploads this data to a server. After uploading, it confirms data transmission and receives notification from the server that the data has been received.

[0135] Step 2:

[0136] The server stores the received medical image data in a database. An AI agent analyzes this data and extracts anatomical features. For example, it identifies information such as bone shape and density and generates a 3D model for the patient.

[0137] Step 3:

[0138] The server designs a custom implant based on a 3D model of the patient. This design is optimized by machine learning algorithms, which learn from past surgical data to suggest materials and shapes.

[0139] Step 4:

[0140] The server sends the designed implant data to the 3D printer and starts the manufacturing process. The server monitors the printer's operation and manages it to ensure that the implant is manufactured successfully.

[0141] Step 5:

[0142] The terminal receives notifications from the server and checks the shipping status of the manufactured implants. It then communicates the arrival schedule to the logistics department and prepares for receipt.

[0143] Step 6:

[0144] The user inspects the implant upon arrival, checking that its appearance and dimensions conform to the design. If there are no problems, they determine that the implant is ready for use in surgery.

[0145] Step 7:

[0146] An emotion engine embedded in the server collects and analyzes emotional data through interactions with users and patients. For example, it analyzes voice input from patients to identify their emotional state.

[0147] Step 8:

[0148] Users develop care plans based on the patient's emotional state. Using the emotional analysis results obtained from the server, they provide mental care to patients during and after surgery, aiming to improve patient satisfaction.

[0149] (Example 2)

[0150] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0151] In the design and manufacture of medical implants, there is a need to efficiently provide high-quality implants while considering the anatomical characteristics and mental state of each individual patient. However, current processes have challenges in adapting to individual patient needs and considering patients' emotional states. Therefore, improvements are needed to enhance both the physical and mental satisfaction of patients.

[0152] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0153] In this invention, the server includes means for receiving medical images of a patient, means for analyzing the medical images to extract anatomical features, means for automatically generating an implant design based on the anatomical features, means for transmitting the generated implant design to a medical institution, means for collecting and analyzing patient emotional data, and means for adjusting the surgical plan based on the analysis results. This enables the optimization of individual medical plans and the provision of high-quality medical services, including emotional care.

[0154] "Patient medical images" refer to visual information of the inside of a patient's body obtained using methods such as CT scans and MRI.

[0155] "Anatomical features" refer to a collection of information that describes specific shapes, dimensions, and arrangements related to a patient's physical structure.

[0156] An "implant" refers to an artificial organ or assistive device designed to be implanted in a patient's body.

[0157] "Automated generation" refers to the process of creating designs and layouts using artificial intelligence and algorithms without any human intervention.

[0158] A "generative AI model" refers to a machine learning algorithm that learns from past data and generates new implant designs.

[0159] "Emotional data" refers to information such as voice, facial expressions, and behavior related to the emotions a patient exhibits.

[0160] A "surgical plan" refers to a detailed outline of the surgical procedure to be performed on a specific patient.

[0161] A "machine learning algorithm" refers to a computational method that uses large amounts of data to discover patterns and rules and predict future actions and outcomes.

[0162] "Evaluation" refers to the process of measuring the quality of a product based on certain criteria and identifying areas for improvement.

[0163] This invention is a system for efficiently designing and manufacturing medical implants that meet the diverse needs of each patient, and for providing care tailored to the patient's emotional state. The system has three central components: a server, a terminal, and a user, and utilizes a generative AI model.

[0164] The server receives patient medical images provided by medical institutions via terminals. This image data includes CT scans and MRI data. The server uses this data for an AI agent to analyze the patient's anatomical features. Based on the analysis results, the generated AI model automatically creates a 3D design for the optimal implant for the patient. This generation process references past medical data, enabling designs that leverage individual anatomical features.

[0165] The terminal is in the hands of the healthcare provider and operates in conjunction with the server. The terminal can view analysis results and generated implant designs received from the server, and also collects emotional data from patients. This emotional data includes voice input and non-verbal cues, and is designed to facilitate smooth data entry. The collected emotional data is sent to the server for analysis by an emotional engine.

[0166] Users, acting as healthcare professionals, utilize feedback from the emotion engine to provide optimal patient care. This can be used to improve patients' mental well-being during surgical explanations and follow-up. For example, if a patient shows anxiety while waiting for surgery, the emotion engine analyzes their state, and the server suggests approaches to alleviate that anxiety to the user.

[0167] Example prompt: "Please explain how individual implant designs are generated in this system."

[0168] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0169] Step 1:

[0170] The server receives CT scan and MRI data from medical institutions via terminals. This data is provided as input, and an AI agent analyzes the images to extract anatomical features. As output, shape data tailored to each individual patient is obtained. At this stage, the server performs the specific operation of rapidly processing a massive amount of image data and quantifying anatomical features.

[0171] Step 2:

[0172] The server automatically generates a 3D design of the implant using a generative AI model based on extracted anatomical features. The input here is numerical data of anatomical features, and the output is a 3D model of the implant optimized for the patient. The generative AI model performs specific calculations to generate the optimal shape, referencing an existing design database.

[0173] Step 3:

[0174] The server sends the generated 3D design to the terminal for user review. The input is a 3D model, and the output is digital design information displayed on the terminal. The server performs specific actions to ensure reliable data transmission and provide an interface that allows healthcare professionals to suggest necessary adjustments.

[0175] Step 4:

[0176] The terminal receives feedback from healthcare professionals, makes necessary adjustments, and then returns the final design to the server. At this stage, the design data, which has been adjusted to reflect user feedback, is output. The terminal then performs specific operations to accurately reflect the input adjustment information and incorporate it into the design.

[0177] Step 5:

[0178] The server uses an emotion engine to collect and analyze the aforementioned emotional data. This process takes voice and nonverbal data provided from the terminal as input and outputs information evaluating the patient's psychological state. The emotion engine analyzes voice tone and patterns to quantify emotions and performs specific emotional evaluations.

[0179] Step 6:

[0180] The server adjusts the surgical plan based on the analysis results and generates the optimal patient care procedure. In this step, the results of the emotional assessment are input, and the adjusted surgical plan and care suggestions are output. Through communication with healthcare professionals, the server performs specific actions to select the optimal process according to the patient's physical and mental condition.

[0181] (Application Example 2)

[0182] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0183] Designing and manufacturing medical implants tailored to individual patient needs while simultaneously improving patient emotional well-being is a challenging task within current medical processes. Conventional technologies make the process of creating implants that meet individual anatomical needs complex and time-consuming, and methods for considering patients' emotional states in real time are limited. Therefore, there is a need to provide a method that comprehensively improves both the physical and mental care of patients.

[0184] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0185] In this invention, the server includes means for receiving medical images of a patient, means for analyzing the medical images to extract anatomical features, means for automatically generating implant designs based on the anatomical features, means for manufacturing the designed implants using a three-dimensional manufacturing device, and means for utilizing an emotion analysis mechanism to analyze the patient's emotional state and adjust care accordingly. This makes it possible to simultaneously achieve individualized implant design and improved emotional care for patients.

[0186] "Medical images" are photographs or videos that visually represent a patient's internal condition and are used for diagnosis and the development of treatment plans.

[0187] "Anatomical features" refer to the detailed characteristics of a patient's physical structure, including information about its shape, tissues, and positional relationships.

[0188] An "implant" is a medical device or material that is inserted or fitted into a patient's body and used to complement or modify the function of that person's body.

[0189] A "three-dimensional manufacturing machine" is a machine that forms a three-dimensional object by layering materials based on digital data.

[0190] An "emotion analysis mechanism" is a technical means of evaluating and judging a person's emotional state based on voice, facial expressions, and other nonverbal cues.

[0191] "Intelligent function" refers to the ability to use artificial intelligence to analyze and judge data, and to independently learn and adapt in order to perform specific tasks.

[0192] A "response" is information generated from system input and analysis, and represents actions or opinions provided for improvement or adjustment in the next steps.

[0193] The system for realizing this invention designs and manufactures medical implants based on the individual needs of patients, and improves patients' mental satisfaction by analyzing their emotional state and utilizing this information in their care.

[0194] The server primarily receives and analyzes patient medical images to extract anatomical features. Specifically, it processes CT scans and MRI data on the platform, using high-precision algorithms to identify necessary features. This enables the design of implants tailored to each individual patient. Leveraging AI technology, the system generates optimal designs based on past treatment data, and then materializes those designs using 3D manufacturing equipment.

[0195] Furthermore, the emotion analysis mechanism analyzes emotions from data such as the patient's voice and facial expressions. This function allows for monitoring the psychological state of patients during surgery or treatment, providing guidance for appropriate care. Wearable devices such as smart glasses allow care staff to access this data in real time and take specific care actions.

[0196] As a concrete example, when used in a nursing home, a system is utilized to monitor the emotional state of patient A. The AI ​​detects when she is feeling anxious, and the data is sent to staff via a server. Based on notifications displayed on her glasses, staff suggest relaxing coping mechanisms. In this way, integrating medical and psychological care makes it possible to improve the overall patient satisfaction.

[0197] An example of a specific prompt for a generative AI model is: "Analyze patient A's voice input and facial expression data to identify her emotional state. Based on her emotions, suggest appropriate care."

[0198] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0199] Step 1:

[0200] The terminal receives CT scan and MRI data from the patient's medical institution. This data is sent to a server for preparation for analysis. The input is medical images, and the output is the transfer to the server. Specifically, the terminal uploads data to the server via the network.

[0201] Step 2:

[0202] The server analyzes received medical images to extract anatomical features. Image processing techniques and machine learning algorithms (e.g., deep learning) are used for the analysis. The input is medical images, and the output is extracted anatomical feature data. Specifically, the algorithm performs pattern recognition to generate the feature data.

[0203] Step 3:

[0204] The server automatically generates implant designs based on extracted anatomical features. This process utilizes AI technology. The input is anatomical feature data, and the output is implant design data. Specifically, the AI ​​generates the design using relevant shape data from an existing database.

[0205] Step 4:

[0206] The server transmits design data to a 3D manufacturing machine to produce the implant. The input is the design data for the implant, and the output is the physical implant. Specifically, the manufacturing machine is operated through the data transmission system, and the implant is formed by layering materials.

[0207] Step 5:

[0208] The server collects voice and facial expression data from the terminal to analyze the patient's emotional state. It uses an emotion analysis mechanism to evaluate emotions. The input is voice and facial expression data, and the output is the analyzed emotional state. Specifically, the server uses a generative AI model to analyze the data and quantitatively evaluate emotions.

[0209] Step 6:

[0210] The user implements appropriate care for the patient based on the analysis results. Specific care actions and communications are selected based on emotional data. The input is the analyzed emotional state, and the output is the care plan. Specifically, the user checks notifications and receives instructions to take appropriate action.

[0211] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0212] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0213] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0214] [Second Embodiment]

[0215] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0216] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0217] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0218] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0219] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0220] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0221] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0222] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0223] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0224] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0225] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0226] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0227] This invention is a system for efficiently designing and manufacturing medical implants tailored to the individual needs of patients. The main components of the system include a server, terminals, and users.

[0228] Server Functions

[0229] The server receives patient CT scans and MRI data sent from medical institutions and securely stores it in a database. This data forms the basis for analyzing the patient's anatomical features. An AI agent within the server analyzes this data and extracts anatomical features using image analysis techniques. Based on these analysis results, the server generates a patient-specific 3D model.

[0230] Next, the server designs the optimal implant for the patient based on the generated 3D model. This design process utilizes artificial intelligence that has learned from past successes and failures to suggest materials and shapes. The designed implant is then converted into a 3D printable format.

[0231] Furthermore, the server connects with the 3D printer and sends the design data to the printer to begin manufacturing the actual implant. During the manufacturing process, the server monitors the printer's operation in real time and issues a rapid alert if any problems occur.

[0232] Device functions

[0233] The terminal is part of the system at the medical institution that collects medical images. The terminal transmits patient CT scans and MRI data, along with their identification information, to the server. Furthermore, when manufactured implants are shipped, the terminal receives notifications from the server and manages the arrival of the implants.

[0234] User involvement

[0235] The user is a healthcare worker responsible for receiving and quality-checking implants. The user receives the manufactured implants and inspects them. If necessary, they provide feedback on the implant quality to the server. This feedback is incorporated as training data for the AI ​​and used to improve future implant design processes.

[0236] As a specific example, when manufacturing a knee implant for patient A, the terminal collects CT data of the patient's knee and sends it to the server. The server's AI agent analyzes the data and designs the optimal implant for patient A's knee. Based on this design, the implant is manufactured using a 3D printer and received by the user. This entire process allows for the rapid provision of implants tailored to individual anatomical characteristics, which is expected to improve patient satisfaction.

[0237] The following describes the processing flow.

[0238] Step 1:

[0239] The terminal acquires patient medical images from CT scanners and MRI machines and uploads this data to the server via a secure channel. After the upload, the terminal confirms the completion of data transmission and receives a notification of receipt from the server.

[0240] Step 2:

[0241] The server stores the received medical image data in a database, and the AI ​​agent performs the necessary preprocessing to analyze the data. This includes, for example, denoising the images and adjusting the resolution.

[0242] Step 3:

[0243] An AI agent on the server analyzes pre-processed images to extract the patient's anatomical features. This analysis uses machine learning algorithms to identify features such as bone shape and density.

[0244] Step 4:

[0245] The server generates a digital 3D model of the implant, tailored to the patient, based on the extracted anatomical features. This model is created using an automated design algorithm and verified to meet specific requirements.

[0246] Step 5:

[0247] The server converts the designed implant model into a format that can be manufactured by a 3D printer and sends the data to the 3D printer. Here, the server places the data in a queue of print jobs and monitors the manufacturing process.

[0248] Step 6:

[0249] The server monitors the printing status in real time to ensure the printer is functioning correctly. If an anomaly is detected, it issues an alert and provides instructions for troubleshooting.

[0250] Step 7:

[0251] After the server confirms that manufacturing is complete, it sends the implant shipment information to the terminal. Based on this information, the terminal notifies the medical institution's logistics department of the expected arrival date of the implant.

[0252] Step 8:

[0253] The user receives the implant upon arrival at the medical institution and verifies its quality and compliance with specifications. After the inspection is complete, the user sends the results as feedback to the server, which records this information in a database for future designs.

[0254] (Example 1)

[0255] Next, we will describe Example 1. 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."

[0256] Conventional medical component design and manufacturing methods have presented challenges in quickly and precisely responding to the individual needs of each patient, leading to increased time costs and human resource requirements. Furthermore, the design phase relied heavily on human experience, making it difficult to efficiently supply medical components of consistent quality. Additionally, delays in anomaly detection during the manufacturing process increased the risk of quality defects.

[0257] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0258] In this invention, the server includes means for receiving patient medical information, means for analyzing and extracting biological characteristics, means for automatically generating medical component designs, means for optimizing the design process using machine learning algorithms, and means for monitoring the manufacturing process and detecting anomalies. This makes it possible to quickly and accurately design and manufacture medical components tailored to individual patients, and to immediately identify and address problems in the manufacturing process.

[0259] "Medical information" refers to data collected to indicate a patient's health status, and encompasses a wide range of information, including image data and diagnostic results.

[0260] "Biological characteristics" refer to data that represents the physiological or anatomical features unique to each individual patient, including, for example, bone shape and tissue density.

[0261] "Medical components" are artificial structures or devices used for the purpose of treating patients, specifically components such as implants and prostheses.

[0262] "Automated generation" refers to a process carried out by machines or programs with minimal human intervention, and specifically refers to the act of efficiently performing complex tasks using software.

[0263] "Machine learning algorithms" refer to mathematical methods and processes that enable computers to learn data patterns from experience and make predictions and decisions about the future.

[0264] "Optimization" is a technique for adjusting processes and designs to obtain the best or most favorable results for a specific purpose, and is performed to improve efficiency and performance.

[0265] "Anomaly detection" is a system function that monitors for states or patterns that deviate from normal processes, quickly identifies them, and issues warnings.

[0266] This invention is a system for efficiently designing and manufacturing medical components that meet the individual needs of each patient. Its main components include a server, terminals, and users.

[0267] The server is a powerful computer device with robust data processing capabilities that securely processes patient medical information received from healthcare institutions. Data processing utilizes domain-specific AI analysis software, particularly for image analysis and pattern recognition. This AI excels at extracting biometric features and generating highly accurate 3D models. These 3D models are then optimized using machine learning algorithms to design medical components. Advanced design support software is used to convert the data into a format suitable for 3D printing. Furthermore, the server interacts with 3D printers, monitoring the manufacturing process and issuing alerts if defects are detected.

[0268] The terminal is connected to the healthcare facility's system and is a device that collects medical information and transmits it to a server. The terminal uses a highly secure data transfer protocol to encrypt and transmit patient CT scans and MRI data. The terminal also receives manufacturing information and quality inspection results transmitted from the server and informs the user.

[0269] The user receives the manufactured medical components and performs quality assurance. The user checks the product dimensions and machining precision and provides feedback to the server. This feedback can then be used by the AI ​​in future designs, contributing to further improvements in accuracy.

[0270] As a concrete example, when manufacturing a knee joint implant for a patient, the terminal collects CT data of the patient's knee and sends it to the server. The server analyzes this data and designs the optimal implant for the patient's anatomy. Based on this data, a 3D printer manufactures the implant, and the user receives it. The server collects this feedback and uses it to improve the design for the next time. An example of a prompt message for the generated AI model could be in the form of, "Design a 3D model of the optimal knee implant using patient A's knee CT data."

[0271] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0272] Step 1:

[0273] The terminal collects patient CT scan and MRI data within the medical facility and receives associated identification information as data input. This data is converted to a specific format and sent to the server using encryption technology. An encrypted, secure data package is generated as output.

[0274] Step 2:

[0275] The server receives encrypted data sent from the terminal and decrypts it to restore the input data. Here, the data's integrity and completeness are verified, and it is stored in a secure database. As output, the data is securely stored and available for use in subsequent analysis steps.

[0276] Step 3:

[0277] The AI agent installed on the server acquires the safely stored medical data as input and extracts biometric features using an image analysis algorithm. As a specific operation, it utilizes a machine learning model to analyze the characteristics of bones and tissues and outputs them as feature vectors.

[0278] Step 4:

[0279] The server receives the extracted biometric features as input and constructs a patient-specific virtual model using 3D model generation software. At this time, an algorithm learned from past successful data and failed data is used to propose optimal material and shape parameters. As output, 3D printable digital design data is generated.

[0280] Step 5:

[0281] The server sends the designed 3D model data to a 3D printer, and the printer receives the data as input and manufactures an actual medical part. During the manufacturing process, the server monitors the status of the printer in real time and issues an alert immediately if an abnormality is detected. As output, a physical medical part is completed.

[0282] Step 6:

[0283] The user receives the manufactured medical part. The user specifically inspects the dimensions, shape, and surface condition of the part and confirms that it meets the quality standards. Based on this confirmation, feedback is provided to the server as input, and it is reused in the system as data for the next improvement. As output, a quality-confirmed medical part and data for continuous learning are generated.

[0284] (Application Example 1)

[0285] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0286] There is a need to efficiently design and manufacture medical implants according to individual needs and appropriately manage the progress and quality feedback of the process. In particular, it is a challenge for medical staff to grasp the progress of design and manufacturing in real time and provide implants suitable for patients quickly and accurately.

[0287] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0288] In this invention, the server includes means for receiving a medical image of a patient, means for analyzing the medical image to extract anatomical features, means for automatically generating a design of an implant based on the anatomical features, means for manufacturing the designed implant using a 3D printer, and means for notifying a user of progress information on the implant design. As a result, medical staff can grasp the progress of the design and manufacturing processes in real time, and it becomes possible to provide implants optimized for each patient.

[0289] The "medical image of a patient" refers to image data such as CT scans and MRIs taken in hospitals and medical facilities, which captures the anatomical features of the patient in detail.

[0290] The "anatomical features" refer to information indicating the internal structure, shape, and position of organs in a patient, and are extracted by analyzing medical images.

[0291] The "means for automatically generating an implant design" is a technology that designs an implant based on anatomical features using a computer system and quickly outputs the design.

[0292] The "3D printer" is a machine that creates a three-dimensional object based on digital design data and is used for manufacturing implants.

[0293] The "progress information on implant design" refers to information regarding the progress of the implant design and manufacturing processes, which is sequentially notified to the user.

[0294] To implement this invention, collaboration between a server, a terminal, and a user is required. The server first receives the patient's medical image data transmitted from the terminal. This includes CT scans and MRI images. The received data is processed by an AI agent to analyze the anatomical characteristics of the specific patient. Through this analysis, the server generates a patient-specific three-dimensional model, and uses this model to design the implant.

[0295] During the design process, the server uses AI, learned from past surgical data and successful and unsuccessful cases, to suggest implant materials and shapes. This AI acts as computational intelligence, monitoring the design progress in real time and making design changes as needed.

[0296] Next, the server sends the data to a 3D printer to manufacture the designed implant. The 3D printer creates the physical implant based on the design. As manufacturing progresses, the server notifies the terminal of the implant design's progress, allowing the user to check the information.

[0297] As a concrete example, consider the case where a hip joint implant is manufactured for patient C. The server analyzes the CT image of the hip joint received from the terminal and designs the optimal implant. Then, it manufactures the implant using a 3D printer and provides progress information to healthcare professionals via the terminal. This allows healthcare professionals to accurately understand the preparation status of the implant and provide the patient with the best possible medical care.

[0298] Examples of prompts to input into a generative AI model:

[0299] "Please tell me how to design a custom implant based on patient C's CT images and start the manufacturing process using a 3D printer."

[0300] As hardware, a smart device with a camera (smartphone or tablet), a server group, and a 3D printer were used. As software, Python, OpenCV, and the requests library were used.

[0301] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0302] Step 1:

[0303] The user uses the terminal to take a medical image of the patient and sends it to the server via the terminal. As input, a CT or MRI image of the patient is acquired and converted into a digital format for compression and transmission. As output, the medical image is sent to the server. As a specific operation, the terminal packetizes the captured image according to the communication protocol and uploads it to the specified endpoint of the server.

[0304] Step 2:

[0305] The server analyzes the received medical image and extracts anatomical features. The input is the medical image data sent from the terminal. The output is digital data containing anatomical features. The AI agent in the server analyzes the image using an image processing algorithm and extracts the necessary features. For this image processing, OpenCV is used, and edge detection and region segmentation techniques are utilized.

[0306] Step 3:

[0307] The server automatically generates the implant design based on the anatomical features. The input is the data of the anatomical features obtained in Step 2. The output is 3D printable design data. The AI model selects the optimal material and shape from the past database and converts it into a 3D design format. At this time, the data processing library of Python is used to perform numerical calculations for generating the design data.

[0308] Step 4:

[0309] The server transmits the designed implant data to the 3D printer and initiates the manufacturing process. The input is the design data, and the output is the physical implant. Specifically, the server connects to the 3D printer's control system, adds the printing task to a queue, and sends execution commands. The implant is formed by layering the specified material using a continuous lamination method.

[0310] Step 5:

[0311] The server notifies the user's device of the progress of implant design and manufacturing. Input is information on the progress of the manufacturing process, and output is a message sent to the user. Specifically, the server provides the user with the latest progress in the design and manufacturing phases via email or in-app notifications. These notifications are sent in stages depending on the situation.

[0312] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0313] This invention is a system that designs and manufactures medical implants tailored to the individual needs of patients, and further improves the patient's psychological satisfaction before and after surgery by taking into account the user's emotional state. The system is mainly composed of three stakeholders: a server, a terminal, and a user, and incorporates an emotion engine within it.

[0314] Server Functions

[0315] The server uses CT scan and MRI data received from medical institutions via terminals to analyze anatomical features using an AI agent. This data analysis automatically generates a 3D model based on the patient's individual needs. The server then designs implants based on this 3D model, utilizing artificial intelligence trained on past surgical data during the design process.

[0316] Furthermore, the emotion engine integrated into the server collects and analyzes user emotional data. This information visualizes the patient's mental state during and after surgery, supporting decision-making during the surgical process.

[0317] Applications of the Emotion Engine

[0318] The emotion engine analyzes the patient's mental state based on user feedback and evaluates emotions through specific speech data and nonverbal cues. For example, if the emotion engine analyzes the patient's voice input and indicates that the patient is feeling anxious or reassured, it will provide care and surgical explanations tailored to those emotions.

[0319] Device functions

[0320] The terminal functions as an interface for healthcare users to communicate with the server. It performs operations such as uploading medical image data, receiving notifications upon implant arrival, and reviewing analysis results from the emotion engine.

[0321] User involvement

[0322] As healthcare professionals, users will understand the patient's emotional state through the emotion engine and utilize this information in post-operative care plans and explanations. Furthermore, after surgery, they will provide feedback to the server regarding the quality and design fit of the received implant, contributing to improvements in future processes.

[0323] As a concrete example, when considering surgery for patient B, the terminal receives her CT data, and the server analyzes it to design an individual implant. During this process, an emotion engine analyzes patient B's emotional state, and the server incorporates this information into the surgical plan and explanation. After inspection, the user provides appropriate follow-up, taking into account patient B's satisfaction. This entire process provides medical care that considers not only the physical aspects but also the psychological aspects.

[0324] The following describes the processing flow.

[0325] Step 1:

[0326] The terminal collects patient CT scan and MRI data from medical facility equipment and securely uploads this data to a server. After uploading, it confirms data transmission and receives notification from the server that the data has been received.

[0327] Step 2:

[0328] The server stores the received medical image data in a database. An AI agent analyzes this data and extracts anatomical features. For example, it identifies information such as bone shape and density and generates a 3D model for the patient.

[0329] Step 3:

[0330] The server designs a custom implant based on a 3D model of the patient. This design is optimized by machine learning algorithms, which learn from past surgical data to suggest materials and shapes.

[0331] Step 4:

[0332] The server sends the designed implant data to the 3D printer and starts the manufacturing process. The server monitors the printer's operation and manages it to ensure that the implant is manufactured successfully.

[0333] Step 5:

[0334] The terminal receives notifications from the server and checks the shipping status of the manufactured implants. It then communicates the arrival schedule to the logistics department and prepares for receipt.

[0335] Step 6:

[0336] The user inspects the implant upon arrival, checking that its appearance and dimensions conform to the design. If there are no problems, they determine that the implant is ready for use in surgery.

[0337] Step 7:

[0338] An emotion engine embedded in the server collects and analyzes emotional data through interactions with users and patients. For example, it analyzes voice input from patients to identify their emotional state.

[0339] Step 8:

[0340] Users develop care plans based on the patient's emotional state. Using the emotional analysis results obtained from the server, they provide mental care to patients during and after surgery, aiming to improve patient satisfaction.

[0341] (Example 2)

[0342] Next, we will describe Example 2. 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".

[0343] In the design and manufacture of medical implants, there is a need to efficiently provide high-quality implants while considering the anatomical characteristics and mental state of each individual patient. However, current processes have challenges in adapting to individual patient needs and considering patients' emotional states. Therefore, improvements are needed to enhance both the physical and mental satisfaction of patients.

[0344] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0345] In this invention, the server includes means for receiving medical images of a patient, means for analyzing the medical images to extract anatomical features, means for automatically generating an implant design based on the anatomical features, means for transmitting the generated implant design to a medical institution, means for collecting and analyzing patient emotional data, and means for adjusting the surgical plan based on the analysis results. This enables the optimization of individual medical plans and the provision of high-quality medical services, including emotional care.

[0346] "Patient medical images" refer to visual information of the inside of a patient's body obtained using methods such as CT scans and MRI.

[0347] "Anatomical features" refer to a collection of information that describes specific shapes, dimensions, and arrangements related to a patient's physical structure.

[0348] An "implant" refers to an artificial organ or assistive device designed to be implanted in a patient's body.

[0349] "Automated generation" refers to the process of creating designs and layouts using artificial intelligence and algorithms without any human intervention.

[0350] A "generative AI model" refers to a machine learning algorithm that learns from past data and generates new implant designs.

[0351] "Emotional data" refers to information such as voice, facial expressions, and behavior related to the emotions a patient exhibits.

[0352] A "surgical plan" refers to a detailed outline of the surgical procedure to be performed on a specific patient.

[0353] A "machine learning algorithm" refers to a computational method that uses large amounts of data to discover patterns and rules and predict future actions and outcomes.

[0354] "Evaluation" refers to the process of measuring the quality of a product based on certain criteria and identifying areas for improvement.

[0355] This invention is a system for efficiently designing and manufacturing medical implants that meet the diverse needs of each patient, and for providing care tailored to the patient's emotional state. The system has three central components: a server, a terminal, and a user, and utilizes a generative AI model.

[0356] The server receives patient medical images provided by medical institutions via terminals. This image data includes CT scans and MRI data. The server uses this data for an AI agent to analyze the patient's anatomical features. Based on the analysis results, the generated AI model automatically creates a 3D design for the optimal implant for the patient. This generation process references past medical data, enabling designs that leverage individual anatomical features.

[0357] The terminal is in the hands of the healthcare provider and operates in conjunction with the server. The terminal can view analysis results and generated implant designs received from the server, and also collects emotional data from patients. This emotional data includes voice input and non-verbal cues, and is designed to facilitate smooth data entry. The collected emotional data is sent to the server for analysis by an emotional engine.

[0358] Users, acting as healthcare professionals, utilize feedback from the emotion engine to provide optimal patient care. This can be used to improve patients' mental well-being during surgical explanations and follow-up. For example, if a patient shows anxiety while waiting for surgery, the emotion engine analyzes their state, and the server suggests approaches to alleviate that anxiety to the user.

[0359] Example prompt: "Please explain how individual implant designs are generated in this system."

[0360] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0361] Step 1:

[0362] The server receives CT scan and MRI data from medical institutions via terminals. This data is provided as input, and an AI agent analyzes the images to extract anatomical features. As output, shape data tailored to each individual patient is obtained. At this stage, the server performs the specific operation of rapidly processing a massive amount of image data and quantifying anatomical features.

[0363] Step 2:

[0364] The server automatically generates a 3D design of the implant using a generative AI model based on extracted anatomical features. The input here is numerical data of anatomical features, and the output is a 3D model of the implant optimized for the patient. The generative AI model performs specific calculations to generate the optimal shape, referencing an existing design database.

[0365] Step 3:

[0366] The server sends the generated 3D design to the terminal for user review. The input is a 3D model, and the output is digital design information displayed on the terminal. The server performs specific actions to ensure reliable data transmission and provide an interface that allows healthcare professionals to suggest necessary adjustments.

[0367] Step 4:

[0368] The terminal receives feedback from healthcare professionals, makes necessary adjustments, and then returns the final design to the server. At this stage, the design data, which has been adjusted to reflect user feedback, is output. The terminal then performs specific operations to accurately reflect the input adjustment information and incorporate it into the design.

[0369] Step 5:

[0370] The server uses an emotion engine to collect and analyze the aforementioned emotional data. This process takes voice and nonverbal data provided from the terminal as input and outputs information evaluating the patient's psychological state. The emotion engine analyzes voice tone and patterns to quantify emotions and performs specific emotional evaluations.

[0371] Step 6:

[0372] The server adjusts the surgical plan based on the analysis results and generates the optimal patient care procedure. In this step, the results of the emotional assessment are input, and the adjusted surgical plan and care suggestions are output. Through communication with healthcare professionals, the server performs specific actions to select the optimal process according to the patient's physical and mental condition.

[0373] (Application Example 2)

[0374] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0375] Designing and manufacturing medical implants tailored to individual patient needs while simultaneously improving patient emotional well-being is a challenging task within current medical processes. Conventional technologies make the process of creating implants that meet individual anatomical needs complex and time-consuming, and methods for considering patients' emotional states in real time are limited. Therefore, there is a need to provide a method that comprehensively improves both the physical and mental care of patients.

[0376] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0377] In this invention, the server includes means for receiving medical images of a patient, means for analyzing the medical images to extract anatomical features, means for automatically generating implant designs based on the anatomical features, means for manufacturing the designed implants using a three-dimensional manufacturing device, and means for utilizing an emotion analysis mechanism to analyze the patient's emotional state and adjust care accordingly. This makes it possible to simultaneously achieve individualized implant design and improved emotional care for patients.

[0378] "Medical images" are photographs or videos that visually represent a patient's internal condition and are used for diagnosis and the development of treatment plans.

[0379] "Anatomical features" refer to the detailed characteristics of a patient's physical structure, including information about its shape, tissues, and positional relationships.

[0380] An "implant" is a medical device or material that is inserted or fitted into a patient's body and used to complement or modify the function of that person's body.

[0381] A "three-dimensional manufacturing machine" is a machine that forms a three-dimensional object by layering materials based on digital data.

[0382] An "emotion analysis mechanism" is a technical means of evaluating and judging a person's emotional state based on voice, facial expressions, and other nonverbal cues.

[0383] "Intelligent function" refers to the ability to use artificial intelligence to analyze and judge data, and to independently learn and adapt in order to perform specific tasks.

[0384] A "response" is information generated from system input and analysis, and represents actions or opinions provided for improvement or adjustment in the next steps.

[0385] The system for realizing this invention designs and manufactures medical implants based on the individual needs of patients, and improves patients' mental satisfaction by analyzing their emotional state and utilizing this information in their care.

[0386] The server primarily receives and analyzes patient medical images to extract anatomical features. Specifically, it processes CT scans and MRI data on the platform, using high-precision algorithms to identify necessary features. This enables the design of implants tailored to each individual patient. Leveraging AI technology, the system generates optimal designs based on past treatment data, and then materializes those designs using 3D manufacturing equipment.

[0387] Furthermore, the emotion analysis mechanism analyzes emotions from data such as the patient's voice and facial expressions. This function allows for monitoring the psychological state of patients during surgery or treatment, providing guidance for appropriate care. Wearable devices such as smart glasses allow care staff to access this data in real time and take specific care actions.

[0388] As a concrete example, when used in a nursing home, a system is utilized to monitor the emotional state of patient A. The AI ​​detects when she is feeling anxious, and the data is sent to staff via a server. Based on notifications displayed on her glasses, staff suggest relaxing coping mechanisms. In this way, integrating medical and psychological care makes it possible to improve the overall patient satisfaction.

[0389] An example of a specific prompt for a generative AI model is: "Analyze patient A's voice input and facial expression data to identify her emotional state. Based on her emotions, suggest appropriate care."

[0390] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0391] Step 1:

[0392] The terminal receives CT scan and MRI data from the patient's medical institution. This data is sent to a server for preparation for analysis. The input is medical images, and the output is the transfer to the server. Specifically, the terminal uploads data to the server via the network.

[0393] Step 2:

[0394] The server analyzes received medical images to extract anatomical features. Image processing techniques and machine learning algorithms (e.g., deep learning) are used for the analysis. The input is medical images, and the output is extracted anatomical feature data. Specifically, the algorithm performs pattern recognition to generate the feature data.

[0395] Step 3:

[0396] The server automatically generates implant designs based on extracted anatomical features. This process utilizes AI technology. The input is anatomical feature data, and the output is implant design data. Specifically, the AI ​​generates the design using relevant shape data from an existing database.

[0397] Step 4:

[0398] The server transmits design data to a 3D manufacturing machine to produce the implant. The input is the design data for the implant, and the output is the physical implant. Specifically, the manufacturing machine is operated through the data transmission system, and the implant is formed by layering materials.

[0399] Step 5:

[0400] The server collects voice and facial expression data from the terminal to analyze the patient's emotional state. It uses an emotion analysis mechanism to evaluate emotions. The input is voice and facial expression data, and the output is the analyzed emotional state. Specifically, the server uses a generative AI model to analyze the data and quantitatively evaluate emotions.

[0401] Step 6:

[0402] The user implements appropriate care for the patient based on the analysis results. Specific care actions and communications are selected based on emotional data. The input is the analyzed emotional state, and the output is the care plan. Specifically, the user checks notifications and receives instructions to take appropriate action.

[0403] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0404] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0405] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0406] [Third Embodiment]

[0407] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0408] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0409] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0410] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0411] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0412] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0413] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0414] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0415] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0416] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0417] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0418] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0419] This invention is a system for efficiently designing and manufacturing medical implants tailored to the individual needs of patients. The main components of the system include a server, terminals, and users.

[0420] Server Functions

[0421] The server receives patient CT scans and MRI data sent from medical institutions and securely stores it in a database. This data forms the basis for analyzing the patient's anatomical features. An AI agent within the server analyzes this data and extracts anatomical features using image analysis techniques. Based on these analysis results, the server generates a patient-specific 3D model.

[0422] Next, the server designs the optimal implant for the patient based on the generated 3D model. This design process utilizes artificial intelligence that has learned from past successes and failures to suggest materials and shapes. The designed implant is then converted into a 3D printable format.

[0423] Furthermore, the server connects with the 3D printer and sends the design data to the printer to begin manufacturing the actual implant. During the manufacturing process, the server monitors the printer's operation in real time and issues a rapid alert if any problems occur.

[0424] Device functions

[0425] The terminal is part of the system at the medical institution that collects medical images. The terminal transmits patient CT scans and MRI data, along with their identification information, to the server. Furthermore, when manufactured implants are shipped, the terminal receives notifications from the server and manages the arrival of the implants.

[0426] User involvement

[0427] The user is a healthcare worker responsible for receiving and quality-checking implants. The user receives the manufactured implants and inspects them. If necessary, they provide feedback on the implant quality to the server. This feedback is incorporated as training data for the AI ​​and used to improve future implant design processes.

[0428] As a specific example, when manufacturing a knee implant for patient A, the terminal collects CT data of the patient's knee and sends it to the server. The server's AI agent analyzes the data and designs the optimal implant for patient A's knee. Based on this design, the implant is manufactured using a 3D printer and received by the user. This entire process allows for the rapid provision of implants tailored to individual anatomical characteristics, which is expected to improve patient satisfaction.

[0429] The following describes the processing flow.

[0430] Step 1:

[0431] The terminal acquires patient medical images from CT scanners and MRI machines and uploads this data to the server via a secure channel. After the upload, the terminal confirms the completion of data transmission and receives a notification of receipt from the server.

[0432] Step 2:

[0433] The server stores the received medical image data in a database, and the AI ​​agent performs the necessary preprocessing to analyze the data. This includes, for example, denoising the images and adjusting the resolution.

[0434] Step 3:

[0435] An AI agent on the server analyzes pre-processed images to extract the patient's anatomical features. This analysis uses machine learning algorithms to identify features such as bone shape and density.

[0436] Step 4:

[0437] The server generates a digital 3D model of the implant, tailored to the patient, based on the extracted anatomical features. This model is created using an automated design algorithm and verified to meet specific requirements.

[0438] Step 5:

[0439] The server converts the designed implant model into a format that can be manufactured by a 3D printer and sends the data to the 3D printer. Here, the server places the data in a queue of print jobs and monitors the manufacturing process.

[0440] Step 6:

[0441] The server monitors the printing status in real time to ensure the printer is functioning correctly. If an anomaly is detected, it issues an alert and provides instructions for troubleshooting.

[0442] Step 7:

[0443] After the server confirms that manufacturing is complete, it sends the implant shipment information to the terminal. Based on this information, the terminal notifies the medical institution's logistics department of the expected arrival date of the implant.

[0444] Step 8:

[0445] The user receives the implant upon arrival at the medical institution and verifies its quality and compliance with specifications. After the inspection is complete, the user sends the results as feedback to the server, which records this information in a database for future designs.

[0446] (Example 1)

[0447] Next, we will describe Example 1. 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."

[0448] Conventional medical component design and manufacturing methods have presented challenges in quickly and precisely responding to the individual needs of each patient, leading to increased time costs and human resource requirements. Furthermore, the design phase relied heavily on human experience, making it difficult to efficiently supply medical components of consistent quality. Additionally, delays in anomaly detection during the manufacturing process increased the risk of quality defects.

[0449] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0450] In this invention, the server includes means for receiving patient medical information, means for analyzing and extracting biological characteristics, means for automatically generating medical component designs, means for optimizing the design process using machine learning algorithms, and means for monitoring the manufacturing process and detecting anomalies. This makes it possible to quickly and accurately design and manufacture medical components tailored to individual patients, and to immediately identify and address problems in the manufacturing process.

[0451] "Medical information" refers to data collected to indicate a patient's health status, and encompasses a wide range of information, including image data and diagnostic results.

[0452] "Biological characteristics" refer to data that represents the physiological or anatomical features unique to each individual patient, including, for example, bone shape and tissue density.

[0453] "Medical components" are artificial structures or devices used for the purpose of treating patients, specifically components such as implants and prostheses.

[0454] "Automated generation" refers to a process carried out by machines or programs with minimal human intervention, and specifically refers to the act of efficiently performing complex tasks using software.

[0455] "Machine learning algorithms" refer to mathematical methods and processes that enable computers to learn data patterns from experience and make predictions and decisions about the future.

[0456] "Optimization" is a technique for adjusting processes and designs to obtain the best or most favorable results for a specific purpose, and is performed to improve efficiency and performance.

[0457] "Anomaly detection" is a system function that monitors for states or patterns that deviate from normal processes, quickly identifies them, and issues warnings.

[0458] This invention is a system for efficiently designing and manufacturing medical components that meet the individual needs of each patient. Its main components include a server, terminals, and users.

[0459] The server is a powerful computer device with robust data processing capabilities that securely processes patient medical information received from healthcare institutions. Data processing utilizes domain-specific AI analysis software, particularly for image analysis and pattern recognition. This AI excels at extracting biometric features and generating highly accurate 3D models. These 3D models are then optimized using machine learning algorithms to design medical components. Advanced design support software is used to convert the data into a format suitable for 3D printing. Furthermore, the server interacts with 3D printers, monitoring the manufacturing process and issuing alerts if defects are detected.

[0460] The terminal is connected to the healthcare facility's system and is a device that collects medical information and transmits it to a server. The terminal uses a highly secure data transfer protocol to encrypt and transmit patient CT scans and MRI data. The terminal also receives manufacturing information and quality inspection results transmitted from the server and informs the user.

[0461] The user receives the manufactured medical components and performs quality assurance. The user checks the product dimensions and machining precision and provides feedback to the server. This feedback can then be used by the AI ​​in future designs, contributing to further improvements in accuracy.

[0462] As a concrete example, when manufacturing a knee joint implant for a patient, the terminal collects CT data of the patient's knee and sends it to the server. The server analyzes this data and designs the optimal implant for the patient's anatomy. Based on this data, a 3D printer manufactures the implant, and the user receives it. The server collects this feedback and uses it to improve the design for the next time. An example of a prompt message for the generated AI model could be in the form of, "Design a 3D model of the optimal knee implant using patient A's knee CT data."

[0463] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0464] Step 1:

[0465] The terminal collects patient CT scan and MRI data within the medical facility and receives associated identification information as data input. This data is converted to a specific format and sent to the server using encryption technology. An encrypted, secure data package is generated as output.

[0466] Step 2:

[0467] The server receives encrypted data sent from the terminal and decrypts it to restore the input data. Here, the data's integrity and completeness are verified, and it is stored in a secure database. As output, the data is securely stored and available for use in subsequent analysis steps.

[0468] Step 3:

[0469] An AI agent installed on the server takes securely stored medical data as input and extracts biological features using image analysis algorithms. Specifically, it uses machine learning models to analyze the characteristics of bones and tissues and outputs them as feature vectors.

[0470] Step 4:

[0471] The server receives extracted biometric data as input and constructs a patient-specific virtual model using 3D model generation software. During this process, an algorithm trained on past success and failure data is used to suggest optimal materials and shape parameters. The output is 3D printable digital design data.

[0472] Step 5:

[0473] The server sends the designed 3D model data to the 3D printer, which then receives the data as input and manufactures the actual medical component. During the manufacturing process, the server monitors the printer's status in real time and immediately issues an alert if any abnormalities are detected. The output is the completed physical medical component.

[0474] Step 6:

[0475] The user receives the manufactured medical component. The user specifically inspects the component's dimensions, shape, and surface condition to confirm that it meets quality standards. Based on this confirmation, feedback is provided to the server as input, which is then used again within the system as data for future improvements. The output consists of the quality-verified medical component and data for continuous learning.

[0476] (Application Example 1)

[0477] Next, we will explain Application Example 1. In the following explanation, 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."

[0478] There is a need for efficient design and manufacturing processes for medical implants tailored to individual needs, as well as proper management of their progress and quality feedback. In particular, a challenge is enabling healthcare professionals to understand the design and manufacturing progress in real time and to provide patients with implants that are appropriate for them quickly and accurately.

[0479] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0480] In this invention, the server includes means for receiving medical images of a patient, means for analyzing the medical images to extract anatomical features, means for automatically generating an implant design based on the anatomical features, means for manufacturing the designed implant using a three-dimensional printer, and means for notifying the user of the progress of the implant design. This enables medical professionals to understand the progress of the design and manufacturing process in real time and to provide implants optimized for each patient.

[0481] "Patient medical images" refer to image data such as CT scans and MRIs taken at hospitals and medical facilities, which capture the patient's anatomical features in detail.

[0482] "Anatomical features" refer to information that describes the internal structure, shape, and location of a patient's organs, and are extracted by analyzing medical images.

[0483] "Methods for automatically generating implant designs" refer to technologies that use a computer system to design implants based on anatomical characteristics and quickly output those designs.

[0484] A "three-dimensional printing machine" is a machine that creates three-dimensional objects based on digital design data, and is used in the manufacture of implants.

[0485] "Implant design progress information" refers to information regarding the progress of the implant design and manufacturing process, which is notified to users as it progresses.

[0486] To implement this invention, collaboration between a server, a terminal, and a user is required. The server first receives the patient's medical image data transmitted from the terminal. This includes CT scans and MRI images. The received data is processed by an AI agent to analyze the anatomical characteristics of the specific patient. Through this analysis, the server generates a patient-specific three-dimensional model, and uses this model to design the implant.

[0487] During the design process, the server uses AI, learned from past surgical data and successful and unsuccessful cases, to suggest implant materials and shapes. This AI acts as computational intelligence, monitoring the design progress in real time and making design changes as needed.

[0488] Next, the server sends the data to a 3D printer to manufacture the designed implant. The 3D printer creates the physical implant based on the design. As manufacturing progresses, the server notifies the terminal of the implant design's progress, allowing the user to check the information.

[0489] As a concrete example, consider the case where a hip joint implant is manufactured for patient C. The server analyzes the CT image of the hip joint received from the terminal and designs the optimal implant. Then, it manufactures the implant using a 3D printer and provides progress information to healthcare professionals via the terminal. This allows healthcare professionals to accurately understand the preparation status of the implant and provide the patient with the best possible medical care.

[0490] Examples of prompts to input into a generative AI model:

[0491] "Please tell me how to design a custom implant based on patient C's CT images and start the manufacturing process using a 3D printer."

[0492] The hardware used included smart devices with cameras (smartphones and tablets), a cluster of servers, and a 3D printer. The software used was Python, OpenCV, and the requests library.

[0493] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0494] Step 1:

[0495] The user uses a device to capture medical images of a patient and sends them to a server. As input, the device acquires CT or MRI images of the patient, compresses them, and converts them into a digital format for transmission. As output, the medical images are sent to the server. Specifically, the device packets the captured images according to the communication protocol and uploads them to a designated endpoint on the server.

[0496] Step 2:

[0497] The server analyzes received medical images and extracts anatomical features. The input is medical image data sent from the terminal. The output is digital data containing anatomical features. An AI agent within the server analyzes the images using image processing algorithms and extracts the necessary features. This image processing uses OpenCV and leverages edge detection and region segmentation techniques.

[0498] Step 3:

[0499] The server automatically generates implant designs based on anatomical features. The input is the anatomical feature data obtained in step 2. The output is 3D printable design data. The AI ​​model selects the optimal material and shape from a historical database and converts it into a 3D design format. During this process, a Python data processing library is used to perform numerical calculations to generate the design data.

[0500] Step 4:

[0501] The server transmits the designed implant data to the 3D printer and initiates the manufacturing process. The input is the design data, and the output is the physical implant. Specifically, the server connects to the 3D printer's control system, adds the printing task to a queue, and sends execution commands. The implant is formed by layering the specified material using a continuous lamination method.

[0502] Step 5:

[0503] The server notifies the user's device of the progress of implant design and manufacturing. Input is information on the progress of the manufacturing process, and output is a message sent to the user. Specifically, the server provides the user with the latest progress in the design and manufacturing phases via email or in-app notifications. These notifications are sent in stages depending on the situation.

[0504] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0505] This invention is a system that designs and manufactures medical implants tailored to the individual needs of patients, and further improves the patient's psychological satisfaction before and after surgery by taking into account the user's emotional state. The system is mainly composed of three stakeholders: a server, a terminal, and a user, and incorporates an emotion engine within it.

[0506] Server Functions

[0507] The server uses CT scan and MRI data received from medical institutions via terminals to analyze anatomical features using an AI agent. This data analysis automatically generates a 3D model based on the patient's individual needs. The server then designs implants based on this 3D model, utilizing artificial intelligence trained on past surgical data during the design process.

[0508] Furthermore, the emotion engine integrated into the server collects and analyzes user emotional data. This information visualizes the patient's mental state during and after surgery, supporting decision-making during the surgical process.

[0509] Applications of the Emotion Engine

[0510] The emotion engine analyzes the patient's mental state based on user feedback and evaluates emotions through specific speech data and nonverbal cues. For example, if the emotion engine analyzes the patient's voice input and indicates that the patient is feeling anxious or reassured, it will provide care and surgical explanations tailored to those emotions.

[0511] Device functions

[0512] The terminal functions as an interface for healthcare users to communicate with the server. It performs operations such as uploading medical image data, receiving notifications upon implant arrival, and reviewing analysis results from the emotion engine.

[0513] User involvement

[0514] As healthcare professionals, users will understand the patient's emotional state through the emotion engine and utilize this information in post-operative care plans and explanations. Furthermore, after surgery, they will provide feedback to the server regarding the quality and design fit of the received implant, contributing to improvements in future processes.

[0515] As a concrete example, when considering surgery for patient B, the terminal receives her CT data, and the server analyzes it to design an individual implant. During this process, an emotion engine analyzes patient B's emotional state, and the server incorporates this information into the surgical plan and explanation. After inspection, the user provides appropriate follow-up, taking into account patient B's satisfaction. This entire process provides medical care that considers not only the physical aspects but also the psychological aspects.

[0516] The following describes the processing flow.

[0517] Step 1:

[0518] The terminal collects patient CT scan and MRI data from medical facility equipment and securely uploads this data to a server. After uploading, it confirms data transmission and receives notification from the server that the data has been received.

[0519] Step 2:

[0520] The server stores the received medical image data in a database. An AI agent analyzes this data and extracts anatomical features. For example, it identifies information such as bone shape and density and generates a 3D model for the patient.

[0521] Step 3:

[0522] The server designs a custom implant based on a 3D model of the patient. This design is optimized by machine learning algorithms, which learn from past surgical data to suggest materials and shapes.

[0523] Step 4:

[0524] The server sends the designed implant data to the 3D printer and starts the manufacturing process. The server monitors the printer's operation and manages it to ensure that the implant is manufactured successfully.

[0525] Step 5:

[0526] The terminal receives notifications from the server and checks the shipping status of the manufactured implants. It then communicates the arrival schedule to the logistics department and prepares for receipt.

[0527] Step 6:

[0528] The user inspects the implant upon arrival, checking that its appearance and dimensions conform to the design. If there are no problems, they determine that the implant is ready for use in surgery.

[0529] Step 7:

[0530] An emotion engine embedded in the server collects and analyzes emotional data through interactions with users and patients. For example, it analyzes voice input from patients to identify their emotional state.

[0531] Step 8:

[0532] Users develop care plans based on the patient's emotional state. Using the emotional analysis results obtained from the server, they provide mental care to patients during and after surgery, aiming to improve patient satisfaction.

[0533] (Example 2)

[0534] Next, we will describe Example 2. 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."

[0535] In the design and manufacture of medical implants, there is a need to efficiently provide high-quality implants while considering the anatomical characteristics and mental state of each individual patient. However, current processes have challenges in adapting to individual patient needs and considering patients' emotional states. Therefore, improvements are needed to enhance both the physical and mental satisfaction of patients.

[0536] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0537] In this invention, the server includes means for receiving medical images of a patient, means for analyzing the medical images to extract anatomical features, means for automatically generating an implant design based on the anatomical features, means for transmitting the generated implant design to a medical institution, means for collecting and analyzing patient emotional data, and means for adjusting the surgical plan based on the analysis results. This enables the optimization of individual medical plans and the provision of high-quality medical services, including emotional care.

[0538] "Patient medical images" refer to visual information of the inside of a patient's body obtained using methods such as CT scans and MRI.

[0539] "Anatomical features" refer to a collection of information that describes specific shapes, dimensions, and arrangements related to a patient's physical structure.

[0540] An "implant" refers to an artificial organ or assistive device designed to be implanted in a patient's body.

[0541] "Automated generation" refers to the process of creating designs and layouts using artificial intelligence and algorithms without any human intervention.

[0542] A "generative AI model" refers to a machine learning algorithm that learns from past data and generates new implant designs.

[0543] "Emotional data" refers to information such as voice, facial expressions, and behavior related to the emotions a patient exhibits.

[0544] A "surgical plan" refers to a detailed outline of the surgical procedure to be performed on a specific patient.

[0545] A "machine learning algorithm" refers to a computational method that uses large amounts of data to discover patterns and rules and predict future actions and outcomes.

[0546] "Evaluation" refers to the process of measuring the quality of a product based on certain criteria and identifying areas for improvement.

[0547] This invention is a system for efficiently designing and manufacturing medical implants that meet the diverse needs of each patient, and for providing care tailored to the patient's emotional state. The system has three central components: a server, a terminal, and a user, and utilizes a generative AI model.

[0548] The server receives patient medical images provided by medical institutions via terminals. This image data includes CT scans and MRI data. The server uses this data for an AI agent to analyze the patient's anatomical features. Based on the analysis results, the generated AI model automatically creates a 3D design for the optimal implant for the patient. This generation process references past medical data, enabling designs that leverage individual anatomical features.

[0549] The terminal is in the hands of the healthcare provider and operates in conjunction with the server. The terminal can view analysis results and generated implant designs received from the server, and also collects emotional data from patients. This emotional data includes voice input and non-verbal cues, and is designed to facilitate smooth data entry. The collected emotional data is sent to the server for analysis by an emotional engine.

[0550] Users, acting as healthcare professionals, utilize feedback from the emotion engine to provide optimal patient care. This can be used to improve patients' mental well-being during surgical explanations and follow-up. For example, if a patient shows anxiety while waiting for surgery, the emotion engine analyzes their state, and the server suggests approaches to alleviate that anxiety to the user.

[0551] Example prompt: "Please explain how individual implant designs are generated in this system."

[0552] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0553] Step 1:

[0554] The server receives CT scan and MRI data from medical institutions via terminals. This data is provided as input, and an AI agent analyzes the images to extract anatomical features. As output, shape data tailored to each individual patient is obtained. At this stage, the server performs the specific operation of rapidly processing a massive amount of image data and quantifying anatomical features.

[0555] Step 2:

[0556] The server automatically generates a 3D design of the implant using a generative AI model based on extracted anatomical features. The input here is numerical data of anatomical features, and the output is a 3D model of the implant optimized for the patient. The generative AI model performs specific calculations to generate the optimal shape, referencing an existing design database.

[0557] Step 3:

[0558] The server sends the generated 3D design to the terminal for user review. The input is a 3D model, and the output is digital design information displayed on the terminal. The server performs specific actions to ensure reliable data transmission and provide an interface that allows healthcare professionals to suggest necessary adjustments.

[0559] Step 4:

[0560] The terminal receives feedback from healthcare professionals, makes necessary adjustments, and then returns the final design to the server. At this stage, the design data, which has been adjusted to reflect user feedback, is output. The terminal then performs specific operations to accurately reflect the input adjustment information and incorporate it into the design.

[0561] Step 5:

[0562] The server uses an emotion engine to collect and analyze the aforementioned emotional data. This process takes voice and nonverbal data provided from the terminal as input and outputs information evaluating the patient's psychological state. The emotion engine analyzes voice tone and patterns to quantify emotions and performs specific emotional evaluations.

[0563] Step 6:

[0564] The server adjusts the surgical plan based on the analysis results and generates the optimal patient care procedure. In this step, the results of the emotional assessment are input, and the adjusted surgical plan and care suggestions are output. Through communication with healthcare professionals, the server performs specific actions to select the optimal process according to the patient's physical and mental condition.

[0565] (Application Example 2)

[0566] Next, we will explain application example 2. In the following explanation, 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."

[0567] Designing and manufacturing medical implants tailored to individual patient needs while simultaneously improving patient emotional well-being is a challenging task within current medical processes. Conventional technologies make the process of creating implants that meet individual anatomical needs complex and time-consuming, and methods for considering patients' emotional states in real time are limited. Therefore, there is a need to provide a method that comprehensively improves both the physical and mental care of patients.

[0568] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0569] In this invention, the server includes means for receiving medical images of a patient, means for analyzing the medical images to extract anatomical features, means for automatically generating implant designs based on the anatomical features, means for manufacturing the designed implants using a three-dimensional manufacturing device, and means for utilizing an emotion analysis mechanism to analyze the patient's emotional state and adjust care accordingly. This makes it possible to simultaneously achieve individualized implant design and improved emotional care for patients.

[0570] "Medical images" are photographs or videos that visually represent a patient's internal condition and are used for diagnosis and the development of treatment plans.

[0571] "Anatomical features" refer to the detailed characteristics of a patient's physical structure, including information about its shape, tissues, and positional relationships.

[0572] An "implant" is a medical device or material that is inserted or fitted into a patient's body and used to complement or modify the function of that person's body.

[0573] A "three-dimensional manufacturing machine" is a machine that forms a three-dimensional object by layering materials based on digital data.

[0574] An "emotion analysis mechanism" is a technical means of evaluating and judging a person's emotional state based on voice, facial expressions, and other nonverbal cues.

[0575] "Intelligent function" refers to the ability to use artificial intelligence to analyze and judge data, and to independently learn and adapt in order to perform specific tasks.

[0576] A "response" is information generated from system input and analysis, and represents actions or opinions provided for improvement or adjustment in the next steps.

[0577] The system for realizing this invention designs and manufactures medical implants based on the individual needs of patients, and improves patients' mental satisfaction by analyzing their emotional state and utilizing this information in their care.

[0578] The server primarily receives and analyzes patient medical images to extract anatomical features. Specifically, it processes CT scans and MRI data on the platform, using high-precision algorithms to identify necessary features. This enables the design of implants tailored to each individual patient. Leveraging AI technology, the system generates optimal designs based on past treatment data, and then materializes those designs using 3D manufacturing equipment.

[0579] Furthermore, the emotion analysis mechanism analyzes emotions from data such as the patient's voice and facial expressions. This function allows for monitoring the psychological state of patients during surgery or treatment, providing guidance for appropriate care. Wearable devices such as smart glasses allow care staff to access this data in real time and take specific care actions.

[0580] As a concrete example, when used in a nursing home, a system is utilized to monitor the emotional state of patient A. The AI ​​detects when she is feeling anxious, and the data is sent to staff via a server. Based on notifications displayed on her glasses, staff suggest relaxing coping mechanisms. In this way, integrating medical and psychological care makes it possible to improve the overall patient satisfaction.

[0581] An example of a specific prompt for a generative AI model is: "Analyze patient A's voice input and facial expression data to identify her emotional state. Based on her emotions, suggest appropriate care."

[0582] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0583] Step 1:

[0584] The terminal receives CT scan and MRI data from the patient's medical institution. This data is sent to a server for preparation for analysis. The input is medical images, and the output is the transfer to the server. Specifically, the terminal uploads data to the server via the network.

[0585] Step 2:

[0586] The server analyzes received medical images to extract anatomical features. Image processing techniques and machine learning algorithms (e.g., deep learning) are used for the analysis. The input is medical images, and the output is extracted anatomical feature data. Specifically, the algorithm performs pattern recognition to generate the feature data.

[0587] Step 3:

[0588] The server automatically generates implant designs based on extracted anatomical features. This process utilizes AI technology. The input is anatomical feature data, and the output is implant design data. Specifically, the AI ​​generates the design using relevant shape data from an existing database.

[0589] Step 4:

[0590] The server transmits design data to a 3D manufacturing machine to produce the implant. The input is the design data for the implant, and the output is the physical implant. Specifically, the manufacturing machine is operated through the data transmission system, and the implant is formed by layering materials.

[0591] Step 5:

[0592] The server collects voice and facial expression data from the terminal to analyze the patient's emotional state. It uses an emotion analysis mechanism to evaluate emotions. The input is voice and facial expression data, and the output is the analyzed emotional state. Specifically, the server uses a generative AI model to analyze the data and quantitatively evaluate emotions.

[0593] Step 6:

[0594] The user implements appropriate care for the patient based on the analysis results. Specific care actions and communications are selected based on emotional data. The input is the analyzed emotional state, and the output is the care plan. Specifically, the user checks notifications and receives instructions to take appropriate action.

[0595] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0596] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0597] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0598] [Fourth Embodiment]

[0599] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0600] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0601] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0602] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0603] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0604] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0605] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0606] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0607] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0608] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0609] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0610] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0611] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0612] This invention is a system for efficiently designing and manufacturing medical implants tailored to the individual needs of patients. The main components of the system include a server, terminals, and users.

[0613] Server Functions

[0614] The server receives patient CT scans and MRI data sent from medical institutions and securely stores it in a database. This data forms the basis for analyzing the patient's anatomical features. An AI agent within the server analyzes this data and extracts anatomical features using image analysis techniques. Based on these analysis results, the server generates a patient-specific 3D model.

[0615] Next, the server designs the optimal implant for the patient based on the generated 3D model. This design process utilizes artificial intelligence that has learned from past successes and failures to suggest materials and shapes. The designed implant is then converted into a 3D printable format.

[0616] Furthermore, the server connects with the 3D printer and sends the design data to the printer to begin manufacturing the actual implant. During the manufacturing process, the server monitors the printer's operation in real time and issues a rapid alert if any problems occur.

[0617] Device functions

[0618] The terminal is part of the system at the medical institution that collects medical images. The terminal transmits patient CT scans and MRI data, along with their identification information, to the server. Furthermore, when manufactured implants are shipped, the terminal receives notifications from the server and manages the arrival of the implants.

[0619] User involvement

[0620] The user is a healthcare worker responsible for receiving and quality-checking implants. The user receives the manufactured implants and inspects them. If necessary, they provide feedback on the implant quality to the server. This feedback is incorporated as training data for the AI ​​and used to improve future implant design processes.

[0621] As a specific example, when manufacturing a knee implant for patient A, the terminal collects CT data of the patient's knee and sends it to the server. The server's AI agent analyzes the data and designs the optimal implant for patient A's knee. Based on this design, the implant is manufactured using a 3D printer and received by the user. This entire process allows for the rapid provision of implants tailored to individual anatomical characteristics, which is expected to improve patient satisfaction.

[0622] The following describes the processing flow.

[0623] Step 1:

[0624] The terminal acquires patient medical images from CT scanners and MRI machines and uploads this data to the server via a secure channel. After the upload, the terminal confirms the completion of data transmission and receives a notification of receipt from the server.

[0625] Step 2:

[0626] The server stores the received medical image data in a database, and the AI ​​agent performs the necessary preprocessing to analyze the data. This includes, for example, denoising the images and adjusting the resolution.

[0627] Step 3:

[0628] An AI agent on the server analyzes pre-processed images to extract the patient's anatomical features. This analysis uses machine learning algorithms to identify features such as bone shape and density.

[0629] Step 4:

[0630] The server generates a digital 3D model of the implant, tailored to the patient, based on the extracted anatomical features. This model is created using an automated design algorithm and verified to meet specific requirements.

[0631] Step 5:

[0632] The server converts the designed implant model into a format that can be manufactured by a 3D printer and sends the data to the 3D printer. Here, the server places the data in a queue of print jobs and monitors the manufacturing process.

[0633] Step 6:

[0634] The server monitors the printing status in real time to ensure the printer is functioning correctly. If an anomaly is detected, it issues an alert and provides instructions for troubleshooting.

[0635] Step 7:

[0636] After the server confirms that manufacturing is complete, it sends the implant shipment information to the terminal. Based on this information, the terminal notifies the medical institution's logistics department of the expected arrival date of the implant.

[0637] Step 8:

[0638] The user receives the implant upon arrival at the medical institution and verifies its quality and compliance with specifications. After the inspection is complete, the user sends the results as feedback to the server, which records this information in a database for future designs.

[0639] (Example 1)

[0640] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0641] Conventional medical component design and manufacturing methods have presented challenges in quickly and precisely responding to the individual needs of each patient, leading to increased time costs and human resource requirements. Furthermore, the design phase relied heavily on human experience, making it difficult to efficiently supply medical components of consistent quality. Additionally, delays in anomaly detection during the manufacturing process increased the risk of quality defects.

[0642] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0643] In this invention, the server includes means for receiving patient medical information, means for analyzing and extracting biological characteristics, means for automatically generating medical component designs, means for optimizing the design process using machine learning algorithms, and means for monitoring the manufacturing process and detecting anomalies. This makes it possible to quickly and accurately design and manufacture medical components tailored to individual patients, and to immediately identify and address problems in the manufacturing process.

[0644] "Medical information" refers to data collected to indicate a patient's health status, and encompasses a wide range of information, including image data and diagnostic results.

[0645] "Biological characteristics" refer to data that represents the physiological or anatomical features unique to each individual patient, including, for example, bone shape and tissue density.

[0646] "Medical components" are artificial structures or devices used for the purpose of treating patients, specifically components such as implants and prostheses.

[0647] "Automated generation" refers to a process carried out by machines or programs with minimal human intervention, and specifically refers to the act of efficiently performing complex tasks using software.

[0648] "Machine learning algorithms" refer to mathematical methods and processes that enable computers to learn data patterns from experience and make predictions and decisions about the future.

[0649] "Optimization" is a technique for adjusting processes and designs to obtain the best or most favorable results for a specific purpose, and is performed to improve efficiency and performance.

[0650] "Anomaly detection" is a system function that monitors for states or patterns that deviate from normal processes, quickly identifies them, and issues warnings.

[0651] This invention is a system for efficiently designing and manufacturing medical components that meet the individual needs of each patient. Its main components include a server, terminals, and users.

[0652] The server is a powerful computer device with robust data processing capabilities that securely processes patient medical information received from healthcare institutions. Data processing utilizes domain-specific AI analysis software, particularly for image analysis and pattern recognition. This AI excels at extracting biometric features and generating highly accurate 3D models. These 3D models are then optimized using machine learning algorithms to design medical components. Advanced design support software is used to convert the data into a format suitable for 3D printing. Furthermore, the server interacts with 3D printers, monitoring the manufacturing process and issuing alerts if defects are detected.

[0653] The terminal is connected to the healthcare facility's system and is a device that collects medical information and transmits it to a server. The terminal uses a highly secure data transfer protocol to encrypt and transmit patient CT scans and MRI data. The terminal also receives manufacturing information and quality inspection results transmitted from the server and informs the user.

[0654] The user receives the manufactured medical components and performs quality assurance. The user checks the product dimensions and machining precision and provides feedback to the server. This feedback can then be used by the AI ​​in future designs, contributing to further improvements in accuracy.

[0655] As a concrete example, when manufacturing a knee joint implant for a patient, the terminal collects CT data of the patient's knee and sends it to the server. The server analyzes this data and designs the optimal implant for the patient's anatomy. Based on this data, a 3D printer manufactures the implant, and the user receives it. The server collects this feedback and uses it to improve the design for the next time. An example of a prompt message for the generated AI model could be in the form of, "Design a 3D model of the optimal knee implant using patient A's knee CT data."

[0656] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0657] Step 1:

[0658] The terminal collects patient CT scan and MRI data within the medical facility and receives associated identification information as data input. This data is converted to a specific format and sent to the server using encryption technology. An encrypted, secure data package is generated as output.

[0659] Step 2:

[0660] The server receives encrypted data sent from the terminal and decrypts it to restore the input data. Here, the data's integrity and completeness are verified, and it is stored in a secure database. As output, the data is securely stored and available for use in subsequent analysis steps.

[0661] Step 3:

[0662] An AI agent installed on the server takes securely stored medical data as input and extracts biological features using image analysis algorithms. Specifically, it uses machine learning models to analyze the characteristics of bones and tissues and outputs them as feature vectors.

[0663] Step 4:

[0664] The server receives extracted biometric data as input and constructs a patient-specific virtual model using 3D model generation software. During this process, an algorithm trained on past success and failure data is used to suggest optimal materials and shape parameters. The output is 3D printable digital design data.

[0665] Step 5:

[0666] The server sends the designed 3D model data to the 3D printer, which then receives the data as input and manufactures the actual medical component. During the manufacturing process, the server monitors the printer's status in real time and immediately issues an alert if any abnormalities are detected. The output is the completed physical medical component.

[0667] Step 6:

[0668] The user receives the manufactured medical component. The user specifically inspects the component's dimensions, shape, and surface condition to confirm that it meets quality standards. Based on this confirmation, feedback is provided to the server as input, which is then used again within the system as data for future improvements. The output consists of the quality-verified medical component and data for continuous learning.

[0669] (Application Example 1)

[0670] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0671] There is a need for efficient design and manufacturing processes for medical implants tailored to individual needs, as well as proper management of their progress and quality feedback. In particular, a challenge is enabling healthcare professionals to understand the design and manufacturing progress in real time and to provide patients with implants that are appropriate for them quickly and accurately.

[0672] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0673] In this invention, the server includes means for receiving medical images of a patient, means for analyzing the medical images to extract anatomical features, means for automatically generating an implant design based on the anatomical features, means for manufacturing the designed implant using a three-dimensional printer, and means for notifying the user of the progress of the implant design. This enables medical professionals to understand the progress of the design and manufacturing process in real time and to provide implants optimized for each patient.

[0674] "Patient medical images" refer to image data such as CT scans and MRIs taken at hospitals and medical facilities, which capture the patient's anatomical features in detail.

[0675] "Anatomical features" refer to information that describes the internal structure, shape, and location of a patient's organs, and are extracted by analyzing medical images.

[0676] "Methods for automatically generating implant designs" refer to technologies that use a computer system to design implants based on anatomical characteristics and quickly output those designs.

[0677] A "three-dimensional printing machine" is a machine that creates three-dimensional objects based on digital design data, and is used in the manufacture of implants.

[0678] "Implant design progress information" refers to information regarding the progress of the implant design and manufacturing process, which is notified to users as it progresses.

[0679] To implement this invention, collaboration between a server, a terminal, and a user is required. The server first receives the patient's medical image data transmitted from the terminal. This includes CT scans and MRI images. The received data is processed by an AI agent to analyze the anatomical characteristics of the specific patient. Through this analysis, the server generates a patient-specific three-dimensional model, and uses this model to design the implant.

[0680] During the design process, the server uses AI, learned from past surgical data and successful and unsuccessful cases, to suggest implant materials and shapes. This AI acts as computational intelligence, monitoring the design progress in real time and making design changes as needed.

[0681] Next, the server sends the data to a 3D printer to manufacture the designed implant. The 3D printer creates the physical implant based on the design. As manufacturing progresses, the server notifies the terminal of the implant design's progress, allowing the user to check the information.

[0682] As a concrete example, consider the case where a hip joint implant is manufactured for patient C. The server analyzes the CT image of the hip joint received from the terminal and designs the optimal implant. Then, it manufactures the implant using a 3D printer and provides progress information to healthcare professionals via the terminal. This allows healthcare professionals to accurately understand the preparation status of the implant and provide the patient with the best possible medical care.

[0683] Examples of prompts to input into a generative AI model:

[0684] "Please tell me how to design a custom implant based on patient C's CT images and start the manufacturing process using a 3D printer."

[0685] The hardware used included smart devices with cameras (smartphones and tablets), a cluster of servers, and a 3D printer. The software used was Python, OpenCV, and the requests library.

[0686] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0687] Step 1:

[0688] The user uses a device to capture medical images of a patient and sends them to a server. As input, the device acquires CT or MRI images of the patient, compresses them, and converts them into a digital format for transmission. As output, the medical images are sent to the server. Specifically, the device packets the captured images according to the communication protocol and uploads them to a designated endpoint on the server.

[0689] Step 2:

[0690] The server analyzes received medical images and extracts anatomical features. The input is medical image data sent from the terminal. The output is digital data containing anatomical features. An AI agent within the server analyzes the images using image processing algorithms and extracts the necessary features. This image processing uses OpenCV and leverages edge detection and region segmentation techniques.

[0691] Step 3:

[0692] The server automatically generates implant designs based on anatomical features. The input is the anatomical feature data obtained in step 2. The output is 3D printable design data. The AI ​​model selects the optimal material and shape from a historical database and converts it into a 3D design format. During this process, a Python data processing library is used to perform numerical calculations to generate the design data.

[0693] Step 4:

[0694] The server transmits the designed implant data to the 3D printer and initiates the manufacturing process. The input is the design data, and the output is the physical implant. Specifically, the server connects to the 3D printer's control system, adds the printing task to a queue, and sends execution commands. The implant is formed by layering the specified material using a continuous lamination method.

[0695] Step 5:

[0696] The server notifies the user's device of the progress of implant design and manufacturing. Input is information on the progress of the manufacturing process, and output is a message sent to the user. Specifically, the server provides the user with the latest progress in the design and manufacturing phases via email or in-app notifications. These notifications are sent in stages depending on the situation.

[0697] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0698] This invention is a system that designs and manufactures medical implants tailored to the individual needs of patients, and further improves the patient's psychological satisfaction before and after surgery by taking into account the user's emotional state. The system is mainly composed of three stakeholders: a server, a terminal, and a user, and incorporates an emotion engine within it.

[0699] Server Functions

[0700] The server uses CT scan and MRI data received from medical institutions via terminals to analyze anatomical features using an AI agent. This data analysis automatically generates a 3D model based on the patient's individual needs. The server then designs implants based on this 3D model, utilizing artificial intelligence trained on past surgical data during the design process.

[0701] Furthermore, the emotion engine integrated into the server collects and analyzes user emotional data. This information visualizes the patient's mental state during and after surgery, supporting decision-making during the surgical process.

[0702] Applications of the Emotion Engine

[0703] The emotion engine analyzes the patient's mental state based on user feedback and evaluates emotions through specific speech data and nonverbal cues. For example, if the emotion engine analyzes the patient's voice input and indicates that the patient is feeling anxious or reassured, it will provide care and surgical explanations tailored to those emotions.

[0704] Device functions

[0705] The terminal functions as an interface for healthcare users to communicate with the server. It performs operations such as uploading medical image data, receiving notifications upon implant arrival, and reviewing analysis results from the emotion engine.

[0706] User involvement

[0707] As healthcare professionals, users will understand the patient's emotional state through the emotion engine and utilize this information in post-operative care plans and explanations. Furthermore, after surgery, they will provide feedback to the server regarding the quality and design fit of the received implant, contributing to improvements in future processes.

[0708] As a concrete example, when considering surgery for patient B, the terminal receives her CT data, and the server analyzes it to design an individual implant. During this process, an emotion engine analyzes patient B's emotional state, and the server incorporates this information into the surgical plan and explanation. After inspection, the user provides appropriate follow-up, taking into account patient B's satisfaction. This entire process provides medical care that considers not only the physical aspects but also the psychological aspects.

[0709] The following describes the processing flow.

[0710] Step 1:

[0711] The terminal collects patient CT scan and MRI data from medical facility equipment and securely uploads this data to a server. After uploading, it confirms data transmission and receives notification from the server that the data has been received.

[0712] Step 2:

[0713] The server stores the received medical image data in a database. An AI agent analyzes this data and extracts anatomical features. For example, it identifies information such as bone shape and density and generates a 3D model for the patient.

[0714] Step 3:

[0715] The server designs a custom implant based on a 3D model of the patient. This design is optimized by machine learning algorithms, which learn from past surgical data to suggest materials and shapes.

[0716] Step 4:

[0717] The server sends the designed implant data to the 3D printer and starts the manufacturing process. The server monitors the printer's operation and manages it to ensure that the implant is manufactured successfully.

[0718] Step 5:

[0719] The terminal receives notifications from the server and checks the shipping status of the manufactured implants. It then communicates the arrival schedule to the logistics department and prepares for receipt.

[0720] Step 6:

[0721] The user inspects the implant upon arrival, checking that its appearance and dimensions conform to the design. If there are no problems, they determine that the implant is ready for use in surgery.

[0722] Step 7:

[0723] An emotion engine embedded in the server collects and analyzes emotional data through interactions with users and patients. For example, it analyzes voice input from patients to identify their emotional state.

[0724] Step 8:

[0725] Users develop care plans based on the patient's emotional state. Using the emotional analysis results obtained from the server, they provide mental care to patients during and after surgery, aiming to improve patient satisfaction.

[0726] (Example 2)

[0727] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0728] In the design and manufacture of medical implants, there is a need to efficiently provide high-quality implants while considering the anatomical characteristics and mental state of each individual patient. However, current processes have challenges in adapting to individual patient needs and considering patients' emotional states. Therefore, improvements are needed to enhance both the physical and mental satisfaction of patients.

[0729] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0730] In this invention, the server includes means for receiving medical images of a patient, means for analyzing the medical images to extract anatomical features, means for automatically generating an implant design based on the anatomical features, means for transmitting the generated implant design to a medical institution, means for collecting and analyzing patient emotional data, and means for adjusting the surgical plan based on the analysis results. This enables the optimization of individual medical plans and the provision of high-quality medical services, including emotional care.

[0731] "Patient medical images" refer to visual information of the inside of a patient's body obtained using methods such as CT scans and MRI.

[0732] "Anatomical features" refer to a collection of information that describes specific shapes, dimensions, and arrangements related to a patient's physical structure.

[0733] An "implant" refers to an artificial organ or assistive device designed to be implanted in a patient's body.

[0734] "Automated generation" refers to the process of creating designs and layouts using artificial intelligence and algorithms without any human intervention.

[0735] A "generative AI model" refers to a machine learning algorithm that learns from past data and generates new implant designs.

[0736] "Emotional data" refers to information such as voice, facial expressions, and behavior related to the emotions a patient exhibits.

[0737] A "surgical plan" refers to a detailed outline of the surgical procedure to be performed on a specific patient.

[0738] A "machine learning algorithm" refers to a computational method that uses large amounts of data to discover patterns and rules and predict future actions and outcomes.

[0739] "Evaluation" refers to the process of measuring the quality of a product based on certain criteria and identifying areas for improvement.

[0740] This invention is a system for efficiently designing and manufacturing medical implants that meet the diverse needs of each patient, and for providing care tailored to the patient's emotional state. The system has three central components: a server, a terminal, and a user, and utilizes a generative AI model.

[0741] The server receives patient medical images provided by medical institutions via terminals. This image data includes CT scans and MRI data. The server uses this data for an AI agent to analyze the patient's anatomical features. Based on the analysis results, the generated AI model automatically creates a 3D design for the optimal implant for the patient. This generation process references past medical data, enabling designs that leverage individual anatomical features.

[0742] The terminal is in the hands of the healthcare provider and operates in conjunction with the server. The terminal can view analysis results and generated implant designs received from the server, and also collects emotional data from patients. This emotional data includes voice input and non-verbal cues, and is designed to facilitate smooth data entry. The collected emotional data is sent to the server for analysis by an emotional engine.

[0743] Users, acting as healthcare professionals, utilize feedback from the emotion engine to provide optimal patient care. This can be used to improve patients' mental well-being during surgical explanations and follow-up. For example, if a patient shows anxiety while waiting for surgery, the emotion engine analyzes their state, and the server suggests approaches to alleviate that anxiety to the user.

[0744] Example prompt: "Please explain how individual implant designs are generated in this system."

[0745] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0746] Step 1:

[0747] The server receives CT scan and MRI data from medical institutions via terminals. This data is provided as input, and an AI agent analyzes the images to extract anatomical features. As output, shape data tailored to each individual patient is obtained. At this stage, the server performs the specific operation of rapidly processing a massive amount of image data and quantifying anatomical features.

[0748] Step 2:

[0749] The server automatically generates a 3D design of the implant using a generative AI model based on extracted anatomical features. The input here is numerical data of anatomical features, and the output is a 3D model of the implant optimized for the patient. The generative AI model performs specific calculations to generate the optimal shape, referencing an existing design database.

[0750] Step 3:

[0751] The server sends the generated 3D design to the terminal for user review. The input is a 3D model, and the output is digital design information displayed on the terminal. The server performs specific actions to ensure reliable data transmission and provide an interface that allows healthcare professionals to suggest necessary adjustments.

[0752] Step 4:

[0753] The terminal receives feedback from healthcare professionals, makes necessary adjustments, and then returns the final design to the server. At this stage, the design data, which has been adjusted to reflect user feedback, is output. The terminal then performs specific operations to accurately reflect the input adjustment information and incorporate it into the design.

[0754] Step 5:

[0755] The server uses an emotion engine to collect and analyze the aforementioned emotional data. This process takes voice and nonverbal data provided from the terminal as input and outputs information evaluating the patient's psychological state. The emotion engine analyzes voice tone and patterns to quantify emotions and performs specific emotional evaluations.

[0756] Step 6:

[0757] The server adjusts the surgical plan based on the analysis results and generates the optimal patient care procedure. In this step, the results of the emotional assessment are input, and the adjusted surgical plan and care suggestions are output. Through communication with healthcare professionals, the server performs specific actions to select the optimal process according to the patient's physical and mental condition.

[0758] (Application Example 2)

[0759] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0760] Designing and manufacturing medical implants tailored to individual patient needs while simultaneously improving patient emotional well-being is a challenging task within current medical processes. Conventional technologies make the process of creating implants that meet individual anatomical needs complex and time-consuming, and methods for considering patients' emotional states in real time are limited. Therefore, there is a need to provide a method that comprehensively improves both the physical and mental care of patients.

[0761] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0762] In this invention, the server includes means for receiving medical images of a patient, means for analyzing the medical images to extract anatomical features, means for automatically generating implant designs based on the anatomical features, means for manufacturing the designed implants using a three-dimensional manufacturing device, and means for utilizing an emotion analysis mechanism to analyze the patient's emotional state and adjust care accordingly. This makes it possible to simultaneously achieve individualized implant design and improved emotional care for patients.

[0763] "Medical images" are photographs or videos that visually represent a patient's internal condition and are used for diagnosis and the development of treatment plans.

[0764] "Anatomical features" refer to the detailed characteristics of a patient's physical structure, including information about its shape, tissues, and positional relationships.

[0765] An "implant" is a medical device or material that is inserted or fitted into a patient's body and used to complement or modify the function of that person's body.

[0766] A "three-dimensional manufacturing machine" is a machine that forms a three-dimensional object by layering materials based on digital data.

[0767] An "emotion analysis mechanism" is a technical means of evaluating and judging a person's emotional state based on voice, facial expressions, and other nonverbal cues.

[0768] "Intelligent function" refers to the ability to use artificial intelligence to analyze and judge data, and to independently learn and adapt in order to perform specific tasks.

[0769] A "response" is information generated from system input and analysis, and represents actions or opinions provided for improvement or adjustment in the next steps.

[0770] The system for realizing this invention designs and manufactures medical implants based on the individual needs of patients, and improves patients' mental satisfaction by analyzing their emotional state and utilizing this information in their care.

[0771] The server primarily receives and analyzes patient medical images to extract anatomical features. Specifically, it processes CT scans and MRI data on the platform, using high-precision algorithms to identify necessary features. This enables the design of implants tailored to each individual patient. Leveraging AI technology, the system generates optimal designs based on past treatment data, and then materializes those designs using 3D manufacturing equipment.

[0772] Furthermore, the emotion analysis mechanism analyzes emotions from data such as the patient's voice and facial expressions. This function allows for monitoring the psychological state of patients during surgery or treatment, providing guidance for appropriate care. Wearable devices such as smart glasses allow care staff to access this data in real time and take specific care actions.

[0773] As a concrete example, when used in a nursing home, a system is utilized to monitor the emotional state of patient A. The AI ​​detects when she is feeling anxious, and the data is sent to staff via a server. Based on notifications displayed on her glasses, staff suggest relaxing coping mechanisms. In this way, integrating medical and psychological care makes it possible to improve the overall patient satisfaction.

[0774] An example of a specific prompt for a generative AI model is: "Analyze patient A's voice input and facial expression data to identify her emotional state. Based on her emotions, suggest appropriate care."

[0775] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0776] Step 1:

[0777] The terminal receives CT scan and MRI data from the patient's medical institution. This data is sent to a server for preparation for analysis. The input is medical images, and the output is the transfer to the server. Specifically, the terminal uploads data to the server via the network.

[0778] Step 2:

[0779] The server analyzes received medical images to extract anatomical features. Image processing techniques and machine learning algorithms (e.g., deep learning) are used for the analysis. The input is medical images, and the output is extracted anatomical feature data. Specifically, the algorithm performs pattern recognition to generate the feature data.

[0780] Step 3:

[0781] The server automatically generates implant designs based on extracted anatomical features. This process utilizes AI technology. The input is anatomical feature data, and the output is implant design data. Specifically, the AI ​​generates the design using relevant shape data from an existing database.

[0782] Step 4:

[0783] The server transmits design data to a 3D manufacturing machine to produce the implant. The input is the design data for the implant, and the output is the physical implant. Specifically, the manufacturing machine is operated through the data transmission system, and the implant is formed by layering materials.

[0784] Step 5:

[0785] The server collects voice and facial expression data from the terminal to analyze the patient's emotional state. It uses an emotion analysis mechanism to evaluate emotions. The input is voice and facial expression data, and the output is the analyzed emotional state. Specifically, the server uses a generative AI model to analyze the data and quantitatively evaluate emotions.

[0786] Step 6:

[0787] The user implements appropriate care for the patient based on the analysis results. Specific care actions and communications are selected based on emotional data. The input is the analyzed emotional state, and the output is the care plan. Specifically, the user checks notifications and receives instructions to take appropriate action.

[0788] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0789] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0790] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0791] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0792] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0793] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0794] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0795] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0796] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0797] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0798] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0799] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0800] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0801] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0802] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0803] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0804] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0805] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0806] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0807] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0808] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0809] The following is further disclosed regarding the embodiments described above.

[0810] (Claim 1)

[0811] A means of receiving patient medical images,

[0812] A means for analyzing the aforementioned medical images and extracting anatomical features,

[0813] A means for automatically generating an implant design based on the aforementioned anatomical features,

[0814] A means for manufacturing the aforementioned designed implant using a 3D printer,

[0815] A system that includes this.

[0816] (Claim 2)

[0817] The system according to claim 1, comprising means of using artificial intelligence capable of learning from past surgical data in the implant design process.

[0818] (Claim 3)

[0819] The system according to claim 1, wherein the artificial intelligence includes means for evaluating the quality of the manufactured implant and providing feedback for improving the design process.

[0820] "Example 1"

[0821] (Claim 1)

[0822] Means of receiving patient medical information,

[0823] A means for analyzing the aforementioned medical information and extracting biological characteristics,

[0824] A means for automatically generating the design of a medical component based on the aforementioned biological characteristics,

[0825] A method for optimizing the design process using machine learning algorithms learned from past medical history,

[0826] A means for manufacturing the aforementioned medical component using a three-dimensional manufacturing apparatus,

[0827] A means of monitoring the manufacturing process and detecting abnormalities,

[0828] A system that includes this.

[0829] (Claim 2)

[0830] The system according to claim 1, comprising means for verifying the quality of the medical component after its manufacture and providing data for improving the design process.

[0831] (Claim 3)

[0832] The system according to claim 1, wherein the machine learning algorithm includes means for suggesting optimal manufacturing conditions based on the characteristics of the designed medical component.

[0833] "Application Example 1"

[0834] (Claim 1)

[0835] A means of receiving patient medical images,

[0836] A means for analyzing the aforementioned medical images and extracting anatomical features,

[0837] A means for automatically generating an implant design based on the aforementioned anatomical features,

[0838] A means for manufacturing the aforementioned designed implant using a three-dimensional printing machine,

[0839] A means for notifying the user of the progress information regarding the implant design,

[0840] A system that includes this.

[0841] (Claim 2)

[0842] The system according to claim 1, comprising means for using computational intelligence that can learn from past surgical data in the implant design process.

[0843] (Claim 3)

[0844] The system according to claim 1, wherein the computer intelligence includes means for evaluating the quality of the manufactured implant and providing feedback for improving the design process.

[0845] "Example 2 of combining an emotion engine"

[0846] (Claim 1)

[0847] A means of receiving patient medical images,

[0848] A means for analyzing the aforementioned medical images and extracting anatomical features,

[0849] A means for automatically generating an implant design based on the aforementioned anatomical features,

[0850] A means of transmitting the generated implant design to a medical institution,

[0851] A means of collecting and analyzing patient emotional data,

[0852] A means of adjusting the surgical plan based on the analysis results,

[0853] A system that includes this.

[0854] (Claim 2)

[0855] The system according to claim 1, comprising means of using a machine learning algorithm that can learn from past medical planning data in the implant design process.

[0856] (Claim 3)

[0857] The system according to claim 1, wherein the machine learning algorithm includes means for evaluating the characteristics of the manufactured implant and providing information for improving the design process.

[0858] "Application example 2 when combining with an emotional engine"

[0859] (Claim 1)

[0860] A means of receiving patient medical images,

[0861] A means for analyzing the aforementioned medical images and extracting anatomical features,

[0862] A means for automatically generating an implant design based on the aforementioned anatomical features,

[0863] A means for manufacturing the aforementioned designed implant using a three-dimensional manufacturing machine,

[0864] A means of using an emotion analysis mechanism to analyze the patient's emotional state and adjust care based on that analysis,

[0865] A system that includes this.

[0866] (Claim 2)

[0867] The system according to claim 1, comprising means for using an intelligent function that can learn from past treatment data in the implant design process.

[0868] (Claim 3)

[0869] The system according to claim 1, wherein the intelligent function includes means for evaluating the quality of the manufactured implant and providing responses for improving the design process. [Explanation of Symbols]

[0870] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving patient medical images, A means for analyzing the aforementioned medical images and extracting anatomical features, A means for automatically generating an implant design based on the aforementioned anatomical features, A means for manufacturing the aforementioned designed implant using a three-dimensional printing machine, A means for notifying the user of the progress information regarding the implant design, A system that includes this.

2. The system according to claim 1, comprising means for using computer intelligence capable of learning from past surgical data in the implant design process.

3. The system according to claim 1, wherein the computer intelligence includes means for evaluating the quality of the manufactured implant and providing feedback for improving the design process.

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