system

Abstract

The system according to this embodiment aims to improve surgical accuracy, enhance diagnostic precision, and provide personalized, adaptive care. [Solution] The system according to the embodiment comprises a surgical suite unit, a diagnostic engine unit, a treatment planner unit, a predictive analysis unit, and a continuous learning unit. The surgical suite unit improves the accuracy of surgery. The diagnostic engine unit performs a diagnosis based on the data obtained by the surgical suite unit. The treatment planner unit generates an individualized treatment plan based on the diagnostic results obtained by the diagnostic engine unit. The predictive analysis unit predicts the patient's outcome based on the treatment plan generated by the treatment planner unit. The continuous learning unit evolves the system based on the results obtained by the predictive analysis unit.

Description

Technical Field

[0001] The technology provided by this 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, an integrated system for improving the accuracy of surgery and the accuracy of diagnosis has not been sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to improve the accuracy of surgery, enhance the accuracy of diagnosis, and provide individualized adaptive care.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a surgical suite unit, a diagnostic engine unit, a treatment planner unit, a predictive analysis unit, and a continuous learning unit. The surgical suite unit improves the accuracy of surgery. The diagnostic engine unit performs a diagnosis based on the data obtained by the surgical suite unit. The treatment planner unit generates an individualized treatment plan based on the diagnostic results obtained by the diagnostic engine unit. The predictive analysis unit predicts the patient's outcome based on the treatment plan generated by the treatment planner unit. The continuous learning unit evolves the system based on the results obtained by the predictive analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can improve the accuracy of surgery, enhance the precision of diagnosis, and provide personalized, adaptive care. [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 applicable 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 12 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 AIHOS system, according to an embodiment of the present invention, is an integrated platform for transforming healthcare delivery through the use of AI. The AIHOS system seamlessly integrates AI algorithms, robotics, and data analytics to improve surgical accuracy, enhance diagnostic precision, and provide personalized, adaptive care. This system optimizes resources and reduces costs while addressing critical healthcare challenges. For example, the AIHOS system features an AI-enhanced surgical suite, combining robotics and real-time AI guidance to achieve superior surgical outcomes. It also includes a comprehensive diagnostic engine, integrating multimodal data for rapid and accurate diagnoses. Furthermore, the AIHOS system includes an adaptive treatment planner, generating and adjusting personalized care plans based on patient responses. The AIHOS system performs predictive healthcare analytics, forecasting patient outcomes and optimizing resource allocation. The AIHOS system incorporates a continuous learning system, evolving through continuous analysis of clinical data and outcomes. Unlike fragmented medical AI solutions, the AIHOS system provides an end-to-end integrated platform covering the entire patient care process. Its ability to learn and adapt in real time sets it apart from static systems, ensuring continuous improvement in the quality and efficiency of care. The goal of the AIHOS system is to integrate cutting-edge AI across healthcare to save millions of lives, drastically reduce healthcare costs, and usher in a new era of personalized, efficient, and high-quality medicine. This enables the AIHOS system to improve surgical precision, enhance diagnostic accuracy, and deliver personalized, adaptive care.

[0029] The AIHOS system according to this embodiment comprises a surgical suite unit, a diagnostic engine unit, a treatment planner unit, a predictive analysis unit, and a continuous learning unit. The surgical suite unit improves the accuracy of surgery. The surgical suite unit includes, for example, a robotics control unit to improve accuracy during surgery. The surgical suite unit also includes a real-time guidance unit that can provide guidance during surgery. The diagnostic engine unit performs a diagnosis based on the data obtained by the surgical suite unit. The diagnostic engine unit integrates multimodal data to perform a rapid and accurate diagnosis. The treatment planner unit generates an individualized treatment plan based on the diagnostic results obtained by the diagnostic engine unit. The treatment planner unit adjusts the treatment plan based on, for example, the patient's response. The predictive analysis unit predicts the patient's outcome based on the treatment plan generated by the treatment planner unit. The predictive analysis unit optimizes resource allocation, for example. The continuous learning unit evolves the system based on the results obtained by the predictive analysis unit. The continuous learning unit performs, for example, continuous analysis of clinical data and results. As a result, the AIHOS system according to this embodiment enables improved surgical precision, enhanced diagnostic accuracy, and the provision of personalized, adaptive care.

[0030] The surgical suite is designed to improve surgical precision. Specifically, it includes a robotics control unit that enhances accuracy during surgery. The robotics control unit combines advanced sensors and actuators to precisely control minute movements. This allows surgeons to perform extremely precise operations during surgery, improving the success rate of the surgery. The surgical suite also includes a real-time guidance unit that provides guidance during surgery. The real-time guidance unit analyzes video and sensor data acquired during surgery and presents the surgeon with optimal operating procedures and points to note in real time. For example, it can accurately identify the location of blood vessels and nerves during surgery, reducing the risk of accidental damage. Furthermore, the surgical suite also has the function of collecting data during surgery in real time and transmitting it to the diagnostic engine unit. This allows for constant monitoring of the situation during surgery and rapid response as needed. By integrating these functions, the surgical suite can significantly improve the precision and safety of surgery.

[0031] The diagnostic engine unit performs diagnoses based on data obtained by the surgical suite unit. Specifically, it integrates multimodal data to perform rapid and accurate diagnoses. Multimodal data refers to multiple different types of data, such as video data, sensor data, and patient biometric information. The diagnostic engine unit integrates and analyzes this data to comprehensively evaluate the patient's condition. The AI-based analysis algorithm can quickly detect abnormal patterns and risk factors based on past data and statistical information. For example, it can identify the location and size of tumors from video data acquired during surgery and monitor the patient's vital signs from sensor data to evaluate the progress of the surgery in real time. Furthermore, the diagnostic engine unit can perform more accurate diagnoses by referring to past diagnostic results and treatment history and comparing them with the current patient condition. As a result, the diagnostic engine unit can rapidly and accurately analyze data during surgery and provide appropriate diagnostic information to surgeons.

[0032] The treatment planner unit generates personalized treatment plans based on the diagnostic results obtained by the diagnostic engine unit. Specifically, it has the function of adjusting the treatment plan based on the patient's response. The treatment planner unit uses AI to analyze the patient's individual condition and response and propose the optimal treatment method. For example, it can monitor the recovery status after surgery and the occurrence of side effects, and modify the treatment plan as needed. The treatment planner unit selects the optimal medication and adjusts the dosage based on the patient's biometric information and past treatment history. In addition, the treatment planner unit can simulate multiple treatment options and select the most effective treatment method. This allows the treatment planner unit to provide the optimal treatment plan for each individual patient and maximize treatment effectiveness. Furthermore, the treatment planner unit can monitor the progress of treatment in real time and take rapid action as needed. This allows the treatment planner unit to provide personalized, adaptive care and improve the patient's treatment effectiveness.

[0033] The Predictive Analytics Department predicts patient outcomes based on treatment plans generated by the Treatment Planner Department. Specifically, it has the function of optimizing resource allocation. The Predictive Analytics Department uses AI to simulate the effectiveness of treatment plans and predict the patient's recovery status and treatment effectiveness. For example, based on the treatment plan, it can predict how long it will take for a patient to recover and what the risk of side effects is. Based on these prediction results, the Predictive Analytics Department makes optimal allocations of medical resources. For example, it optimizes operating room schedules and the allocation of medical staff to achieve efficient medical care delivery. In addition, the Predictive Analytics Department can also perform long-term risk assessments and trend analyses using historical data and statistical information. This allows the Predictive Analytics Department to predict the effectiveness of treatment plans with high accuracy and support the optimal allocation of medical resources. Furthermore, the Predictive Analytics Department can continuously revise prediction results based on real-time updated data and respond to the latest situations. This allows the Predictive Analytics Department to always provide highly accurate predictions based on the latest information and support quick and appropriate responses.

[0034] The Continuous Learning Unit evolves the system based on the results obtained by the Predictive Analytics Unit. Specifically, it performs continuous analysis of clinical data and results. The Continuous Learning Unit uses AI to learn from newly acquired data and improve the accuracy and performance of the system. For example, by collecting surgical and treatment result data and updating the AI ​​model based on this data, the accuracy of diagnoses and treatment plans can be improved. The Continuous Learning Unit integrates and analyzes historical data and newly acquired data to optimize the overall system performance. In addition, the Continuous Learning Unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the Continuous Learning Unit to improve the reliability and safety of the system. Furthermore, the Continuous Learning Unit can collect feedback from users and use it to improve the system. For example, it can improve the functions of the surgical suite and diagnostic engine based on feedback from surgeons and medical staff. This allows the Continuous Learning Unit to continuously improve the overall performance of the system and realize more accurate and reliable medical care.

[0035] The surgical suite includes a robotics control unit. The surgical suite, for example, uses the robotics control unit to improve the accuracy and safety of surgery. The robotics control unit precisely controls the robot's movements using control algorithms, for example. Furthermore, the robotics control unit can monitor the situation during surgery in real time using sensors and perform appropriate control. In addition, the robotics control unit can collect data during surgery and adjust the control algorithm according to the progress of the surgery. Thus, by including a robotics control unit, the surgical suite improves the accuracy and safety of surgery.

[0036] The surgical suite includes a real-time guidance unit. The surgical suite provides guidance during surgery, for example, using the real-time guidance unit. The real-time guidance unit visually displays the situation during surgery, for example, using image guidance. The real-time guidance unit can also provide instructions during surgery using voice guidance. Furthermore, the real-time guidance unit can analyze data during surgery in real time and provide appropriate guidance. Thus, by including the real-time guidance unit, the surgical suite enables real-time guidance during surgery.

[0037] The diagnostic engine integrates multimodal data. For example, the diagnostic engine integrates multimodal data such as image data, text data, and audio data to improve diagnostic accuracy. For instance, the diagnostic engine analyzes image data to detect lesions. It can also analyze text data to understand the patient's medical history and symptoms. Furthermore, it can analyze audio data to detect changes in the patient's voice. As a result, the diagnostic engine improves diagnostic accuracy by integrating multimodal data.

[0038] The treatment planning department adjusts treatment plans based on the patient's responses. For example, the treatment planning department monitors the patient's physiological responses and adjusts treatment plans. For example, the treatment planning department monitors the patient's heart rate and blood pressure and adjusts treatment plans. The treatment planning department can also monitor the patient's behavioral responses and adjust treatment plans. Furthermore, the treatment planning department can adjust treatment plans based on patient feedback. In this way, the treatment planning department provides individualized treatment plans by adjusting treatment plans based on the patient's responses.

[0039] The predictive analytics unit optimizes resource allocation. For example, it optimizes the placement of medical staff. For example, it adjusts the placement of medical staff based on the patient's condition. The predictive analytics unit can also optimize the use of medical equipment. For example, it adjusts the use of medical equipment based on the surgery schedule. Furthermore, the predictive analytics unit can optimize the allocation of medical resources. For example, it adjusts the allocation of medical resources based on patient demand. In this way, the predictive analytics unit enables efficient resource allocation by optimizing resource allocation.

[0040] The Continuing Learning Unit performs continuous analysis of clinical data and results. For example, the Continuing Learning Unit collects and continuously analyzes clinical data such as patient medical history and test results. For example, the Continuing Learning Unit analyzes patient medical history to evaluate treatment effectiveness. Furthermore, the Continuing Learning Unit can analyze test results to assess the progress of treatment. In addition, the Continuing Learning Unit can analyze treatment outcomes to improve the accuracy and effectiveness of the system. Thus, by continuously analyzing clinical data and results, the accuracy and effectiveness of the system are continuously improved by the Continuing Learning Unit.

[0041] The surgical suite analyzes data acquired during surgery in real time and optimizes robotic movements according to the progress of the surgery. For example, the surgical suite can analyze image data acquired during surgery in real time and fine-tune robotic movements. For example, the surgical suite can analyze biological data (heart rate, blood pressure, etc.) during surgery in real time and adjust robotic movements. For example, the surgical suite can analyze environmental data (temperature, humidity, etc.) during surgery in real time and optimize robotic movements. This enables the surgical suite to provide optimal robotic control according to the progress of the surgery.

[0042] The surgical suite applies customizable robotics control algorithms tailored to different surgical techniques. For example, it can apply a robotics control algorithm specifically designed for laparoscopic surgery to improve surgical accuracy. It can also apply a robotics control algorithm specifically designed for cardiac surgery to ensure surgical safety. Furthermore, it can apply a robotics control algorithm specifically designed for neurosurgery to increase the success rate of surgery. This enables the surgical suite to provide optimal robotics control tailored to each surgical technique.

[0043] The surgical suite adjusts robotic operation based on environmental data during surgery. For example, if the operating room temperature is high, the surgical suite can slow down robotic operation to prevent equipment overheating. For example, if the operating room humidity is low, the surgical suite can adjust robotic operation to prevent static electricity generation. The surgical suite can also monitor operating room environmental data in real time to maintain optimal robotic operation. This enables the surgical suite to provide optimal robotic control according to the environment during surgery.

[0044] The surgical suite analyzes the movements of medical staff during surgery and optimizes robotic movements. For example, the surgical suite analyzes the movements of medical staff in real time and adjusts robotic movements accordingly. For example, the surgical suite can optimize robotic movements in response to the movements of medical staff, thereby improving surgical efficiency. The surgical suite can also predict the movements of medical staff and pre-adjust robotic movements. This enables the surgical suite to provide optimal robotic control in response to the movements of medical staff.

[0045] The diagnostic engine unit improves diagnostic accuracy by integrating data from different diagnostic modalities. For example, the diagnostic engine unit can integrate CT scan and MRI data to perform a more accurate diagnosis. For example, the diagnostic engine unit can integrate X-ray and ultrasound data to improve diagnostic accuracy. For example, the diagnostic engine unit can integrate data from different diagnostic modalities in real time to perform a rapid diagnosis. In this way, the diagnostic engine unit improves diagnostic accuracy by integrating data from different diagnostic modalities.

[0046] The diagnostic engine optimizes the diagnostic algorithm by referring to past diagnostic data. For example, the diagnostic engine analyzes past diagnostic data to optimize the diagnostic algorithm. For example, the diagnostic engine can improve the accuracy of the diagnostic algorithm based on past diagnostic data. The diagnostic engine can also adjust the diagnostic algorithm in real time by referring to past diagnostic data. As a result, the diagnostic engine improves the accuracy of the diagnostic algorithm by optimizing it based on past diagnostic data.

[0047] The diagnostic engine adjusts the diagnostic results by considering the patient's lifestyle data. For example, the diagnostic engine can adjust the diagnostic results by considering the patient's dietary data. For example, the diagnostic engine can adjust the diagnostic results by considering the patient's exercise habits data. For example, the diagnostic engine can also adjust the diagnostic results by considering the patient's sleep pattern data. As a result, the diagnostic engine provides diagnostic results that are tailored to the patient's lifestyle.

[0048] The diagnostic engine unit will add a function to provide real-time feedback of diagnostic results to medical staff. For example, the diagnostic engine unit can notify medical staff of diagnostic results in real time, enabling a rapid response. For example, the diagnostic engine unit can display diagnostic results on medical staff's devices in real time, providing immediate feedback. For example, the diagnostic engine unit can share diagnostic results with medical staff in real time, facilitating a rapid response across the entire team. This enables the diagnostic engine unit to provide rapid feedback of diagnostic results.

[0049] The treatment planner unit generates an optimal treatment plan by referring to the patient's past treatment history. For example, the treatment planner unit analyzes the patient's past treatment history to generate an optimal treatment plan. For example, the treatment planner unit can customize the treatment plan based on the patient's past treatment history. For example, the treatment planner unit can also adjust the treatment plan in real time by referring to the patient's past treatment history. This ensures that the treatment planner unit provides an optimal treatment plan based on the patient's past treatment history.

[0050] The treatment planner unit applies customizable treatment plan generation algorithms tailored to different treatment methods. For example, the treatment planner unit can apply a treatment plan generation algorithm specialized for chemotherapy to provide the optimal treatment plan. For example, the treatment planner unit can apply a treatment plan generation algorithm specialized for radiotherapy to improve the accuracy of treatment. For example, the treatment planner unit can apply a treatment plan generation algorithm specialized for surgery to increase the success rate of treatment. In this way, the treatment planner unit provides the optimal treatment plan according to the treatment method.

[0051] The treatment planning department adjusts treatment plans by considering the patient's living environment data. For example, the treatment planning department can adjust treatment plans by considering the patient's home environment data. For example, the treatment planning department can adjust treatment plans by considering the patient's work environment data. For example, the treatment planning department can also adjust treatment plans by considering the patient's lifestyle data. In this way, the treatment planning department provides treatment plans that are tailored to the patient's living environment.

[0052] The treatment planning department monitors the progress of treatment plans in real time and modifies them as needed. For example, the treatment planning department can notify medical staff of the progress of treatment plans in real time, enabling a quick response. The treatment planning department can also provide real-time feedback to patients on the progress of treatment plans, allowing them to confirm the effectiveness of the treatment. This enables the treatment planning department to provide optimal treatment tailored to the progress of the treatment plan.

[0053] The predictive analytics unit optimizes the prediction algorithm by referring to past patient data. For example, the predictive analytics unit analyzes past patient data to optimize the prediction algorithm. For example, the predictive analytics unit can improve the accuracy of the prediction algorithm based on past patient data. The predictive analytics unit can also adjust the prediction algorithm in real time by referring to past patient data. This allows the predictive analytics unit to improve the accuracy of the prediction algorithm by optimizing it with reference to past patient data.

[0054] The predictive analytics unit improves prediction accuracy by combining different prediction models. For example, it can improve prediction accuracy by combining machine learning models and statistical models. For example, it can improve prediction accuracy by combining different machine learning algorithms. For example, it can also improve prediction accuracy by integrating information from different data sources. Thus, the predictive analytics unit improves prediction accuracy by combining different prediction models.

[0055] The predictive analytics unit adjusts the prediction results by considering the patient's lifestyle data. For example, the predictive analytics unit can adjust the prediction results by considering the patient's dietary data. For example, the predictive analytics unit can adjust the prediction results by considering the patient's exercise habits data. For example, the predictive analytics unit can also adjust the prediction results by considering the patient's sleep pattern data. As a result, the predictive analytics unit provides prediction results that are tailored to the patient's lifestyle.

[0056] The predictive analytics unit will add a function to provide real-time feedback of prediction results to medical staff. For example, the predictive analytics unit can notify medical staff of prediction results in real time, enabling a rapid response. For example, the predictive analytics unit can display prediction results on medical staff's devices in real time, providing immediate feedback. For example, the predictive analytics unit can share prediction results with medical staff in real time, facilitating a rapid response across the entire team. This enables the predictive analytics unit to provide rapid feedback of prediction results.

[0057] The continuous learning unit optimizes the learning algorithm by referring to past training data. For example, the continuous learning unit analyzes past training data to optimize the learning algorithm. For example, the continuous learning unit can improve the accuracy of the learning algorithm based on past training data. For example, the continuous learning unit can also adjust the learning algorithm in real time by referring to past training data. As a result, the accuracy of the learning algorithm is improved by the continuous learning unit optimizing the learning algorithm by referring to past training data.

[0058] The continuous learning unit improves learning accuracy by combining different learning models. For example, the continuous learning unit improves learning accuracy by combining machine learning models and statistical models. For example, the continuous learning unit can improve learning accuracy by combining different machine learning algorithms. For example, the continuous learning unit can improve learning accuracy by integrating information from different data sources. In this way, the continuous learning unit improves learning accuracy by combining different learning models.

[0059] The continuous learning unit adjusts the learning data considering the patient's lifestyle data. For example, the continuous learning unit can adjust the learning data considering the patient's dietary data. For example, the continuous learning unit can adjust the learning data considering the patient's exercise habits data. For example, the continuous learning unit can also adjust the learning data considering the patient's sleep pattern data. As a result, the continuous learning unit provides learning data tailored to the patient's lifestyle.

[0060] The Continuing Education Unit will add a function to provide real-time feedback on learning results to medical staff. For example, the Continuing Education Unit can notify medical staff of learning results in real time, enabling a rapid response. For example, the Continuing Education Unit can display learning results on medical staff's devices in real time, providing immediate feedback. For example, the Continuing Education Unit can share learning results with medical staff in real time, facilitating a rapid response across the entire team. This enables the Continuing Education Unit to provide rapid feedback on learning results.

[0061] The continuous learning unit optimizes the overall system performance based on the learning results. For example, the continuous learning unit can optimize the overall system performance based on the learning results. For example, the continuous learning unit can analyze the learning results in real time and improve system performance. For example, the continuous learning unit can automatically adjust system settings based on the learning results to maintain optimal performance. In this way, the continuous learning unit optimizes the overall system performance based on the learning results, thereby optimizing the overall system performance.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The AIHOS system may also include a lifestyle adaptation unit that adjusts the diagnostic results by considering the patient's lifestyle data. For example, the lifestyle adaptation unit can adjust the diagnostic results by considering the patient's dietary data. It can also adjust the diagnostic results by considering the patient's exercise data. Furthermore, it can adjust the diagnostic results by considering the patient's sleep pattern data. This allows the lifestyle adaptation unit to provide diagnostic results tailored to the patient's lifestyle.

[0064] The AIHOS system can also include a modality integration unit that integrates data from different diagnostic modalities to improve diagnostic accuracy. For example, the modality integration unit can integrate CT scan and MRI data for a more accurate diagnosis. It can also integrate X-ray and ultrasound data to improve diagnostic accuracy. Furthermore, it can integrate data from different diagnostic modalities in real time for rapid diagnosis. Thus, the modality integration unit improves diagnostic accuracy by integrating data from different diagnostic modalities.

[0065] The AIHOS system can also include a treatment history reference unit that generates an optimal treatment plan by referring to the patient's past treatment history. For example, the treatment history reference unit analyzes the patient's past treatment history and generates an optimal treatment plan. It can also customize the treatment plan based on the patient's past treatment history. Furthermore, it can adjust the treatment plan in real time by referring to the patient's past treatment history. This allows the treatment history reference unit to provide an optimal treatment plan based on past treatment history.

[0066] The AIHOS system can also include a prediction model integration unit that combines different prediction models to improve prediction accuracy. For example, the prediction model integration unit can combine machine learning models and statistical models to improve prediction accuracy. It can also combine different machine learning algorithms to improve prediction accuracy. Furthermore, it can integrate information from different data sources to improve prediction accuracy. Thus, the prediction model integration unit improves prediction accuracy by combining different prediction models.

[0067] The AIHOS system can further include a living environment adaptation unit that adjusts the treatment plan by considering the patient's living environment data. For example, the living environment adaptation unit can adjust the treatment plan by considering the patient's home environment data. It can also adjust the treatment plan by considering the patient's work environment data. Furthermore, it can adjust the treatment plan by considering the patient's lifestyle data. This allows the living environment adaptation unit to provide a treatment plan tailored to the patient's living environment.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The surgical suite improves surgical precision. The surgical suite includes a robotics control unit to enhance precision during surgery. It also includes a real-time guidance unit to provide guidance during surgery. Step 2: The diagnostic engine unit performs a diagnosis based on the data obtained by the surgical suite unit. The diagnostic engine unit integrates multimodal data to perform a rapid and accurate diagnosis. Step 3: The treatment planner unit generates an individualized treatment plan based on the diagnostic results obtained by the diagnostic engine unit. The treatment planner unit adjusts the treatment plan based on the patient's response. Step 4: The predictive analytics unit predicts patient outcomes based on the treatment plan generated by the treatment planner unit. The predictive analytics unit optimizes resource allocation. Step 5: The Continuing Learning Unit evolves the system based on the results obtained by the Predictive Analytics Unit. The Continuing Learning Unit performs continuous analysis of clinical data and results.

[0070] (Example of form 2) The AIHOS system, according to an embodiment of the present invention, is an integrated platform for transforming healthcare delivery through the use of AI. The AIHOS system seamlessly integrates AI algorithms, robotics, and data analytics to improve surgical accuracy, enhance diagnostic precision, and provide personalized, adaptive care. This system optimizes resources and reduces costs while addressing critical healthcare challenges. For example, the AIHOS system features an AI-enhanced surgical suite, combining robotics and real-time AI guidance to achieve superior surgical outcomes. It also includes a comprehensive diagnostic engine, integrating multimodal data for rapid and accurate diagnoses. Furthermore, the AIHOS system includes an adaptive treatment planner, generating and adjusting personalized care plans based on patient responses. The AIHOS system performs predictive healthcare analytics, forecasting patient outcomes and optimizing resource allocation. The AIHOS system incorporates a continuous learning system, evolving through continuous analysis of clinical data and outcomes. Unlike fragmented medical AI solutions, the AIHOS system provides an end-to-end integrated platform covering the entire patient care process. Its ability to learn and adapt in real time sets it apart from static systems, ensuring continuous improvement in the quality and efficiency of care. The goal of the AIHOS system is to integrate cutting-edge AI across healthcare to save millions of lives, drastically reduce healthcare costs, and usher in a new era of personalized, efficient, and high-quality medicine. This enables the AIHOS system to improve surgical precision, enhance diagnostic accuracy, and deliver personalized, adaptive care.

[0071] The AIHOS system according to this embodiment comprises a surgical suite unit, a diagnostic engine unit, a treatment planner unit, a predictive analysis unit, and a continuous learning unit. The surgical suite unit improves the accuracy of surgery. The surgical suite unit includes, for example, a robotics control unit to improve accuracy during surgery. The surgical suite unit also includes a real-time guidance unit that can provide guidance during surgery. The diagnostic engine unit performs a diagnosis based on the data obtained by the surgical suite unit. The diagnostic engine unit integrates multimodal data to perform a rapid and accurate diagnosis. The treatment planner unit generates an individualized treatment plan based on the diagnostic results obtained by the diagnostic engine unit. The treatment planner unit adjusts the treatment plan based on, for example, the patient's response. The predictive analysis unit predicts the patient's outcome based on the treatment plan generated by the treatment planner unit. The predictive analysis unit optimizes resource allocation, for example. The continuous learning unit evolves the system based on the results obtained by the predictive analysis unit. The continuous learning unit performs, for example, continuous analysis of clinical data and results. As a result, the AIHOS system according to this embodiment enables improved surgical precision, enhanced diagnostic accuracy, and the provision of personalized, adaptive care.

[0072] The surgical suite is designed to improve surgical precision. Specifically, it includes a robotics control unit that enhances accuracy during surgery. The robotics control unit combines advanced sensors and actuators to precisely control minute movements. This allows surgeons to perform extremely precise operations during surgery, improving the success rate of the surgery. The surgical suite also includes a real-time guidance unit that provides guidance during surgery. The real-time guidance unit analyzes video and sensor data acquired during surgery and presents the surgeon with optimal operating procedures and points to note in real time. For example, it can accurately identify the location of blood vessels and nerves during surgery, reducing the risk of accidental damage. Furthermore, the surgical suite also has the function of collecting data during surgery in real time and transmitting it to the diagnostic engine unit. This allows for constant monitoring of the situation during surgery and rapid response as needed. By integrating these functions, the surgical suite can significantly improve the precision and safety of surgery.

[0073] The diagnostic engine unit performs diagnoses based on data obtained by the surgical suite unit. Specifically, it integrates multimodal data to perform rapid and accurate diagnoses. Multimodal data refers to multiple different types of data, such as video data, sensor data, and patient biometric information. The diagnostic engine unit integrates and analyzes this data to comprehensively evaluate the patient's condition. The AI-based analysis algorithm can quickly detect abnormal patterns and risk factors based on past data and statistical information. For example, it can identify the location and size of tumors from video data acquired during surgery and monitor the patient's vital signs from sensor data to evaluate the progress of the surgery in real time. Furthermore, the diagnostic engine unit can perform more accurate diagnoses by referring to past diagnostic results and treatment history and comparing them with the current patient condition. As a result, the diagnostic engine unit can rapidly and accurately analyze data during surgery and provide appropriate diagnostic information to surgeons.

[0074] The treatment planner unit generates personalized treatment plans based on the diagnostic results obtained by the diagnostic engine unit. Specifically, it has the function of adjusting the treatment plan based on the patient's response. The treatment planner unit uses AI to analyze the patient's individual condition and response and propose the optimal treatment method. For example, it can monitor the recovery status after surgery and the occurrence of side effects, and modify the treatment plan as needed. The treatment planner unit selects the optimal medication and adjusts the dosage based on the patient's biometric information and past treatment history. In addition, the treatment planner unit can simulate multiple treatment options and select the most effective treatment method. This allows the treatment planner unit to provide the optimal treatment plan for each individual patient and maximize treatment effectiveness. Furthermore, the treatment planner unit can monitor the progress of treatment in real time and take rapid action as needed. This allows the treatment planner unit to provide personalized, adaptive care and improve the patient's treatment effectiveness.

[0075] The Predictive Analytics Department predicts patient outcomes based on treatment plans generated by the Treatment Planner Department. Specifically, it has the function of optimizing resource allocation. The Predictive Analytics Department uses AI to simulate the effectiveness of treatment plans and predict the patient's recovery status and treatment effectiveness. For example, based on the treatment plan, it can predict how long it will take for a patient to recover and what the risk of side effects is. Based on these prediction results, the Predictive Analytics Department makes optimal allocations of medical resources. For example, it optimizes operating room schedules and the allocation of medical staff to achieve efficient medical care delivery. In addition, the Predictive Analytics Department can also perform long-term risk assessments and trend analyses using historical data and statistical information. This allows the Predictive Analytics Department to predict the effectiveness of treatment plans with high accuracy and support the optimal allocation of medical resources. Furthermore, the Predictive Analytics Department can continuously revise prediction results based on real-time updated data and respond to the latest situations. This allows the Predictive Analytics Department to always provide highly accurate predictions based on the latest information and support quick and appropriate responses.

[0076] The Continuous Learning Unit evolves the system based on the results obtained by the Predictive Analytics Unit. Specifically, it performs continuous analysis of clinical data and results. The Continuous Learning Unit uses AI to learn from newly acquired data and improve the accuracy and performance of the system. For example, by collecting surgical and treatment result data and updating the AI ​​model based on this data, the accuracy of diagnoses and treatment plans can be improved. The Continuous Learning Unit integrates and analyzes historical data and newly acquired data to optimize the overall system performance. In addition, the Continuous Learning Unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the Continuous Learning Unit to improve the reliability and safety of the system. Furthermore, the Continuous Learning Unit can collect feedback from users and use it to improve the system. For example, it can improve the functions of the surgical suite and diagnostic engine based on feedback from surgeons and medical staff. This allows the Continuous Learning Unit to continuously improve the overall performance of the system and realize more accurate and reliable medical care.

[0077] The surgical suite includes a robotics control unit. The surgical suite, for example, uses the robotics control unit to improve the accuracy and safety of surgery. The robotics control unit precisely controls the robot's movements using control algorithms, for example. Furthermore, the robotics control unit can monitor the situation during surgery in real time using sensors and perform appropriate control. In addition, the robotics control unit can collect data during surgery and adjust the control algorithm according to the progress of the surgery. Thus, by including a robotics control unit, the surgical suite improves the accuracy and safety of surgery.

[0078] The surgical suite includes a real-time guidance unit. The surgical suite provides guidance during surgery, for example, using the real-time guidance unit. The real-time guidance unit visually displays the situation during surgery, for example, using image guidance. The real-time guidance unit can also provide instructions during surgery using voice guidance. Furthermore, the real-time guidance unit can analyze data during surgery in real time and provide appropriate guidance. Thus, by including the real-time guidance unit, the surgical suite enables real-time guidance during surgery.

[0079] The diagnostic engine integrates multimodal data. For example, the diagnostic engine integrates multimodal data such as image data, text data, and audio data to improve diagnostic accuracy. For instance, the diagnostic engine analyzes image data to detect lesions. It can also analyze text data to understand the patient's medical history and symptoms. Furthermore, it can analyze audio data to detect changes in the patient's voice. As a result, the diagnostic engine improves diagnostic accuracy by integrating multimodal data.

[0080] The treatment planning department adjusts treatment plans based on the patient's responses. For example, the treatment planning department monitors the patient's physiological responses and adjusts treatment plans. For example, the treatment planning department monitors the patient's heart rate and blood pressure and adjusts treatment plans. The treatment planning department can also monitor the patient's behavioral responses and adjust treatment plans. Furthermore, the treatment planning department can adjust treatment plans based on patient feedback. In this way, the treatment planning department provides individualized treatment plans by adjusting treatment plans based on the patient's responses.

[0081] The predictive analytics unit optimizes resource allocation. For example, it optimizes the placement of medical staff. For example, it adjusts the placement of medical staff based on the patient's condition. The predictive analytics unit can also optimize the use of medical equipment. For example, it adjusts the use of medical equipment based on the surgery schedule. Furthermore, the predictive analytics unit can optimize the allocation of medical resources. For example, it adjusts the allocation of medical resources based on patient demand. In this way, the predictive analytics unit enables efficient resource allocation by optimizing resource allocation.

[0082] The Continuing Learning Unit performs continuous analysis of clinical data and results. For example, the Continuing Learning Unit collects and continuously analyzes clinical data such as patient medical history and test results. For example, the Continuing Learning Unit analyzes patient medical history to evaluate treatment effectiveness. Furthermore, the Continuing Learning Unit can analyze test results to assess the progress of treatment. In addition, the Continuing Learning Unit can analyze treatment outcomes to improve the accuracy and effectiveness of the system. Thus, by continuously analyzing clinical data and results, the accuracy and effectiveness of the system are continuously improved by the Continuing Learning Unit.

[0083] The surgical suite estimates the patient's emotions and adjusts robotic control during surgery based on the estimated emotions. For example, if the patient is anxious, the surgical suite will slow down the robotic movements and proceed cautiously with the surgery. For example, if the patient is relaxed, the surgical suite will operate the robotic movements normally and proceed smoothly with the surgery. For example, if the patient is agitated, the surgical suite can temporarily stop the robotic movements and wait until the patient's condition stabilizes. This allows the surgical suite to proceed with surgery in accordance with the patient's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The surgical suite analyzes data acquired during surgery in real time and optimizes robotic movements according to the progress of the surgery. For example, the surgical suite can analyze image data acquired during surgery in real time and fine-tune robotic movements. For example, the surgical suite can analyze biological data (heart rate, blood pressure, etc.) during surgery in real time and adjust robotic movements. For example, the surgical suite can analyze environmental data (temperature, humidity, etc.) during surgery in real time and optimize robotic movements. This enables the surgical suite to provide optimal robotic control according to the progress of the surgery.

[0085] The surgical suite applies customizable robotics control algorithms tailored to different surgical techniques. For example, it can apply a robotics control algorithm specifically designed for laparoscopic surgery to improve surgical accuracy. It can also apply a robotics control algorithm specifically designed for cardiac surgery to ensure surgical safety. Furthermore, it can apply a robotics control algorithm specifically designed for neurosurgery to increase the success rate of surgery. This enables the surgical suite to provide optimal robotics control tailored to each surgical technique.

[0086] The surgical suite estimates the patient's emotions and adjusts the guidance display during surgery based on the estimated emotions. For example, if the patient is anxious, the surgical suite simplifies the guidance display to reduce visual stress. For example, if the patient is relaxed, the surgical suite can provide detailed guidance displays to clarify the progress of the surgery. For example, if the patient is agitated, the surgical suite can temporarily stop the guidance display and wait until the patient's condition stabilizes. This allows the surgical suite to provide guidance displays that are appropriate to the patient's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The surgical suite adjusts robotic operation based on environmental data during surgery. For example, if the operating room temperature is high, the surgical suite can slow down robotic operation to prevent equipment overheating. For example, if the operating room humidity is low, the surgical suite can adjust robotic operation to prevent static electricity generation. The surgical suite can also monitor operating room environmental data in real time to maintain optimal robotic operation. This enables the surgical suite to provide optimal robotic control according to the environment during surgery.

[0088] The surgical suite analyzes the movements of medical staff during surgery and optimizes robotic movements. For example, the surgical suite analyzes the movements of medical staff in real time and adjusts robotic movements accordingly. For example, the surgical suite can optimize robotic movements in response to the movements of medical staff, thereby improving surgical efficiency. The surgical suite can also predict the movements of medical staff and pre-adjust robotic movements. This enables the surgical suite to provide optimal robotic control in response to the movements of medical staff.

[0089] The diagnostic engine estimates the patient's emotions and adjusts the presentation of the diagnostic results based on the estimated emotions. For example, if the patient is feeling anxious, the diagnostic engine presents the results simply to reduce visual stress. For example, if the patient is relaxed, the diagnostic engine provides detailed results to deepen understanding. For example, if the patient is agitated, the diagnostic engine may temporarily withhold the presentation of the diagnostic results and wait until the patient's condition stabilizes. This allows the diagnostic engine to present diagnostic results in accordance with the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The diagnostic engine unit improves diagnostic accuracy by integrating data from different diagnostic modalities. For example, the diagnostic engine unit can integrate CT scan and MRI data to perform a more accurate diagnosis. For example, the diagnostic engine unit can integrate X-ray and ultrasound data to improve diagnostic accuracy. For example, the diagnostic engine unit can integrate data from different diagnostic modalities in real time to perform a rapid diagnosis. In this way, the diagnostic engine unit improves diagnostic accuracy by integrating data from different diagnostic modalities.

[0091] The diagnostic engine optimizes the diagnostic algorithm by referring to past diagnostic data. For example, the diagnostic engine analyzes past diagnostic data to optimize the diagnostic algorithm. For example, the diagnostic engine can improve the accuracy of the diagnostic algorithm based on past diagnostic data. The diagnostic engine can also adjust the diagnostic algorithm in real time by referring to past diagnostic data. As a result, the diagnostic engine improves the accuracy of the diagnostic algorithm by optimizing it based on past diagnostic data.

[0092] The diagnostic engine estimates the patient's emotions and prioritizes the diagnostic results based on the estimated emotions. For example, if the patient is feeling anxious, the diagnostic engine will prioritize presenting important diagnostic results. For example, if the patient is relaxed, the diagnostic engine can sequentially present detailed diagnostic results. For example, if the patient is agitated, the diagnostic engine can temporarily withhold the presentation of diagnostic results and wait until the patient's condition stabilizes. This allows the diagnostic engine to determine the priority of diagnostic results according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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.

[0093] The diagnostic engine adjusts the diagnostic results by considering the patient's lifestyle data. For example, the diagnostic engine can adjust the diagnostic results by considering the patient's dietary data. For example, the diagnostic engine can adjust the diagnostic results by considering the patient's exercise habits data. For example, the diagnostic engine can also adjust the diagnostic results by considering the patient's sleep pattern data. As a result, the diagnostic engine provides diagnostic results that are tailored to the patient's lifestyle.

[0094] The diagnostic engine unit will add a function to provide real-time feedback of diagnostic results to medical staff. For example, the diagnostic engine unit can notify medical staff of diagnostic results in real time, enabling a rapid response. For example, the diagnostic engine unit can display diagnostic results on medical staff's devices in real time, providing immediate feedback. For example, the diagnostic engine unit can share diagnostic results with medical staff in real time, facilitating a rapid response across the entire team. This enables the diagnostic engine unit to provide rapid feedback of diagnostic results.

[0095] The treatment planner department estimates the patient's emotions and adjusts the presentation of the treatment plan based on the estimated emotions. For example, if the patient is anxious, the treatment planner department may present the treatment plan simply to reduce visual stress. If the patient is relaxed, for example, the treatment planner department may provide a detailed treatment plan to deepen understanding. If the patient is agitated, for example, the treatment planner department may temporarily refrain from presenting the treatment plan and wait until the patient's condition stabilizes. This allows the treatment planner department to present a treatment plan that is appropriate to the patient's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The treatment planner unit generates an optimal treatment plan by referring to the patient's past treatment history. For example, the treatment planner unit analyzes the patient's past treatment history to generate an optimal treatment plan. For example, the treatment planner unit can customize the treatment plan based on the patient's past treatment history. For example, the treatment planner unit can also adjust the treatment plan in real time by referring to the patient's past treatment history. This ensures that the treatment planner unit provides an optimal treatment plan based on the patient's past treatment history.

[0097] The treatment planner unit applies customizable treatment plan generation algorithms tailored to different treatment methods. For example, the treatment planner unit can apply a treatment plan generation algorithm specialized for chemotherapy to provide the optimal treatment plan. For example, the treatment planner unit can apply a treatment plan generation algorithm specialized for radiotherapy to improve the accuracy of treatment. For example, the treatment planner unit can apply a treatment plan generation algorithm specialized for surgery to increase the success rate of treatment. In this way, the treatment planner unit provides the optimal treatment plan according to the treatment method.

[0098] The treatment planner unit estimates the patient's emotions and prioritizes treatment plans based on those estimated emotions. For example, if the patient is feeling anxious, the treatment planner unit will prioritize presenting important treatment plans. For example, if the patient is relaxed, the treatment planner unit can present detailed treatment plans sequentially. For example, if the patient is agitated, the treatment planner unit can temporarily refrain from presenting treatment plans and wait until the patient's condition stabilizes. This allows the treatment planner unit to determine treatment plan priorities according to the patient's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The treatment planning department adjusts treatment plans by considering the patient's living environment data. For example, the treatment planning department can adjust treatment plans by considering the patient's home environment data. For example, the treatment planning department can adjust treatment plans by considering the patient's work environment data. For example, the treatment planning department can also adjust treatment plans by considering the patient's lifestyle data. In this way, the treatment planning department provides treatment plans that are tailored to the patient's living environment.

[0100] The treatment planning department monitors the progress of treatment plans in real time and modifies them as needed. For example, the treatment planning department can notify medical staff of the progress of treatment plans in real time, enabling a quick response. The treatment planning department can also provide real-time feedback to patients on the progress of treatment plans, allowing them to confirm the effectiveness of the treatment. This enables the treatment planning department to provide optimal treatment tailored to the progress of the treatment plan.

[0101] The predictive analytics unit estimates the patient's emotions and adjusts how the prediction results are presented based on the estimated emotions. For example, if the patient is anxious, the predictive analytics unit presents the prediction results simply to reduce visual stress. For example, if the patient is relaxed, the predictive analytics unit can provide detailed prediction results to deepen understanding. For example, if the patient is agitated, the predictive analytics unit can temporarily withhold the presentation of prediction results and wait until the patient's condition stabilizes. This allows the predictive analytics unit to present prediction results in accordance with the patient's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The predictive analytics unit optimizes the prediction algorithm by referring to past patient data. For example, the predictive analytics unit analyzes past patient data to optimize the prediction algorithm. For example, the predictive analytics unit can improve the accuracy of the prediction algorithm based on past patient data. The predictive analytics unit can also adjust the prediction algorithm in real time by referring to past patient data. This allows the predictive analytics unit to improve the accuracy of the prediction algorithm by optimizing it with reference to past patient data.

[0103] The predictive analytics unit improves prediction accuracy by combining different prediction models. For example, it can improve prediction accuracy by combining machine learning models and statistical models. For example, it can improve prediction accuracy by combining different machine learning algorithms. For example, it can also improve prediction accuracy by integrating information from different data sources. Thus, the predictive analytics unit improves prediction accuracy by combining different prediction models.

[0104] The predictive analytics unit estimates the patient's emotions and prioritizes prediction results based on the estimated emotions. For example, if the patient is feeling anxious, the predictive analytics unit will prioritize presenting important prediction results. For example, if the patient is relaxed, the predictive analytics unit can sequentially present detailed prediction results. For example, if the patient is agitated, the predictive analytics unit may temporarily withhold the presentation of prediction results and wait until the patient's condition stabilizes. This allows the predictive analytics unit to determine the priority of prediction results according to the patient's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The predictive analytics unit adjusts the prediction results by considering the patient's lifestyle data. For example, the predictive analytics unit can adjust the prediction results by considering the patient's dietary data. For example, the predictive analytics unit can adjust the prediction results by considering the patient's exercise habits data. For example, the predictive analytics unit can also adjust the prediction results by considering the patient's sleep pattern data. As a result, the predictive analytics unit provides prediction results that are tailored to the patient's lifestyle.

[0106] The predictive analytics unit will add a function to provide real-time feedback of prediction results to medical staff. For example, the predictive analytics unit can notify medical staff of prediction results in real time, enabling a rapid response. For example, the predictive analytics unit can display prediction results on medical staff's devices in real time, providing immediate feedback. For example, the predictive analytics unit can share prediction results with medical staff in real time, facilitating a rapid response across the entire team. This enables the predictive analytics unit to provide rapid feedback of prediction results.

[0107] The continuous learning unit estimates the patient's emotions and selects training data based on the estimated emotions. For example, if the patient is feeling anxious, the continuous learning unit will select training data that is less stressful. For example, if the patient is relaxed, the continuous learning unit can select detailed training data. For example, if the patient is agitated, the continuous learning unit can temporarily refrain from selecting training data and wait until the patient's condition stabilizes. This allows the continuous learning unit to select training data in accordance with the patient'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.

[0108] The continuous learning unit optimizes the learning algorithm by referring to past training data. For example, the continuous learning unit analyzes past training data to optimize the learning algorithm. For example, the continuous learning unit can improve the accuracy of the learning algorithm based on past training data. For example, the continuous learning unit can also adjust the learning algorithm in real time by referring to past training data. As a result, the accuracy of the learning algorithm is improved by the continuous learning unit optimizing the learning algorithm by referring to past training data.

[0109] The continuous learning unit improves learning accuracy by combining different learning models. For example, the continuous learning unit improves learning accuracy by combining machine learning models and statistical models. For example, the continuous learning unit can improve learning accuracy by combining different machine learning algorithms. For example, the continuous learning unit can improve learning accuracy by integrating information from different data sources. In this way, the continuous learning unit improves learning accuracy by combining different learning models.

[0110] The continuous learning unit estimates the patient's emotions and adjusts the learning frequency based on the estimated emotions. For example, if the patient is feeling anxious, the continuous learning unit reduces the learning frequency to alleviate stress. For example, if the patient is relaxed, the continuous learning unit can increase the learning frequency to collect more detailed data. For example, if the patient is agitated, the continuous learning unit can temporarily reduce the learning frequency and wait until the patient's condition stabilizes. In this way, the continuous learning unit adjusts the learning frequency according to the patient'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.

[0111] The continuous learning unit adjusts the learning data considering the patient's lifestyle data. For example, the continuous learning unit can adjust the learning data considering the patient's dietary data. For example, the continuous learning unit can adjust the learning data considering the patient's exercise habits data. For example, the continuous learning unit can also adjust the learning data considering the patient's sleep pattern data. As a result, the continuous learning unit provides learning data tailored to the patient's lifestyle.

[0112] The Continuing Education Unit will add a function to provide real-time feedback on learning results to medical staff. For example, the Continuing Education Unit can notify medical staff of learning results in real time, enabling a rapid response. For example, the Continuing Education Unit can display learning results on medical staff's devices in real time, providing immediate feedback. For example, the Continuing Education Unit can share learning results with medical staff in real time, facilitating a rapid response across the entire team. This enables the Continuing Education Unit to provide rapid feedback on learning results.

[0113] The continuous learning unit optimizes the overall system performance based on the learning results. For example, the continuous learning unit can optimize the overall system performance based on the learning results. For example, the continuous learning unit can analyze the learning results in real time and improve system performance. For example, the continuous learning unit can automatically adjust system settings based on the learning results to maintain optimal performance. In this way, the continuous learning unit optimizes the overall system performance based on the learning results, thereby optimizing the overall system performance.

[0114] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0115] The AIHOS system can also include an emotion adaptation unit that estimates the patient's emotions and adjusts the treatment plan based on those emotions. For example, if the patient is feeling anxious, the emotion adaptation unit can simplify the treatment plan to reduce the patient's stress. If the patient is relaxed, it can provide a more detailed treatment plan to deepen the patient's understanding. Furthermore, if the patient is agitated, it can temporarily withhold the presentation of the treatment plan and wait until the patient's condition stabilizes. This allows the emotion adaptation unit to provide the optimal treatment plan tailored to the patient's emotions.

[0116] The AIHOS system may also include a lifestyle adaptation unit that adjusts the diagnostic results by considering the patient's lifestyle data. For example, the lifestyle adaptation unit can adjust the diagnostic results by considering the patient's dietary data. It can also adjust the diagnostic results by considering the patient's exercise data. Furthermore, it can adjust the diagnostic results by considering the patient's sleep pattern data. This allows the lifestyle adaptation unit to provide diagnostic results tailored to the patient's lifestyle.

[0117] The AIHOS system can also include a modality integration unit that integrates data from different diagnostic modalities to improve diagnostic accuracy. For example, the modality integration unit can integrate CT scan and MRI data for a more accurate diagnosis. It can also integrate X-ray and ultrasound data to improve diagnostic accuracy. Furthermore, it can integrate data from different diagnostic modalities in real time for rapid diagnosis. Thus, the modality integration unit improves diagnostic accuracy by integrating data from different diagnostic modalities.

[0118] The AIHOS system can further incorporate an emotion-adaptive robotics unit that estimates the patient's emotions and adjusts robotic control during surgery based on those estimated emotions. For example, if the patient is feeling anxious, the emotion-adaptive robotics unit can slow down the robot's movements and proceed cautiously with the surgery. If the patient is relaxed, the robot's movements can proceed normally, allowing the surgery to proceed smoothly. Furthermore, if the patient is agitated, the robot's movements can be temporarily stopped and the system can wait until the patient's condition stabilizes. This enables the emotion-adaptive robotics unit to conduct surgery in accordance with the patient's emotions.

[0119] The AIHOS system can also include a treatment history reference unit that generates an optimal treatment plan by referring to the patient's past treatment history. For example, the treatment history reference unit analyzes the patient's past treatment history and generates an optimal treatment plan. It can also customize the treatment plan based on the patient's past treatment history. Furthermore, it can adjust the treatment plan in real time by referring to the patient's past treatment history. This allows the treatment history reference unit to provide an optimal treatment plan based on past treatment history.

[0120] The AIHOS system can also include an emotion-adaptive diagnostic unit that estimates the patient's emotions and adjusts the presentation of diagnostic results based on those estimated emotions. For example, if the patient is feeling anxious, the emotion-adaptive diagnostic unit may present the diagnostic results simply to reduce visual stress. If the patient is relaxed, it may provide detailed diagnostic results to deepen understanding. Furthermore, if the patient is agitated, it may temporarily withhold the presentation of diagnostic results and wait until the patient's condition stabilizes. This enables the emotion-adaptive diagnostic unit to present diagnostic results in a manner that is appropriate to the patient's emotions.

[0121] The AIHOS system can also include a prediction model integration unit that combines different prediction models to improve prediction accuracy. For example, the prediction model integration unit can combine machine learning models and statistical models to improve prediction accuracy. It can also combine different machine learning algorithms to improve prediction accuracy. Furthermore, it can integrate information from different data sources to improve prediction accuracy. Thus, the prediction model integration unit improves prediction accuracy by combining different prediction models.

[0122] The AIHOS system can further include an emotion-adaptive prediction unit that estimates the patient's emotions and adjusts the presentation method of the prediction results based on the estimated emotions. For example, if the patient is feeling anxious, the emotion-adaptive prediction unit may present the prediction results simply to reduce visual stress. If the patient is relaxed, it may provide detailed prediction results to deepen understanding. Furthermore, if the patient is agitated, it may temporarily withhold the presentation of the prediction results and wait until the patient's condition stabilizes. This enables the emotion-adaptive prediction unit to present prediction results in accordance with the patient's emotions.

[0123] The AIHOS system can further include a living environment adaptation unit that adjusts the treatment plan by considering the patient's living environment data. For example, the living environment adaptation unit can adjust the treatment plan by considering the patient's home environment data. It can also adjust the treatment plan by considering the patient's work environment data. Furthermore, it can adjust the treatment plan by considering the patient's lifestyle data. This allows the living environment adaptation unit to provide a treatment plan tailored to the patient's living environment.

[0124] The AIHOS system can also be equipped with an emotion-adaptive learning unit that estimates the patient's emotions and selects training data based on those estimated emotions. For example, if the patient is feeling anxious, the emotion-adaptive learning unit will select training data that reduces stress. If the patient is relaxed, it can select more detailed training data. Furthermore, if the patient is agitated, it can temporarily refrain from selecting training data and wait until the patient's condition stabilizes. This allows the emotion-adaptive learning unit to select training data that is appropriate for the patient's emotions.

[0125] The following briefly describes the processing flow for example form 2.

[0126] Step 1: The surgical suite improves surgical precision. The surgical suite includes a robotics control unit to enhance precision during surgery. It also includes a real-time guidance unit to provide guidance during surgery. Step 2: The diagnostic engine unit performs a diagnosis based on the data obtained by the surgical suite unit. The diagnostic engine unit integrates multimodal data to perform a rapid and accurate diagnosis. Step 3: The treatment planner unit generates an individualized treatment plan based on the diagnostic results obtained by the diagnostic engine unit. The treatment planner unit adjusts the treatment plan based on the patient's response. Step 4: The predictive analytics unit predicts patient outcomes based on the treatment plan generated by the treatment planner unit. The predictive analytics unit optimizes resource allocation. Step 5: The Continuing Learning Unit evolves the system based on the results obtained by the Predictive Analytics Unit. The Continuing Learning Unit performs continuous analysis of clinical data and results.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each element of the AIHOS system described above is implemented by at least one of the smart device 14 and the data processing device 12. For example, the surgical suite is implemented by the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing device 12. The diagnostic engine is implemented by the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing device 12. The treatment planner is implemented by the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing device 12. The predictive analytics unit is implemented by the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing device 12. The continuous learning unit is implemented by the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0131] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each element of the AIHOS system described above is implemented by at least one of the smart glasses 214 and the data processing device 12. For example, the surgical suite is implemented by the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing device 12. The diagnostic engine is implemented by the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing device 12. The treatment planner is implemented by the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing device 12. The predictive analysis unit is implemented by the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing device 12. The continuous learning unit is implemented by the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing device 12. The correspondence between each part and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0147] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each element of the AIHOS system described above is implemented by at least one of the headset terminal 314 and the data processing device 12. For example, the surgical suite is implemented by the control unit 46A of the headset terminal 314 and the specific processing unit 290 of the data processing device 12. The diagnostic engine is implemented by the control unit 46A of the headset terminal 314 and the specific processing unit 290 of the data processing device 12. The treatment planner is implemented by the control unit 46A of the headset terminal 314 and the specific processing unit 290 of the data processing device 12. The predictive analysis unit is implemented by the control unit 46A of the headset terminal 314 and the specific processing unit 290 of the data processing device 12. The continuous learning unit is implemented by the control unit 46A of the headset terminal 314 and the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0163] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0168] 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).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.).

[0176] 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.

[0177] 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.

[0178] 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.

[0179] Each element of the AIHOS system described above is implemented by, for example, at least one of the robot 414 and the data processing device 12. For example, the surgical suite is implemented by the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing device 12. The diagnostic engine is implemented by the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing device 12. The treatment planner is implemented by the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing device 12. The predictive analysis unit is implemented by the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing device 12. The continuous learning unit is implemented by the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing device 12. The correspondence between each part and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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."

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] (Note 1) A surgical suite that improves the precision of surgery, A diagnostic engine unit that performs a diagnosis based on the data obtained by the surgical suite unit, A treatment planner unit that generates an individualized treatment plan based on the diagnostic results obtained by the diagnostic engine unit, A predictive analysis unit that predicts the patient's outcome based on the treatment plan generated by the aforementioned treatment planner unit, The system includes a continuous learning unit that evolves the system based on the results obtained by the predictive analysis unit. A system characterized by the following features. (Note 2) The aforementioned surgical suite section is Equipped with a robotics control unit. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned surgical suite section is Equipped with a real-time guidance unit. The system described in Appendix 1, characterized by the features described herein. (Note 4) The diagnostic engine unit is Integrating multimodal data The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned treatment planning department, Adjust the treatment plan based on the patient's response. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned predictive analysis unit, Optimize resource allocation The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned continuous learning unit, Conduct continuous analysis of clinical data and results. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned surgical suite section is The system estimates the patient's emotions and adjusts robotic controls during surgery based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 9) The aforementioned surgical suite section is The system analyzes data acquired during surgery in real time and optimizes robotic movements according to the progress of the surgery. The system described in Appendix 2, characterized by the features described herein. (Note 10) The aforementioned surgical suite section is Apply customizable robotic control algorithms tailored to different surgical techniques. The system described in Appendix 2, characterized by the features described herein. (Note 11) The aforementioned surgical suite section is The system estimates the patient's emotions and adjusts the guidance displayed during surgery based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 12) The aforementioned surgical suite section is Adjusting robotic movements based on environmental data during surgery The system described in Appendix 2, characterized by the features described herein. (Note 13) The aforementioned surgical suite section is Analyze the movements of medical staff during surgery and optimize robotic movements. The system described in Appendix 2, characterized by the features described herein. (Note 14) The diagnostic engine unit is The system estimates the patient's emotions and adjusts the presentation of diagnostic results based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 15) The diagnostic engine unit is Integrating data from different diagnostic modalities improves diagnostic accuracy. The system described in Appendix 4, characterized by the features described herein. (Note 16) The diagnostic engine unit is Optimize the diagnostic algorithm by referring to past diagnostic data. The system described in Appendix 4, characterized by the features described herein. (Note 17) The diagnostic engine unit is The system estimates the patient's emotions and prioritizes diagnostic results based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 18) The diagnostic engine unit is The diagnostic results will be adjusted considering the patient's lifestyle data. The system described in Appendix 4, characterized by the features described herein. (Note 19) The diagnostic engine unit is Add a feature to provide real-time feedback of diagnostic results to medical staff. The system described in Appendix 4, characterized by the features described herein. (Note 20) The aforementioned treatment planning department, We estimate the patient's emotions and adjust the way the treatment plan is presented based on those estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 21) The aforementioned treatment planning department, The optimal treatment plan is generated by referring to the patient's past treatment history. The system described in Appendix 5, characterized by the features described herein. (Note 22) The aforementioned treatment planning department, Apply a customizable treatment plan generation algorithm tailored to different treatment methods. The system described in Appendix 5, characterized by the features described herein. (Note 23) The aforementioned treatment planning department, The system estimates the patient's emotions and prioritizes treatment plans based on those estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 24) The aforementioned treatment planning department, Adjust the treatment plan considering the patient's living environment data. The system described in Appendix 5, characterized by the features described herein. (Note 25) The aforementioned treatment planning department, The progress of the treatment plan is monitored in real time, and the plan is modified as needed. The system described in Appendix 5, characterized by the features described herein. (Note 26) The aforementioned predictive analysis unit, The system estimates the patient's emotions and adjusts the presentation method of the prediction results based on the estimated emotions. The system described in Appendix 6, characterized by the features described herein. (Note 27) The aforementioned predictive analysis unit, Optimize the predictive algorithm by referring to past patient data. The system described in Appendix 6, characterized by the features described herein. (Note 28) The aforementioned predictive analysis unit, Combining different prediction models improves prediction accuracy. The system described in Appendix 6, characterized by the features described herein. (Note 29) The aforementioned predictive analysis unit, The system estimates the patient's emotions and prioritizes prediction results based on the estimated emotions. The system described in Appendix 6, characterized by the features described herein. (Note 30) The aforementioned predictive analysis unit, Adjusting prediction results to take into account the patient's lifestyle data. The system described in Appendix 6, characterized by the features described herein. (Note 31) The aforementioned predictive analysis unit, Add a feature to provide real-time feedback of prediction results to medical staff. The system described in Appendix 6, characterized by the features described herein. (Note 32) The aforementioned continuous learning unit, The system estimates the patient's emotions and selects training data based on the estimated emotions. The system described in Appendix 7, characterized by the features described herein. (Note 33) The aforementioned continuous learning unit, Optimize the learning algorithm by referring to past training data. The system described in Appendix 7, characterized by the features described herein. (Note 34) The aforementioned continuous learning unit, Combining different learning models improves learning accuracy. The system described in Appendix 7, characterized by the features described herein. (Note 35) The aforementioned continuous learning unit, The system estimates the patient's emotions and adjusts the learning frequency based on the estimated emotions. The system described in Appendix 7, characterized by the features described herein. (Note 36) The aforementioned continuous learning unit, Adjust the training data considering the patient's lifestyle data. The system described in Appendix 7, characterized by the features described herein. (Note 37) The aforementioned continuous learning unit, Add a feature to provide real-time feedback on learning results to medical staff. The system described in Appendix 7, characterized by the features described herein. (Note 38) The aforementioned continuous learning unit, Optimize the overall system performance based on the learning results. The system described in Appendix 7, characterized by the features described herein. [Explanation of symbols]

[0199] 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. The surgical suite improves the precision of the surgery, A diagnostic engine unit that performs a diagnosis based on the data obtained by the surgical suite unit, A treatment planner unit that generates an individualized treatment plan based on the diagnostic results obtained by the diagnostic engine unit, A predictive analysis unit that predicts the patient's outcome based on the treatment plan generated by the aforementioned treatment planner unit, The system includes a continuous learning unit that evolves the system based on the results obtained by the predictive analysis unit. A system characterized by the following features.

2. The aforementioned surgical suite section is Equipped with a robotics control unit. The system according to feature 1.

3. The aforementioned surgical suite section is Equipped with a real-time guidance unit. The system according to feature 1.

4. The diagnostic engine unit is Integrating multimodal data The system according to feature 1.

5. The aforementioned treatment planning department, Adjust the treatment plan based on the patient's response. The system according to feature 1.

6. The aforementioned predictive analysis unit, Optimize resource allocation The system according to feature 1.

7. The aforementioned continuous learning unit, Conduct continuous analysis of clinical data and results. The system according to feature 1.

8. The aforementioned surgical suite section is The system estimates the patient's emotions and adjusts robotic controls during surgery based on those estimated emotions. The system according to feature 2.

9. The aforementioned surgical suite section is The system analyzes data acquired during surgery in real time and optimizes robotic movements according to the progress of the surgery. The system according to feature 2.

10. The aforementioned surgical suite section is Apply customizable robotics control algorithms tailored to different surgical techniques. The system according to feature 2.

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