system

Abstract

We provide the system. [Solution] A means for acquiring medical image data of a patient and storing it in a database, A means for preprocessing acquired medical image data and extracting anatomical features, A means to optimize the design of medical implants based on anatomical features extracted using an artificial intelligence model, A method for sending optimized medical implant designs to a 3D printer for manufacturing, A system that includes means for recording surgical results and the patient's recovery process, and for using that data to modify an artificial intelligence model.

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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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] Medical implants need to be adapted to the anatomical characteristics of individual patients. However, in the prior art, general designs are often used, which may cause problems in postoperative compatibility. For this reason, reoperation or the postoperative recovery time is prolonged, increasing the burden on patients. In addition, current custom-made implants are costly and inaccessible to many patients.

Means for Solving the Problems

[0005] This invention includes the steps of acquiring medical image data from a patient and preprocessing that data to extract anatomical features. Next, an optimized implant design is created based on the extracted features using an artificial intelligence model. This design is manufactured using a 3D printer and used in surgery. Furthermore, patient recovery data is recorded after surgery, and this data is used to improve the artificial intelligence model, thereby achieving a more suitable implant design. This series of processes makes it possible to rapidly provide patient-specific, customized medical implants at low cost, solving conventional problems.

[0006] "Patient medical imaging data" refers to medical images such as CT scans and MRIs, which are digital data used to visually understand the internal structure of a patient's body.

[0007] A "database" is a system for efficiently storing and managing multiple digital data sets, allowing for quick access to any given data.

[0008] An "artificial intelligence model" is a computer algorithm or system that learns from past data and can perform specific tasks, and is particularly capable of automatically making appropriate decisions regarding new data.

[0009] A "medical implant" is a medical device that is permanently or temporarily implanted in a patient's body and is used to replace the function of bones, joints, and other structures.

[0010] "Anatomical features" refer to specific morphological characteristics of the human body, such as the shape and size of the skeleton and organs.

[0011] A "3D printer" is a device that generates three-dimensional objects by sequentially layering materials based on digital design data.

[0012] "Biocompatibility" refers to the degree to which medical materials and designs are physiologically accepted by the human body, and encompasses safety and a low risk of rejection.

[0013] "Mechanical strength" refers to the ability of a material or structure to resist external forces, representing the degree of force it can withstand before deformation or fracture occurs. [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, when an emotion engine is combined. [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 numbered processor (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), and the like.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) 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 numbered storage 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] In this invention, the system provides a platform for efficiently designing and manufacturing customized medical implants to meet the specific medical requirements of patients.

[0036] First, the user, a medical staff member or doctor, uploads the patient's CT scan or MRI data to the medical information system. This allows for the acquisition of detailed internal structure data of the patient in digital format.

[0037] Next, the server receives the uploaded medical image data and stores it in a database. This data is preprocessed to remove noise and accurately capture anatomical features. For example, when designing a knee joint implant, image processing techniques are used to extract the knee bone and surrounding tissues and filter out noise.

[0038] Once preprocessing is complete, the server automatically generates implant designs based on anatomical features extracted using an artificial intelligence model. Here, it provides an optimal design tailored to the patient, based on past surgical data and implant design guidelines. For example, it can suggest an implant shape that matches the patient's specific bone structure.

[0039] Once the implant design is complete, the user (doctor or engineer) can review it and make adjustments as needed. This includes customization to meet specific needs during surgery and the patient's lifestyle.

[0040] The final design is sent to a 3D printer, and the implant is manufactured using biocompatible materials. For example, it is possible to create durable implants using titanium or biocompatible plastics.

[0041] After the surgery is complete, the server collects data on the patient's recovery progress and the functionality of the implant, and uses this data to improve the artificial intelligence model. This is expected to improve the accuracy of future implant designs and achieve even higher patient satisfaction.

[0042] This system makes it possible to provide implants that accommodate the unique anatomical conditions of each patient, which were previously difficult to address, at a low cost, thereby supporting effective treatment in medical settings.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] Users upload patient CT scan or MRI data to the medical information system. This allows for the acquisition of detailed image data of the patient's body in digital format.

[0046] Step 2:

[0047] The server receives the uploaded image data and stores it in the database while ensuring security. At this stage, it is confirmed that the data is properly accessible and processable.

[0048] Step 3:

[0049] The server preprocesses the received medical image data. It applies a noise filter to remove unwanted information and uses an edge detection algorithm to enhance anatomical features.

[0050] Step 4:

[0051] The server constructs a 3D model based on pre-processed data. Specifically, it uses algorithms such as the Marching Cube algorithm to generate a 3D polygon model from 2D images, faithfully reproducing the patient's anatomical details.

[0052] Step 5:

[0053] The server uses an AI agent to design medical implants based on the constructed 3D model. During this process, it refers to data from past successes and failures to suggest the optimal shape and structure.

[0054] Step 6:

[0055] Users can review the AI-generated implant design and request adjustments as needed. They can verify whether the implant shape and size meet the patient's specific requirements and request adjustments.

[0056] Step 7:

[0057] The 3D printer, acting as the terminal, receives the finalized implant design data and manufactures the implant using biocompatible materials. Depending on the material selection, adjustments to strength and durability are also made.

[0058] Step 8:

[0059] The server receives patient data during and after surgery and monitors their recovery. This data is analyzed and used to continuously improve the AI ​​model. This is expected to lead to more accurate implant designs for future procedures.

[0060] (Example 1)

[0061] 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."

[0062] Current design and manufacturing processes for medical implants do not adequately address the unique anatomical characteristics of each patient, resulting in difficulties in providing optimal fit and function. Furthermore, existing systems rely heavily on manual intervention in fine-tuning implant design and managing materials during manufacturing, leaving challenges in terms of accuracy and efficiency. Therefore, there is a need for technologies that can rapidly and automatically design and manufacture more precise implants, continuously improving the patient's recovery process.

[0063] 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.

[0064] In this invention, the server includes means for acquiring patient medical data and storing it in an information aggregation device, means for preprocessing the acquired medical data and extracting biological structural features, and means for optimizing implant design based on the extracted biological structural features using a machine learning model. This enables the automated design and manufacture of personalized implants based on the patient's unique anatomical characteristics.

[0065] "Patient medical data" refers to information related to a patient's health status and treatment, specifically image data obtained from CT scans, MRI scans, and other imaging methods.

[0066] An "information aggregation device" is a device for efficiently storing and managing large amounts of digital information, and typically functions as a database.

[0067] "Biological structural features" refer to characteristic information that describes the unique shape, size, and positional relationships of tissues and organs within a living organism.

[0068] A "machine learning model" is a mathematically and statistically oriented algorithm that computers use to analyze data and make predictions or classifications based on that analysis.

[0069] "Implant design" is the process of defining the details of the shape, structure, and function of a medical device to be implanted in a patient's body.

[0070] "Manufacturing equipment" refers to a machine or device used to physically shape the designed implant, and typically involves the use of a 3D printer.

[0071] This invention is configured as a system for efficiently designing and manufacturing patient-specific medical implants. Specific embodiments are described below.

[0072] First, medical staff and doctors, who are the users, upload detailed medical data about patients' health conditions, specifically image data obtained from CT scans and MRIs, to the medical information management system. This medical information system uses common database technology as an information aggregation device, and all uploaded data is stored in digital format.

[0073] Next, the server is responsible for preprocessing the received medical data. This preprocessing uses image processing software (for example, an open-source image analysis program) to remove noise and extract biological structural features. For example, when designing a knee joint implant, accurately capturing the shape of the knee bone and cartilage improves the accuracy in the subsequent design phase.

[0074] Subsequently, the server automates implant design using a generative AI model. Here, the machine learning model proposes an implant design suitable for each patient's individual biostructure. This process references historical data and design guidelines, and implements algorithms that support the optimal shape. The generative AI model primarily utilizes frameworks such as TENSORFLOW® and PyTorch.

[0075] After the implant design is generated, the user (doctor or engineer) reviews and fine-tunes the design. This fine-tuning is done according to the patient's lifestyle and specific surgical requirements. For example, for patients who enjoy sports, the design can be modified to increase its strength.

[0076] Once the final design is complete, the manufacturing equipment, specifically a 3D printer, creates the implant from biocompatible materials. This printer is a commonly used technology that can precisely shape implants using materials such as durable metals or bioplastics.

[0077] As a concrete example of a prompt, the AI ​​model is prompted with the question, "To design a knee joint implant, please propose the optimal shape based on CT data of a 50-year-old male patient with a BMI of 25 and an exercise habit." This invention makes it possible to provide implants that are tailored to the individual anatomical characteristics of each patient in a timely and accurate manner, maximizing the efficiency and effectiveness of treatment in medical settings.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The user uploads patient medical data to the medical information management system. Inputs include CT scans and MRI images, and output is data stored digitally in an information aggregation device. This process involves the user clicking an upload button and selecting data files.

[0081] Step 2:

[0082] The server preprocesses the medical data received from the data aggregation device. The input is the data stored in step 1, and the output is preprocessed data from which noise has been removed and biological structural features have been extracted. Specifically, the server applies an image analysis algorithm to extract shape data of bones and cartilage.

[0083] Step 3:

[0084] The server generates implant designs based on bio-structural features using a generative AI model. The input is the pre-processed data obtained in step 2, and the output is the implant design data. The server refers to historical data and design guidelines, and the process includes the specific operation of automatically generating designs using machine learning algorithms.

[0085] Step 4:

[0086] The user reviews and fine-tunes the generated implant design. The input is the design data generated in step 3, and the output is the final design optimized by the physician or engineer. The user uses CAD software to make adjustments to specific dimensions and shapes.

[0087] Step 5:

[0088] The manufacturing device, which acts as the terminal, generates the implant based on the final design. The input is the design data confirmed and fine-tuned in step 4, and the output is the actual implant made from biocompatible material. Specifically, the 3D printer performs a technical process in which it builds the implant by stacking layers.

[0089] Step 6:

[0090] The server collects data on the patient's recovery status and implant functionality after surgery. The input is recovery data reported by the patient, and the output is updated information used to improve future implant designs. The server analyzes this data and performs specific actions to adjust the parameters of the generated AI model.

[0091] (Application Example 1)

[0092] 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."

[0093] Conventional methods for designing and manufacturing medical implants have made it difficult to address the individual needs of each patient. Furthermore, the lack of mechanisms to quickly incorporate feedback from caregivers when introducing implants in nursing care settings has made it difficult to provide patients with the most suitable implants.

[0094] 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.

[0095] In this invention, the server includes means for acquiring patient body image information and storing it in an information aggregation system, means for processing the acquired body image information and identifying morphological features, and means for improving the design of medical components based on the identified morphological features using an intelligent program model. This enables the efficient design and manufacture of medical implants that meet the individual needs of patients, and allows caregivers to quickly incorporate feedback into implant designs.

[0096] "Patient body image information" refers to digital image data that includes detailed information about the internal and external structures of the patient's body.

[0097] An "information aggregation system" is a system that securely stores acquired data and maintains it in a format that can be used for subsequent processing and analysis.

[0098] "Data processing" refers to a series of operations that convert acquired digital data into an analyzable format and remove unwanted noise.

[0099] "Morphological features" refer to information about the structural and functional characteristics of an object extracted from data.

[0100] An "intelligent program model" is an algorithm that uses artificial intelligence to analyze data and perform inference and pattern recognition for specific problems.

[0101] A "medical component" is a part designed to be attached to or implanted in a patient's body, serving a specific medical purpose.

[0102] "Adjustment" refers to the act of precisely setting or modifying a design or process to achieve optimal performance.

[0103] A "care worker" is a professional who provides support and medical assistance to improve the quality of life for patients.

[0104] "Knowledge aggregation means" are methods and devices for accumulating knowledge in a specific field and disseminating it in a usable form.

[0105] The following describes embodiments for carrying out the invention.

[0106] The server first acquires the patient's physical image information and stores it in the information aggregation system. Specifically, medical staff upload CT scans and MRI data using smartphones, securely saving the data in digital format.

[0107] Next, the server processes the acquired body image information and identifies morphological features. This process uses Python and TensorFlow and includes calculations to remove noise from the images and extract the patient's unique body structures.

[0108] Subsequently, the design of medical components is improved based on morphological features identified using intelligent program models. AI inference algorithms are leveraged to provide optimal designs for patients, referencing historical data. Generative AI models are used in this step.

[0109] Users can then review the design proposal through their smartphone interface. This allows caregivers to quickly provide feedback that reflects the patient's lifestyle and specific needs.

[0110] The final design, transferred to the 3D printer as a terminal, is constructed from materials that consider biocompatibility and mechanical durability, and is completed as a medical component.

[0111] An example of a prompt message might be, "Upload knee CT scan data and generate a patient-specific implant design." This prompt prompts the system to perform appropriate data processing and automatically generate a highly accurate implant design. This makes it possible to provide medical solutions optimized for individual patients.

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] Users input patient CT scan and MRI data using their smartphones and upload the data to a server. This data contains detailed images of the body, which the server stores in an information aggregation system. The input is in digital image format, and the output is stored in a secure database.

[0115] Step 2:

[0116] The system processes the body image data acquired by the server. Specifically, it uses Python and TensorFlow to perform noise filtering and identify morphological features. The input is the body image data acquired in step 1, and the output is clean data representing the patient's specific body structure.

[0117] Step 3:

[0118] The server uses an intelligent program model to design medical components based on morphological features. At this stage, the generating AI model outputs design proposals by referencing past design guidelines and surgical data. The input is the clean data obtained in step 2 and past design data, and the output is an optimized implant design proposal.

[0119] Step 4:

[0120] This is a process where users can review design proposals through a smartphone app. In this scenario, caregivers can provide feedback on the implant designs. The input is the design proposal received from the server, and the output is the caregiver's feedback.

[0121] Step 5:

[0122] The server receives feedback from the user and modifies the intelligent program model as needed. The input is user feedback information, and the output is the adjusted new implant design.

[0123] Step 6:

[0124] The server transfers the final implant design to the 3D printer, which acts as the terminal. The 3D printer then manufactures the components using the specified biocompatible material. At this stage, the input is the final implant design, and the output is the materialized medical component.

[0125] 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.

[0126] This invention enhances the user experience and improves the quality of medical services by combining an emotion engine with a system for designing and manufacturing medical implants for patients.

[0127] First, healthcare professionals, acting as users, upload patient CT scans and MRI data to the medical information system. This data is received by a server and preprocessed, including noise reduction and extraction of anatomical features. This process generates a 3D model optimized for each patient.

[0128] Next, the server uses an artificial intelligence model to automatically generate implant designs based on the patient's anatomical characteristics. During this process, an emotion engine is activated to analyze the user's voice, facial expressions, and text comments, collecting feedback. For example, it captures significant reactions shown by physicians during reviews and evaluates the suitability of the design.

[0129] The generated implant design is reviewed and considered by the user, and feedback through the emotional engine is incorporated into the design. If the user appears anxious, the implant design may be revised, and customized suggestions may be presented.

[0130] Subsequently, the final design of the medical implant is sent to a 3D printer, which is the terminal for manufacturing using specified biocompatible materials. For example, when manufacturing a titanium implant that fits a patient's knee joint using a 3D printer, a design that prioritizes comfort, taking into account the user's emotional state, may be adopted.

[0131] Once manufactured, the implants are used in surgery, and the post-operative results and the patient's recovery process are recorded and analyzed by a server. This allows the system to enhance its AI model and emotional engine, laying the foundation for providing better implants. In this way, the system, incorporating an emotional engine, can provide an optimal solution for both healthcare professionals and patients.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] Users upload patient CT scans and MRI data to a dedicated medical information system. This inputs digital data that provides a detailed representation of the patient's internal structure into the system.

[0135] Step 2:

[0136] The server receives the uploaded data and automatically saves it to the database. After receiving the data, it is sent to a preprocessing module where noise reduction and image correction processes are performed.

[0137] Step 3:

[0138] The server extracts anatomical features from the processed data. This process utilizes image analysis algorithms such as segmentation techniques to extract specific regions and edge detection.

[0139] Step 4:

[0140] The server generates a 3D model based on the extracted features. Three-dimensional reconstruction techniques, such as the Marching Cubes algorithm, are used for model generation. The generated model is then optimized to improve anatomical accuracy.

[0141] Step 5:

[0142] The server utilizes artificial intelligence models to design medical implants based on the generated 3D models. During this process, an emotion engine operates, analyzing the user's facial expressions and voice tone, and using this as feedback input in the design process.

[0143] Step 6:

[0144] The user reviews the analysis results obtained from the emotion engine and adjusts the implant design. If the user expresses anxiety or dissatisfaction with the design, the design parameters are reviewed and new suggestions are made.

[0145] Step 7:

[0146] The finalized implant design is sent to a 3D printer, which manufactures the implant using the specified biocompatible material. During the manufacturing process, material selection and the precision of the implant's shape are strictly controlled.

[0147] Step 8:

[0148] After the surgery is complete, the server collects postoperative patient data and evaluates the recovery status and the functionality of the implants used. Based on this, the artificial intelligence model and emotion engine are further optimized and used for future designs.

[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 devices, rapid and accurate device design based on the individual anatomical characteristics of each patient is required. Furthermore, conventional systems struggle to incorporate device design fit and user feedback, hindering improvements in user experience and the quality of medical services. These challenges directly impact material selection and quality assurance in the manufacturing process, thus requiring comprehensive solutions.

[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 acquiring patient medical data and storing it in an information management system, means for preprocessing the acquired medical data and extracting the characteristics of the biological structure, and means for optimizing the design of a medical device based on the characteristics of the biological structure extracted using a generative AI model. This enables the rapid design of a medical device optimized for each patient and the provision of an advanced user experience.

[0154] "Patient medical data" refers to digital data containing information related to a patient's health status and physical structure, including CT scans, MRI images, and blood test results.

[0155] An "information management system" refers to a computer system that comprehensively manages medical data and stores, retrieves, and processes the data as needed.

[0156] "Preprocessing" refers to the initial data processing steps involved in removing noise and filtering medical data to prepare it for analysis.

[0157] "Biostructural characteristics" refer to the anatomical structures and shapes that make up the patient's body, and include information necessary for device design.

[0158] A "generative AI model" is a program model that uses artificial intelligence technology to generate a specific output from input data, and it primarily utilizes deep learning technology.

[0159] A "medical device" is an instrument designed to achieve a specific medical purpose, including those that are surgically placed, such as implants.

[0160] "Emotion analysis function" refers to technology that identifies a user's emotional state from their voice, facial expressions, or text, and predicts their response based on that.

[0161] "Feedback" refers to information collected from users, such as their reactions and opinions, that is used to improve and optimize the system.

[0162] "Manufacturing equipment" refers to mechanical devices used to form devices using specific materials, and 3D printers are an example of this.

[0163] "Biocompatibility" refers to the properties of materials and devices that allow them to function without causing side effects when in contact with living tissue.

[0164] The system based on the present invention is for the rapid and effective design and manufacture of personalized medical devices for patients. Specific embodiments thereof are described below.

[0165] First, healthcare professionals, as users, upload patient CT scans and MRI data to the medical information management system. This operation is typically performed through a graphical user interface (GUI) on a medical workstation. The system is operated by numerous data processing and calculations performed by a server.

[0166] Next, the server performs preprocessing on the received medical data, such as noise reduction and extraction of anatomical features. Standard server racks are used as the specific hardware. The software used includes Python and its OpenCV library, or MATLAB®. These tools prepare high-quality data appropriate to the patient's physical structure.

[0167] Subsequently, the server uses a generative AI model to optimize the design of medical devices based on the extracted bio-structure features. Deep learning libraries such as TensorFlow and PyTorch are primarily used. This process involves sentiment analysis, which analyzes the user's voice, facial expressions, and text comments. Commercial sentiment recognition APIs are commonly used for this function.

[0168] Here, user feedback plays a crucial role. By collecting feedback and incorporating it into the design, it is possible to improve the fit of medical devices and user satisfaction. For example, when designing knee joint implants, an approach that prioritizes comfort based on user comments can be employed. At this stage, the following text might be used as a prompt in the generative AI model:

[0169] "Generate a design for the patient's knee joint implant. Please propose a model that prioritizes comfort and ease of use, taking into consideration the physician's review."

[0170] Finally, the finalized device design is sent to a 3D printer, the terminal for manufacturing using specified biocompatible materials. A professional-grade 3D printer, commonly used in the medical field, is suitable. This entire process provides high-quality, patient-optimized medical devices, enabling a better experience for both patients and healthcare professionals.

[0171] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0172] Step 1:

[0173] Users upload patient CT scans and MRI data to the medical information management system. The data, as input, is transferred via a GUI on a medical workstation. This data, which includes the patient's anatomical information, is received and stored by the information management system.

[0174] Step 2:

[0175] The server receives the uploaded image data and first performs denoising. Denoising is carried out using the Python and OpenCV libraries. The input data is raw image data, and the output is a clear image with the noise removed. This process improves the accuracy of subsequent analysis.

[0176] Step 3:

[0177] The server then extracts anatomical features from the image data. This is done using Matlab or an advanced image processing library. Pre-processed images are used as input, and data for 3D models representing anatomical regions is generated as output. This results in data specific to each individual patient.

[0178] Step 4:

[0179] The server applies a generative AI model and optimizes the design of medical devices based on the extracted features. The software used is TensorFlow or PyTorch. Anatomical feature data is used as input, and a 3D model of the optimized device design is generated as output. Model generation and adjustments are performed using prompts.

[0180] Step 5:

[0181] The server uses emotion analysis capabilities to collect feedback from users. It analyzes user voice, facial expressions, and text comments as input to obtain feedback data. The technology used is a commercial emotion recognition API. This provides guidance for improving the device design.

[0182] Step 6:

[0183] The user reviews the 3D device design and requests modifications as needed. An interactive 3D viewer is used to review the design. The input is the generated device design, and the output is user feedback on its suitability.

[0184] Step 7:

[0185] The 3D printer, acting as the terminal, begins manufacturing based on the final device design. The device uses specified biocompatible materials to transform the 3D design into a physical product. The input is device design information, and the output is a physical medical device. This results in a patient-specific device.

[0186] Step 8:

[0187] The server records surgical results and the patient's recovery process, and uses this data to improve the AI ​​model. Database software is used, with postoperative patient data as input and an improved AI model as output. This process improves the accuracy of future device designs.

[0188] (Application Example 2)

[0189] 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".

[0190] In modern medicine, providing patients with optimal medical components is crucial. However, conventional design methods struggle to comprehensively consider each patient's individual biological data and emotional state, and they cannot adequately evaluate the impact of design on patient comfort and recovery speed. Furthermore, in postoperative patient care, the provision of support methods utilizing consumer electronics is insufficient, often resulting in a lack of patient comfort.

[0191] 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.

[0192] In this invention, the server includes means for acquiring a patient's biological data and storing it in a data set; means for analyzing the user's emotional state using an emotion analysis device and adjusting the design of medical components based on the feedback; and means for providing information about the patient's biological state in their daily environment using consumer electronics and communicating the information to a medical facility as needed. This makes it possible to design and provide medical components that meet the individual needs of each patient, thereby enhancing the patient's sense of security and comfort after surgery.

[0193] "Biometric data" refers to all information about a patient's body, including medical images such as CT scans and MRIs, blood test results, and vital data such as heart rate and blood pressure.

[0194] A "data collection" refers to a database or data repository built to efficiently store and utilize various types of acquired data.

[0195] An "emotion analysis device" refers to a device or software that analyzes a person's facial expressions, voice tone, text comments, etc., to identify their individual emotional state.

[0196] "Medical components" refer to implants and prosthetics that are designed and manufactured to achieve specific therapeutic purposes based on the patient's anatomical characteristics.

[0197] "Consumer-use machinery" refers to robots and electrical appliances used by general consumers in their daily lives, and which are designed to be optimized for medical applications.

[0198] A "three-dimensional manufacturing device" refers to a 3D printer, which constructs physical objects from digital designs. It is a device that generates three-dimensional objects using specified materials.

[0199] The system that realizes this invention is mainly composed of a server, a terminal, and user interaction. The server starts by acquiring the patient's biometric data and storing it in a data collection. In this process, various types of data are handled, including medical images such as CT scans and MRIs, and vital data.

[0200] Next, the server uses an emotion analysis device to analyze the user's emotional state in real time and adjusts the design of the medical components based on this data. The emotion analysis uses sophisticated algorithms that read emotions from human facial expressions, voice, and text.

[0201] The terminal controls a 3D printer, a three-dimensional manufacturing device, to generate objects based on optimized medical component designs sent from the server. In this process, appropriate materials are selected to maintain biocompatibility and mechanical strength.

[0202] Users receive necessary support in the patient's daily environment through consumer-grade devices. These devices monitor the patient's condition and transmit information to medical facilities if any abnormalities are detected.

[0203] One specific example is a consumer robot that checks the facial expressions of patients while they are relaxing at home after surgery and recommends relaxing music. This allows patients to recover while feeling reassured.

[0204] An example of an input prompt for a generative AI model is, "Analyze the patient's emotional state and suggest music to listen to during the remaining recovery period."

[0205] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0206] Step 1:

[0207] The server receives biometric data provided by patients and stores it in a data set. Specifically, it acquires medical images such as CT scans and MRIs, as well as vital data, removes noise from this data, and converts it into a standardized format so that it can be stored in the database. The input is biometric data, and the output is standardized data with noise reduction.

[0208] Step 2:

[0209] The server activates an emotion analysis device and analyzes the user's facial expressions and voice data in real time. The emotional state obtained (e.g., reassurance, tension, etc.) is then used to adjust the design of medical components. The input consists of facial expression data and voice data, and the output is data indicating the analyzed emotional state.

[0210] Step 3:

[0211] The server uses a generative AI model to automatically generate designs for medical components based on emotional states and anatomical features. User emotional feedback is incorporated into the design during this process. The input consists of emotional states and anatomical features, while the output is optimized medical component design data.

[0212] Step 4:

[0213] The terminal retrieves optimized medical component designs transmitted from the server and controls a 3D printer (a three-dimensional manufacturing device) to carry out the manufacturing process. The materials used are biocompatible and guarantee mechanical strength. The input is optimized design data, and the output is the manufactured physical medical component.

[0214] Step 5:

[0215] The user monitors the patient's biological state in their daily environment using consumer-grade equipment and transmits information to a medical facility in real time if an abnormality is detected. Specifically, it reassessss the patient's emotional state from their facial expressions and voice, and issues an alarm to coordinate with medical staff as needed. Inputs include facial expression data and voice data, and outputs are reports to the user and the medical facility.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] [Second Embodiment]

[0220] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0221] 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.

[0222] 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).

[0223] 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.

[0224] 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.

[0225] 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).

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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".

[0232] In this invention, the system provides a platform for efficiently designing and manufacturing customized medical implants to meet the specific medical requirements of patients.

[0233] First, the user, a medical staff member or doctor, uploads the patient's CT scan or MRI data to the medical information system. This allows for the acquisition of detailed internal structure data of the patient in digital format.

[0234] Next, the server receives the uploaded medical image data and stores it in a database. This data is preprocessed to remove noise and accurately capture anatomical features. For example, when designing a knee joint implant, image processing techniques are used to extract the knee bone and surrounding tissues and filter out noise.

[0235] Once preprocessing is complete, the server automatically generates implant designs based on anatomical features extracted using an artificial intelligence model. Here, it provides an optimal design tailored to the patient, based on past surgical data and implant design guidelines. For example, it can suggest an implant shape that matches the patient's specific bone structure.

[0236] Once the implant design is complete, the user (doctor or engineer) can review it and make adjustments as needed. This includes customization to meet specific needs during surgery and the patient's lifestyle.

[0237] The final design is sent to a 3D printer, and the implant is manufactured using biocompatible materials. For example, it is possible to create durable implants using titanium or biocompatible plastics.

[0238] After the surgery is complete, the server collects data on the patient's recovery progress and the functionality of the implant, and uses this data to improve the artificial intelligence model. This is expected to improve the accuracy of future implant designs and achieve even higher patient satisfaction.

[0239] This system makes it possible to provide implants that accommodate the unique anatomical conditions of each patient, which were previously difficult to address, at a low cost, thereby supporting effective treatment in medical settings.

[0240] The following describes the processing flow.

[0241] Step 1:

[0242] Users upload patient CT scan or MRI data to the medical information system. This allows for the acquisition of detailed image data of the patient's body in digital format.

[0243] Step 2:

[0244] The server receives the uploaded image data and stores it in the database while ensuring security. At this stage, it is confirmed that the data is properly accessible and processable.

[0245] Step 3:

[0246] The server preprocesses the received medical image data. It applies a noise filter to remove unwanted information and uses an edge detection algorithm to enhance anatomical features.

[0247] Step 4:

[0248] The server constructs a 3D model based on pre-processed data. Specifically, it uses algorithms such as the Marching Cube algorithm to generate a 3D polygon model from 2D images, faithfully reproducing the patient's anatomical details.

[0249] Step 5:

[0250] The server uses an AI agent to design medical implants based on the constructed 3D model. During this process, it refers to data from past successes and failures to suggest the optimal shape and structure.

[0251] Step 6:

[0252] Users can review the AI-generated implant design and request adjustments as needed. They can verify whether the implant shape and size meet the patient's specific requirements and request adjustments.

[0253] Step 7:

[0254] The 3D printer, acting as the terminal, receives the finalized implant design data and manufactures the implant using biocompatible materials. Depending on the material selection, adjustments to strength and durability are also made.

[0255] Step 8:

[0256] The server receives patient data during and after surgery and monitors their recovery. This data is analyzed and used to continuously improve the AI ​​model. This is expected to lead to more accurate implant designs for future procedures.

[0257] (Example 1)

[0258] 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."

[0259] Current design and manufacturing processes for medical implants do not adequately address the unique anatomical characteristics of each patient, resulting in difficulties in providing optimal fit and function. Furthermore, existing systems rely heavily on manual intervention in fine-tuning implant design and managing materials during manufacturing, leaving challenges in terms of accuracy and efficiency. Therefore, there is a need for technologies that can rapidly and automatically design and manufacture more precise implants, continuously improving the patient's recovery process.

[0260] 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.

[0261] In this invention, the server includes means for acquiring patient medical data and storing it in an information aggregation device, means for preprocessing the acquired medical data and extracting biological structural features, and means for optimizing implant design based on the extracted biological structural features using a machine learning model. This enables the automated design and manufacture of personalized implants based on the patient's unique anatomical characteristics.

[0262] "Patient medical data" refers to information related to a patient's health status and treatment, specifically image data obtained from CT scans, MRI scans, and other imaging methods.

[0263] An "information aggregation device" is a device for efficiently storing and managing large amounts of digital information, and typically functions as a database.

[0264] "Biological structural features" refer to characteristic information that describes the unique shape, size, and positional relationships of tissues and organs within a living organism.

[0265] A "machine learning model" is a mathematically and statistically oriented algorithm that computers use to analyze data and make predictions or classifications based on that analysis.

[0266] "Implant design" is the process of defining the details of the shape, structure, and function of a medical device to be implanted in a patient's body.

[0267] "Manufacturing equipment" refers to a machine or device used to physically shape the designed implant, and typically involves the use of a 3D printer.

[0268] This invention is configured as a system for efficiently designing and manufacturing patient-specific medical implants. Specific embodiments are described below.

[0269] First, medical staff and doctors, who are the users, upload detailed medical data about patients' health conditions, specifically image data obtained from CT scans and MRIs, to the medical information management system. This medical information system uses common database technology as an information aggregation device, and all uploaded data is stored in digital format.

[0270] Next, the server is responsible for preprocessing the received medical data. This preprocessing uses image processing software (for example, an open-source image analysis program) to remove noise and extract biological structural features. For example, when designing a knee joint implant, accurately capturing the shape of the knee bone and cartilage improves the accuracy in the subsequent design phase.

[0271] Subsequently, the server automates implant design using a generative AI model. Here, the machine learning model proposes an implant design suitable for each patient's individual biological structure. This process references historical data and design guidelines, and implements algorithms that support the optimal shape. The generative AI model primarily utilizes frameworks such as TensorFlow and PyTorch.

[0272] After the implant design is generated, the user (doctor or engineer) reviews and fine-tunes the design. This fine-tuning is done according to the patient's lifestyle and specific surgical requirements. For example, for patients who enjoy sports, the design can be modified to increase its strength.

[0273] Once the final design is complete, the manufacturing equipment, specifically a 3D printer, creates the implant from biocompatible materials. This printer is a commonly used technology that can precisely shape implants using materials such as durable metals or bioplastics.

[0274] As a concrete example of a prompt, the AI ​​model is prompted with the question, "To design a knee joint implant, please propose the optimal shape based on CT data of a 50-year-old male patient with a BMI of 25 and an exercise habit." This invention makes it possible to provide implants that are tailored to the individual anatomical characteristics of each patient in a timely and accurate manner, maximizing the efficiency and effectiveness of treatment in medical settings.

[0275] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0276] Step 1:

[0277] The user uploads patient medical data to the medical information management system. Inputs include CT scans and MRI images, and output is data stored digitally in an information aggregation device. This process involves the user clicking an upload button and selecting data files.

[0278] Step 2:

[0279] The server preprocesses the medical data received from the data aggregation device. The input is the data stored in step 1, and the output is preprocessed data from which noise has been removed and biological structural features have been extracted. Specifically, the server applies an image analysis algorithm to extract shape data of bones and cartilage.

[0280] Step 3:

[0281] The server generates implant designs based on bio-structural features using a generative AI model. The input is the pre-processed data obtained in step 2, and the output is the implant design data. The server refers to historical data and design guidelines, and the process includes the specific operation of automatically generating designs using machine learning algorithms.

[0282] Step 4:

[0283] The user checks the generated implant design and makes fine adjustments. The input is the design data generated in step 3, and the output is the final design optimized by doctors or engineers. The user utilizes CAD software and includes operations for adjusting specific dimensions and shapes.

[0284] Step 5:

[0285] The manufacturing device, which is a terminal, generates an implant based on the final design. The input is the design data confirmed and finely adjusted in step 4, and the output is the actual implant made of a biocompatible material. As a specific operation, a technical process is executed where a 3D printer forms the implant by stacking layers.

[0286] Step 6:

[0287] The server collects data on the patient's recovery status and the functionality of the implant after surgery. The input is the recovery data reported by the patient, and the output is the updated information for utilization in improving the implant design in subsequent times. The server includes specific operations for analyzing this data and adjusting the parameters of the generated AI model.

[0288] (Application Example 1)

[0289] 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".

[0290] Conventional methods for the design and manufacture of medical implants have been difficult to meet the individual requirements of each patient. Also, when introducing implants in the caregiving field, there has been a lack of a mechanism to quickly reflect the feedback of caregiving staff, making it difficult to provide the optimal implant for patients.

[0291] 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.

[0292] In this invention, the server includes means for acquiring patient body image information and storing it in an information aggregation system, means for processing the acquired body image information and identifying morphological features, and means for improving the design of medical components based on the identified morphological features using an intelligent program model. This enables the efficient design and manufacture of medical implants that meet the individual needs of patients, and allows caregivers to quickly incorporate feedback into implant designs.

[0293] "Patient body image information" refers to digital image data that includes detailed information about the internal and external structures of the patient's body.

[0294] An "information aggregation system" is a system that securely stores acquired data and maintains it in a format that can be used for subsequent processing and analysis.

[0295] "Data processing" refers to a series of operations that convert acquired digital data into an analyzable format and remove unwanted noise.

[0296] "Morphological features" refer to information about the structural and functional characteristics of an object extracted from data.

[0297] An "intelligent program model" is an algorithm that uses artificial intelligence to analyze data and perform inference and pattern recognition for specific problems.

[0298] A "medical component" is a part designed to be attached to or implanted in a patient's body, serving a specific medical purpose.

[0299] "Adjustment" refers to the act of precisely setting or modifying a design or process to achieve optimal performance.

[0300] A "caregiver" is a professional who provides support and medical assistance to improve the quality of life of patients.

[0301] A "knowledge aggregation means" is a method or device for accumulating knowledge in a specific field and presenting it in a usable form.

[0302] The embodiments for implementing the invention are shown below.

[0303] First, the server acquires the patient's body image information and stores it in the information aggregation system. Specifically, medical staff upload CT scan and MRI data using a smartphone to safely store the data in digital format.

[0304] Next, the server processes the acquired body image information to identify morphological features. This processing uses Python and TensorFlow and includes operations to remove noise from the image and extract the patient's unique body structure.

[0305] After that, based on the morphological features identified using the intelligent program model, the design of medical components is improved. By referring to past data and leveraging the inference algorithm of AI, an optimal design for the patient is provided. In this step, a generative AI model is used.

[0306] The user can then view the design plan through the smartphone interface. Caregivers can quickly provide feedback that reflects the lifestyle and specific requirements of the target patient.

[0307] The final design transferred to the 3D printer as a terminal is constructed of a material that takes into account biocompatibility and mechanical durability and is completed as a medical component.

[0308] An example of a prompt message might be, "Upload knee CT scan data and generate a patient-specific implant design." This prompt prompts the system to perform appropriate data processing and automatically generate a highly accurate implant design. This makes it possible to provide medical solutions optimized for individual patients.

[0309] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0310] Step 1:

[0311] Users input patient CT scan and MRI data using their smartphones and upload the data to a server. This data contains detailed images of the body, which the server stores in an information aggregation system. The input is in digital image format, and the output is stored in a secure database.

[0312] Step 2:

[0313] The system processes the body image data acquired by the server. Specifically, it uses Python and TensorFlow to perform noise filtering and identify morphological features. The input is the body image data acquired in step 1, and the output is clean data representing the patient's specific body structure.

[0314] Step 3:

[0315] The server uses an intelligent program model to design medical components based on morphological features. At this stage, the generating AI model outputs design proposals by referencing past design guidelines and surgical data. The input is the clean data obtained in step 2 and past design data, and the output is an optimized implant design proposal.

[0316] Step 4:

[0317] This is a process where users can review design proposals through a smartphone app. In this scenario, caregivers can provide feedback on the implant designs. The input is the design proposal received from the server, and the output is the caregiver's feedback.

[0318] Step 5:

[0319] The server receives feedback from the user and modifies the intelligent program model as needed. The input is user feedback information, and the output is the adjusted new implant design.

[0320] Step 6:

[0321] The server transfers the final implant design to the 3D printer, which acts as the terminal. The 3D printer then manufactures the components using the specified biocompatible material. At this stage, the input is the final implant design, and the output is the materialized medical component.

[0322] 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.

[0323] This invention enhances the user experience and improves the quality of medical services by combining an emotion engine with a system for designing and manufacturing medical implants for patients.

[0324] First, healthcare professionals, acting as users, upload patient CT scans and MRI data to the medical information system. This data is received by a server and preprocessed, including noise reduction and extraction of anatomical features. This process generates a 3D model optimized for each patient.

[0325] Next, the server uses an artificial intelligence model to automatically generate implant designs based on the patient's anatomical characteristics. During this process, an emotion engine is activated to analyze the user's voice, facial expressions, and text comments, collecting feedback. For example, it captures significant reactions shown by physicians during reviews and evaluates the suitability of the design.

[0326] The generated implant design is reviewed and considered by the user, and feedback through the emotional engine is incorporated into the design. If the user appears anxious, the implant design may be revised, and customized suggestions may be presented.

[0327] Subsequently, the final design of the medical implant is sent to a 3D printer, which is the terminal for manufacturing using specified biocompatible materials. For example, when manufacturing a titanium implant that fits a patient's knee joint using a 3D printer, a design that prioritizes comfort, taking into account the user's emotional state, may be adopted.

[0328] Once manufactured, the implants are used in surgery, and the post-operative results and the patient's recovery process are recorded and analyzed by a server. This allows the system to enhance its AI model and emotional engine, laying the foundation for providing better implants. In this way, the system, incorporating an emotional engine, can provide an optimal solution for both healthcare professionals and patients.

[0329] The following describes the processing flow.

[0330] Step 1:

[0331] Users upload patient CT scans and MRI data to a dedicated medical information system. This inputs digital data that provides a detailed representation of the patient's internal structure into the system.

[0332] Step 2:

[0333] The server receives the uploaded data and automatically saves it to the database. After receiving the data, it is sent to a preprocessing module where noise reduction and image correction processes are performed.

[0334] Step 3:

[0335] The server extracts anatomical features from the processed data. This process utilizes image analysis algorithms such as segmentation techniques to extract specific regions and edge detection.

[0336] Step 4:

[0337] The server generates a 3D model based on the extracted features. Three-dimensional reconstruction techniques, such as the Marching Cubes algorithm, are used for model generation. The generated model is then optimized to improve anatomical accuracy.

[0338] Step 5:

[0339] The server utilizes artificial intelligence models to design medical implants based on the generated 3D models. During this process, an emotion engine operates, analyzing the user's facial expressions and voice tone, and using this as feedback input in the design process.

[0340] Step 6:

[0341] The user reviews the analysis results obtained from the emotion engine and adjusts the implant design. If the user expresses anxiety or dissatisfaction with the design, the design parameters are reviewed and new suggestions are made.

[0342] Step 7:

[0343] The finalized implant design is sent to a 3D printer, which manufactures the implant using the specified biocompatible material. During the manufacturing process, material selection and the precision of the implant's shape are strictly controlled.

[0344] Step 8:

[0345] After the surgery is complete, the server collects postoperative patient data and evaluates the recovery status and the functionality of the implants used. Based on this, the artificial intelligence model and emotion engine are further optimized and used for future designs.

[0346] (Example 2)

[0347] 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".

[0348] In the design and manufacture of medical devices, rapid and accurate device design based on the individual anatomical characteristics of each patient is required. Furthermore, conventional systems struggle to incorporate device design fit and user feedback, hindering improvements in user experience and the quality of medical services. These challenges directly impact material selection and quality assurance in the manufacturing process, thus requiring comprehensive solutions.

[0349] 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.

[0350] In this invention, the server includes means for acquiring patient medical data and storing it in an information management system, means for preprocessing the acquired medical data and extracting the characteristics of the biological structure, and means for optimizing the design of a medical device based on the characteristics of the biological structure extracted using a generative AI model. This enables the rapid design of a medical device optimized for each patient and the provision of an advanced user experience.

[0351] "Patient medical data" refers to digital data containing information related to a patient's health status and physical structure, including CT scans, MRI images, and blood test results.

[0352] An "information management system" refers to a computer system that comprehensively manages medical data and stores, retrieves, and processes the data as needed.

[0353] "Preprocessing" refers to the initial data processing steps involved in removing noise and filtering medical data to prepare it for analysis.

[0354] "Biostructural characteristics" refer to the anatomical structures and shapes that make up the patient's body, and include information necessary for device design.

[0355] A "generative AI model" is a program model that uses artificial intelligence technology to generate a specific output from input data, and it primarily utilizes deep learning technology.

[0356] A "medical device" is an instrument designed to achieve a specific medical purpose, including those that are surgically placed, such as implants.

[0357] "Emotion analysis function" refers to technology that identifies a user's emotional state from their voice, facial expressions, or text, and predicts their response based on that.

[0358] "Feedback" refers to information collected from users, such as their reactions and opinions, that is used to improve and optimize the system.

[0359] "Manufacturing equipment" refers to mechanical devices used to form devices using specific materials, and 3D printers are an example of this.

[0360] "Biocompatibility" refers to the properties of materials and devices that allow them to function without causing side effects when in contact with living tissue.

[0361] The system based on the present invention is for the rapid and effective design and manufacture of personalized medical devices for patients. Specific embodiments thereof are described below.

[0362] First, healthcare professionals, as users, upload patient CT scans and MRI data to the medical information management system. This operation is typically performed through a graphical user interface (GUI) on a medical workstation. The system is operated by numerous data processing and calculations performed by a server.

[0363] Next, the server performs preprocessing on the received medical data, such as noise reduction and extraction of anatomical features. Standard server racks are used as the specific hardware. The software used is either Python and its OpenCV library, or Matlab. These tools prepare high-quality data appropriate for the patient's physical structure.

[0364] Subsequently, the server uses a generative AI model to optimize the design of medical devices based on the extracted bio-structure features. Deep learning libraries such as TensorFlow and PyTorch are primarily used. This process involves sentiment analysis, which analyzes the user's voice, facial expressions, and text comments. Commercial sentiment recognition APIs are commonly used for this function.

[0365] Here, user feedback plays a crucial role. By collecting feedback and incorporating it into the design, it is possible to improve the fit of medical devices and user satisfaction. For example, when designing knee joint implants, an approach that prioritizes comfort based on user comments can be employed. At this stage, the following text might be used as a prompt in the generative AI model:

[0366] "Generate a design for the patient's knee joint implant. Please propose a model that prioritizes comfort and ease of use, taking into consideration the physician's review."

[0367] Finally, the finalized device design is sent to a 3D printer, the terminal for manufacturing using specified biocompatible materials. A professional-grade 3D printer, commonly used in the medical field, is suitable. This entire process provides high-quality, patient-optimized medical devices, enabling a better experience for both patients and healthcare professionals.

[0368] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0369] Step 1:

[0370] Users upload patient CT scans and MRI data to the medical information management system. The data, as input, is transferred via a GUI on a medical workstation. This data, which includes the patient's anatomical information, is received and stored by the information management system.

[0371] Step 2:

[0372] The server receives the uploaded image data and first performs denoising. Denoising is carried out using the Python and OpenCV libraries. The input data is raw image data, and the output is a clear image with the noise removed. This process improves the accuracy of subsequent analysis.

[0373] Step 3:

[0374] The server then extracts anatomical features from the image data. This is done using Matlab or an advanced image processing library. Pre-processed images are used as input, and data for 3D models representing anatomical regions is generated as output. This results in data specific to each individual patient.

[0375] Step 4:

[0376] The server applies a generative AI model and optimizes the design of medical devices based on the extracted features. The software used is TensorFlow or PyTorch. Anatomical feature data is used as input, and a 3D model of the optimized device design is generated as output. Model generation and adjustments are performed using prompts.

[0377] Step 5:

[0378] The server uses emotion analysis capabilities to collect feedback from users. It analyzes user voice, facial expressions, and text comments as input to obtain feedback data. The technology used is a commercial emotion recognition API. This provides guidance for improving the device design.

[0379] Step 6:

[0380] The user reviews the 3D device design and requests modifications as needed. An interactive 3D viewer is used to review the design. The input is the generated device design, and the output is user feedback on its suitability.

[0381] Step 7:

[0382] The 3D printer, acting as the terminal, begins manufacturing based on the final device design. The device uses specified biocompatible materials to transform the 3D design into a physical product. The input is device design information, and the output is a physical medical device. This results in a patient-specific device.

[0383] Step 8:

[0384] The server records surgical results and the patient's recovery process, and uses this data to improve the AI ​​model. Database software is used, with postoperative patient data as input and an improved AI model as output. This process improves the accuracy of future device designs.

[0385] (Application Example 2)

[0386] 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."

[0387] In modern medicine, providing patients with optimal medical components is crucial. However, conventional design methods struggle to comprehensively consider each patient's individual biological data and emotional state, and they cannot adequately evaluate the impact of design on patient comfort and recovery speed. Furthermore, in postoperative patient care, the provision of support methods utilizing consumer electronics is insufficient, often resulting in a lack of patient comfort.

[0388] 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.

[0389] In this invention, the server includes means for acquiring a patient's biological data and storing it in a data set; means for analyzing the user's emotional state using an emotion analysis device and adjusting the design of medical components based on the feedback; and means for providing information about the patient's biological state in their daily environment using consumer electronics and communicating the information to a medical facility as needed. This makes it possible to design and provide medical components that meet the individual needs of each patient, thereby enhancing the patient's sense of security and comfort after surgery.

[0390] "Biometric data" refers to all information about a patient's body, including medical images such as CT scans and MRIs, blood test results, and vital data such as heart rate and blood pressure.

[0391] A "data collection" refers to a database or data repository built to efficiently store and utilize various types of acquired data.

[0392] An "emotion analysis device" refers to a device or software that analyzes a person's facial expressions, voice tone, text comments, etc., to identify their individual emotional state.

[0393] "Medical components" refer to implants and prosthetics that are designed and manufactured to achieve specific therapeutic purposes based on the patient's anatomical characteristics.

[0394] "Consumer-use machinery" refers to robots and electrical appliances used by general consumers in their daily lives, and which are designed to be optimized for medical applications.

[0395] A "three-dimensional manufacturing device" refers to a 3D printer, which constructs physical objects from digital designs. It is a device that generates three-dimensional objects using specified materials.

[0396] The system that realizes this invention is mainly composed of a server, a terminal, and user interaction. The server starts by acquiring the patient's biometric data and storing it in a data collection. In this process, various types of data are handled, including medical images such as CT scans and MRIs, and vital data.

[0397] Next, the server uses an emotion analysis device to analyze the user's emotional state in real time and adjusts the design of the medical components based on this data. The emotion analysis uses sophisticated algorithms that read emotions from human facial expressions, voice, and text.

[0398] The terminal controls a 3D printer, a three-dimensional manufacturing device, to generate objects based on optimized medical component designs sent from the server. In this process, appropriate materials are selected to maintain biocompatibility and mechanical strength.

[0399] Users receive necessary support in the patient's daily environment through consumer-grade devices. These devices monitor the patient's condition and transmit information to medical facilities if any abnormalities are detected.

[0400] One specific example is a consumer robot that checks the facial expressions of patients while they are relaxing at home after surgery and recommends relaxing music. This allows patients to recover while feeling reassured.

[0401] An example of an input prompt for a generative AI model is, "Analyze the patient's emotional state and suggest music to listen to during the remaining recovery period."

[0402] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0403] Step 1:

[0404] The server receives biometric data provided by patients and stores it in a data set. Specifically, it acquires medical images such as CT scans and MRIs, as well as vital data, removes noise from this data, and converts it into a standardized format so that it can be stored in the database. The input is biometric data, and the output is standardized data with noise reduction.

[0405] Step 2:

[0406] The server activates an emotion analysis device and analyzes the user's facial expressions and voice data in real time. The emotional state obtained (e.g., reassurance, tension, etc.) is then used to adjust the design of medical components. The input consists of facial expression data and voice data, and the output is data indicating the analyzed emotional state.

[0407] Step 3:

[0408] The server uses a generative AI model to automatically generate designs for medical components based on emotional states and anatomical features. User emotional feedback is incorporated into the design during this process. The input consists of emotional states and anatomical features, while the output is optimized medical component design data.

[0409] Step 4:

[0410] The terminal retrieves optimized medical component designs transmitted from the server and controls a 3D printer (a three-dimensional manufacturing device) to carry out the manufacturing process. The materials used are biocompatible and guarantee mechanical strength. The input is optimized design data, and the output is the manufactured physical medical component.

[0411] Step 5:

[0412] The user monitors the patient's biological state in their daily environment using consumer-grade equipment and transmits information to a medical facility in real time if an abnormality is detected. Specifically, it reassessss the patient's emotional state from their facial expressions and voice, and issues an alarm to coordinate with medical staff as needed. Inputs include facial expression data and voice data, and outputs are reports to the user and the medical facility.

[0413] 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.

[0414] 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.

[0415] 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.

[0416] [Third Embodiment]

[0417] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0418] 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.

[0419] 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).

[0420] 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.

[0421] 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.

[0422] 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).

[0423] 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.

[0424] 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.

[0425] 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.

[0426] 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.

[0427] 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.

[0428] 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".

[0429] In this invention, the system provides a platform for efficiently designing and manufacturing customized medical implants to meet the specific medical requirements of patients.

[0430] First, the user, a medical staff member or doctor, uploads the patient's CT scan or MRI data to the medical information system. This allows for the acquisition of detailed internal structure data of the patient in digital format.

[0431] Next, the server receives the uploaded medical image data and stores it in a database. This data is preprocessed to remove noise and accurately capture anatomical features. For example, when designing a knee joint implant, image processing techniques are used to extract the knee bone and surrounding tissues and filter out noise.

[0432] Once preprocessing is complete, the server automatically generates implant designs based on anatomical features extracted using an artificial intelligence model. Here, it provides an optimal design tailored to the patient, based on past surgical data and implant design guidelines. For example, it can suggest an implant shape that matches the patient's specific bone structure.

[0433] Once the implant design is complete, the user (doctor or engineer) can review it and make adjustments as needed. This includes customization to meet specific needs during surgery and the patient's lifestyle.

[0434] The final design is sent to a 3D printer, and the implant is manufactured using biocompatible materials. For example, it is possible to create durable implants using titanium or biocompatible plastics.

[0435] After the surgery is complete, the server collects data on the patient's recovery progress and the functionality of the implant, and uses this data to improve the artificial intelligence model. This is expected to improve the accuracy of future implant designs and achieve even higher patient satisfaction.

[0436] This system makes it possible to provide implants that accommodate the unique anatomical conditions of each patient, which were previously difficult to address, at a low cost, thereby supporting effective treatment in medical settings.

[0437] The following describes the processing flow.

[0438] Step 1:

[0439] Users upload patient CT scan or MRI data to the medical information system. This allows for the acquisition of detailed image data of the patient's body in digital format.

[0440] Step 2:

[0441] The server receives the uploaded image data and stores it in the database while ensuring security. At this stage, it is confirmed that the data is properly accessible and processable.

[0442] Step 3:

[0443] The server preprocesses the received medical image data. It applies a noise filter to remove unwanted information and uses an edge detection algorithm to enhance anatomical features.

[0444] Step 4:

[0445] The server constructs a 3D model based on pre-processed data. Specifically, it uses algorithms such as the Marching Cube algorithm to generate a 3D polygon model from 2D images, faithfully reproducing the patient's anatomical details.

[0446] Step 5:

[0447] The server uses an AI agent to design medical implants based on the constructed 3D model. During this process, it refers to data from past successes and failures to suggest the optimal shape and structure.

[0448] Step 6:

[0449] Users can review the AI-generated implant design and request adjustments as needed. They can verify whether the implant shape and size meet the patient's specific requirements and request adjustments.

[0450] Step 7:

[0451] The 3D printer, acting as the terminal, receives the finalized implant design data and manufactures the implant using biocompatible materials. Depending on the material selection, adjustments to strength and durability are also made.

[0452] Step 8:

[0453] The server receives patient data during and after surgery and monitors their recovery. This data is analyzed and used to continuously improve the AI ​​model. This is expected to lead to more accurate implant designs for future procedures.

[0454] (Example 1)

[0455] 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."

[0456] Current design and manufacturing processes for medical implants do not adequately address the unique anatomical characteristics of each patient, resulting in difficulties in providing optimal fit and function. Furthermore, existing systems rely heavily on manual intervention in fine-tuning implant design and managing materials during manufacturing, leaving challenges in terms of accuracy and efficiency. Therefore, there is a need for technologies that can rapidly and automatically design and manufacture more precise implants, continuously improving the patient's recovery process.

[0457] 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.

[0458] In this invention, the server includes means for acquiring patient medical data and storing it in an information aggregation device, means for preprocessing the acquired medical data and extracting biological structural features, and means for optimizing implant design based on the extracted biological structural features using a machine learning model. This enables the automated design and manufacture of personalized implants based on the patient's unique anatomical characteristics.

[0459] "Patient medical data" refers to information related to a patient's health status and treatment, specifically image data obtained from CT scans, MRI scans, and other imaging methods.

[0460] An "information aggregation device" is a device for efficiently storing and managing large amounts of digital information, and typically functions as a database.

[0461] "Biological structural features" refer to characteristic information that describes the unique shape, size, and positional relationships of tissues and organs within a living organism.

[0462] A "machine learning model" is a mathematically and statistically oriented algorithm that computers use to analyze data and make predictions or classifications based on that analysis.

[0463] "Implant design" is the process of defining the details of the shape, structure, and function of a medical device to be implanted in a patient's body.

[0464] "Manufacturing equipment" refers to a machine or device used to physically shape the designed implant, and typically involves the use of a 3D printer.

[0465] This invention is configured as a system for efficiently designing and manufacturing patient-specific medical implants. Specific embodiments are described below.

[0466] First, medical staff and doctors, who are the users, upload detailed medical data about patients' health conditions, specifically image data obtained from CT scans and MRIs, to the medical information management system. This medical information system uses common database technology as an information aggregation device, and all uploaded data is stored in digital format.

[0467] Next, the server is responsible for preprocessing the received medical data. This preprocessing uses image processing software (for example, an open-source image analysis program) to remove noise and extract biological structural features. For example, when designing a knee joint implant, accurately capturing the shape of the knee bone and cartilage improves the accuracy in the subsequent design phase.

[0468] Subsequently, the server automates implant design using a generative AI model. Here, the machine learning model proposes an implant design suitable for each patient's individual biological structure. This process references historical data and design guidelines, and implements algorithms that support the optimal shape. The generative AI model primarily utilizes frameworks such as TensorFlow and PyTorch.

[0469] After the implant design is generated, the user (doctor or engineer) reviews and fine-tunes the design. This fine-tuning is done according to the patient's lifestyle and specific surgical requirements. For example, for patients who enjoy sports, the design can be modified to increase its strength.

[0470] Once the final design is complete, the manufacturing equipment, specifically a 3D printer, creates the implant from biocompatible materials. This printer is a commonly used technology that can precisely shape implants using materials such as durable metals or bioplastics.

[0471] As a concrete example of a prompt, the AI ​​model is prompted with the question, "To design a knee joint implant, please propose the optimal shape based on CT data of a 50-year-old male patient with a BMI of 25 and an exercise habit." This invention makes it possible to provide implants that are tailored to the individual anatomical characteristics of each patient in a timely and accurate manner, maximizing the efficiency and effectiveness of treatment in medical settings.

[0472] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0473] Step 1:

[0474] The user uploads patient medical data to the medical information management system. Inputs include CT scans and MRI images, and output is data stored digitally in an information aggregation device. This process involves the user clicking an upload button and selecting data files.

[0475] Step 2:

[0476] The server preprocesses the medical data received from the data aggregation device. The input is the data stored in step 1, and the output is preprocessed data from which noise has been removed and biological structural features have been extracted. Specifically, the server applies an image analysis algorithm to extract shape data of bones and cartilage.

[0477] Step 3:

[0478] The server generates implant designs based on bio-structural features using a generative AI model. The input is the pre-processed data obtained in step 2, and the output is the implant design data. The server refers to historical data and design guidelines, and the process includes the specific operation of automatically generating designs using machine learning algorithms.

[0479] Step 4:

[0480] The user reviews and fine-tunes the generated implant design. The input is the design data generated in step 3, and the output is the final design optimized by the physician or engineer. The user uses CAD software to make adjustments to specific dimensions and shapes.

[0481] Step 5:

[0482] The manufacturing device, which acts as the terminal, generates the implant based on the final design. The input is the design data confirmed and fine-tuned in step 4, and the output is the actual implant made from biocompatible material. Specifically, the 3D printer performs a technical process in which it builds the implant by stacking layers.

[0483] Step 6:

[0484] The server collects data on the patient's recovery status and implant functionality after surgery. The input is recovery data reported by the patient, and the output is updated information used to improve future implant designs. The server analyzes this data and performs specific actions to adjust the parameters of the generated AI model.

[0485] (Application Example 1)

[0486] 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."

[0487] Conventional methods for designing and manufacturing medical implants have made it difficult to address the individual needs of each patient. Furthermore, the lack of mechanisms to quickly incorporate feedback from caregivers when introducing implants in nursing care settings has made it difficult to provide patients with the most suitable implants.

[0488] 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.

[0489] In this invention, the server includes means for acquiring patient body image information and storing it in an information aggregation system, means for processing the acquired body image information and identifying morphological features, and means for improving the design of medical components based on the identified morphological features using an intelligent program model. This enables the efficient design and manufacture of medical implants that meet the individual needs of patients, and allows caregivers to quickly incorporate feedback into implant designs.

[0490] "Patient body image information" refers to digital image data that includes detailed information about the internal and external structures of the patient's body.

[0491] An "information aggregation system" is a system that securely stores acquired data and maintains it in a format that can be used for subsequent processing and analysis.

[0492] "Data processing" refers to a series of operations that convert acquired digital data into an analyzable format and remove unwanted noise.

[0493] "Morphological features" refer to information about the structural and functional characteristics of an object extracted from data.

[0494] An "intelligent program model" is an algorithm that uses artificial intelligence to analyze data and perform inference and pattern recognition for specific problems.

[0495] A "medical component" is a part designed to be attached to or implanted in a patient's body, serving a specific medical purpose.

[0496] "Adjustment" refers to the act of precisely setting or modifying a design or process to achieve optimal performance.

[0497] A "care worker" is a professional who provides support and medical assistance to improve the quality of life for patients.

[0498] "Knowledge aggregation means" are methods and devices for accumulating knowledge in a specific field and disseminating it in a usable form.

[0499] The following describes embodiments for carrying out the invention.

[0500] The server first acquires the patient's physical image information and stores it in the information aggregation system. Specifically, medical staff upload CT scans and MRI data using smartphones, securely saving the data in digital format.

[0501] Next, the server processes the acquired body image information and identifies morphological features. This process uses Python and TensorFlow and includes calculations to remove noise from the images and extract the patient's unique body structures.

[0502] Subsequently, the design of medical components is improved based on morphological features identified using intelligent program models. AI inference algorithms are leveraged to provide optimal designs for patients, referencing historical data. Generative AI models are used in this step.

[0503] Users can then review the design proposal through their smartphone interface. This allows caregivers to quickly provide feedback that reflects the patient's lifestyle and specific needs.

[0504] The final design, transferred to the 3D printer as a terminal, is constructed from materials that consider biocompatibility and mechanical durability, and is completed as a medical component.

[0505] An example of a prompt message might be, "Upload knee CT scan data and generate a patient-specific implant design." This prompt prompts the system to perform appropriate data processing and automatically generate a highly accurate implant design. This makes it possible to provide medical solutions optimized for individual patients.

[0506] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0507] Step 1:

[0508] Users input patient CT scan and MRI data using their smartphones and upload the data to a server. This data contains detailed images of the body, which the server stores in an information aggregation system. The input is in digital image format, and the output is stored in a secure database.

[0509] Step 2:

[0510] The system processes the body image data acquired by the server. Specifically, it uses Python and TensorFlow to perform noise filtering and identify morphological features. The input is the body image data acquired in step 1, and the output is clean data representing the patient's specific body structure.

[0511] Step 3:

[0512] The server uses an intelligent program model to design medical components based on morphological features. At this stage, the generating AI model outputs design proposals by referencing past design guidelines and surgical data. The input is the clean data obtained in step 2 and past design data, and the output is an optimized implant design proposal.

[0513] Step 4:

[0514] This is a process where users can review design proposals through a smartphone app. In this scenario, caregivers can provide feedback on the implant designs. The input is the design proposal received from the server, and the output is the caregiver's feedback.

[0515] Step 5:

[0516] The server receives feedback from the user and modifies the intelligent program model as needed. The input is user feedback information, and the output is the adjusted new implant design.

[0517] Step 6:

[0518] The server transfers the final implant design to the 3D printer, which acts as the terminal. The 3D printer then manufactures the components using the specified biocompatible material. At this stage, the input is the final implant design, and the output is the materialized medical component.

[0519] 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.

[0520] This invention enhances the user experience and improves the quality of medical services by combining an emotion engine with a system for designing and manufacturing medical implants for patients.

[0521] First, healthcare professionals, acting as users, upload patient CT scans and MRI data to the medical information system. This data is received by a server and preprocessed, including noise reduction and extraction of anatomical features. This process generates a 3D model optimized for each patient.

[0522] Next, the server uses an artificial intelligence model to automatically generate implant designs based on the patient's anatomical characteristics. During this process, an emotion engine is activated to analyze the user's voice, facial expressions, and text comments, collecting feedback. For example, it captures significant reactions shown by physicians during reviews and evaluates the suitability of the design.

[0523] The generated implant design is reviewed and considered by the user, and feedback through the emotional engine is incorporated into the design. If the user appears anxious, the implant design may be revised, and customized suggestions may be presented.

[0524] Subsequently, the final design of the medical implant is sent to a 3D printer, which is the terminal for manufacturing using specified biocompatible materials. For example, when manufacturing a titanium implant that fits a patient's knee joint using a 3D printer, a design that prioritizes comfort, taking into account the user's emotional state, may be adopted.

[0525] Once manufactured, the implants are used in surgery, and the post-operative results and the patient's recovery process are recorded and analyzed by a server. This allows the system to enhance its AI model and emotional engine, laying the foundation for providing better implants. In this way, the system, incorporating an emotional engine, can provide an optimal solution for both healthcare professionals and patients.

[0526] The following describes the processing flow.

[0527] Step 1:

[0528] Users upload patient CT scans and MRI data to a dedicated medical information system. This inputs digital data that provides a detailed representation of the patient's internal structure into the system.

[0529] Step 2:

[0530] The server receives the uploaded data and automatically saves it to the database. After receiving the data, it is sent to a preprocessing module where noise reduction and image correction processes are performed.

[0531] Step 3:

[0532] The server extracts anatomical features from the processed data. This process utilizes image analysis algorithms such as segmentation techniques to extract specific regions and edge detection.

[0533] Step 4:

[0534] The server generates a 3D model based on the extracted features. Three-dimensional reconstruction techniques, such as the Marching Cubes algorithm, are used for model generation. The generated model is then optimized to improve anatomical accuracy.

[0535] Step 5:

[0536] The server utilizes artificial intelligence models to design medical implants based on the generated 3D models. During this process, an emotion engine operates, analyzing the user's facial expressions and voice tone, and using this as feedback input in the design process.

[0537] Step 6:

[0538] The user reviews the analysis results obtained from the emotion engine and adjusts the implant design. If the user expresses anxiety or dissatisfaction with the design, the design parameters are reviewed and new suggestions are made.

[0539] Step 7:

[0540] The finalized implant design is sent to a 3D printer, which manufactures the implant using the specified biocompatible material. During the manufacturing process, material selection and the precision of the implant's shape are strictly controlled.

[0541] Step 8:

[0542] After the surgery is complete, the server collects postoperative patient data and evaluates the recovery status and the functionality of the implants used. Based on this, the artificial intelligence model and emotion engine are further optimized and used for future designs.

[0543] (Example 2)

[0544] 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."

[0545] In the design and manufacture of medical devices, rapid and accurate device design based on the individual anatomical characteristics of each patient is required. Furthermore, conventional systems struggle to incorporate device design fit and user feedback, hindering improvements in user experience and the quality of medical services. These challenges directly impact material selection and quality assurance in the manufacturing process, thus requiring comprehensive solutions.

[0546] 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.

[0547] In this invention, the server includes means for acquiring patient medical data and storing it in an information management system, means for preprocessing the acquired medical data and extracting the characteristics of the biological structure, and means for optimizing the design of a medical device based on the characteristics of the biological structure extracted using a generative AI model. This enables the rapid design of a medical device optimized for each patient and the provision of an advanced user experience.

[0548] "Patient medical data" refers to digital data containing information related to a patient's health status and physical structure, including CT scans, MRI images, and blood test results.

[0549] An "information management system" refers to a computer system that comprehensively manages medical data and stores, retrieves, and processes the data as needed.

[0550] "Preprocessing" refers to the initial data processing steps involved in removing noise and filtering medical data to prepare it for analysis.

[0551] "Biostructural characteristics" refer to the anatomical structures and shapes that make up the patient's body, and include information necessary for device design.

[0552] A "generative AI model" is a program model that uses artificial intelligence technology to generate a specific output from input data, and it primarily utilizes deep learning technology.

[0553] A "medical device" is an instrument designed to achieve a specific medical purpose, including those that are surgically placed, such as implants.

[0554] "Emotion analysis function" refers to technology that identifies a user's emotional state from their voice, facial expressions, or text, and predicts their response based on that.

[0555] "Feedback" refers to information collected from users, such as their reactions and opinions, that is used to improve and optimize the system.

[0556] "Manufacturing equipment" refers to mechanical devices used to form devices using specific materials, and 3D printers are an example of this.

[0557] "Biocompatibility" refers to the properties of materials and devices that allow them to function without causing side effects when in contact with living tissue.

[0558] The system based on the present invention is for the rapid and effective design and manufacture of personalized medical devices for patients. Specific embodiments thereof are described below.

[0559] First, healthcare professionals, as users, upload patient CT scans and MRI data to the medical information management system. This operation is typically performed through a graphical user interface (GUI) on a medical workstation. The system is operated by numerous data processing and calculations performed by a server.

[0560] Next, the server performs preprocessing on the received medical data, such as noise reduction and extraction of anatomical features. Standard server racks are used as the specific hardware. The software used is either Python and its OpenCV library, or Matlab. These tools prepare high-quality data appropriate for the patient's physical structure.

[0561] Subsequently, the server uses a generative AI model to optimize the design of medical devices based on the extracted bio-structure features. Deep learning libraries such as TensorFlow and PyTorch are primarily used. This process involves sentiment analysis, which analyzes the user's voice, facial expressions, and text comments. Commercial sentiment recognition APIs are commonly used for this function.

[0562] Here, user feedback plays a crucial role. By collecting feedback and incorporating it into the design, it is possible to improve the fit of medical devices and user satisfaction. For example, when designing knee joint implants, an approach that prioritizes comfort based on user comments can be employed. At this stage, the following text might be used as a prompt in the generative AI model:

[0563] "Generate a design for the patient's knee joint implant. Please propose a model that prioritizes comfort and ease of use, taking into consideration the physician's review."

[0564] Finally, the finalized device design is sent to a 3D printer, the terminal for manufacturing using specified biocompatible materials. A professional-grade 3D printer, commonly used in the medical field, is suitable. This entire process provides high-quality, patient-optimized medical devices, enabling a better experience for both patients and healthcare professionals.

[0565] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0566] Step 1:

[0567] Users upload patient CT scans and MRI data to the medical information management system. The data, as input, is transferred via a GUI on a medical workstation. This data, which includes the patient's anatomical information, is received and stored by the information management system.

[0568] Step 2:

[0569] The server receives the uploaded image data and first performs denoising. Denoising is carried out using the Python and OpenCV libraries. The input data is raw image data, and the output is a clear image with the noise removed. This process improves the accuracy of subsequent analysis.

[0570] Step 3:

[0571] The server then extracts anatomical features from the image data. This is done using Matlab or an advanced image processing library. Pre-processed images are used as input, and data for 3D models representing anatomical regions is generated as output. This results in data specific to each individual patient.

[0572] Step 4:

[0573] The server applies a generative AI model and optimizes the design of medical devices based on the extracted features. The software used is TensorFlow or PyTorch. Anatomical feature data is used as input, and a 3D model of the optimized device design is generated as output. Model generation and adjustments are performed using prompts.

[0574] Step 5:

[0575] The server uses emotion analysis capabilities to collect feedback from users. It analyzes user voice, facial expressions, and text comments as input to obtain feedback data. The technology used is a commercial emotion recognition API. This provides guidance for improving the device design.

[0576] Step 6:

[0577] The user reviews the 3D device design and requests modifications as needed. An interactive 3D viewer is used to review the design. The input is the generated device design, and the output is user feedback on its suitability.

[0578] Step 7:

[0579] The 3D printer, acting as the terminal, begins manufacturing based on the final device design. The device uses specified biocompatible materials to transform the 3D design into a physical product. The input is device design information, and the output is a physical medical device. This results in a patient-specific device.

[0580] Step 8:

[0581] The server records surgical results and the patient's recovery process, and uses this data to improve the AI ​​model. Database software is used, with postoperative patient data as input and an improved AI model as output. This process improves the accuracy of future device designs.

[0582] (Application Example 2)

[0583] 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."

[0584] In modern medicine, providing patients with optimal medical components is crucial. However, conventional design methods struggle to comprehensively consider each patient's individual biological data and emotional state, and they cannot adequately evaluate the impact of design on patient comfort and recovery speed. Furthermore, in postoperative patient care, the provision of support methods utilizing consumer electronics is insufficient, often resulting in a lack of patient comfort.

[0585] 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.

[0586] In this invention, the server includes means for acquiring a patient's biological data and storing it in a data set; means for analyzing the user's emotional state using an emotion analysis device and adjusting the design of medical components based on the feedback; and means for providing information about the patient's biological state in their daily environment using consumer electronics and communicating the information to a medical facility as needed. This makes it possible to design and provide medical components that meet the individual needs of each patient, thereby enhancing the patient's sense of security and comfort after surgery.

[0587] "Biometric data" refers to all information about a patient's body, including medical images such as CT scans and MRIs, blood test results, and vital data such as heart rate and blood pressure.

[0588] A "data collection" refers to a database or data repository built to efficiently store and utilize various types of acquired data.

[0589] An "emotion analysis device" refers to a device or software that analyzes a person's facial expressions, voice tone, text comments, etc., to identify their individual emotional state.

[0590] "Medical components" refer to implants and prosthetics that are designed and manufactured to achieve specific therapeutic purposes based on the patient's anatomical characteristics.

[0591] "Consumer-use machinery" refers to robots and electrical appliances used by general consumers in their daily lives, and which are designed to be optimized for medical applications.

[0592] A "three-dimensional manufacturing device" refers to a 3D printer, which constructs physical objects from digital designs. It is a device that generates three-dimensional objects using specified materials.

[0593] The system that realizes this invention is mainly composed of a server, a terminal, and user interaction. The server starts by acquiring the patient's biometric data and storing it in a data collection. In this process, various types of data are handled, including medical images such as CT scans and MRIs, and vital data.

[0594] Next, the server uses an emotion analysis device to analyze the user's emotional state in real time and adjusts the design of the medical components based on this data. The emotion analysis uses sophisticated algorithms that read emotions from human facial expressions, voice, and text.

[0595] The terminal controls a 3D printer, a three-dimensional manufacturing device, to generate objects based on optimized medical component designs sent from the server. In this process, appropriate materials are selected to maintain biocompatibility and mechanical strength.

[0596] Users receive necessary support in the patient's daily environment through consumer-grade devices. These devices monitor the patient's condition and transmit information to medical facilities if any abnormalities are detected.

[0597] One specific example is a consumer robot that checks the facial expressions of patients while they are relaxing at home after surgery and recommends relaxing music. This allows patients to recover while feeling reassured.

[0598] An example of an input prompt for a generative AI model is, "Analyze the patient's emotional state and suggest music to listen to during the remaining recovery period."

[0599] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0600] Step 1:

[0601] The server receives biometric data provided by patients and stores it in a data set. Specifically, it acquires medical images such as CT scans and MRIs, as well as vital data, removes noise from this data, and converts it into a standardized format so that it can be stored in the database. The input is biometric data, and the output is standardized data with noise reduction.

[0602] Step 2:

[0603] The server activates an emotion analysis device and analyzes the user's facial expressions and voice data in real time. The emotional state obtained (e.g., reassurance, tension, etc.) is then used to adjust the design of medical components. The input consists of facial expression data and voice data, and the output is data indicating the analyzed emotional state.

[0604] Step 3:

[0605] The server uses a generative AI model to automatically generate designs for medical components based on emotional states and anatomical features. User emotional feedback is incorporated into the design during this process. The input consists of emotional states and anatomical features, while the output is optimized medical component design data.

[0606] Step 4:

[0607] The terminal retrieves optimized medical component designs transmitted from the server and controls a 3D printer (a three-dimensional manufacturing device) to carry out the manufacturing process. The materials used are biocompatible and guarantee mechanical strength. The input is optimized design data, and the output is the manufactured physical medical component.

[0608] Step 5:

[0609] The user monitors the patient's biological state in their daily environment using consumer-grade equipment and transmits information to a medical facility in real time if an abnormality is detected. Specifically, it reassessss the patient's emotional state from their facial expressions and voice, and issues an alarm to coordinate with medical staff as needed. Inputs include facial expression data and voice data, and outputs are reports to the user and the medical facility.

[0610] 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.

[0611] 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.

[0612] 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.

[0613] [Fourth Embodiment]

[0614] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0615] 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.

[0616] 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).

[0617] 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.

[0618] 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.

[0619] 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).

[0620] 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.

[0621] 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.

[0622] 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.

[0623] 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.

[0624] 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.

[0625] 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.

[0626] 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".

[0627] In this invention, the system provides a platform for efficiently designing and manufacturing customized medical implants to meet the specific medical requirements of patients.

[0628] First, the user, a medical staff member or doctor, uploads the patient's CT scan or MRI data to the medical information system. This allows for the acquisition of detailed internal structure data of the patient in digital format.

[0629] Next, the server receives the uploaded medical image data and stores it in a database. This data is preprocessed to remove noise and accurately capture anatomical features. For example, when designing a knee joint implant, image processing techniques are used to extract the knee bone and surrounding tissues and filter out noise.

[0630] Once preprocessing is complete, the server automatically generates implant designs based on anatomical features extracted using an artificial intelligence model. Here, it provides an optimal design tailored to the patient, based on past surgical data and implant design guidelines. For example, it can suggest an implant shape that matches the patient's specific bone structure.

[0631] Once the implant design is complete, the user (doctor or engineer) can review it and make adjustments as needed. This includes customization to meet specific needs during surgery and the patient's lifestyle.

[0632] The final design is sent to a 3D printer, and the implant is manufactured using biocompatible materials. For example, it is possible to create durable implants using titanium or biocompatible plastics.

[0633] After the surgery is complete, the server collects data on the patient's recovery progress and the functionality of the implant, and uses this data to improve the artificial intelligence model. This is expected to improve the accuracy of future implant designs and achieve even higher patient satisfaction.

[0634] This system makes it possible to provide implants that accommodate the unique anatomical conditions of each patient, which were previously difficult to address, at a low cost, thereby supporting effective treatment in medical settings.

[0635] The following describes the processing flow.

[0636] Step 1:

[0637] Users upload patient CT scan or MRI data to the medical information system. This allows for the acquisition of detailed image data of the patient's body in digital format.

[0638] Step 2:

[0639] The server receives the uploaded image data and stores it in the database while ensuring security. At this stage, it is confirmed that the data is properly accessible and processable.

[0640] Step 3:

[0641] The server preprocesses the received medical image data. It applies a noise filter to remove unwanted information and uses an edge detection algorithm to enhance anatomical features.

[0642] Step 4:

[0643] The server constructs a 3D model based on pre-processed data. Specifically, it uses algorithms such as the Marching Cube algorithm to generate a 3D polygon model from 2D images, faithfully reproducing the patient's anatomical details.

[0644] Step 5:

[0645] The server uses an AI agent to design medical implants based on the constructed 3D model. During this process, it refers to data from past successes and failures to suggest the optimal shape and structure.

[0646] Step 6:

[0647] Users can review the AI-generated implant design and request adjustments as needed. They can verify whether the implant shape and size meet the patient's specific requirements and request adjustments.

[0648] Step 7:

[0649] The 3D printer, acting as the terminal, receives the finalized implant design data and manufactures the implant using biocompatible materials. Depending on the material selection, adjustments to strength and durability are also made.

[0650] Step 8:

[0651] The server receives patient data during and after surgery and monitors their recovery. This data is analyzed and used to continuously improve the AI ​​model. This is expected to lead to more accurate implant designs for future procedures.

[0652] (Example 1)

[0653] 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".

[0654] Current design and manufacturing processes for medical implants do not adequately address the unique anatomical characteristics of each patient, resulting in difficulties in providing optimal fit and function. Furthermore, existing systems rely heavily on manual intervention in fine-tuning implant design and managing materials during manufacturing, leaving challenges in terms of accuracy and efficiency. Therefore, there is a need for technologies that can rapidly and automatically design and manufacture more precise implants, continuously improving the patient's recovery process.

[0655] 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.

[0656] In this invention, the server includes means for acquiring patient medical data and storing it in an information aggregation device, means for preprocessing the acquired medical data and extracting biological structural features, and means for optimizing implant design based on the extracted biological structural features using a machine learning model. This enables the automated design and manufacture of personalized implants based on the patient's unique anatomical characteristics.

[0657] "Patient medical data" refers to information related to a patient's health status and treatment, specifically image data obtained from CT scans, MRI scans, and other imaging methods.

[0658] An "information aggregation device" is a device for efficiently storing and managing large amounts of digital information, and typically functions as a database.

[0659] "Biological structural features" refer to characteristic information that describes the unique shape, size, and positional relationships of tissues and organs within a living organism.

[0660] A "machine learning model" is a mathematically and statistically oriented algorithm that computers use to analyze data and make predictions or classifications based on that analysis.

[0661] "Implant design" is the process of defining the details of the shape, structure, and function of a medical device to be implanted in a patient's body.

[0662] "Manufacturing equipment" refers to a machine or device used to physically shape the designed implant, and typically involves the use of a 3D printer.

[0663] This invention is configured as a system for efficiently designing and manufacturing patient-specific medical implants. Specific embodiments are described below.

[0664] First, medical staff and doctors, who are the users, upload detailed medical data about patients' health conditions, specifically image data obtained from CT scans and MRIs, to the medical information management system. This medical information system uses common database technology as an information aggregation device, and all uploaded data is stored in digital format.

[0665] Next, the server is responsible for preprocessing the received medical data. This preprocessing uses image processing software (for example, an open-source image analysis program) to remove noise and extract biological structural features. For example, when designing a knee joint implant, accurately capturing the shape of the knee bone and cartilage improves the accuracy in the subsequent design phase.

[0666] Subsequently, the server automates implant design using a generative AI model. Here, the machine learning model proposes an implant design suitable for each patient's individual biological structure. This process references historical data and design guidelines, and implements algorithms that support the optimal shape. The generative AI model primarily utilizes frameworks such as TensorFlow and PyTorch.

[0667] After the implant design is generated, the user (doctor or engineer) reviews and fine-tunes the design. This fine-tuning is done according to the patient's lifestyle and specific surgical requirements. For example, for patients who enjoy sports, the design can be modified to increase its strength.

[0668] Once the final design is complete, the manufacturing equipment, specifically a 3D printer, creates the implant from biocompatible materials. This printer is a commonly used technology that can precisely shape implants using materials such as durable metals or bioplastics.

[0669] As a concrete example of a prompt, the AI ​​model is prompted with the question, "To design a knee joint implant, please propose the optimal shape based on CT data of a 50-year-old male patient with a BMI of 25 and an exercise habit." This invention makes it possible to provide implants that are tailored to the individual anatomical characteristics of each patient in a timely and accurate manner, maximizing the efficiency and effectiveness of treatment in medical settings.

[0670] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0671] Step 1:

[0672] The user uploads patient medical data to the medical information management system. Inputs include CT scans and MRI images, and output is data stored digitally in an information aggregation device. This process involves the user clicking an upload button and selecting data files.

[0673] Step 2:

[0674] The server preprocesses the medical data received from the data aggregation device. The input is the data stored in step 1, and the output is preprocessed data from which noise has been removed and biological structural features have been extracted. Specifically, the server applies an image analysis algorithm to extract shape data of bones and cartilage.

[0675] Step 3:

[0676] The server generates implant designs based on bio-structural features using a generative AI model. The input is the pre-processed data obtained in step 2, and the output is the implant design data. The server refers to historical data and design guidelines, and the process includes the specific operation of automatically generating designs using machine learning algorithms.

[0677] Step 4:

[0678] The user reviews and fine-tunes the generated implant design. The input is the design data generated in step 3, and the output is the final design optimized by the physician or engineer. The user uses CAD software to make adjustments to specific dimensions and shapes.

[0679] Step 5:

[0680] The manufacturing device, which acts as the terminal, generates the implant based on the final design. The input is the design data confirmed and fine-tuned in step 4, and the output is the actual implant made from biocompatible material. Specifically, the 3D printer performs a technical process in which it builds the implant by stacking layers.

[0681] Step 6:

[0682] The server collects data on the patient's recovery status and implant functionality after surgery. The input is recovery data reported by the patient, and the output is updated information used to improve future implant designs. The server analyzes this data and performs specific actions to adjust the parameters of the generated AI model.

[0683] (Application Example 1)

[0684] 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".

[0685] Conventional methods for designing and manufacturing medical implants have made it difficult to address the individual needs of each patient. Furthermore, the lack of mechanisms to quickly incorporate feedback from caregivers when introducing implants in nursing care settings has made it difficult to provide patients with the most suitable implants.

[0686] 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.

[0687] In this invention, the server includes means for acquiring patient body image information and storing it in an information aggregation system, means for processing the acquired body image information and identifying morphological features, and means for improving the design of medical components based on the identified morphological features using an intelligent program model. This enables the efficient design and manufacture of medical implants that meet the individual needs of patients, and allows caregivers to quickly incorporate feedback into implant designs.

[0688] "Patient body image information" refers to digital image data that includes detailed information about the internal and external structures of the patient's body.

[0689] An "information aggregation system" is a system that securely stores acquired data and maintains it in a format that can be used for subsequent processing and analysis.

[0690] "Data processing" refers to a series of operations that convert acquired digital data into an analyzable format and remove unwanted noise.

[0691] "Morphological features" refer to information about the structural and functional characteristics of an object extracted from data.

[0692] An "intelligent program model" is an algorithm that uses artificial intelligence to analyze data and perform inference and pattern recognition for specific problems.

[0693] A "medical component" is a part designed to be attached to or implanted in a patient's body, serving a specific medical purpose.

[0694] "Adjustment" refers to the act of precisely setting or modifying a design or process to achieve optimal performance.

[0695] A "care worker" is a professional who provides support and medical assistance to improve the quality of life for patients.

[0696] "Knowledge aggregation means" are methods and devices for accumulating knowledge in a specific field and disseminating it in a usable form.

[0697] The following describes embodiments for carrying out the invention.

[0698] The server first acquires the patient's physical image information and stores it in the information aggregation system. Specifically, medical staff upload CT scans and MRI data using smartphones, securely saving the data in digital format.

[0699] Next, the server processes the acquired body image information and identifies morphological features. This process uses Python and TensorFlow and includes calculations to remove noise from the images and extract the patient's unique body structures.

[0700] Subsequently, the design of medical components is improved based on morphological features identified using intelligent program models. AI inference algorithms are leveraged to provide optimal designs for patients, referencing historical data. Generative AI models are used in this step.

[0701] Users can then review the design proposal through their smartphone interface. This allows caregivers to quickly provide feedback that reflects the patient's lifestyle and specific needs.

[0702] The final design, transferred to the 3D printer as a terminal, is constructed from materials that consider biocompatibility and mechanical durability, and is completed as a medical component.

[0703] An example of a prompt message might be, "Upload knee CT scan data and generate a patient-specific implant design." This prompt prompts the system to perform appropriate data processing and automatically generate a highly accurate implant design. This makes it possible to provide medical solutions optimized for individual patients.

[0704] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0705] Step 1:

[0706] Users input patient CT scan and MRI data using their smartphones and upload the data to a server. This data contains detailed images of the body, which the server stores in an information aggregation system. The input is in digital image format, and the output is stored in a secure database.

[0707] Step 2:

[0708] The system processes the body image data acquired by the server. Specifically, it uses Python and TensorFlow to perform noise filtering and identify morphological features. The input is the body image data acquired in step 1, and the output is clean data representing the patient's specific body structure.

[0709] Step 3:

[0710] The server uses an intelligent program model to design medical components based on morphological features. At this stage, the generating AI model outputs design proposals by referencing past design guidelines and surgical data. The input is the clean data obtained in step 2 and past design data, and the output is an optimized implant design proposal.

[0711] Step 4:

[0712] This is a process where users can review design proposals through a smartphone app. In this scenario, caregivers can provide feedback on the implant designs. The input is the design proposal received from the server, and the output is the caregiver's feedback.

[0713] Step 5:

[0714] The server receives feedback from the user and modifies the intelligent program model as needed. The input is user feedback information, and the output is the adjusted new implant design.

[0715] Step 6:

[0716] The server transfers the final implant design to the 3D printer, which acts as the terminal. The 3D printer then manufactures the components using the specified biocompatible material. At this stage, the input is the final implant design, and the output is the materialized medical component.

[0717] 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.

[0718] This invention enhances the user experience and improves the quality of medical services by combining an emotion engine with a system for designing and manufacturing medical implants for patients.

[0719] First, healthcare professionals, acting as users, upload patient CT scans and MRI data to the medical information system. This data is received by a server and preprocessed, including noise reduction and extraction of anatomical features. This process generates a 3D model optimized for each patient.

[0720] Next, the server uses an artificial intelligence model to automatically generate implant designs based on the patient's anatomical characteristics. During this process, an emotion engine is activated to analyze the user's voice, facial expressions, and text comments, collecting feedback. For example, it captures significant reactions shown by physicians during reviews and evaluates the suitability of the design.

[0721] The generated implant design is reviewed and considered by the user, and feedback through the emotional engine is incorporated into the design. If the user appears anxious, the implant design may be revised, and customized suggestions may be presented.

[0722] Subsequently, the final design of the medical implant is sent to a 3D printer, which is the terminal for manufacturing using specified biocompatible materials. For example, when manufacturing a titanium implant that fits a patient's knee joint using a 3D printer, a design that prioritizes comfort, taking into account the user's emotional state, may be adopted.

[0723] Once manufactured, the implants are used in surgery, and the post-operative results and the patient's recovery process are recorded and analyzed by a server. This allows the system to enhance its AI model and emotional engine, laying the foundation for providing better implants. In this way, the system, incorporating an emotional engine, can provide an optimal solution for both healthcare professionals and patients.

[0724] The following describes the processing flow.

[0725] Step 1:

[0726] Users upload patient CT scans and MRI data to a dedicated medical information system. This inputs digital data that provides a detailed representation of the patient's internal structure into the system.

[0727] Step 2:

[0728] The server receives the uploaded data and automatically saves it to the database. After receiving the data, it is sent to a preprocessing module where noise reduction and image correction processes are performed.

[0729] Step 3:

[0730] The server extracts anatomical features from the processed data. This process utilizes image analysis algorithms such as segmentation techniques to extract specific regions and edge detection.

[0731] Step 4:

[0732] The server generates a 3D model based on the extracted features. Three-dimensional reconstruction techniques, such as the Marching Cubes algorithm, are used for model generation. The generated model is then optimized to improve anatomical accuracy.

[0733] Step 5:

[0734] The server utilizes artificial intelligence models to design medical implants based on the generated 3D models. During this process, an emotion engine operates, analyzing the user's facial expressions and voice tone, and using this as feedback input in the design process.

[0735] Step 6:

[0736] The user reviews the analysis results obtained from the emotion engine and adjusts the implant design. If the user expresses anxiety or dissatisfaction with the design, the design parameters are reviewed and new suggestions are made.

[0737] Step 7:

[0738] The finalized implant design is sent to a 3D printer, which manufactures the implant using the specified biocompatible material. During the manufacturing process, material selection and the precision of the implant's shape are strictly controlled.

[0739] Step 8:

[0740] After the surgery is complete, the server collects postoperative patient data and evaluates the recovery status and the functionality of the implants used. Based on this, the artificial intelligence model and emotion engine are further optimized and used for future designs.

[0741] (Example 2)

[0742] 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".

[0743] In the design and manufacture of medical devices, rapid and accurate device design based on the individual anatomical characteristics of each patient is required. Furthermore, conventional systems struggle to incorporate device design fit and user feedback, hindering improvements in user experience and the quality of medical services. These challenges directly impact material selection and quality assurance in the manufacturing process, thus requiring comprehensive solutions.

[0744] 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.

[0745] In this invention, the server includes means for acquiring patient medical data and storing it in an information management system, means for preprocessing the acquired medical data and extracting the characteristics of the biological structure, and means for optimizing the design of a medical device based on the characteristics of the biological structure extracted using a generative AI model. This enables the rapid design of a medical device optimized for each patient and the provision of an advanced user experience.

[0746] "Patient medical data" refers to digital data containing information related to a patient's health status and physical structure, including CT scans, MRI images, and blood test results.

[0747] An "information management system" refers to a computer system that comprehensively manages medical data and stores, retrieves, and processes the data as needed.

[0748] "Preprocessing" refers to the initial data processing steps involved in removing noise and filtering medical data to prepare it for analysis.

[0749] "Biostructural characteristics" refer to the anatomical structures and shapes that make up the patient's body, and include information necessary for device design.

[0750] A "generative AI model" is a program model that uses artificial intelligence technology to generate a specific output from input data, and it primarily utilizes deep learning technology.

[0751] A "medical device" is an instrument designed to achieve a specific medical purpose, including those that are surgically placed, such as implants.

[0752] "Emotion analysis function" refers to technology that identifies a user's emotional state from their voice, facial expressions, or text, and predicts their response based on that.

[0753] "Feedback" refers to information collected from users, such as their reactions and opinions, that is used to improve and optimize the system.

[0754] "Manufacturing equipment" refers to mechanical devices used to form devices using specific materials, and 3D printers are an example of this.

[0755] "Biocompatibility" refers to the properties of materials and devices that allow them to function without causing side effects when in contact with living tissue.

[0756] The system based on the present invention is for the rapid and effective design and manufacture of personalized medical devices for patients. Specific embodiments thereof are described below.

[0757] First, healthcare professionals, as users, upload patient CT scans and MRI data to the medical information management system. This operation is typically performed through a graphical user interface (GUI) on a medical workstation. The system is operated by numerous data processing and calculations performed by a server.

[0758] Next, the server performs preprocessing on the received medical data, such as noise reduction and extraction of anatomical features. Standard server racks are used as the specific hardware. The software used is either Python and its OpenCV library, or Matlab. These tools prepare high-quality data appropriate for the patient's physical structure.

[0759] Subsequently, the server uses a generative AI model to optimize the design of medical devices based on the extracted bio-structure features. Deep learning libraries such as TensorFlow and PyTorch are primarily used. This process involves sentiment analysis, which analyzes the user's voice, facial expressions, and text comments. Commercial sentiment recognition APIs are commonly used for this function.

[0760] Here, user feedback plays a crucial role. By collecting feedback and incorporating it into the design, it is possible to improve the fit of medical devices and user satisfaction. For example, when designing knee joint implants, an approach that prioritizes comfort based on user comments can be employed. At this stage, the following text might be used as a prompt in the generative AI model:

[0761] "Generate a design for the patient's knee joint implant. Please propose a model that prioritizes comfort and ease of use, taking into consideration the physician's review."

[0762] Finally, the finalized device design is sent to a 3D printer, the terminal for manufacturing using specified biocompatible materials. A professional-grade 3D printer, commonly used in the medical field, is suitable. This entire process provides high-quality, patient-optimized medical devices, enabling a better experience for both patients and healthcare professionals.

[0763] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0764] Step 1:

[0765] Users upload patient CT scans and MRI data to the medical information management system. The data, as input, is transferred via a GUI on a medical workstation. This data, which includes the patient's anatomical information, is received and stored by the information management system.

[0766] Step 2:

[0767] The server receives the uploaded image data and first performs denoising. Denoising is carried out using the Python and OpenCV libraries. The input data is raw image data, and the output is a clear image with the noise removed. This process improves the accuracy of subsequent analysis.

[0768] Step 3:

[0769] The server then extracts anatomical features from the image data. This is done using Matlab or an advanced image processing library. Pre-processed images are used as input, and data for 3D models representing anatomical regions is generated as output. This results in data specific to each individual patient.

[0770] Step 4:

[0771] The server applies a generative AI model and optimizes the design of medical devices based on the extracted features. The software used is TensorFlow or PyTorch. Anatomical feature data is used as input, and a 3D model of the optimized device design is generated as output. Model generation and adjustments are performed using prompts.

[0772] Step 5:

[0773] The server uses emotion analysis capabilities to collect feedback from users. It analyzes user voice, facial expressions, and text comments as input to obtain feedback data. The technology used is a commercial emotion recognition API. This provides guidance for improving the device design.

[0774] Step 6:

[0775] The user reviews the 3D device design and requests modifications as needed. An interactive 3D viewer is used to review the design. The input is the generated device design, and the output is user feedback on its suitability.

[0776] Step 7:

[0777] The 3D printer, acting as the terminal, begins manufacturing based on the final device design. The device uses specified biocompatible materials to transform the 3D design into a physical product. The input is device design information, and the output is a physical medical device. This results in a patient-specific device.

[0778] Step 8:

[0779] The server records surgical results and the patient's recovery process, and uses this data to improve the AI ​​model. Database software is used, with postoperative patient data as input and an improved AI model as output. This process improves the accuracy of future device designs.

[0780] (Application Example 2)

[0781] 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".

[0782] In modern medicine, providing patients with optimal medical components is crucial. However, conventional design methods struggle to comprehensively consider each patient's individual biological data and emotional state, and they cannot adequately evaluate the impact of design on patient comfort and recovery speed. Furthermore, in postoperative patient care, the provision of support methods utilizing consumer electronics is insufficient, often resulting in a lack of patient comfort.

[0783] 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.

[0784] In this invention, the server includes means for acquiring a patient's biological data and storing it in a data set; means for analyzing the user's emotional state using an emotion analysis device and adjusting the design of medical components based on the feedback; and means for providing information about the patient's biological state in their daily environment using consumer electronics and communicating the information to a medical facility as needed. This makes it possible to design and provide medical components that meet the individual needs of each patient, thereby enhancing the patient's sense of security and comfort after surgery.

[0785] "Biometric data" refers to all information about a patient's body, including medical images such as CT scans and MRIs, blood test results, and vital data such as heart rate and blood pressure.

[0786] A "data collection" refers to a database or data repository built to efficiently store and utilize various types of acquired data.

[0787] An "emotion analysis device" refers to a device or software that analyzes a person's facial expressions, voice tone, text comments, etc., to identify their individual emotional state.

[0788] "Medical components" refer to implants and prosthetics that are designed and manufactured to achieve specific therapeutic purposes based on the patient's anatomical characteristics.

[0789] "Consumer-use machinery" refers to robots and electrical appliances used by general consumers in their daily lives, and which are designed to be optimized for medical applications.

[0790] A "three-dimensional manufacturing device" refers to a 3D printer, which constructs physical objects from digital designs. It is a device that generates three-dimensional objects using specified materials.

[0791] The system that realizes this invention is mainly composed of a server, a terminal, and user interaction. The server starts by acquiring the patient's biometric data and storing it in a data collection. In this process, various types of data are handled, including medical images such as CT scans and MRIs, and vital data.

[0792] Next, the server uses an emotion analysis device to analyze the user's emotional state in real time and adjusts the design of the medical components based on this data. The emotion analysis uses sophisticated algorithms that read emotions from human facial expressions, voice, and text.

[0793] The terminal controls a 3D printer, a three-dimensional manufacturing device, to generate objects based on optimized medical component designs sent from the server. In this process, appropriate materials are selected to maintain biocompatibility and mechanical strength.

[0794] Users receive necessary support in the patient's daily environment through consumer-grade devices. These devices monitor the patient's condition and transmit information to medical facilities if any abnormalities are detected.

[0795] One specific example is a consumer robot that checks the facial expressions of patients while they are relaxing at home after surgery and recommends relaxing music. This allows patients to recover while feeling reassured.

[0796] An example of an input prompt for a generative AI model is, "Analyze the patient's emotional state and suggest music to listen to during the remaining recovery period."

[0797] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0798] Step 1:

[0799] The server receives biometric data provided by patients and stores it in a data set. Specifically, it acquires medical images such as CT scans and MRIs, as well as vital data, removes noise from this data, and converts it into a standardized format so that it can be stored in the database. The input is biometric data, and the output is standardized data with noise reduction.

[0800] Step 2:

[0801] The server activates an emotion analysis device and analyzes the user's facial expressions and voice data in real time. The emotional state obtained (e.g., reassurance, tension, etc.) is then used to adjust the design of medical components. The input consists of facial expression data and voice data, and the output is data indicating the analyzed emotional state.

[0802] Step 3:

[0803] The server uses a generative AI model to automatically generate designs for medical components based on emotional states and anatomical features. User emotional feedback is incorporated into the design during this process. The input consists of emotional states and anatomical features, while the output is optimized medical component design data.

[0804] Step 4:

[0805] The terminal retrieves optimized medical component designs transmitted from the server and controls a 3D printer (a three-dimensional manufacturing device) to carry out the manufacturing process. The materials used are biocompatible and guarantee mechanical strength. The input is optimized design data, and the output is the manufactured physical medical component.

[0806] Step 5:

[0807] The user monitors the patient's biological state in their daily environment using consumer-grade equipment and transmits information to a medical facility in real time if an abnormality is detected. Specifically, it reassessss the patient's emotional state from their facial expressions and voice, and issues an alarm to coordinate with medical staff as needed. Inputs include facial expression data and voice data, and outputs are reports to the user and the medical facility.

[0808] 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.

[0809] 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.

[0810] 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 robot 414.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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."

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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 to be incorporated by reference.

[0829] The following is further disclosed regarding the embodiments described above.

[0830] (Claim 1)

[0831] A means of acquiring medical image data of patients and storing it in a database,

[0832] A means for preprocessing acquired medical image data and extracting anatomical features,

[0833] A means to optimize the design of medical implants based on anatomical features extracted using an artificial intelligence model,

[0834] A method for sending optimized medical implant designs to a 3D printer for manufacturing,

[0835] A system that includes means for recording surgical results and the patient's recovery process, and for using that data to modify an artificial intelligence model.

[0836] (Claim 2)

[0837] The system according to claim 1, which receives user feedback and modifies an artificial intelligence model based on this feedback in order to fine-tune the design of a medical implant.

[0838] (Claim 3)

[0839] The system according to claim 1, which automatically manages the use of specified materials in the 3D printing process to ensure biocompatibility and mechanical strength.

[0840] "Example 1"

[0841] (Claim 1)

[0842] A means for acquiring patient medical data and storing it in an information aggregation device,

[0843] A means for preprocessing acquired medical data and extracting biological structural features,

[0844] A means for optimizing implant design based on biological structural features extracted using a machine learning model,

[0845] A means for transmitting optimized implant design information to a manufacturing device for generation,

[0846] A system that includes means for recording treatment results and the patient's recovery progress, and for using that information to improve machine learning models.

[0847] (Claim 2)

[0848] The system according to claim 1, which receives feedback from users and improves a machine learning model based on this feedback in order to fine-tune the design of the implant.

[0849] (Claim 3)

[0850] The system according to claim 1, which automatically manages the use of specified components during the production process by the manufacturing apparatus to ensure biocompatibility and structural strength.

[0851] "Application Example 1"

[0852] (Claim 1)

[0853] A means of acquiring patient body image information and storing it in an information aggregation system,

[0854] A means for processing acquired body image information and identifying morphological features,

[0855] A means for improving the design of medical components based on morphological features identified using an intelligent program model,

[0856] A means of transferring improved medical component designs to a 3D printing device for construction,

[0857] A means for recording treatment results and the patient's recovery progress, and for using that data to adjust an intelligent program model,

[0858] A system that allows care workers to easily upload physical image information on their devices, review proposed designs, and quickly incorporate their feedback.

[0859] (Claim 2)

[0860] The system according to claim 1, which collects user feedback and modifies an intelligent program model based on this feedback in order to fine-tune the design of medical components.

[0861] (Claim 3)

[0862] The system according to claim 1, which automatically controls the use of specified constituent materials during the construction process using a 3D printing device to ensure biocompatibility and mechanical durability.

[0863] "Example 2 of combining an emotion engine"

[0864] (Claim 1)

[0865] A means of acquiring patient medical data and storing it in an information management system,

[0866] A means for preprocessing acquired medical data and extracting features of biological structures,

[0867] A means for optimizing the design of medical devices based on the characteristics of biological structures extracted using a generative AI model,

[0868] A means of collecting user feedback using emotion analysis functions and incorporating it into device design,

[0869] A means of transmitting an optimized medical device design to a manufacturing device and carrying out the manufacturing process,

[0870] A system that includes means for recording treatment results and the patient's improvement process, and for using that data to modify the generated AI model.

[0871] (Claim 2)

[0872] The system according to claim 1, which collects emotion-based feedback and incorporates it into a generative AI model and design in order to fine-tune the design of a medical device.

[0873] (Claim 3)

[0874] The system according to claim 1, which automatically controls the use of specified materials in the manufacturing process using a manufacturing device, thereby maintaining biocompatibility and mechanical strength.

[0875] "Application example 2 when combining with an emotional engine"

[0876] (Claim 1)

[0877] A means for acquiring a patient's biometric data and storing it in a data set,

[0878] A means for preprocessing acquired biological data and extracting anatomical features,

[0879] A means for optimizing the design of medical components based on anatomical features extracted using a machine learning model,

[0880] A means of transmitting an optimized medical component design to a 3D manufacturing machine for manufacturing,

[0881] A means of recording surgical results and the patient's recovery process, and using that data to modify machine learning models,

[0882] A means of analyzing a user's emotional state using an emotion analysis device and adjusting the design of medical components based on that feedback,

[0883] A system that uses consumer-grade equipment to provide information about a patient's biological condition in their everyday environment and includes means for communicating this information to medical facilities as needed.

[0884] (Claim 2)

[0885] The system according to claim 1, which receives user feedback and modifies a machine learning model based on this feedback in order to fine-tune the design of a medical component.

[0886] (Claim 3)

[0887] The system according to claim 1, which automatically manages the use of specified materials in the manufacturing process using a three-dimensional manufacturing apparatus to ensure biocompatibility and mechanical strength. [Explanation of Symbols]

[0888] 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 acquiring medical image data of patients and storing it in a database, A means for preprocessing acquired medical image data and extracting anatomical features, A means to optimize the design of medical implants based on anatomical features extracted using an artificial intelligence model, A method for sending optimized medical implant designs to a 3D printer for manufacturing, A system that includes means for recording surgical results and the patient's recovery process, and for using that data to modify an artificial intelligence model.

2. The system according to claim 1, which receives user feedback and modifies an artificial intelligence model based on this feedback in order to fine-tune the design of a medical implant.

3. The system according to claim 1, which automatically manages the use of specified materials in the manufacturing process using a 3D printer to ensure biocompatibility and mechanical strength.

Images