system

Abstract

The system according to this embodiment aims to provide real-time feedback and personalized simulations in surgical training and planning. [Solution] The system according to the embodiment comprises a data collection unit, a data generation unit, an analysis unit, and a data provision unit. The data collection unit collects data on the surgeon's movements and actual patient data. The data generation unit generates a virtual patient model based on the data collected by the data collection unit. The analysis unit analyzes the virtual patient model generated by the data generation unit and performs a surgical simulation. The data provision unit provides feedback and guidance based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of 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] In the prior art, there was a problem that real-time feedback and individualized simulations were not sufficiently performed in surgical training and planning.

[0005] The system according to the embodiment aims to provide real-time feedback and individualized simulations in surgical training and planning.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, a data generation unit, an analysis unit, and a data provision unit. The data collection unit collects data on the surgeon's movements and actual patient data. The data generation unit generates a virtual patient model based on the data collected by the data collection unit. The analysis unit analyzes the virtual patient model generated by the data generation unit and performs a surgical simulation. The data provision unit provides feedback and guidance based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide real-time feedback and personalized simulations in surgical training and planning. [Brief explanation of the drawing]

[0008] [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. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 1 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example of form 1) The surgical training and planning platform according to an embodiment of the present invention is a next-generation system that integrates AI technology and VR / AR technology. This system allows surgeons to simulate surgery in a highly realistic 3D virtual environment, with AI providing real-time feedback and guidance. Furthermore, it enables detailed pre-operative planning and rehearsals using personalized virtual patient models generated by AI based on actual patient data. For example, a surgeon simulates surgery in a 3D virtual environment using VR / AR technology. This virtual environment is highly realistic, providing a similar sensation to actual surgery. Next, the AI ​​analyzes the surgeon's movements in real time and visually presents optimal techniques and points of caution within the VR space. In addition, the AI ​​generates personalized virtual patient models based on actual patient data, enabling detailed pre-operative planning and rehearsals. The AI ​​also analyzes the results of the surgical simulation and predicts the prognosis and potential risks in actual surgery. Furthermore, the AI ​​evaluates the surgeon's skill level and provides a training program optimized to individual needs. In this way, SurgeonVerse accelerates the improvement of surgeons' skills, enhances patient safety, and contributes to the overall improvement of medical quality. Furthermore, it facilitates collaboration with specialists in remote locations and promotes the rapid dissemination of new surgical techniques, contributing to the improvement of global medical standards. In this way, the surgical training and surgical planning platform can accelerate the improvement of surgeons' skills, enhance patient safety, and contribute to the overall improvement of medical quality.

[0029] The surgical training and surgical planning platform according to the embodiment comprises an acquisition unit, a generation unit, an analysis unit, and a provision unit. The acquisition unit collects data on the surgeon's movements and actual patient data. The acquisition unit can collect, for example, the surgeon's hand movements, instrument manipulation, and eye movements. The acquisition unit can also collect actual patient data such as the patient's medical history, image data, and biosignals. For example, the acquisition unit can collect the surgeon's hand movements using motion capture technology. The acquisition unit can also collect patient image data using MRI or CT scans. Furthermore, the acquisition unit can collect the patient's biosignals using sensors. The generation unit generates a virtual patient model based on the data collected by the acquisition unit. The generation unit can generate a virtual patient model using, for example, a 3D model or a simulation algorithm. For example, the generation unit generates a 3D model based on the collected image data. The generation unit can also generate a virtual patient model using a simulation algorithm. Furthermore, the generation unit can also generate a virtual patient model based on the collected biosignals. The analysis unit analyzes the virtual patient model generated by the generation unit and performs a surgical simulation. The analysis unit can perform surgical simulations based on, for example, simulation scenarios and the software used. For example, the analysis unit performs surgical simulations using simulation software. The analysis unit can also perform surgical simulations based on simulation scenarios. Furthermore, the analysis unit can perform surgical simulations based on virtual patient models. The service unit provides feedback and guidance based on the analysis results obtained by the analysis unit. The service unit can provide feedback and guidance based on, for example, evaluation criteria and the format of guidance (oral, written, video, etc.). For example, the service unit provides oral feedback. The service unit can also provide written feedback. Furthermore, the service unit can provide video feedback.As a result, the surgical training and surgical planning platform according to the embodiment can generate a virtual patient model based on the surgeon's movements and actual patient data, perform surgical simulations, and provide feedback and guidance.

[0030] The data collection unit collects data on the surgeon's movements and actual patient data. For example, it can collect the surgeon's hand movements, instrument manipulation, and eye movements. Specifically, it uses motion capture technology to record the surgeon's hand movements with high precision, allowing for a detailed understanding of subtle movements and force applied during surgery. For instrument manipulation, sensors attached to surgical instruments are used to collect real-time data on the instrument's position, angle, and force application. For eye movements, an eye-tracking device is used to track which part the surgeon is focusing on. This allows for analysis of the correlation between the surgeon's eye movements and hand movements. Furthermore, the data collection unit can also collect actual patient data such as patient history, image data, and biosignals. For example, patient history is obtained from the electronic medical record system, and detailed medical information including past surgical history and allergy information is collected. For image data, high-resolution 3D images are obtained using MRI and CT scans to gain a detailed understanding of the patient's internal structure. For biosignals, data such as electrocardiograms, pulse, and blood pressure are collected in real-time using sensors to monitor the patient's physiological state. This allows the data collection unit to integrate and collect detailed medical data on the surgeon's movements and the patient, providing the information necessary for surgical preparation and simulation.

[0031] The generation unit generates a virtual patient model based on the data collected by the acquisition unit. The generation unit can generate virtual patient models using, for example, 3D models or simulation algorithms. Specifically, it generates detailed 3D models of the patient's organs and tissues based on the collected image data. This 3D model possesses anatomical accuracy and plays a crucial role in surgical simulations. Furthermore, the generation unit can use simulation algorithms to simulate the effects of surgery on the virtual patient model in real time. For example, it can simulate tissue changes and bleeding when incisions and sutures are performed on the virtual patient model. In addition, the generation unit can reproduce the physiological state of the virtual patient model based on collected biosignals. This allows the virtual patient model to exhibit responses similar to those of a real patient, providing a realistic experience in surgical simulations. The generation unit integrates this data to provide highly accurate virtual patient models for surgeons to plan and train for surgeries.

[0032] The analysis unit analyzes the virtual patient model generated by the generation unit and performs surgical simulations. For example, the analysis unit can perform surgical simulations based on the simulation scenario and the software used. Specifically, it uses simulation software to simulate each step of the surgery on the virtual patient model in detail. For instance, it simulates each surgical process, such as the depth and angle of the incision, the selection of instruments, and the suturing method, and analyzes the results. Furthermore, the analysis unit can compare and examine different surgical techniques and instrument usage methods based on the simulation scenario. This helps in selecting the optimal surgical technique and instruments. In addition, the analysis unit can identify risks and problems during surgery in advance based on the virtual patient model and take countermeasures. For example, if a particular surgical technique increases the risk of bleeding, it can propose alternative methods to reduce that risk. Based on these analysis results, the analysis unit can provide specific feedback to surgeons, thereby improving the success rate of surgery.

[0033] The service provider provides feedback and guidance based on the analysis results obtained by the analysis department. For example, the service provider can provide feedback and guidance based on evaluation criteria and the format of guidance (oral, written, video, etc.). Specifically, based on the analysis results, they evaluate the surgeon's surgical techniques and instrument handling, and provide oral feedback on the evaluation results. The service provider can also create a detailed evaluation report and provide written feedback. This report details the evaluation and areas for improvement at each step of the surgery, serving as reference material for the surgeon's self-assessment. Furthermore, the service provider can provide feedback using video. For example, by playing a video of a surgical simulation and pointing out specific areas for improvement and points to note, they can provide visually easy-to-understand feedback. This allows the service provider to provide specific guidance to surgeons to improve their skills and increase the success rate of surgeries. Additionally, the service provider can collect feedback from surgeons and use it to improve the system and develop new training programs. This allows the service provider to continuously support the improvement of surgeons' skills and enhance the safety and effectiveness of surgeries.

[0034] The service provider can analyze the results of surgical simulations and predict the prognosis and potential risks in actual surgery. For example, the service provider can predict the prognosis based on the results of surgical simulations. For example, the service provider can predict the recovery period based on the results of surgical simulations. The service provider can also predict the incidence of complications based on the results of surgical simulations. Furthermore, the service provider can predict postoperative risks based on the results of surgical simulations. For example, the service provider can predict the risk of postoperative infection based on the results of surgical simulations. The service provider can also predict the risk of postoperative bleeding based on the results of surgical simulations. In this way, by analyzing the results of surgical simulations, it is possible to predict the prognosis and potential risks in actual surgery.

[0035] The service provider can assess the proficiency of surgeons and provide training programs optimized to their individual needs. For example, the service provider can assess a surgeon's proficiency based on factors such as surgical success rate, surgical time, and error rate. For instance, the service provider can assess a surgeon's proficiency based on their surgical success rate. It can also assess a surgeon's proficiency based on their surgical time. Furthermore, it can assess a surgeon's proficiency based on their error rate. Based on the proficiency assessment results, the service provider provides training programs optimized to individual needs. For example, it can provide a training program on basic techniques for novice surgeons. It can also provide a training program on advanced techniques for intermediate surgeons. Furthermore, it can provide a training program on the latest surgical techniques for advanced surgeons. This allows the service provider to support the improvement of surgeons' skills by assessing their proficiency and providing training programs tailored to their individual needs.

[0036] The data collection unit can analyze a surgeon's past surgical history and select the optimal data collection method. For example, the data collection unit can select a data collection method for similar surgeries based on data from surgeons' past successful surgeries. For example, the data collection unit can select a data collection method that takes improvements into account based on data from surgeons' past unsuccessful surgeries. For example, the data collection unit can customize a data collection method for a specific procedure based on the surgeon's surgical history. This enables efficient data collection by selecting the optimal data collection method based on the surgeon's past surgical history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the surgeon's past surgical data into a generating AI and have the generating AI select the optimal data collection method.

[0037] The data collection unit can filter data based on the surgeon's current surgical skills and areas of interest during data collection. For example, the data collection unit may prioritize collecting data related to techniques in which the surgeon excels. For example, the data collection unit may collect data related to new surgical techniques that the surgeon is interested in. For example, the data collection unit may collect data of appropriate difficulty level according to the surgeon's current skill level. This allows for the collection of highly relevant data by filtering the data based on the surgeon's current surgical skills and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit may input data related to the surgeon's areas of interest into a generating AI and have the generating AI perform data filtering.

[0038] The data collection unit can prioritize the collection of highly relevant data by considering the surgeon's geographical location during data collection. For example, if a surgeon performs surgery in a specific region, the data collection unit will prioritize the collection of data relevant to that region. For example, if a surgeon performs surgery in a remote location, the data collection unit will collect data related to the medical environment of that region. For example, if a surgeon performs surgery internationally, the data collection unit will collect data based on the medical guidelines of each country. This allows for the priority collection of highly relevant data by considering the surgeon's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the surgeon's geographical location into a generating AI and have the generating AI perform the collection of highly relevant data.

[0039] The data collection unit can analyze the surgeon's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data on surgical techniques that the surgeon has shown interest in on social media. For example, the data collection unit can collect data based on the opinions and research of experts that the surgeon follows. For example, the data collection unit can collect relevant data based on the content of online discussions that the surgeon has participated in. In this way, relevant data can be collected by analyzing the surgeon's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the surgeon's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0040] The generation unit can adjust the level of detail of a virtual patient model based on the patient's importance. For example, for a high-importance patient, the generation unit generates a model with detailed anatomical structures. For example, for a low-importance patient, the generation unit generates a model with basic anatomical structures. For example, for a high-urgency patient, the generation unit generates a simplified model that can be generated quickly. This allows for efficient generation of virtual patient models by adjusting the level of detail of the model based on the patient's importance. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input patient importance data into a generation AI and have the generation AI perform the adjustment of the model's level of detail.

[0041] The generation unit can apply different generation algorithms depending on the patient category when generating virtual patient models. For example, in the case of pediatric patients, the generation unit applies a generation algorithm appropriate to the growth stage. For example, in the case of elderly patients, the generation unit applies a generation algorithm that takes into account anatomical changes associated with aging. For example, in the case of patients with specific diseases, the generation unit applies a generation algorithm specific to that disease. By applying different generation algorithms depending on the patient category, a more appropriate virtual patient model can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input patient category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0042] The generation unit can determine the generation priority based on the patient submission date when generating virtual patient models. For example, the generation unit may prioritize using data from patients with high urgency to generate virtual patient models. For example, the generation unit may prioritize using data from patients with older submission dates to generate virtual patient models. For example, the generation unit may prioritize using data from patients with newer submission dates to generate virtual patient models. This enables efficient generation of virtual patient models by determining the generation priority based on the patient submission date. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit may input patient submission date data into a generation AI and have the generation AI perform the determination of the generation priority.

[0043] The generation unit can adjust the generation order based on patient relevance when generating virtual patient models. For example, the generation unit may prioritize using data highly relevant to patients the surgeon has operated on in the past to generate virtual patient models. For example, the generation unit may prioritize using data related to diseases the surgeon is currently interested in to generate virtual patient models. For example, the generation unit may prioritize using data related to patients the surgeon plans to operate on in the future to generate virtual patient models. This allows for efficient generation of virtual patient models by adjusting the generation order based on patient relevance. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit may input patient relevance data into a generation AI and have the generation AI perform the adjustment of the generation order.

[0044] The analysis unit can improve the accuracy of the analysis by considering the interrelationships between virtual patient models during the analysis. For example, the analysis unit integrates data from multiple virtual patient models and analyzes their interrelationships. For example, the analysis unit extracts commonalities between virtual patient models to improve the accuracy of the analysis. For example, the analysis unit predicts the success rate of surgery based on the interrelationships between virtual patient models. This improves the accuracy of the analysis by considering the interrelationships between virtual patient models. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data from multiple virtual patient models into a generating AI and have the generating AI perform the analysis of the interrelationships.

[0045] The analysis unit can perform analysis while considering the patient's attribute information. For example, the analysis unit performs analysis based on attribute information such as the patient's age, sex, and medical history. For example, the analysis unit performs analysis while considering the patient's lifestyle and environmental factors. For example, the analysis unit performs personalized analysis based on the patient's genetic information. This makes it possible to perform more personalized analysis by considering the patient's attribute information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the patient's attribute information into a generating AI and have the generating AI perform the analysis.

[0046] The analysis unit can perform analysis while considering the geographical distribution of patients. For example, the analysis unit can perform analysis while considering the medical environment in the area where the patient lives. For example, the analysis unit can analyze region-specific disease risks based on the geographical distribution of patients. For example, the analysis unit can predict the success rate of surgery based on the geographical distribution of patients. In this way, region-specific disease risks can be analyzed by considering the geographical distribution of patients. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical distribution data of patients into a generating AI and have the generating AI perform the analysis.

[0047] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the patient during the analysis process. For example, the analysis unit performs analysis by referring to the latest research papers related to the patient's disease. For example, the analysis unit performs analysis based on past literature on the patient's treatment methods. For example, the analysis unit performs personalized analysis by referring to literature on the patient's genetic information. This improves the accuracy of the analysis by referring to relevant literature on the patient. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input patient-related literature data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0048] The service provider can provide optimal feedback by referring to the surgeon's past surgical history during feedback and guidance. For example, the service provider can provide feedback based on the surgeon's past successful surgical techniques. For example, the service provider can point out areas for improvement in surgeries that the surgeon has failed in the past. For example, the service provider can customize feedback for specific techniques based on the surgeon's surgical history. This allows for the provision of more appropriate feedback by referring to the surgeon's past surgical history. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the surgeon's past surgical history data into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0049] The service provider can customize the content of instruction based on the surgeon's current skill level during feedback and guidance sessions. For example, if the surgeon is a beginner, the service provider will provide instruction on basic techniques. If the surgeon is an intermediate level, the service provider will provide instruction on advanced techniques. If the surgeon is an advanced level, the service provider will provide instruction on the latest surgical techniques. By customizing the instruction content based on the surgeon's current skill level, more effective instruction becomes possible. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the surgeon's skill level data into a generating AI and have the generating AI perform the customization of the instruction content.

[0050] The service provider can provide optimal feedback and guidance by taking into account the surgeon's geographical location. For example, if the surgeon performs surgery in a specific region, the service provider can provide feedback relevant to that region. For example, if the surgeon performs surgery in a remote location, the service provider can provide feedback regarding the medical environment of that region. For example, if the surgeon performs surgery internationally, the service provider can provide feedback based on the medical guidelines of each country. This allows for the provision of more appropriate feedback by taking into account the surgeon's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the surgeon's geographical location into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0051] The service provider can analyze a surgeon's social media activity and suggest methods for providing feedback and guidance. For example, the service provider can provide feedback on surgical techniques that the surgeon has shown interest in on social media. For example, the service provider can provide feedback based on the opinions and research of experts that the surgeon follows. For example, the service provider can provide relevant feedback based on the content of online discussions that the surgeon has participated in. By analyzing the surgeon's social media activity, the service provider can suggest more appropriate methods for providing feedback. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the surgeon's social media activity data into a generating AI and have the generating AI suggest methods for providing feedback.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The generation unit can apply algorithms to predict the success rate of surgeries based on the surgeon's past surgical data. For example, the generation unit analyzes data from surgeries performed by the surgeon in the past and identifies procedures with high success rates. Based on the identified procedures, the generation unit can generate a virtual patient model and predict the success rate of the surgery. The generation unit can also apply algorithms to predict the risk of surgery based on the surgeon's past surgical data. This allows for the generation of more appropriate virtual patient models by predicting the success rate and risk of surgery based on the surgeon's past surgical data.

[0054] The data collection unit can monitor the surgeon's vital signs in real time during surgery and issue alerts if abnormalities are detected. For example, the unit can monitor the surgeon's heart rate and blood pressure and issue alerts if abnormalities are detected. The unit can also monitor the surgeon's oxygen saturation and issue alerts if abnormalities are detected. Furthermore, the unit can monitor the surgeon's body temperature and issue alerts if abnormalities are detected. This allows for real-time monitoring of the surgeon's vital signs, enabling a rapid response if abnormalities are detected.

[0055] The analysis unit can analyze the surgeon's motion data during surgery and evaluate the efficiency of the surgery. For example, the analysis unit can analyze the surgeon's hand movements and instrument manipulation to evaluate the efficiency of the surgery. The analysis unit can also analyze the surgeon's eye movements to evaluate the efficiency of the surgery. Furthermore, based on the surgeon's motion data, the analysis unit can suggest improvements to enhance the efficiency of the surgery. In this way, by analyzing the surgeon's motion data during surgery, the efficiency of the surgery can be evaluated and improvements can be suggested.

[0056] The data collection unit can collect environmental data from surgeons during surgery and optimize the surgical environment. For example, it can monitor the temperature and humidity of the operating room to maintain an optimal environment. It can also monitor the lighting conditions of the operating room and provide optimal lighting. Furthermore, it can monitor the noise level of the operating room to maintain a quiet environment. By optimizing the surgical environment in this way, surgeons can improve their performance.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The data acquisition unit collects data on the surgeon's movements and actual patient data. For example, it collects the surgeon's hand movements, instrument manipulation, eye movements, patient medical history, image data, and biosignals. The data acquisition unit uses motion capture technology to collect the surgeon's hand movements, MRI and CT scans to collect patient image data, and sensors to collect patient biosignals. Step 2: The generation unit generates a virtual patient model based on the data collected by the acquisition unit. For example, a virtual patient model may be generated using a 3D model or a simulation algorithm, or a virtual patient model may be generated based on collected image data and biosignals. Step 3: The analysis unit analyzes the virtual patient model generated by the generation unit and performs a surgical simulation. For example, it performs a surgical simulation based on simulation software or scenarios. Step 4: The service provider provides feedback and guidance based on the analysis results obtained by the analysis provider. For example, they provide feedback and guidance based on evaluation criteria and the format of guidance (oral, written, video, etc.).

[0059] (Example of form 2) The surgical training and planning platform according to an embodiment of the present invention is a next-generation system that integrates AI technology and VR / AR technology. This system allows surgeons to simulate surgery in a highly realistic 3D virtual environment, with AI providing real-time feedback and guidance. Furthermore, it enables detailed pre-operative planning and rehearsals using personalized virtual patient models generated by AI based on actual patient data. For example, a surgeon simulates surgery in a 3D virtual environment using VR / AR technology. This virtual environment is highly realistic, providing a similar sensation to actual surgery. Next, the AI ​​analyzes the surgeon's movements in real time and visually presents optimal techniques and points of caution within the VR space. In addition, the AI ​​generates personalized virtual patient models based on actual patient data, enabling detailed pre-operative planning and rehearsals. The AI ​​also analyzes the results of the surgical simulation and predicts the prognosis and potential risks in actual surgery. Furthermore, the AI ​​evaluates the surgeon's skill level and provides a training program optimized to individual needs. In this way, SurgeonVerse accelerates the improvement of surgeons' skills, enhances patient safety, and contributes to the overall improvement of medical quality. Furthermore, it facilitates collaboration with specialists in remote locations and promotes the rapid dissemination of new surgical techniques, contributing to the improvement of global medical standards. In this way, the surgical training and surgical planning platform can accelerate the improvement of surgeons' skills, enhance patient safety, and contribute to the overall improvement of medical quality.

[0060] The surgical training and surgical planning platform according to the embodiment comprises an acquisition unit, a generation unit, an analysis unit, and a provision unit. The acquisition unit collects data on the surgeon's movements and actual patient data. The acquisition unit can collect, for example, the surgeon's hand movements, instrument manipulation, and eye movements. The acquisition unit can also collect actual patient data such as the patient's medical history, image data, and biosignals. For example, the acquisition unit can collect the surgeon's hand movements using motion capture technology. The acquisition unit can also collect patient image data using MRI or CT scans. Furthermore, the acquisition unit can collect the patient's biosignals using sensors. The generation unit generates a virtual patient model based on the data collected by the acquisition unit. The generation unit can generate a virtual patient model using, for example, a 3D model or a simulation algorithm. For example, the generation unit generates a 3D model based on the collected image data. The generation unit can also generate a virtual patient model using a simulation algorithm. Furthermore, the generation unit can also generate a virtual patient model based on the collected biosignals. The analysis unit analyzes the virtual patient model generated by the generation unit and performs a surgical simulation. The analysis unit can perform surgical simulations based on, for example, simulation scenarios and the software used. For example, the analysis unit performs surgical simulations using simulation software. The analysis unit can also perform surgical simulations based on simulation scenarios. Furthermore, the analysis unit can perform surgical simulations based on virtual patient models. The service unit provides feedback and guidance based on the analysis results obtained by the analysis unit. The service unit can provide feedback and guidance based on, for example, evaluation criteria and the format of guidance (oral, written, video, etc.). For example, the service unit provides oral feedback. The service unit can also provide written feedback. Furthermore, the service unit can provide video feedback.As a result, the surgical training and surgical planning platform according to the embodiment can generate a virtual patient model based on the surgeon's movements and actual patient data, perform surgical simulations, and provide feedback and guidance.

[0061] The data collection unit collects data on the surgeon's movements and actual patient data. For example, it can collect the surgeon's hand movements, instrument manipulation, and eye movements. Specifically, it uses motion capture technology to record the surgeon's hand movements with high precision, allowing for a detailed understanding of subtle movements and force applied during surgery. For instrument manipulation, sensors attached to surgical instruments are used to collect real-time data on the instrument's position, angle, and force application. For eye movements, an eye-tracking device is used to track which part the surgeon is focusing on. This allows for analysis of the correlation between the surgeon's eye movements and hand movements. Furthermore, the data collection unit can also collect actual patient data such as patient history, image data, and biosignals. For example, patient history is obtained from the electronic medical record system, and detailed medical information including past surgical history and allergy information is collected. For image data, high-resolution 3D images are obtained using MRI and CT scans to gain a detailed understanding of the patient's internal structure. For biosignals, data such as electrocardiograms, pulse, and blood pressure are collected in real-time using sensors to monitor the patient's physiological state. This allows the data collection unit to integrate and collect detailed medical data on the surgeon's movements and the patient, providing the information necessary for surgical preparation and simulation.

[0062] The generation unit generates a virtual patient model based on the data collected by the acquisition unit. The generation unit can generate virtual patient models using, for example, 3D models or simulation algorithms. Specifically, it generates detailed 3D models of the patient's organs and tissues based on the collected image data. This 3D model possesses anatomical accuracy and plays a crucial role in surgical simulations. Furthermore, the generation unit can use simulation algorithms to simulate the effects of surgery on the virtual patient model in real time. For example, it can simulate tissue changes and bleeding when incisions and sutures are performed on the virtual patient model. In addition, the generation unit can reproduce the physiological state of the virtual patient model based on collected biosignals. This allows the virtual patient model to exhibit responses similar to those of a real patient, providing a realistic experience in surgical simulations. The generation unit integrates this data to provide highly accurate virtual patient models for surgeons to plan and train for surgeries.

[0063] The analysis unit analyzes the virtual patient model generated by the generation unit and performs surgical simulations. For example, the analysis unit can perform surgical simulations based on the simulation scenario and the software used. Specifically, it uses simulation software to simulate each step of the surgery on the virtual patient model in detail. For instance, it simulates each surgical process, such as the depth and angle of the incision, the selection of instruments, and the suturing method, and analyzes the results. Furthermore, the analysis unit can compare and examine different surgical techniques and instrument usage methods based on the simulation scenario. This helps in selecting the optimal surgical technique and instruments. In addition, the analysis unit can identify risks and problems during surgery in advance based on the virtual patient model and take countermeasures. For example, if a particular surgical technique increases the risk of bleeding, it can propose alternative methods to reduce that risk. Based on these analysis results, the analysis unit can provide specific feedback to surgeons, thereby improving the success rate of surgery.

[0064] The service provider provides feedback and guidance based on the analysis results obtained by the analysis department. For example, the service provider can provide feedback and guidance based on evaluation criteria and the format of guidance (oral, written, video, etc.). Specifically, based on the analysis results, they evaluate the surgeon's surgical techniques and instrument handling, and provide oral feedback on the evaluation results. The service provider can also create a detailed evaluation report and provide written feedback. This report details the evaluation and areas for improvement at each step of the surgery, serving as reference material for the surgeon's self-assessment. Furthermore, the service provider can provide feedback using video. For example, by playing a video of a surgical simulation and pointing out specific areas for improvement and points to note, they can provide visually easy-to-understand feedback. This allows the service provider to provide specific guidance to surgeons to improve their skills and increase the success rate of surgeries. Additionally, the service provider can collect feedback from surgeons and use it to improve the system and develop new training programs. This allows the service provider to continuously support the improvement of surgeons' skills and enhance the safety and effectiveness of surgeries.

[0065] The service provider can analyze the results of surgical simulations and predict the prognosis and potential risks in actual surgery. For example, the service provider can predict the prognosis based on the results of surgical simulations. For example, the service provider can predict the recovery period based on the results of surgical simulations. The service provider can also predict the incidence of complications based on the results of surgical simulations. Furthermore, the service provider can predict postoperative risks based on the results of surgical simulations. For example, the service provider can predict the risk of postoperative infection based on the results of surgical simulations. The service provider can also predict the risk of postoperative bleeding based on the results of surgical simulations. In this way, by analyzing the results of surgical simulations, it is possible to predict the prognosis and potential risks in actual surgery.

[0066] The service provider can assess the proficiency of surgeons and provide training programs optimized to their individual needs. For example, the service provider can assess a surgeon's proficiency based on factors such as surgical success rate, surgical time, and error rate. For instance, the service provider can assess a surgeon's proficiency based on their surgical success rate. It can also assess a surgeon's proficiency based on their surgical time. Furthermore, it can assess a surgeon's proficiency based on their error rate. Based on the proficiency assessment results, the service provider provides training programs optimized to individual needs. For example, it can provide a training program on basic techniques for novice surgeons. It can also provide a training program on advanced techniques for intermediate surgeons. Furthermore, it can provide a training program on the latest surgical techniques for advanced surgeons. This allows the service provider to support the improvement of surgeons' skills by assessing their proficiency and providing training programs tailored to their individual needs.

[0067] The data collection unit can estimate the surgeon's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the surgeon is tense, the data collection unit reduces the frequency of data collection and waits until the surgeon relaxes. For example, if the surgeon is concentrating, the data collection unit increases the frequency of data collection to obtain more detailed data. For example, if the surgeon is tired, the data collection unit temporarily stops data collection and resumes it after a break. This allows for more appropriate data collection by adjusting the timing of data collection according to the surgeon's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input the surgeon's facial expression data into the generative AI and have the generative AI perform the estimation of the surgeon's emotions.

[0068] The data collection unit can analyze a surgeon's past surgical history and select the optimal data collection method. For example, the data collection unit can select a data collection method for similar surgeries based on data from surgeons' past successful surgeries. For example, the data collection unit can select a data collection method that takes improvements into account based on data from surgeons' past unsuccessful surgeries. For example, the data collection unit can customize a data collection method for a specific procedure based on the surgeon's surgical history. This enables efficient data collection by selecting the optimal data collection method based on the surgeon's past surgical history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the surgeon's past surgical data into a generating AI and have the generating AI select the optimal data collection method.

[0069] The data collection unit can filter data based on the surgeon's current surgical skills and areas of interest during data collection. For example, the data collection unit may prioritize collecting data related to techniques in which the surgeon excels. For example, the data collection unit may collect data related to new surgical techniques that the surgeon is interested in. For example, the data collection unit may collect data of appropriate difficulty level according to the surgeon's current skill level. This allows for the collection of highly relevant data by filtering the data based on the surgeon's current surgical skills and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit may input data related to the surgeon's areas of interest into a generating AI and have the generating AI perform data filtering.

[0070] The data collection unit can estimate the surgeon's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the surgeon is tense, the data collection unit will prioritize collecting less important data. If the surgeon is relaxed, the data collection unit will prioritize collecting more important data. If the surgeon is focused, the data collection unit will prioritize collecting data directly related to the progress of the surgery. This allows for the priority collection of important data by prioritizing data according to the surgeon's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the surgeon's facial expression data into a generative AI and have the generative AI perform the estimation of the surgeon's emotions.

[0071] The data collection unit can prioritize the collection of highly relevant data by considering the surgeon's geographical location during data collection. For example, if a surgeon performs surgery in a specific region, the data collection unit will prioritize the collection of data relevant to that region. For example, if a surgeon performs surgery in a remote location, the data collection unit will collect data related to the medical environment of that region. For example, if a surgeon performs surgery internationally, the data collection unit will collect data based on the medical guidelines of each country. This allows for the priority collection of highly relevant data by considering the surgeon's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the surgeon's geographical location into a generating AI and have the generating AI perform the collection of highly relevant data.

[0072] The data collection unit can analyze the surgeon's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data on surgical techniques that the surgeon has shown interest in on social media. For example, the data collection unit can collect data based on the opinions and research of experts that the surgeon follows. For example, the data collection unit can collect relevant data based on the content of online discussions that the surgeon has participated in. In this way, relevant data can be collected by analyzing the surgeon's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the surgeon's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0073] The generation unit can estimate the surgeon's emotions and adjust the method of generating the virtual patient model based on the estimated emotions of the surgeon. For example, if the surgeon is relaxed, the generation unit generates a detailed virtual patient model. For example, if the surgeon is in a hurry, the generation unit generates a simplified virtual patient model. For example, if the surgeon is excited, the generation unit generates a visually stimulating virtual patient model. This allows for the generation of more appropriate virtual patient models by adjusting the method of generating the virtual patient model according to the surgeon's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the surgeon's facial expression data into the generative AI and have the generative AI perform the estimation of the surgeon's emotions.

[0074] The generation unit can adjust the level of detail of a virtual patient model based on the patient's importance. For example, for a high-importance patient, the generation unit generates a model with detailed anatomical structures. For example, for a low-importance patient, the generation unit generates a model with basic anatomical structures. For example, for a high-urgency patient, the generation unit generates a simplified model that can be generated quickly. This allows for efficient generation of virtual patient models by adjusting the level of detail of the model based on the patient's importance. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input patient importance data into a generation AI and have the generation AI perform the adjustment of the model's level of detail.

[0075] The generation unit can apply different generation algorithms depending on the patient category when generating virtual patient models. For example, in the case of pediatric patients, the generation unit applies a generation algorithm appropriate to the growth stage. For example, in the case of elderly patients, the generation unit applies a generation algorithm that takes into account anatomical changes associated with aging. For example, in the case of patients with specific diseases, the generation unit applies a generation algorithm specific to that disease. By applying different generation algorithms depending on the patient category, a more appropriate virtual patient model can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input patient category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0076] The generation unit can estimate the surgeon's emotions and adjust the generation order of virtual patient models based on the estimated emotions. For example, if the surgeon is nervous, the generation unit generates virtual patient models in order from simple surgeries. If the surgeon is relaxed, the generation unit generates virtual patient models in order from complex surgeries. If the surgeon is focused, the generation unit generates virtual patient models randomly, regardless of the difficulty of the surgery. This allows for efficient generation of virtual patient models by adjusting the generation order according to the surgeon's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input the surgeon's facial expression data into the generation AI and have the generation AI perform the estimation of the surgeon's emotions.

[0077] The generation unit can determine the generation priority based on the patient submission date when generating virtual patient models. For example, the generation unit may prioritize using data from patients with high urgency to generate virtual patient models. For example, the generation unit may prioritize using data from patients with older submission dates to generate virtual patient models. For example, the generation unit may prioritize using data from patients with newer submission dates to generate virtual patient models. This enables efficient generation of virtual patient models by determining the generation priority based on the patient submission date. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit may input patient submission date data into a generation AI and have the generation AI perform the determination of the generation priority.

[0078] The generation unit can adjust the generation order based on patient relevance when generating virtual patient models. For example, the generation unit may prioritize using data highly relevant to patients the surgeon has operated on in the past to generate virtual patient models. For example, the generation unit may prioritize using data related to diseases the surgeon is currently interested in to generate virtual patient models. For example, the generation unit may prioritize using data related to patients the surgeon plans to operate on in the future to generate virtual patient models. This allows for efficient generation of virtual patient models by adjusting the generation order based on patient relevance. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit may input patient relevance data into a generation AI and have the generation AI perform the adjustment of the generation order.

[0079] The analysis unit can estimate the surgeon's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the surgeon is tense, the analysis unit can relax the analysis criteria and wait until the surgeon relaxes. For example, if the surgeon is relaxed, the analysis unit can tighten the analysis criteria and perform a detailed analysis. For example, if the surgeon is focused, the analysis unit can adjust the analysis criteria and perform an efficient analysis. This allows for a more appropriate analysis by adjusting the analysis criteria according to the surgeon's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the surgeon's facial expression data into the generative AI and have the generative AI perform the estimation of the surgeon's emotions.

[0080] The analysis unit can improve the accuracy of the analysis by considering the interrelationships between virtual patient models during the analysis. For example, the analysis unit integrates data from multiple virtual patient models and analyzes their interrelationships. For example, the analysis unit extracts commonalities between virtual patient models to improve the accuracy of the analysis. For example, the analysis unit predicts the success rate of surgery based on the interrelationships between virtual patient models. This improves the accuracy of the analysis by considering the interrelationships between virtual patient models. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data from multiple virtual patient models into a generating AI and have the generating AI perform the analysis of the interrelationships.

[0081] The analysis unit can perform analysis while considering the patient's attribute information. For example, the analysis unit performs analysis based on attribute information such as the patient's age, sex, and medical history. For example, the analysis unit performs analysis while considering the patient's lifestyle and environmental factors. For example, the analysis unit performs personalized analysis based on the patient's genetic information. This makes it possible to perform more personalized analysis by considering the patient's attribute information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the patient's attribute information into a generating AI and have the generating AI perform the analysis.

[0082] The analysis unit can estimate the surgeon's emotions and adjust the display order of the analysis results based on the estimated emotions. For example, if the surgeon is tense, the analysis unit will display less important analysis results first. If the surgeon is relaxed, the analysis unit will display more important analysis results first. If the surgeon is focused, the analysis unit will display analysis results directly related to the progress of the surgery first. By adjusting the display order of the analysis results according to the surgeon's emotions, important information can be provided at the appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the surgeon's facial expression data into the generative AI and have the generative AI perform the estimation of the surgeon's emotions.

[0083] The analysis unit can perform analysis while considering the geographical distribution of patients. For example, the analysis unit can perform analysis while considering the medical environment in the area where the patient lives. For example, the analysis unit can analyze region-specific disease risks based on the geographical distribution of patients. For example, the analysis unit can predict the success rate of surgery based on the geographical distribution of patients. In this way, region-specific disease risks can be analyzed by considering the geographical distribution of patients. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical distribution data of patients into a generating AI and have the generating AI perform the analysis.

[0084] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the patient during the analysis process. For example, the analysis unit performs analysis by referring to the latest research papers related to the patient's disease. For example, the analysis unit performs analysis based on past literature on the patient's treatment methods. For example, the analysis unit performs personalized analysis by referring to literature on the patient's genetic information. This improves the accuracy of the analysis by referring to relevant literature on the patient. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input patient-related literature data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0085] The service provider can estimate the surgeon's emotions and adjust the method of feedback and guidance based on the estimated emotions. For example, if the surgeon is tense, the service provider will provide feedback in a gentle tone. For example, if the surgeon is relaxed, the service provider will provide detailed guidance. For example, if the surgeon is focused, the service provider will point out specific areas for improvement. This allows for more effective guidance by adjusting the method of feedback and guidance according to the surgeon's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input the surgeon's facial expression data into the generative AI and have the generative AI perform the estimation of the surgeon's emotions.

[0086] The service provider can provide optimal feedback by referring to the surgeon's past surgical history during feedback and guidance. For example, the service provider can provide feedback based on the surgeon's past successful surgical techniques. For example, the service provider can point out areas for improvement in surgeries that the surgeon has failed in the past. For example, the service provider can customize feedback for specific techniques based on the surgeon's surgical history. This allows for the provision of more appropriate feedback by referring to the surgeon's past surgical history. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the surgeon's past surgical history data into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0087] The service provider can customize the content of instruction based on the surgeon's current skill level during feedback and guidance sessions. For example, if the surgeon is a beginner, the service provider will provide instruction on basic techniques. If the surgeon is an intermediate level, the service provider will provide instruction on advanced techniques. If the surgeon is an advanced level, the service provider will provide instruction on the latest surgical techniques. By customizing the instruction content based on the surgeon's current skill level, more effective instruction becomes possible. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the surgeon's skill level data into a generating AI and have the generating AI perform the customization of the instruction content.

[0088] The service provider can estimate the surgeon's emotions and prioritize feedback and guidance based on the estimated emotions. For example, if the surgeon is tense, the service provider will provide less important feedback first. If the surgeon is relaxed, the service provider will provide more important feedback first. If the surgeon is focused, the service provider will provide feedback directly related to the progress of the surgery first. This allows important information to be provided at the appropriate time by prioritizing feedback and guidance according to the surgeon's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input the surgeon's facial expression data into the generative AI and have the generative AI perform the estimation of the surgeon's emotions.

[0089] The service provider can provide optimal feedback and guidance by taking into account the surgeon's geographical location. For example, if the surgeon performs surgery in a specific region, the service provider can provide feedback relevant to that region. For example, if the surgeon performs surgery in a remote location, the service provider can provide feedback regarding the medical environment of that region. For example, if the surgeon performs surgery internationally, the service provider can provide feedback based on the medical guidelines of each country. This allows for the provision of more appropriate feedback by taking into account the surgeon's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the surgeon's geographical location into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0090] The service provider can analyze a surgeon's social media activity and suggest methods for providing feedback and guidance. For example, the service provider can provide feedback on surgical techniques that the surgeon has shown interest in on social media. For example, the service provider can provide feedback based on the opinions and research of experts that the surgeon follows. For example, the service provider can provide relevant feedback based on the content of online discussions that the surgeon has participated in. By analyzing the surgeon's social media activity, the service provider can suggest more appropriate methods for providing feedback. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the surgeon's social media activity data into a generating AI and have the generating AI suggest methods for providing feedback.

[0091] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0092] The data collection unit can estimate the surgeon's stress level by collecting audio data during surgery and performing audio analysis. For example, the unit can estimate the stress level by analyzing the surgeon's voice tone and speaking speed. If the surgeon is stressed, the unit can reduce the frequency of data collection and wait until the surgeon is relaxed. Conversely, if the surgeon is relaxed, the unit can increase the frequency of data collection to obtain more detailed data. This allows for more appropriate data collection by adjusting the timing of data collection according to the surgeon's stress level.

[0093] The generation unit can apply algorithms to predict the success rate of surgeries based on the surgeon's past surgical data. For example, the generation unit analyzes data from surgeries performed by the surgeon in the past and identifies procedures with high success rates. Based on the identified procedures, the generation unit can generate a virtual patient model and predict the success rate of the surgery. The generation unit can also apply algorithms to predict the risk of surgery based on the surgeon's past surgical data. This allows for the generation of more appropriate virtual patient models by predicting the success rate and risk of surgery based on the surgeon's past surgical data.

[0094] The analysis unit can estimate the surgeon's emotions and prioritize analyses based on those emotions. For example, if the surgeon is tense, the analysis unit can perform less important analyses first and wait until the surgeon relaxes. If the surgeon is relaxed, the analysis unit can perform more important analyses first. Also, if the surgeon is focused, the analysis unit can prioritize analyses directly related to the progress of the surgery. In this way, by prioritizing analyses according to the surgeon's emotions, important analyses can be performed at the appropriate time.

[0095] The system can estimate the surgeon's emotions and adjust the feedback format based on that estimation. For example, if the surgeon is tense, the system can provide feedback in a gentle tone. If the surgeon is relaxed, the system can provide detailed instructions. If the surgeon is focused, the system can point out specific areas for improvement. By adjusting the feedback format according to the surgeon's emotions, more effective instruction becomes possible.

[0096] The data collection unit can monitor the surgeon's vital signs in real time during surgery and issue alerts if abnormalities are detected. For example, the unit can monitor the surgeon's heart rate and blood pressure and issue alerts if abnormalities are detected. The unit can also monitor the surgeon's oxygen saturation and issue alerts if abnormalities are detected. Furthermore, the unit can monitor the surgeon's body temperature and issue alerts if abnormalities are detected. This allows for real-time monitoring of the surgeon's vital signs, enabling a rapid response if abnormalities are detected.

[0097] The generation unit can estimate the surgeon's emotions and adjust the method of generating the virtual patient model based on the estimated emotions. For example, if the surgeon is relaxed, the generation unit can generate a detailed virtual patient model. If the surgeon is in a hurry, the generation unit can generate a simplified virtual patient model. Furthermore, if the surgeon is agitated, the generation unit can generate a visually stimulating virtual patient model. By adjusting the method of generating the virtual patient model according to the surgeon's emotions, a more appropriate virtual patient model can be generated.

[0098] The analysis unit can analyze the surgeon's motion data during surgery and evaluate the efficiency of the surgery. For example, the analysis unit can analyze the surgeon's hand movements and instrument manipulation to evaluate the efficiency of the surgery. The analysis unit can also analyze the surgeon's eye movements to evaluate the efficiency of the surgery. Furthermore, based on the surgeon's motion data, the analysis unit can suggest improvements to enhance the efficiency of the surgery. In this way, by analyzing the surgeon's motion data during surgery, the efficiency of the surgery can be evaluated and improvements can be suggested.

[0099] The service provider can estimate the surgeon's emotions and prioritize feedback and guidance based on those estimates. For example, if the surgeon is tense, the service provider can provide less important feedback first. If the surgeon is relaxed, the service provider can provide more important feedback first. Also, if the surgeon is focused, the service provider can provide feedback directly related to the progress of the surgery first. By prioritizing feedback and guidance according to the surgeon's emotions, important information can be delivered at the right time.

[0100] The data collection unit can collect environmental data from surgeons during surgery and optimize the surgical environment. For example, it can monitor the temperature and humidity of the operating room to maintain an optimal environment. It can also monitor the lighting conditions of the operating room and provide optimal lighting. Furthermore, it can monitor the noise level of the operating room to maintain a quiet environment. By optimizing the surgical environment in this way, surgeons can improve their performance.

[0101] The system can estimate the surgeon's emotions and adjust its feedback and instruction methods based on those estimates. For example, if the surgeon is tense, the system can provide feedback in a gentle tone. If the surgeon is relaxed, the system can provide detailed instruction. Furthermore, if the surgeon is focused, the system can point out specific areas for improvement. By adjusting feedback and instruction methods according to the surgeon's emotions, more effective instruction becomes possible.

[0102] The following briefly describes the processing flow for example form 2.

[0103] Step 1: The data acquisition unit collects data on the surgeon's movements and actual patient data. For example, it collects the surgeon's hand movements, instrument manipulation, eye movements, patient medical history, image data, and biosignals. The data acquisition unit uses motion capture technology to collect the surgeon's hand movements, MRI and CT scans to collect patient image data, and sensors to collect patient biosignals. Step 2: The generation unit generates a virtual patient model based on the data collected by the acquisition unit. For example, a virtual patient model may be generated using a 3D model or a simulation algorithm, or a virtual patient model may be generated based on collected image data and biosignals. Step 3: The analysis unit analyzes the virtual patient model generated by the generation unit and performs a surgical simulation. For example, it performs a surgical simulation based on simulation software or scenarios. Step 4: The service provider provides feedback and guidance based on the analysis results obtained by the analysis provider. For example, they provide feedback and guidance based on evaluation criteria and the format of guidance (oral, written, video, etc.).

[0104] 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.

[0105] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0106] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] Each of the multiple elements described above, including the collection unit, generation unit, analysis unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects data on the surgeon's movements and the patient's movements using the camera 42 and sensors of the smart device 14. The generation unit generates a virtual patient model, for example, by the specific processing unit 290 of the data processing unit 12. The analysis unit performs a surgical simulation, for example, by the specific processing unit 290 of the data processing unit 12. The provision unit provides feedback and guidance, for example, by the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0108] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0109] 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.

[0110] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0111] 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.

[0112] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0113] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0114] 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.

[0115] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0116] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0117] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0118] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0120] 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.

[0121] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0122] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0123] Each of the multiple elements described above, including the collection unit, generation unit, analysis unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects data on the surgeon's movements and the patient's movements using the camera 42 and sensors of the smart glasses 214. The generation unit generates a virtual patient model, for example, using the specific processing unit 290 of the data processing unit 12. The analysis unit performs a surgical simulation, for example, using the specific processing unit 290 of the data processing unit 12. The provision unit provides feedback and guidance, for example, using the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0124] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0125] 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.

[0126] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0127] 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.

[0128] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0129] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0130] 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.

[0131] 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.

[0132] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0133] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0134] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0136] 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.

[0137] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0138] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0139] Each of the multiple elements described above, including the collection unit, generation unit, analysis unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects data on the surgeon's movements and the patient's movements using the camera 42 and sensors of the headset terminal 314. The generation unit generates a virtual patient model using the specific processing unit 290 of the data processing unit 12. The analysis unit performs a surgical simulation using the specific processing unit 290 of the data processing unit 12. The provision unit provides feedback and guidance using the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0140] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0143] 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.

[0144] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0145] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0146] 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.

[0147] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0148] 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.

[0149] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] 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.

[0154] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements described above, including the data collection unit, generation unit, analysis unit, and provision unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects data on the surgeon's movements and the patient's movements using the camera 42 and sensors of the robot 414. The generation unit generates a virtual patient model, for example, using the specific processing unit 290 of the data processing unit 12. The analysis unit performs a surgical simulation, for example, using the specific processing unit 290 of the data processing unit 12. The provision unit provides feedback and guidance, for example, using the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0157] 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.

[0158] Figure 9 shows the 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.

[0159] 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.

[0160] 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.

[0161] 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, and motorcycles, 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 based, for example, 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.

[0162] 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."

[0163] 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.

[0164] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0173] 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 other things 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.

[0174] 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.

[0175] (Note 1) A data collection unit that collects data on the surgeons' movements and actual patient data, A generation unit generates a virtual patient model based on the data collected by the aforementioned collection unit, An analysis unit analyzes the virtual patient model generated by the generation unit and performs surgical simulations, The system includes a providing unit that provides feedback and guidance based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned supply unit is, By analyzing the results of surgical simulations, we predict the prognosis and potential risks in actual surgery. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, We assess the skill level of surgeons and provide training programs optimized to their individual needs. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is We estimate the surgeon's emotions and adjust the timing of data collection based on the estimated emotions of the surgeon. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Analyze the surgeon's past surgical history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is During data collection, filtering is performed based on the surgeon's current surgical skills and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the emotions of surgeons and prioritizes the data to collect based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, the geographical location of surgeons is taken into consideration to prioritize the collection of highly relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, analyze the social media activity of surgeons and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is We estimate the surgeon's emotions and adjust the method of generating the virtual patient model based on the estimated surgeon's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is When generating a virtual patient model, adjust the level of detail of the model based on the patient's importance. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is When generating virtual patient models, different generation algorithms are applied depending on the patient category. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is The system estimates the surgeon's emotions and adjusts the generation order of the virtual patient model based on the estimated surgeon's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating virtual patient models, the generation priority is determined based on when the patient was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating virtual patient models, the generation order is adjusted based on patient relevance. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, We estimate the surgeon's emotions and adjust the analysis criteria based on the estimated emotions of the surgeon. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, consider the interrelationships between virtual patient models to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During the analysis, patient attribute information will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, The system estimates the surgeon's emotions and adjusts the display order of the analysis results based on the estimated emotions of the surgeon. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During the analysis, the geographical distribution of patients will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, During analysis, we refer to relevant patient literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, The system estimates the surgeon's emotions and adjusts feedback and guidance methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing feedback or guidance, refer to the surgeon's past surgical history to provide optimal feedback. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, During feedback and instruction, the content of the instruction will be customized based on the surgeon's current skill level. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, The system estimates the surgeon's emotions and determines the priority of feedback and guidance based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing feedback or guidance, consider the surgeon's geographical location to provide optimal feedback. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing feedback or guidance, we analyze the surgeon's social media activity and propose methods for providing feedback. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data on the surgeons' movements and actual patient data, A generation unit generates a virtual patient model based on the data collected by the aforementioned collection unit, An analysis unit analyzes the virtual patient model generated by the generation unit and performs surgical simulations, The system includes a providing unit that provides feedback and guidance based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned supply unit is, By analyzing the results of surgical simulations, we predict the prognosis and potential risks in actual surgery. The system according to feature 1.

3. The aforementioned supply unit is, We assess the skill level of surgeons and provide training programs optimized to their individual needs. The system according to feature 1.

4. The aforementioned collection unit is We estimate the surgeon's emotions and adjust the timing of data collection based on the estimated emotions of the surgeon. The system according to feature 1.

5. The aforementioned collection unit is Analyze the surgeon's past surgical history and select the optimal data collection method. The system according to feature 1.

6. The aforementioned collection unit is During data collection, filtering is performed based on the surgeon's current surgical skills and areas of interest. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the emotions of surgeons and prioritizes the data to collect based on these estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is During data collection, the geographical location of surgeons is taken into consideration to prioritize the collection of highly relevant data. The system according to feature 1.

9. The aforementioned collection unit is During data collection, analyze the social media activity of surgeons and collect relevant data. The system according to feature 1.

10. The generating unit is We estimate the surgeon's emotions and adjust the method of generating the virtual patient model based on the estimated surgeon's emotions. The system according to feature 1.

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