system

Abstract

The system according to this embodiment aims to streamline pre-operative explanations by doctors and enable patients to understand the details of their surgery without having to visit the hospital. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and a sharing unit. The reception unit uploads the doctor's voice, facial image, and surgical procedure. The generation unit analyzes the information uploaded by the reception unit and generates a surgical explanation video using the doctor's image and voice. The sharing unit shares the surgical explanation video generated by the generation unit with the patient online.

Description

Technical Field

[0004] ,

[0006] , , , , , ,

[0005] , , , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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] <00000​​​​​​​​​​​​​​​​​​​​​​​The system according to this embodiment comprises a reception unit, a generation unit, and a sharing unit. The reception unit uploads the doctor's voice, facial image, and surgical procedure. The generation unit analyzes the information uploaded by the reception unit and generates a surgical explanation video using the doctor's image and voice. The sharing unit shares the surgical explanation video generated by the generation unit with the patient online. [Effects of the Invention]

[0007] The system according to this embodiment can streamline pre-operative explanations by doctors and enable patients to understand the details of the surgery without having to visit the hospital. [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 controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 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 surgical explanation system according to an embodiment of the present invention is a system that solves the challenges faced by doctors and patients in pre-operative explanations. This surgical explanation system allows doctors to upload their voice, facial image, and surgical procedure, and a generating AI creates an easy-to-understand surgical explanation video using the doctor's appearance and voice, which is then shared with the patient online. This reduces the time spent on surgical explanations and eliminates the need for hospital visits, allowing patients to undergo surgery with a full understanding of the procedure. For example, a doctor uploads the surgical procedure, facial image, and voice to the generating AI. For instance, when explaining total knee replacement surgery, the doctor records a statement such as, "Total knee replacement surgery involves removing bone and replacing it with a metal joint," and uploads it to the generating AI along with a facial image. Next, the generating AI generates a surgical explanation video using the doctor's appearance and voice based on the uploaded information. The generating AI analyzes the doctor's facial image and voice to create a video that explains the appropriate surgical procedure. For example, the generating AI creates a doctor avatar and explains the surgical procedure and precautions with easy-to-understand illustrations. The generated surgical explanation video is shared with the patient online. Patients can view the surgical explanation video anytime, anywhere using their smartphone or computer. This eliminates the need for patients to visit the hospital and allows them to deepen their understanding of the surgery by reviewing the procedure multiple times. Furthermore, if patients have any questions after watching the video, they can ask the doctor online. The doctor can answer online and obtain consent forms if necessary. This facilitates smooth communication between doctors and patients and fosters a good doctor-patient relationship. This service reduces the time doctors spend explaining procedures, and patients can understand the procedure without having to visit the hospital. In addition, the easy-to-understand surgical explanation videos generated by AI reduce patients' anxiety about surgery and allow them to undergo the procedure with confidence. For example, by repeatedly reviewing explanations such as "Artificial knee joint replacement surgery is a procedure in which bone is shaved down and replaced with a metal joint," patients can more easily understand the procedure. In this way, this service solves challenges for both doctors and patients, improving the efficiency of pre-operative explanations and promoting patient understanding.This allows the surgical explanation system to reduce the time spent on explanations and eliminate the need for patients to visit the hospital. By uploading the doctor's voice, facial image, and surgical procedure, the AI ​​generates a surgical explanation video, which is then shared with the patient online.

[0029] The surgical explanation system according to this embodiment comprises a reception unit, a generation unit, and a sharing unit. The reception unit uploads the doctor's voice, facial image, and surgical procedure. The reception unit allows, for example, a doctor to upload the surgical procedure, facial image, and voice to the generation AI. The generation unit analyzes the information uploaded by the reception unit and generates a surgical explanation video using the doctor's appearance and voice. The generation unit, for example, analyzes the doctor's facial image and voice to create a video explaining the appropriate surgical procedure. The generation unit uses the generation AI to create a doctor avatar and generates a video explaining the surgical procedure and precautions with easy-to-understand illustrations. The generation unit, for example, uses the generation AI to analyze the doctor's facial image and voice and create a doctor avatar. The generation unit can also use the generation AI to generate a video explaining the surgical procedure and precautions with easy-to-understand illustrations. The sharing unit shares the surgical explanation video generated by the generation unit with the patient online. The sharing unit allows, for example, the patient to view the surgical explanation video using a smartphone or computer. The sharing unit can also allow the patient to ask questions to the doctor online after watching the video. The sharing section allows, for example, patients to ask questions to doctors online after watching a video. The sharing section can also allow doctors to answer questions online and obtain consent forms as needed. The sharing section allows doctors to answer questions online and obtain consent forms as needed. As a result, the surgical explanation system according to the embodiment uploads the doctor's voice and facial image and surgical procedure, a generating AI generates a surgical explanation video, and shares it with the patient online, thereby reducing the time spent on surgical explanations and eliminating the need for hospital visits.

[0030] The reception desk uploads the doctor's voice, facial image, and surgical procedure. Specifically, doctors can upload detailed surgical procedures, facial images, and voice recordings to the system through a dedicated interface. This interface is designed for easy operation by doctors and features an intuitive user interface. For example, when a doctor explains the surgical procedure, they record their voice using a microphone and take a facial image using a camera. This data is securely uploaded to the system and used for analysis in the generation department. Furthermore, the reception desk has a function to check the quality of the uploaded data and verify that there is no missing information or inappropriate data. For example, if the audio data is unclear or the facial image is blurry, it can prompt the doctor to provide the data again. This allows the reception desk to provide accurate and detailed data for the generation department to generate high-quality surgical explanation videos. In addition, the reception desk encrypts and stores the uploaded data to ensure data security. This protects the doctor's privacy and prevents unauthorized access to the data.

[0031] The generation unit analyzes information uploaded by the reception unit and generates surgical explanation videos using the doctor's image and voice. Specifically, the generation AI analyzes the doctor's facial image and voice to create a doctor avatar. This avatar faithfully reproduces the doctor's characteristics and can explain the surgery in a way that is easy for patients to understand. The generation AI learns the doctor's voice tone and speaking style to generate natural speech. In addition, to generate videos that explain the surgical procedure and precautions with easy-to-understand illustrations, it analyzes each step of the surgery in detail and creates appropriate visuals. For example, it visually explains the entire process from the start to the end of the surgery using animation and diagrams. The generation unit integrates these elements to create a video that explains the content of the surgery in a way that is easy for patients to understand. Furthermore, the generation unit also has a function to check the quality of the generated video and make corrections as needed. For example, if the audio in the generated video is unclear or the avatar's movements are unnatural, the generation AI will automatically correct it and generate an optimal video. The generation unit also saves the generated video in multiple formats so that it can be played on various devices. This allows the generation unit to efficiently produce high-quality, easy-to-understand surgical explanation videos and provide them to patients.

[0032] The sharing unit shares surgical explanation videos generated by the generation unit with patients online. Specifically, it provides a dedicated platform that allows patients to view the surgical explanation videos using their smartphones or computers. This platform is designed for easy patient access, allowing for intuitive basic operations such as playing, pausing, rewinding, and fast-forwarding. The sharing unit also has a function to record video viewing history, allowing doctors to see which parts the patient has watched. This allows doctors to confirm that the patient fully understands the surgical procedure and provide additional explanations as needed. Furthermore, the sharing unit provides a function that allows patients to ask questions to doctors online after watching the video. For example, if a patient has questions after watching the video, they can send them to the doctor through a dedicated chat function. Doctors will answer these questions in real time, alleviating the patient's concerns. The sharing unit also has a function that allows doctors to answer questions online and obtain consent forms as needed. For example, if a patient consents to the surgical procedure, they can submit a consent form online using an electronic signature. In this way, the sharing unit can streamline communication between patients and doctors and improve the efficiency of surgical explanations.

[0033] The generation unit can analyze a doctor's facial image and voice to create a doctor avatar. For example, the generation unit can analyze a doctor's facial image and voice to create a doctor avatar. The generation unit can use 3D modeling technology to create a doctor avatar based on a doctor's facial image. The generation unit can also use speech synthesis technology to generate the voice of the doctor avatar based on a doctor's voice. For example, the generation unit can analyze a doctor's facial image using 3D modeling technology to create a doctor avatar. Furthermore, the generation unit can use speech synthesis technology to generate the voice of the doctor avatar based on a doctor's voice. This allows for the creation of more realistic surgical explanation videos by analyzing a doctor's facial image and voice to create a doctor avatar.

[0034] The generation unit can generate videos that explain surgical procedures and precautions with easy-to-understand illustrations. For example, the generation unit can generate videos that explain surgical procedures and precautions with easy-to-understand illustrations. The generation unit can use generation AI to generate videos that explain surgical procedures and precautions with easy-to-understand illustrations. For example, the generation AI generates videos that explain surgical procedures and precautions with easy-to-understand illustrations. The generation unit can also create videos that explain surgical procedures and precautions in an easy-to-understand way by carefully considering the style and color scheme of the illustrations. For example, the generation unit creates videos that explain surgical procedures and precautions in an easy-to-understand way by carefully considering the style and color scheme of the illustrations. As a result, by generating videos that explain surgical procedures and precautions with easy-to-understand illustrations, patients will be able to understand the details of the surgery more easily.

[0035] The shared section allows patients to view surgical explanation videos using their smartphones or computers. The shared section can, for example, enable patients to view surgical explanation videos using their smartphones or computers. The shared section can also allow patients to view surgical explanation videos through a dedicated app or website. The shared section can also, for example, enable patients to view surgical explanation videos through a dedicated app or website. The shared section can also display surgical explanation videos in an optimal format to fit the screen size of smartphones and computers. This allows patients to view surgical explanation videos using their smartphones or computers, enabling them to access explanations anytime, anywhere.

[0036] The shared section allows patients to ask questions to doctors online after watching a video. The shared section allows patients to ask questions to doctors online via chat or video calls. The shared section can also record the questions asked by patients so that doctors can review them later. This makes it easier to resolve patients' doubts by allowing them to ask questions to doctors online after watching a video.

[0037] The shared section allows physicians to respond online and obtain consent forms as needed. The shared section allows physicians to obtain consent forms online through electronic signatures or PDF consent forms. The shared section allows physicians to obtain consent forms online through electronic signatures or PDF consent forms. The shared section can also implement security measures to ensure that consent forms are obtained safely. This facilitates communication between physicians and patients by allowing physicians to respond online and obtain consent forms as needed.

[0038] The reception desk can analyze a doctor's past upload history and select the optimal upload method. For example, the reception desk can analyze a doctor's past upload history and select the optimal upload method. The reception desk can use AI to analyze a doctor's past upload history. For example, the reception desk can use AI to analyze a doctor's past upload history. The reception desk can prioritize suggesting upload methods that doctors have frequently used in the past. For example, the reception desk prioritizes suggesting upload methods that doctors have frequently used in the past. The reception desk can also select the most efficient upload method from a doctor's past upload history. For example, the reception desk selects the most efficient upload method from a doctor's past upload history. The reception desk can also analyze a doctor's past upload history and suggest methods with a high success rate. For example, the reception desk analyzes a doctor's past upload history and suggests methods with a high success rate. This allows the optimal upload method to be selected by analyzing a doctor's past upload history.

[0039] The reception system can filter uploads based on the physician's specialty and experience. For example, the reception system can filter uploads based on the physician's specialty and experience. The reception system can use AI to filter uploads based on the physician's specialty and experience. For example, the reception system can use AI to filter uploads based on the physician's specialty and experience. The reception system can filter uploads to ensure only information related to the physician's specialty is uploaded. For example, the reception system filters uploads to ensure only information related to the physician's specialty is uploaded. The reception system can also select and upload appropriate information based on the physician's experience. For example, the reception system selects and uploads appropriate information based on the physician's experience. The reception system can also upload the most suitable information by considering the physician's specialty and experience. For example, the reception system uploads the most suitable information by considering the physician's specialty and experience. This allows for the uploading of appropriate information by filtering based on the physician's specialty and experience.

[0040] The reception system can prioritize uploading highly relevant information by considering the doctor's geographical location during the upload process. For example, the reception system prioritizes uploading highly relevant information by considering the doctor's geographical location during the upload process. The reception system can use AI to prioritize uploading highly relevant information by considering the doctor's geographical location. For example, the reception system uses AI to prioritize uploading highly relevant information by considering the doctor's geographical location. If a doctor is active in a specific region, the reception system can prioritize uploading information related to that region. For example, if a doctor is active in a specific region, the reception system prioritizes uploading information related to that region. If a doctor is active in different regions, the reception system can appropriately filter and upload information relevant to each region. For example, if a doctor is active in different regions, the reception system appropriately filters and uploads information relevant to each region. The reception system can also select and upload the most relevant information based on the doctor's geographical location. For example, the reception system selects and uploads the most relevant information based on the doctor's geographical location. This allows for the prioritization of uploading highly relevant information, taking into account the geographical location of doctors, thereby providing appropriate information relevant to the region.

[0041] The reception desk can analyze a doctor's social media activity during the upload process and upload relevant information. For example, the reception desk can analyze a doctor's social media activity during the upload process and upload relevant information. The reception desk can use AI to analyze a doctor's social media activity. For example, the reception desk can use AI to analyze a doctor's social media activity. The reception desk can extract topics of interest from a doctor's social media activity and upload relevant information. For example, the reception desk can extract topics of interest from a doctor's social media activity and upload relevant information. The reception desk can also analyze a doctor's social media activity and prioritize uploading the information of the highest interest. For example, the reception desk analyzes a doctor's social media activity and prioritizes uploading the information of the highest interest. The reception desk can also select and upload relevant information based on a doctor's social media activity. For example, the reception desk selects and uploads relevant information based on a doctor's social media activity. This allows for the uploading of highly relevant information by analyzing a doctor's social media activity.

[0042] The generation unit can adjust the level of detail in a video based on the importance of the surgery during video generation. For example, the generation unit can adjust the level of detail in a video based on the importance of the surgery during video generation. The generation unit can use AI to evaluate the importance of the surgery and adjust the level of detail in the video. For example, the generation unit can use AI to evaluate the importance of the surgery and adjust the level of detail in the video. For important surgeries, the generation unit can generate videos that include detailed procedures and precautions. For example, for important surgeries, the generation unit can generate videos that include detailed procedures and precautions. For simple surgeries, the generation unit can also generate videos that include concise explanations. For example, for simple surgeries, the generation unit can generate videos that include concise explanations. The generation unit can also generate videos with an appropriate level of detail depending on the importance of the surgery. For example, the generation unit can generate videos with an appropriate level of detail depending on the importance of the surgery. This allows for the provision of videos with an appropriate amount of information by adjusting the level of detail in the video based on the importance of the surgery.

[0043] The generation unit can apply different generation algorithms depending on the type of surgery when generating videos. For example, the generation unit can apply different generation algorithms depending on the type of surgery when generating videos. The generation unit can use generation AI to select the optimal generation algorithm depending on the type of surgery. For example, the generation unit uses generation AI to select the optimal generation algorithm depending on the type of surgery. In the case of complex surgeries, the generation unit can apply an algorithm that explains the detailed procedure. For example, the generation unit can apply an algorithm that explains the detailed procedure in the case of complex surgeries. In the case of simple surgeries, the generation unit can also apply an algorithm that provides a concise explanation. For example, the generation unit can apply an algorithm that provides a concise explanation in the case of simple surgeries. The generation unit can also select the optimal generation algorithm depending on the type of surgery. For example, the generation unit selects the optimal generation algorithm depending on the type of surgery. This allows for the generation of optimal videos by applying different generation algorithms depending on the type of surgery.

[0044] The generation unit can determine the priority of videos based on the timing of the surgeries when generating them. For example, the generation unit can determine the priority of videos based on the timing of the surgeries when generating them. The generation unit can use generation AI to evaluate the timing of surgeries and determine the priority of videos. For example, the generation unit can use generation AI to evaluate the timing of surgeries and determine the priority of videos. The generation unit can prioritize the generation of videos for surgeries to be performed in the near future. For example, the generation unit prioritizes the generation of videos for surgeries to be performed in the near future. The generation unit can also generate videos at the appropriate time depending on the timing of the surgeries. For example, the generation unit generates videos at the appropriate time depending on the timing of the surgeries. The generation unit can also determine the optimal priority of video generation based on the surgery schedule. For example, the generation unit determines the optimal priority of video generation based on the surgery schedule. This allows for the provision of videos at the appropriate time by prioritizing videos based on the timing of the surgeries.

[0045] The generation unit can adjust the order of videos based on the relevance of the surgeries during video generation. For example, the generation unit can adjust the order of videos based on the relevance of the surgeries during video generation. The generation unit can use generation AI to evaluate the relevance of surgeries and adjust the order of videos. For example, the generation unit uses generation AI to evaluate the relevance of surgeries and adjust the order of videos. The generation unit can prioritize the generation of videos for highly relevant surgeries. For example, the generation unit prioritizes the generation of videos for highly relevant surgeries. The generation unit can also generate videos in an appropriate order according to the relevance of the surgeries. For example, the generation unit generates videos in an appropriate order according to the relevance of the surgeries. The generation unit can also determine the optimal video generation order by considering the relevance of the surgeries. For example, the generation unit determines the optimal video generation order by considering the relevance of the surgeries. This allows for the priority provision of highly relevant information by adjusting the order of videos based on the relevance of the surgeries.

[0046] The sharing unit can select the optimal display method by referring to the patient's past viewing history when sharing videos. For example, the sharing unit can select the optimal display method by referring to the patient's past viewing history when sharing videos. The sharing unit can analyze the patient's past viewing history using AI. For example, the sharing unit can analyze the patient's past viewing history using AI. The sharing unit can select the optimal display method based on the content of videos the patient has previously watched. For example, the sharing unit can select the optimal display method based on the content of videos the patient has previously watched. The sharing unit can also prioritize displaying information of high interest from the patient's viewing history. For example, the sharing unit prioritizes displaying information of high interest from the patient's viewing history. The sharing unit can also analyze the patient's viewing history and select the most effective display method. For example, the sharing unit analyzes the patient's viewing history and selects the most effective display method. This allows the system to provide the optimal display method by referring to the patient's past viewing history.

[0047] The sharing unit can select the optimal display method when sharing videos, taking into account the patient's device information. For example, the sharing unit can select the optimal display method when sharing videos, taking into account the patient's device information. The sharing unit can analyze the patient's device information using AI. For example, the sharing unit can analyze the patient's device information using AI. If the patient is using a smartphone, the sharing unit can provide a display method that matches the screen size. For example, if the patient is using a smartphone, the sharing unit can provide a display method that matches the screen size. If the patient is using a tablet, the sharing unit can also provide a display method optimized for larger screens. For example, if the patient is using a tablet, the sharing unit can provide a display method optimized for larger screens. If the patient is using a computer, the sharing unit can also provide a display method that includes detailed information. For example, if the patient is using a computer, the sharing unit can provide a display method that includes detailed information. This allows the optimal display method to be provided by taking the patient's device information into account.

[0048] The sharing function can select the optimal display method when sharing videos, taking into account the patient's geographical location. For example, the sharing function selects the optimal display method when sharing videos, taking into account the patient's geographical location. The sharing function can analyze the patient's geographical location using AI. For example, the sharing function analyzes the patient's geographical location using AI. If the patient is in a specific region, the sharing function can prioritize displaying information related to that region. For example, if the patient is in a specific region, the sharing function prioritizes displaying information related to that region. The sharing function can also display the most relevant information based on the patient's geographical location. For example, the sharing function displays the most relevant information based on the patient's geographical location. The sharing function can also select the optimal display method, taking into account the patient's geographical location. For example, the sharing function selects the optimal display method, taking into account the patient's geographical location. This allows the system to provide the optimal display method by considering the patient's geographical location.

[0049] The sharing function can analyze the patient's social media activity when sharing videos and display relevant information. For example, the sharing function can analyze the patient's social media activity when sharing videos and display relevant information. The sharing function can use AI to analyze the patient's social media activity. For example, the sharing function can use AI to analyze the patient's social media activity. The sharing function can extract topics of interest from the patient's social media activity and display relevant information. For example, the sharing function can extract topics of interest from the patient's social media activity and display relevant information. The sharing function can also analyze the patient's social media activity and prioritize displaying the information of highest interest. For example, the sharing function can analyze the patient's social media activity and prioritize displaying the information of highest interest. The sharing function can also select and display relevant information based on the patient's social media activity. For example, the sharing function can select and display relevant information based on the patient's social media activity. This allows for the provision of highly relevant information by analyzing the patient's social media activity.

[0050] The shared section allows patients to ask questions to doctors online after watching a video. The shared section allows patients to ask questions to doctors online via chat or video calls. The shared section can also record the questions asked by patients so that doctors can review them later. This makes it easier to resolve patients' doubts by allowing them to ask questions to doctors online after watching a video.

[0051] The shared section allows physicians to respond online and obtain consent forms as needed. The shared section allows physicians to obtain consent forms online through electronic signatures or PDF consent forms. The shared section allows physicians to obtain consent forms online through electronic signatures or PDF consent forms. The shared section can also implement security measures to ensure that consent forms are obtained safely. This facilitates communication between physicians and patients by allowing physicians to respond online and obtain consent forms as needed.

[0052] The shared section can select the optimal display method by referring to the patient's past question history when displaying answers. The shared section can, for example, select the optimal display method by referring to the patient's past question history when displaying answers. The shared section can analyze the patient's past question history using AI. The shared section can, for example, analyze the patient's past question history using AI. The shared section can select the optimal display method based on the content of questions the patient has asked in the past. The shared section can, for example, select the optimal display method based on the content of questions the patient has asked in the past. The shared section can also prioritize displaying information of high interest from the patient's question history. The shared section can, for example, prioritize displaying information of high interest from the patient's question history. The shared section can also analyze the patient's question history and select the most effective display method. The shared section can, for example, analyze the patient's question history and select the most effective display method. This allows the system to provide the optimal display method by referring to the patient's past question history.

[0053] The shared unit can select the optimal display method when displaying responses, taking into account the patient's device information. For example, the shared unit selects the optimal display method when displaying responses, taking into account the patient's device information. The shared unit can analyze the patient's device information using AI. For example, the shared unit analyzes the patient's device information using AI. If the patient is using a smartphone, the shared unit can provide a display method adapted to the screen size. For example, if the patient is using a smartphone, the shared unit can provide a display method adapted to the screen size. If the patient is using a tablet, the shared unit can also provide a display method optimized for larger screens. For example, if the patient is using a tablet, the shared unit can provide a display method optimized for larger screens. If the patient is using a computer, the shared unit can also provide a display method that includes detailed information. For example, if the patient is using a computer, the shared unit can provide a display method that includes detailed information. This allows the system to provide the optimal display method by taking the patient's device information into consideration.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The reception desk can analyze a doctor's past upload history and select the optimal upload method. For example, it can prioritize suggesting upload methods that the doctor has frequently used in the past. It can also select the most efficient upload method based on the doctor's past upload history. Furthermore, it can analyze the doctor's past upload history and suggest methods with a high success rate. In this way, the optimal upload method can be selected by analyzing the doctor's past upload history.

[0056] The reception system can filter uploads based on the physician's specialty and experience. For example, it can filter to upload only information related to the physician's specialty. It can also select and upload appropriate information based on the physician's experience. Furthermore, it can upload the most relevant information considering the physician's specialty and experience. This ensures that appropriate information is uploaded by filtering based on the physician's specialty and experience.

[0057] The reception system can prioritize uploading highly relevant information by considering the doctor's geographical location during the upload process. For example, if a doctor works in a specific region, information related to that region can be prioritized. Furthermore, if a doctor works in different regions, the system can appropriately filter and upload information relevant to each region. It can also select and upload the most relevant information based on the doctor's geographical location. This allows for the appropriate provision of region-specific information by prioritizing the upload of highly relevant information based on the doctor's geographical location.

[0058] The reception desk can analyze a doctor's social media activity during the upload process and upload relevant information. For example, it can extract topics of interest from a doctor's social media activity and upload related information. It can also analyze a doctor's social media activity and prioritize uploading the information of highest interest. Furthermore, it can select and upload relevant information based on a doctor's social media activity. This allows for the uploading of information of high interest by analyzing a doctor's social media activity.

[0059] The generation unit can adjust the level of detail in a video based on the importance of the surgery during video generation. For example, for important surgeries, it can generate videos that include detailed procedures and precautions. For simpler surgeries, it can generate videos with concise explanations. Furthermore, it can generate videos with an appropriate level of detail depending on the importance of the surgery. This allows for the provision of videos with an appropriate amount of information by adjusting the level of detail based on the importance of the surgery.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The reception desk uploads the doctor's voice, facial image, and surgical procedure. For example, a doctor can upload the surgical procedure, facial image, and voice to the AI ​​generator. Step 2: The generation unit analyzes the information uploaded by the reception unit and generates a surgical explanation video using the doctor's image and voice. For example, it uses a generation AI to analyze the doctor's facial image and voice, creates a doctor avatar, and generates a video explaining the surgical procedure and precautions with easy-to-understand illustrations. Step 3: The sharing unit shares the surgical explanation video generated by the generation unit with the patient online. For example, the patient can view the surgical explanation video on their smartphone or computer, and after watching the video, they can ask questions to the doctor online. The doctor can also answer questions online and obtain consent forms if necessary.

[0062] (Example of form 2) The surgical explanation system according to an embodiment of the present invention is a system that solves the challenges faced by doctors and patients in pre-operative explanations. This surgical explanation system allows doctors to upload their voice, facial image, and surgical procedure, and a generating AI creates an easy-to-understand surgical explanation video using the doctor's appearance and voice, which is then shared with the patient online. This reduces the time spent on surgical explanations and eliminates the need for hospital visits, allowing patients to undergo surgery with a full understanding of the procedure. For example, a doctor uploads the surgical procedure, facial image, and voice to the generating AI. For instance, when explaining total knee replacement surgery, the doctor records a statement such as, "Total knee replacement surgery involves removing bone and replacing it with a metal joint," and uploads it to the generating AI along with a facial image. Next, the generating AI generates a surgical explanation video using the doctor's appearance and voice based on the uploaded information. The generating AI analyzes the doctor's facial image and voice to create a video that explains the appropriate surgical procedure. For example, the generating AI creates a doctor avatar and explains the surgical procedure and precautions with easy-to-understand illustrations. The generated surgical explanation video is shared with the patient online. Patients can view the surgical explanation video anytime, anywhere using their smartphone or computer. This eliminates the need for patients to visit the hospital and allows them to deepen their understanding of the surgery by reviewing the procedure multiple times. Furthermore, if patients have any questions after watching the video, they can ask the doctor online. The doctor can answer online and obtain consent forms if necessary. This facilitates smooth communication between doctors and patients and fosters a good doctor-patient relationship. This service reduces the time doctors spend explaining procedures, and patients can understand the procedure without having to visit the hospital. In addition, the easy-to-understand surgical explanation videos generated by AI reduce patients' anxiety about surgery and allow them to undergo the procedure with confidence. For example, by repeatedly reviewing explanations such as "Artificial knee joint replacement surgery is a procedure in which bone is shaved down and replaced with a metal joint," patients can more easily understand the procedure. In this way, this service solves challenges for both doctors and patients, improving the efficiency of pre-operative explanations and promoting patient understanding.This allows the surgical explanation system to reduce the time spent on explanations and eliminate the need for patients to visit the hospital. By uploading the doctor's voice, facial image, and surgical procedure, the AI ​​generates a surgical explanation video, which is then shared with the patient online.

[0063] The surgical explanation system according to this embodiment comprises a reception unit, a generation unit, and a sharing unit. The reception unit uploads the doctor's voice, facial image, and surgical procedure. The reception unit allows, for example, a doctor to upload the surgical procedure, facial image, and voice to the generation AI. The generation unit analyzes the information uploaded by the reception unit and generates a surgical explanation video using the doctor's appearance and voice. The generation unit, for example, analyzes the doctor's facial image and voice to create a video explaining the appropriate surgical procedure. The generation unit uses the generation AI to create a doctor avatar and generates a video explaining the surgical procedure and precautions with easy-to-understand illustrations. The generation unit, for example, uses the generation AI to analyze the doctor's facial image and voice and create a doctor avatar. The generation unit can also use the generation AI to generate a video explaining the surgical procedure and precautions with easy-to-understand illustrations. The sharing unit shares the surgical explanation video generated by the generation unit with the patient online. The sharing unit allows, for example, the patient to view the surgical explanation video using a smartphone or computer. The sharing unit can also allow the patient to ask questions to the doctor online after watching the video. The sharing section allows, for example, patients to ask questions to doctors online after watching a video. The sharing section can also allow doctors to answer questions online and obtain consent forms as needed. The sharing section allows doctors to answer questions online and obtain consent forms as needed. As a result, the surgical explanation system according to the embodiment uploads the doctor's voice and facial image and surgical procedure, a generating AI generates a surgical explanation video, and shares it with the patient online, thereby reducing the time spent on surgical explanations and eliminating the need for hospital visits.

[0064] The reception desk uploads the doctor's voice, facial image, and surgical procedure. Specifically, doctors can upload detailed surgical procedures, facial images, and voice recordings to the system through a dedicated interface. This interface is designed for easy operation by doctors and features an intuitive user interface. For example, when a doctor explains the surgical procedure, they record their voice using a microphone and take a facial image using a camera. This data is securely uploaded to the system and used for analysis in the generation department. Furthermore, the reception desk has a function to check the quality of the uploaded data and verify that there is no missing information or inappropriate data. For example, if the audio data is unclear or the facial image is blurry, it can prompt the doctor to provide the data again. This allows the reception desk to provide accurate and detailed data for the generation department to generate high-quality surgical explanation videos. In addition, the reception desk encrypts and stores the uploaded data to ensure data security. This protects the doctor's privacy and prevents unauthorized access to the data.

[0065] The generation unit analyzes information uploaded by the reception unit and generates surgical explanation videos using the doctor's image and voice. Specifically, the generation AI analyzes the doctor's facial image and voice to create a doctor avatar. This avatar faithfully reproduces the doctor's characteristics and can explain the surgery in a way that is easy for patients to understand. The generation AI learns the doctor's voice tone and speaking style to generate natural speech. In addition, to generate videos that explain the surgical procedure and precautions with easy-to-understand illustrations, it analyzes each step of the surgery in detail and creates appropriate visuals. For example, it visually explains the entire process from the start to the end of the surgery using animation and diagrams. The generation unit integrates these elements to create a video that explains the content of the surgery in a way that is easy for patients to understand. Furthermore, the generation unit also has a function to check the quality of the generated video and make corrections as needed. For example, if the audio in the generated video is unclear or the avatar's movements are unnatural, the generation AI will automatically correct it and generate an optimal video. The generation unit also saves the generated video in multiple formats so that it can be played on various devices. This allows the generation unit to efficiently produce high-quality, easy-to-understand surgical explanation videos and provide them to patients.

[0066] The sharing unit shares surgical explanation videos generated by the generation unit with patients online. Specifically, it provides a dedicated platform that allows patients to view the surgical explanation videos using their smartphones or computers. This platform is designed for easy patient access, allowing for intuitive basic operations such as playing, pausing, rewinding, and fast-forwarding. The sharing unit also has a function to record video viewing history, allowing doctors to see which parts the patient has watched. This allows doctors to confirm that the patient fully understands the surgical procedure and provide additional explanations as needed. Furthermore, the sharing unit provides a function that allows patients to ask questions to doctors online after watching the video. For example, if a patient has questions after watching the video, they can send them to the doctor through a dedicated chat function. Doctors will answer these questions in real time, alleviating the patient's concerns. The sharing unit also has a function that allows doctors to answer questions online and obtain consent forms as needed. For example, if a patient consents to the surgical procedure, they can submit a consent form online using an electronic signature. In this way, the sharing unit can streamline communication between patients and doctors and improve the efficiency of surgical explanations.

[0067] The generation unit can analyze a doctor's facial image and voice to create a doctor avatar. For example, the generation unit can analyze a doctor's facial image and voice to create a doctor avatar. The generation unit can use 3D modeling technology to create a doctor avatar based on a doctor's facial image. The generation unit can also use speech synthesis technology to generate the voice of the doctor avatar based on a doctor's voice. For example, the generation unit can analyze a doctor's facial image using 3D modeling technology to create a doctor avatar. Furthermore, the generation unit can use speech synthesis technology to generate the voice of the doctor avatar based on a doctor's voice. This allows for the creation of more realistic surgical explanation videos by analyzing a doctor's facial image and voice to create a doctor avatar.

[0068] The generation unit can generate videos that explain surgical procedures and precautions with easy-to-understand illustrations. For example, the generation unit can generate videos that explain surgical procedures and precautions with easy-to-understand illustrations. The generation unit can use generation AI to generate videos that explain surgical procedures and precautions with easy-to-understand illustrations. For example, the generation AI generates videos that explain surgical procedures and precautions with easy-to-understand illustrations. The generation unit can also create videos that explain surgical procedures and precautions in an easy-to-understand way by carefully considering the style and color scheme of the illustrations. For example, the generation unit creates videos that explain surgical procedures and precautions in an easy-to-understand way by carefully considering the style and color scheme of the illustrations. As a result, by generating videos that explain surgical procedures and precautions with easy-to-understand illustrations, patients will be able to understand the details of the surgery more easily.

[0069] The shared section allows patients to view surgical explanation videos using their smartphones or computers. The shared section can, for example, enable patients to view surgical explanation videos using their smartphones or computers. The shared section can also allow patients to view surgical explanation videos through a dedicated app or website. The shared section can also, for example, enable patients to view surgical explanation videos through a dedicated app or website. The shared section can also display surgical explanation videos in an optimal format to fit the screen size of smartphones and computers. This allows patients to view surgical explanation videos using their smartphones or computers, enabling them to access explanations anytime, anywhere.

[0070] The shared section allows patients to ask questions to doctors online after watching a video. The shared section allows patients to ask questions to doctors online via chat or video calls. The shared section can also record the questions asked by patients so that doctors can review them later. This makes it easier to resolve patients' doubts by allowing them to ask questions to doctors online after watching a video.

[0071] The shared section allows physicians to respond online and obtain consent forms as needed. The shared section allows physicians to obtain consent forms online through electronic signatures or PDF consent forms. The shared section allows physicians to obtain consent forms online through electronic signatures or PDF consent forms. The shared section can also implement security measures to ensure that consent forms are obtained safely. This facilitates communication between physicians and patients by allowing physicians to respond online and obtain consent forms as needed.

[0072] The reception desk can estimate the doctor's emotions and adjust the upload timing based on the estimated emotions. The reception desk can estimate the doctor's emotions using an emotion engine or generative AI. The reception desk can estimate the doctor's emotions using an emotion engine or generative AI. If the doctor is tired, the reception desk can delay the upload timing to allow time for rest. If the doctor is busy, the reception desk can adjust the upload to be completed in a short amount of time. If the doctor is relaxed, the reception desk can provide time for uploading detailed information. This reduces the burden on doctors by adjusting the upload timing based on their emotions.

[0073] The reception desk can analyze a doctor's past upload history and select the optimal upload method. For example, the reception desk can analyze a doctor's past upload history and select the optimal upload method. The reception desk can use AI to analyze a doctor's past upload history. For example, the reception desk can use AI to analyze a doctor's past upload history. The reception desk can prioritize suggesting upload methods that doctors have frequently used in the past. For example, the reception desk prioritizes suggesting upload methods that doctors have frequently used in the past. The reception desk can also select the most efficient upload method from a doctor's past upload history. For example, the reception desk selects the most efficient upload method from a doctor's past upload history. The reception desk can also analyze a doctor's past upload history and suggest methods with a high success rate. For example, the reception desk analyzes a doctor's past upload history and suggests methods with a high success rate. This allows the optimal upload method to be selected by analyzing a doctor's past upload history.

[0074] The reception system can filter uploads based on the physician's specialty and experience. For example, the reception system can filter uploads based on the physician's specialty and experience. The reception system can use AI to filter uploads based on the physician's specialty and experience. For example, the reception system can use AI to filter uploads based on the physician's specialty and experience. The reception system can filter uploads to ensure only information related to the physician's specialty is uploaded. For example, the reception system filters uploads to ensure only information related to the physician's specialty is uploaded. The reception system can also select and upload appropriate information based on the physician's experience. For example, the reception system selects and uploads appropriate information based on the physician's experience. The reception system can also upload the most suitable information by considering the physician's specialty and experience. For example, the reception system uploads the most suitable information by considering the physician's specialty and experience. This allows for the uploading of appropriate information by filtering based on the physician's specialty and experience.

[0075] The reception desk can estimate the doctor's emotions and prioritize the information to upload based on the estimated emotions. For example, the reception desk can estimate the doctor's emotions and prioritize the information to upload based on the estimated emotions. The reception desk can estimate the doctor's emotions using an emotion engine or generative AI. For example, the reception desk can estimate the doctor's emotions using an emotion engine or generative AI. If the doctor is tired, the reception desk can prioritize uploading important information. For example, if the doctor is tired, the reception desk prioritizes uploading important information. If the doctor is relaxed, the reception desk can prioritize uploading detailed information. For example, if the doctor is relaxed, the reception desk prioritizes uploading detailed information. If the doctor is busy, the reception desk can prioritize uploading concise information. For example, if the doctor is busy, the reception desk prioritizes uploading concise information. This allows for prioritizing the upload of important information by determining the priority of the information to upload based on the doctor's emotions.

[0076] The reception system can prioritize uploading highly relevant information by considering the doctor's geographical location during the upload process. For example, the reception system prioritizes uploading highly relevant information by considering the doctor's geographical location during the upload process. The reception system can use AI to prioritize uploading highly relevant information by considering the doctor's geographical location. For example, the reception system uses AI to prioritize uploading highly relevant information by considering the doctor's geographical location. If a doctor is active in a specific region, the reception system can prioritize uploading information related to that region. For example, if a doctor is active in a specific region, the reception system prioritizes uploading information related to that region. If a doctor is active in different regions, the reception system can appropriately filter and upload information relevant to each region. For example, if a doctor is active in different regions, the reception system appropriately filters and uploads information relevant to each region. The reception system can also select and upload the most relevant information based on the doctor's geographical location. For example, the reception system selects and uploads the most relevant information based on the doctor's geographical location. This allows for the prioritization of uploading highly relevant information, taking into account the geographical location of doctors, thereby providing appropriate information relevant to the region.

[0077] The reception desk can analyze a doctor's social media activity during the upload process and upload relevant information. For example, the reception desk can analyze a doctor's social media activity during the upload process and upload relevant information. The reception desk can use AI to analyze a doctor's social media activity. For example, the reception desk can use AI to analyze a doctor's social media activity. The reception desk can extract topics of interest from a doctor's social media activity and upload relevant information. For example, the reception desk can extract topics of interest from a doctor's social media activity and upload relevant information. The reception desk can also analyze a doctor's social media activity and prioritize uploading the information of the highest interest. For example, the reception desk analyzes a doctor's social media activity and prioritizes uploading the information of the highest interest. The reception desk can also select and upload relevant information based on a doctor's social media activity. For example, the reception desk selects and uploads relevant information based on a doctor's social media activity. This allows for the uploading of highly relevant information by analyzing a doctor's social media activity.

[0078] The generation unit can estimate the patient's emotions and adjust the video's presentation based on the estimated emotions. For example, the generation unit can estimate the patient's emotions and adjust the video's presentation based on the estimated emotions. The generation unit can estimate the patient's emotions using an emotion engine or generative AI. For example, the generation unit can estimate the patient's emotions using an emotion engine or generative AI. If the patient is feeling anxious, the generation unit can generate a video with a calm tone of explanation. For example, if the patient is feeling anxious, the generation unit can generate a video with a calm tone of explanation. If the patient is relaxed, the generation unit can also generate a video with a detailed explanation. For example, if the patient is relaxed, the generation unit can generate a video with a detailed explanation. If the patient is agitated, the generation unit can also generate a video with visually stimulating effects. For example, if the patient is agitated, the generation unit can generate a video with visually stimulating effects. This allows for the provision of videos that are easier for patients to understand by adjusting the video's presentation based on their emotions.

[0079] The generation unit can adjust the level of detail in a video based on the importance of the surgery during video generation. For example, the generation unit can adjust the level of detail in a video based on the importance of the surgery during video generation. The generation unit can use AI to evaluate the importance of the surgery and adjust the level of detail in the video. For example, the generation unit can use AI to evaluate the importance of the surgery and adjust the level of detail in the video. For important surgeries, the generation unit can generate videos that include detailed procedures and precautions. For example, for important surgeries, the generation unit can generate videos that include detailed procedures and precautions. For simple surgeries, the generation unit can also generate videos that include concise explanations. For example, for simple surgeries, the generation unit can generate videos that include concise explanations. The generation unit can also generate videos with an appropriate level of detail depending on the importance of the surgery. For example, the generation unit can generate videos with an appropriate level of detail depending on the importance of the surgery. This allows for the provision of videos with an appropriate amount of information by adjusting the level of detail in the video based on the importance of the surgery.

[0080] The generation unit can apply different generation algorithms depending on the type of surgery when generating videos. For example, the generation unit can apply different generation algorithms depending on the type of surgery when generating videos. The generation unit can use generation AI to select the optimal generation algorithm depending on the type of surgery. For example, the generation unit uses generation AI to select the optimal generation algorithm depending on the type of surgery. In the case of complex surgeries, the generation unit can apply an algorithm that explains the detailed procedure. For example, the generation unit can apply an algorithm that explains the detailed procedure in the case of complex surgeries. In the case of simple surgeries, the generation unit can also apply an algorithm that provides a concise explanation. For example, the generation unit can apply an algorithm that provides a concise explanation in the case of simple surgeries. The generation unit can also select the optimal generation algorithm depending on the type of surgery. For example, the generation unit selects the optimal generation algorithm depending on the type of surgery. This allows for the generation of optimal videos by applying different generation algorithms depending on the type of surgery.

[0081] The generation unit can estimate the patient's emotions and adjust the video length based on the estimated emotions. The generation unit can estimate the patient's emotions using an emotion engine or generative AI. The generation unit can estimate the patient's emotions using an emotion engine or generative AI. If the patient is feeling anxious, the generation unit can generate a short, concise video. If the patient is relaxed, the generation unit can generate a longer video with detailed explanations. If the patient is agitated, the generation unit can generate a video with visually stimulating effects. This allows the system to provide patients with videos of the optimal length by adjusting the video length based on their emotions.

[0082] The generation unit can determine the priority of videos based on the timing of the surgeries when generating them. For example, the generation unit can determine the priority of videos based on the timing of the surgeries when generating them. The generation unit can use generation AI to evaluate the timing of surgeries and determine the priority of videos. For example, the generation unit can use generation AI to evaluate the timing of surgeries and determine the priority of videos. The generation unit can prioritize the generation of videos for surgeries to be performed in the near future. For example, the generation unit prioritizes the generation of videos for surgeries to be performed in the near future. The generation unit can also generate videos at the appropriate time depending on the timing of the surgeries. For example, the generation unit generates videos at the appropriate time depending on the timing of the surgeries. The generation unit can also determine the optimal priority of video generation based on the surgery schedule. For example, the generation unit determines the optimal priority of video generation based on the surgery schedule. This allows for the provision of videos at the appropriate time by prioritizing videos based on the timing of the surgeries.

[0083] The generation unit can adjust the order of videos based on the relevance of the surgeries during video generation. For example, the generation unit can adjust the order of videos based on the relevance of the surgeries during video generation. The generation unit can use generation AI to evaluate the relevance of surgeries and adjust the order of videos. For example, the generation unit uses generation AI to evaluate the relevance of surgeries and adjust the order of videos. The generation unit can prioritize the generation of videos for highly relevant surgeries. For example, the generation unit prioritizes the generation of videos for highly relevant surgeries. The generation unit can also generate videos in an appropriate order according to the relevance of the surgeries. For example, the generation unit generates videos in an appropriate order according to the relevance of the surgeries. The generation unit can also determine the optimal video generation order by considering the relevance of the surgeries. For example, the generation unit determines the optimal video generation order by considering the relevance of the surgeries. This allows for the priority provision of highly relevant information by adjusting the order of videos based on the relevance of the surgeries.

[0084] The shared section can estimate the patient's emotions and adjust how the video is displayed based on the estimated emotions. For example, the shared section can estimate the patient's emotions and adjust how the video is displayed based on the estimated emotions. The shared section can estimate the patient's emotions using an emotion engine or generative AI. For example, the shared section can estimate the patient's emotions using an emotion engine or generative AI. If the patient is feeling anxious, the shared section can display a video with a calming tone of explanation. For example, if the patient is feeling anxious, the shared section can display a video with a calming tone of explanation. If the patient is relaxed, the shared section can also display a video with a detailed explanation. For example, if the patient is relaxed, the shared section can display a video with a detailed explanation. If the patient is agitated, the shared section can also display a video with visually stimulating effects. For example, if the patient is agitated, the shared section can display a video with visually stimulating effects. This allows the system to provide the optimal viewing experience for the patient by adjusting how the video is displayed based on their emotions.

[0085] The sharing unit can select the optimal display method by referring to the patient's past viewing history when sharing videos. For example, the sharing unit can select the optimal display method by referring to the patient's past viewing history when sharing videos. The sharing unit can analyze the patient's past viewing history using AI. For example, the sharing unit can analyze the patient's past viewing history using AI. The sharing unit can select the optimal display method based on the content of videos the patient has previously watched. For example, the sharing unit can select the optimal display method based on the content of videos the patient has previously watched. The sharing unit can also prioritize displaying information of high interest from the patient's viewing history. For example, the sharing unit prioritizes displaying information of high interest from the patient's viewing history. The sharing unit can also analyze the patient's viewing history and select the most effective display method. For example, the sharing unit analyzes the patient's viewing history and selects the most effective display method. This allows the system to provide the optimal display method by referring to the patient's past viewing history.

[0086] The sharing unit can select the optimal display method when sharing videos, taking into account the patient's device information. For example, the sharing unit can select the optimal display method when sharing videos, taking into account the patient's device information. The sharing unit can analyze the patient's device information using AI. For example, the sharing unit can analyze the patient's device information using AI. If the patient is using a smartphone, the sharing unit can provide a display method that matches the screen size. For example, if the patient is using a smartphone, the sharing unit can provide a display method that matches the screen size. If the patient is using a tablet, the sharing unit can also provide a display method optimized for larger screens. For example, if the patient is using a tablet, the sharing unit can provide a display method optimized for larger screens. If the patient is using a computer, the sharing unit can also provide a display method that includes detailed information. For example, if the patient is using a computer, the sharing unit can provide a display method that includes detailed information. This allows the optimal display method to be provided by taking the patient's device information into account.

[0087] The shared unit can estimate the patient's emotions and adjust the video's operation procedures based on the estimated emotions. The shared unit can estimate the patient's emotions using an emotion engine or generative AI. The shared unit can provide simple operation procedures if the patient is feeling anxious. The shared unit can also provide detailed operation procedures if the patient is relaxed. The shared unit can also provide visually stimulating operation procedures if the patient is agitated. This allows the system to provide the optimal operation procedures for the patient by adjusting the video's operation procedures based on the patient's emotions.

[0088] The sharing function can select the optimal display method when sharing videos, taking into account the patient's geographical location. For example, the sharing function selects the optimal display method when sharing videos, taking into account the patient's geographical location. The sharing function can analyze the patient's geographical location using AI. For example, the sharing function analyzes the patient's geographical location using AI. If the patient is in a specific region, the sharing function can prioritize displaying information related to that region. For example, if the patient is in a specific region, the sharing function prioritizes displaying information related to that region. The sharing function can also display the most relevant information based on the patient's geographical location. For example, the sharing function displays the most relevant information based on the patient's geographical location. The sharing function can also select the optimal display method, taking into account the patient's geographical location. For example, the sharing function selects the optimal display method, taking into account the patient's geographical location. This allows the system to provide the optimal display method by considering the patient's geographical location.

[0089] The sharing function can analyze the patient's social media activity when sharing videos and display relevant information. For example, the sharing function can analyze the patient's social media activity when sharing videos and display relevant information. The sharing function can use AI to analyze the patient's social media activity. For example, the sharing function can use AI to analyze the patient's social media activity. The sharing function can extract topics of interest from the patient's social media activity and display relevant information. For example, the sharing function can extract topics of interest from the patient's social media activity and display relevant information. The sharing function can also analyze the patient's social media activity and prioritize displaying the information of highest interest. For example, the sharing function can analyze the patient's social media activity and prioritize displaying the information of highest interest. The sharing function can also select and display relevant information based on the patient's social media activity. For example, the sharing function can select and display relevant information based on the patient's social media activity. This allows for the provision of highly relevant information by analyzing the patient's social media activity.

[0090] The shared section allows patients to ask questions to doctors online after watching a video. The shared section allows patients to ask questions to doctors online via chat or video calls. The shared section can also record the questions asked by patients so that doctors can review them later. This makes it easier to resolve patients' doubts by allowing them to ask questions to doctors online after watching a video.

[0091] The shared section allows physicians to respond online and obtain consent forms as needed. The shared section allows physicians to obtain consent forms online through electronic signatures or PDF consent forms. The shared section allows physicians to obtain consent forms online through electronic signatures or PDF consent forms. The shared section can also implement security measures to ensure that consent forms are obtained safely. This facilitates communication between physicians and patients by allowing physicians to respond online and obtain consent forms as needed.

[0092] The shared section can estimate the patient's emotions and adjust how the responses are displayed based on the estimated emotions. The shared section can estimate the patient's emotions using an emotion engine or generative AI. If the patient is feeling anxious, the shared section can display responses explained in a calm tone. If the patient is relaxed, the shared section can also display responses with detailed explanations. If the patient is agitated, the shared section can also display responses with visually stimulating effects. This allows for the provision of the most optimal display method for the patient by adjusting how the responses are displayed based on their emotions.

[0093] The shared section can select the optimal display method by referring to the patient's past question history when displaying answers. The shared section can, for example, select the optimal display method by referring to the patient's past question history when displaying answers. The shared section can analyze the patient's past question history using AI. The shared section can, for example, analyze the patient's past question history using AI. The shared section can select the optimal display method based on the content of questions the patient has asked in the past. The shared section can, for example, select the optimal display method based on the content of questions the patient has asked in the past. The shared section can also prioritize displaying information of high interest from the patient's question history. The shared section can, for example, prioritize displaying information of high interest from the patient's question history. The shared section can also analyze the patient's question history and select the most effective display method. The shared section can, for example, analyze the patient's question history and select the most effective display method. This allows the system to provide the optimal display method by referring to the patient's past question history.

[0094] The shared unit can select the optimal display method when displaying responses, taking into account the patient's device information. For example, the shared unit selects the optimal display method when displaying responses, taking into account the patient's device information. The shared unit can analyze the patient's device information using AI. For example, the shared unit analyzes the patient's device information using AI. If the patient is using a smartphone, the shared unit can provide a display method adapted to the screen size. For example, if the patient is using a smartphone, the shared unit can provide a display method adapted to the screen size. If the patient is using a tablet, the shared unit can also provide a display method optimized for larger screens. For example, if the patient is using a tablet, the shared unit can provide a display method optimized for larger screens. If the patient is using a computer, the shared unit can also provide a display method that includes detailed information. For example, if the patient is using a computer, the shared unit can provide a display method that includes detailed information. This allows the system to provide the optimal display method by taking the patient's device information into consideration.

[0095] The shared unit can estimate the patient's emotions and adjust the response procedure based on the estimated emotions. The shared unit can estimate the patient's emotions using an emotion engine or generative AI. The shared unit can estimate the patient's emotions using an emotion engine or generative AI. The shared unit can provide simple instructions if the patient is feeling anxious. The shared unit can also provide detailed instructions if the patient is relaxed. The shared unit can also provide visually stimulating instructions if the patient is agitated. This allows the system to provide the optimal procedure for the patient by adjusting the response procedure based on the patient's emotions.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The reception desk can estimate the doctor's mood and adjust the upload timing based on that estimate. For example, if the doctor is tired, the upload timing can be delayed to allow time for rest. If the doctor is busy, the upload can be adjusted to be completed in a short time. Furthermore, if the doctor is relaxed, time can be given to upload detailed information. In this way, the burden on doctors can be reduced by adjusting the upload timing based on their mood.

[0098] The generation unit can estimate the patient's emotions and adjust the video's presentation based on those emotions. For example, if the patient is feeling anxious, it can generate a video with a calm tone of explanation. If the patient is relaxed, it can generate a video with detailed explanations. Furthermore, if the patient is agitated, it can generate a video with visually stimulating effects. By adjusting the video's presentation based on the patient's emotions, it can provide videos that are easy for the patient to understand.

[0099] The shared section can estimate the patient's emotions and adjust how videos are displayed based on those estimates. For example, if the patient is feeling anxious, a video with a calming tone can be displayed. If the patient is relaxed, a video with detailed explanations can be displayed. Furthermore, if the patient is agitated, a video with visually stimulating effects can be displayed. By adjusting how videos are displayed based on the patient's emotions, the system can provide the most optimal viewing experience for the patient.

[0100] The generation unit can estimate the patient's emotions and adjust the video length based on those estimates. For example, if the patient is feeling anxious, it can generate a short, concise video. If the patient is relaxed, it can generate a longer video with more detailed explanations. Furthermore, if the patient is agitated, it can generate a video with visually stimulating effects. By adjusting the video length based on the patient's emotions, it can provide a video of the optimal length for the patient.

[0101] The shared section can estimate the patient's emotions and adjust how responses are displayed based on those estimates. For example, if the patient is feeling anxious, a response explained in a calm tone can be displayed. If the patient is relaxed, a response with a more detailed explanation can be displayed. Furthermore, if the patient is agitated, a response with visually stimulating effects can be displayed. This allows the system to provide the most appropriate display method for the patient by adjusting how responses are displayed based on their emotions.

[0102] The reception desk can analyze a doctor's past upload history and select the optimal upload method. For example, it can prioritize suggesting upload methods that the doctor has frequently used in the past. It can also select the most efficient upload method based on the doctor's past upload history. Furthermore, it can analyze the doctor's past upload history and suggest methods with a high success rate. In this way, the optimal upload method can be selected by analyzing the doctor's past upload history.

[0103] The reception system can filter uploads based on the physician's specialty and experience. For example, it can filter to upload only information related to the physician's specialty. It can also select and upload appropriate information based on the physician's experience. Furthermore, it can upload the most relevant information considering the physician's specialty and experience. This ensures that appropriate information is uploaded by filtering based on the physician's specialty and experience.

[0104] The reception system can prioritize uploading highly relevant information by considering the doctor's geographical location during the upload process. For example, if a doctor works in a specific region, information related to that region can be prioritized. Furthermore, if a doctor works in different regions, the system can appropriately filter and upload information relevant to each region. It can also select and upload the most relevant information based on the doctor's geographical location. This allows for the appropriate provision of region-specific information by prioritizing the upload of highly relevant information based on the doctor's geographical location.

[0105] The reception desk can analyze a doctor's social media activity during the upload process and upload relevant information. For example, it can extract topics of interest from a doctor's social media activity and upload related information. It can also analyze a doctor's social media activity and prioritize uploading the information of highest interest. Furthermore, it can select and upload relevant information based on a doctor's social media activity. This allows for the uploading of information of high interest by analyzing a doctor's social media activity.

[0106] The generation unit can adjust the level of detail in a video based on the importance of the surgery during video generation. For example, for important surgeries, it can generate videos that include detailed procedures and precautions. For simpler surgeries, it can generate videos with concise explanations. Furthermore, it can generate videos with an appropriate level of detail depending on the importance of the surgery. This allows for the provision of videos with an appropriate amount of information by adjusting the level of detail based on the importance of the surgery.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The reception desk uploads the doctor's voice, facial image, and surgical procedure. For example, a doctor can upload the surgical procedure, facial image, and voice to the AI ​​generator. Step 2: The generation unit analyzes the information uploaded by the reception unit and generates a surgical explanation video using the doctor's image and voice. For example, it uses a generation AI to analyze the doctor's facial image and voice, creates a doctor avatar, and generates a video explaining the surgical procedure and precautions with easy-to-understand illustrations. Step 3: The sharing unit shares the surgical explanation video generated by the generation unit with the patient online. For example, the patient can view the surgical explanation video on their smartphone or computer, and after watching the video, they can ask questions to the doctor online. The doctor can also answer questions online and obtain consent forms if necessary.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements, including the reception unit, generation unit, and sharing unit described above, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and uploads the doctor's voice and facial image and surgical procedure. The generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the uploaded information to generate a surgical explanation video with the doctor's image and voice. The sharing unit is implemented by, for example, the control unit 46A of the smart device 14 and shares the generated surgical explanation video with the patient online. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements, including the reception unit, generation unit, and sharing unit described above, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and uploads the doctor's voice and facial image and surgical procedure. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the uploaded information to generate a surgical explanation video with the doctor's image and voice. The sharing unit is implemented, for example, by the control unit 46A of the smart glasses 214 and shares the generated surgical explanation video with the patient online. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements, including the reception unit, generation unit, and sharing unit described above, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and uploads the doctor's voice and facial image and surgical procedure. The generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the uploaded information to generate a surgical explanation video with the doctor's image and voice. The sharing unit is implemented by, for example, the control unit 46A of the headset terminal 314 and shares the generated surgical explanation video with the patient online. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements, including the reception unit, generation unit, and sharing unit described above, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and uploads the doctor's voice and facial image and surgical procedure. The generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the uploaded information to generate a surgical explanation video with the doctor's image and voice. The sharing unit is implemented by, for example, the control unit 46A of the robot 414 and shares the generated surgical explanation video with the patient online. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A reception area where you can upload the doctor's voice, facial image, and surgical procedure, The aforementioned reception unit analyzes the information uploaded and generates a surgical explanation video using the doctor's image and voice, The system includes a sharing unit that shares the surgical explanation video generated by the generation unit with the patient online. A system characterized by the following features. (Note 2) The generating unit is Analyze a doctor's facial image and voice to create a doctor's avatar. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Generate a video explaining surgical procedures and precautions with easy-to-understand illustrations. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned shared portion is, The goal is to allow patients to view surgical explanation videos using their smartphones or computers. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned shared portion is, Allows patients to ask questions to their doctor online after watching the video. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned shared portion is, This allows doctors to respond online and obtain consent forms as needed. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the doctor's emotions and adjusts the upload timing based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the doctor's past upload history to select the optimal upload method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is During the upload process, filtering is performed based on the doctor's specialty and experience. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the doctor's emotions and prioritizes the information to upload based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is During the upload process, the system prioritizes uploading highly relevant information, taking into account the doctor's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When uploading, the system analyzes the doctor's social media activity and uploads relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is The system estimates the patient's emotions and adjusts the video's presentation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating videos, adjust the level of detail based on the importance of the surgery. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating videos, different generation algorithms are applied depending on the type of surgery. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is The system estimates the patient's emotions and adjusts the video length based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating videos, the priority of the videos is determined based on when the surgeries were performed. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating videos, the order of the videos is adjusted based on the relevance of the surgeries. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned shared portion is, The system estimates the patient's emotions and adjusts how the video is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned shared portion is, When sharing videos, the system selects the optimal display method by referring to the patient's past viewing history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned shared portion is, When sharing videos, the optimal display method is selected considering the patient's device information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned shared portion is, The system estimates the patient's emotions and adjusts the video's operation instructions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned shared portion is, When sharing videos, the optimal display method is selected considering the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned shared portion is, When sharing videos, the system analyzes the patient's social media activity and displays relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned shared portion is, Allows patients to ask questions to their doctor online after watching the video. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned shared portion is, This allows doctors to respond online and obtain consent forms as needed. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned shared portion is, The system estimates the patient's emotions and adjusts how responses are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned shared portion is, When displaying the answers, the system will refer to the patient's past question history to select the most appropriate display method. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned shared portion is, When displaying responses, the optimal display method is selected considering the patient's device information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned shared portion is, The system estimates the patient's emotions and adjusts the response procedure based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where you can upload the doctor's voice, facial image, and surgical procedure, The aforementioned reception unit analyzes the information uploaded and generates a surgical explanation video using the doctor's image and voice, The system includes a sharing unit that shares the surgical explanation video generated by the generation unit with the patient online. A system characterized by the following features.

2. The generating unit is Analyze a doctor's facial image and voice to create a doctor's avatar. The system according to feature 1.

3. The generating unit is Generate a video explaining surgical procedures and precautions with easy-to-understand illustrations. The system according to feature 1.

4. The aforementioned shared portion is, The goal is to allow patients to view surgical explanation videos using their smartphones or computers. The system according to feature 1.

5. The aforementioned shared portion is, Allows patients to ask questions to their doctor online after watching the video. The system according to feature 1.

6. The aforementioned shared portion is, This allows doctors to respond online and obtain consent forms as needed. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the doctor's emotions and adjusts the upload timing based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the doctor's past upload history to select the optimal upload method. The system according to feature 1.

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