Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of claims
The following claims have been rejected or allowed for the following reasons:
Claim(s) 1-20 is rejected under 35 USC § 103
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. 17/731,008, filed on 4/27/22.
Information Disclosure Statement
The information disclosure statement/statements (IDS) were filed on 2/2/23. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 3-4, 7-8, 10-11, 14-15, 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over as applied to Shelton (US 20210350897 A1), in further view of Goncalves (US 20170279734 A1).
Regarding claim 1 Shelton teaches determining that sensor data received during the surgical procedure indicates an adverse patient condition; (Shelton [0012] reads “in another aspect, a drug administration method is provided that in one embodiment includes delivering a drug from a drug administration device to a patient, sensing information, using a sensor, relating to a condition regarding at least one of the patient, … to at least one of analyzing an outcome of a surgical procedure performed on the patient,”);
generating, using a machine learning module executed on the particular computer, instructions for a surgical robot to modify an amount of medication administered to a patient or adjust anesthesiology parameters based on the sensor data; (Shelton [0138] reads “The artificial intelligence and machine learning system configured within the system 700 can include data processing components, each associated with a data processor, to monitor the effectiveness of the drug that is delivered via the drug administration device 500 (and/or additional drug administration device(s) 500 and/or drug housing(s) 630). In at least some embodiments, the system 700 can be configured to process the drug administration device usage and drug delivery data that has been aggregated with the clinical outcome data to determine how well the drug provides a therapeutic benefit and if the drug causes the patient to experience any side effects which may be reported via the clinical outcome data, including post-operative data. For example, the system 700 may determine a correlation between a particular drug (or a particular drug delivery schedule) and self-reported and/or sensed symptoms of nausea. The system 700 may further process data associated with an individual patient's medical history to determine a suitable dosage or delivery schedule which is less likely to cause nausea. In this way, new drugs or drug delivery regimens can be determined which produce a desired clinical outcome for a patient population. For another example, the system 700 may determine that patients receiving a different drug than the drug delivered to the patient did not experience a side effect experience by the patient receiving the drug and/or experienced the side effect less severely than the patient receiving the drug. The system 700 may thus determine that the drug received by the other patients would be a good alternative to suggest for the patient receiving the drug in an effort to stop the patient from experiencing the side effect or to reduce the side effect's severity.”);
and executing, by the surgical robot, the instructions during the surgical procedure to stabilize the sensor data and avoid the adverse patient condition. (Shelton [0139] reads “In some embodiments, the system 700 can be configured to electronically transmit an instruction, which is based on the system's analysis of previously received data, to the drug administration device 500. The drug administration device 500 can be configured to execute the received instruction on board the drug administration device 500 to change at least one aspect of the device's/housing's functionality. The system 700 can thus be configured to remotely control the drug administration device 500.”);
Shelton does not teach A computer-implemented method comprising: identifying a particular computer based on a complexity of a surgical procedure and a computation time of the particular computer for providing the real-time surgical assistance;
Goncalves in analogous art, teaches A computer-implemented method comprising: identifying a particular computer based on a complexity of a surgical procedure and a computation time of the particular computer for providing the real-time surgical assistance; (Goncalves [0047] reads “Each computing task and/or sub-task is preferably associated with a corresponding type 306 that may be used, in some embodiments, for determining which of a plurality of computer resources to which to assign the sub-task. … For example, a set of available sub-task types may include descriptions of different types of tasks, such as, without limitation: “Arithmetic,” “Linear regression,” and “Volume calculation,” (e.g., in the context of computing tasks related to mathematics); or “Simple injury,” “Complex injury,” and “Surgical procedure” (e.g., in the context of computing tasks related to medical care processes and/or the automated processing of medical records).” And [0082] reads “In another example, a computer resource allocation rule may comprise a formula for determining, based on a respective current computing workload for a networked computer resource, whether a respective maximum computing workload for that networked computer resource would be exceeded for an associated predetermined period of time if at least one of the first sub-task and the second sub-task were assigned to that networked computer resource.”);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Shelton with that of Goncalves to provide a system that allows for the dynamic allocation of computer resources. (Goncalves [0002] reads “Some types of computing systems are configured to receive and process a plurality of computing tasks (e.g., from different sources). Despite the importance of managing computing tasks in an efficient manner, known methods for allocating computer resources to handle requests to process computing tasks fail to take into account the suitability of different computer resources to handle a given computing task and/or fail to efficiently allocate sub-tasks of a given computing task.”);
Regarding claim 3 Shelton/Goncalves teaches The method of claim 1, wherein avoiding the adverse condition comprises: reducing a recovery time and post-operative pain of the patient. (Shelton [0107] reads “Applying cloud computer data processing techniques on the data collected by the devices 1 a-1 n/2 a-2 m the surgical data network may provide improved surgical outcomes, reduced costs, and/or improved patient satisfaction.” One with ordinary skill in the art would appreciate that the improved surgical outcomes and patient satisfaction comprises a reduction in recover time and a decrease in the pain felt by the patient.);
Regarding claim 4 Shelton/Goncalves teaches The method of claim 1, wherein determining that the sensor data indicates the adverse patient condition comprises: performing at least one of a linear correlation, a parabolic correlation, or a logarithmic regression correlation. (Shelton [0118] reads “The computer system that receives the data from the drug administration device (and/or other computer system that receives the data therefrom) can be configured to analyze the data received from the drug administration device in a variety of ways to help achieve one or more of these and/or other goals, such as by any one or more of correlating the patient's use of the drug with the patient's clinical outcome, performing a cost analysis that includes comparing the patient's clinical outcome with clinical outcomes of other patients receiving a different drug than the drug delivered to the patient via the drug administration device, comparing side effects experienced by the patient with side effects experienced by other patients receiving a different drug than the drug delivered to the patient, determining whether the drug was delivered to the patient in compliance with the patient's treatment plan, identifying a malfunction in the administration of the drug, determining that additional data is needed from the drug administration device and triggering a request for the additional data to be wirelessly transmitted from the other device to the drug administration device, and predictive modeling of the patient's clinical outcome.” One with ordinary skill in the art would appreciate that one would use standard mathematical correlations to understand these features. This would include the correlation methods stated.);
Regarding claim 7 Shelton/Goncalves teaches The method of claim 1, comprising determining a physical condition of the patient by analyzing diagnostic images of the patient taken using one or more imaging devices. (Shelton [0119] reads “In various embodiments, a sensor includes an image capturing device such as a camera, and a processor is configured to analyze image(s) and/or video(s) captured by the image capturing device,” and [0008] reads “a sensor configured to sense information relating to a condition regarding at least one of the patient, the drug, and the drug administration device,”);
Regarding claim 8 Shelton teaches determine that sensor data received during the surgical procedure indicates an adverse patient condition; (Shelton [0012] reads “in another aspect, a drug administration method is provided that in one embodiment includes delivering a drug from a drug administration device to a patient, sensing information, using a sensor, relating to a condition regarding at least one of the patient, … to at least one of analyzing an outcome of a surgical procedure performed on the patient,”);
generate, using a machine learning module executed on the particular computer, instructions for a surgical robot to modify an amount of medication administered to a patient or adjust anesthesiology parameters based on the sensor data; (Shelton [0138] reads “The artificial intelligence and machine learning system configured within the system 700 can include data processing components, each associated with a data processor, to monitor the effectiveness of the drug that is delivered via the drug administration device 500 (and/or additional drug administration device(s) 500 and/or drug housing(s) 630). In at least some embodiments, the system 700 can be configured to process the drug administration device usage and drug delivery data that has been aggregated with the clinical outcome data to determine how well the drug provides a therapeutic benefit and if the drug causes the patient to experience any side effects which may be reported via the clinical outcome data, including post-operative data. For example, the system 700 may determine a correlation between a particular drug (or a particular drug delivery schedule) and self-reported and/or sensed symptoms of nausea. The system 700 may further process data associated with an individual patient's medical history to determine a suitable dosage or delivery schedule which is less likely to cause nausea. In this way, new drugs or drug delivery regimens can be determined which produce a desired clinical outcome for a patient population. For another example, the system 700 may determine that patients receiving a different drug than the drug delivered to the patient did not experience a side effect experience by the patient receiving the drug and/or experienced the side effect less severely than the patient receiving the drug. The system 700 may thus determine that the drug received by the other patients would be a good alternative to suggest for the patient receiving the drug in an effort to stop the patient from experiencing the side effect or to reduce the side effect's severity.”);
and execute, by the surgical robot, the instructions during the surgical procedure to stabilize the sensor data and avoid the adverse patient condition. (Shelton [0139] reads “In some embodiments, the system 700 can be configured to electronically transmit an instruction, which is based on the system's analysis of previously received data, to the drug administration device 500. The drug administration device 500 can be configured to execute the received instruction on board the drug administration device 500 to change at least one aspect of the device's/housing's functionality. The system 700 can thus be configured to remotely control the drug administration device 500.”);
Shelton does not teach A non-transitory computer-readable storage medium storing computer instructions, which when executed by one or more computer processors, cause the one or more computer processors to: identify a particular computer based on a complexity of a surgical procedure and a computation time of the particular computer for providing the real-time surgical assistance;
Goncalves in analogous art, teaches A non-transitory computer-readable storage medium storing computer instructions, which when executed by one or more computer processors, cause the one or more computer processors to: identify a particular computer based on a complexity of a surgical procedure and a computation time of the particular computer for providing the real-time surgical assistance; (Goncalves [0047] reads “Each computing task and/or sub-task is preferably associated with a corresponding type 306 that may be used, in some embodiments, for determining which of a plurality of computer resources to which to assign the sub-task. … For example, a set of available sub-task types may include descriptions of different types of tasks, such as, without limitation: “Arithmetic,” “Linear regression,” and “Volume calculation,” (e.g., in the context of computing tasks related to mathematics); or “Simple injury,” “Complex injury,” and “Surgical procedure” (e.g., in the context of computing tasks related to medical care processes and/or the automated processing of medical records).” And [0082] reads “In another example, a computer resource allocation rule may comprise a formula for determining, based on a respective current computing workload for a networked computer resource, whether a respective maximum computing workload for that networked computer resource would be exceeded for an associated predetermined period of time if at least one of the first sub-task and the second sub-task were assigned to that networked computer resource.”);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Shelton with that of Goncalves to provide a system that allows for the dynamic allocation of computer resources. (Goncalves [0002] reads “Some types of computing systems are configured to receive and process a plurality of computing tasks (e.g., from different sources). Despite the importance of managing computing tasks in an efficient manner, known methods for allocating computer resources to handle requests to process computing tasks fail to take into account the suitability of different computer resources to handle a given computing task and/or fail to efficiently allocate sub-tasks of a given computing task.”);
Regarding claim 10 Shelton/Goncalves teaches The non-transitory computer-readable storage medium of claim 8, wherein avoiding the adverse condition comprises: reducing a recovery time and post-operative pain of the patient. (Shelton [0107] reads “Applying cloud computer data processing techniques on the data collected by the devices 1 a-1 n/2 a-2 m the surgical data network may provide improved surgical outcomes, reduced costs, and/or improved patient satisfaction.” One with ordinary skill in the art would appreciate that the improved surgical outcomes and patient satisfaction comprises a reduction in recover time and a decrease in the pain felt by the patient.);
Regarding claim 11 Shelton/Goncalves teaches The non-transitory computer-readable storage medium of claim 8, wherein determining that the sensor data indicates the adverse patient condition comprises: performing at least one of a linear correlation, a parabolic correlation, or a logarithmic regression correlation. (Shelton [0118] reads “The computer system that receives the data from the drug administration device (and/or other computer system that receives the data therefrom) can be configured to analyze the data received from the drug administration device in a variety of ways to help achieve one or more of these and/or other goals, such as by any one or more of correlating the patient's use of the drug with the patient's clinical outcome, performing a cost analysis that includes comparing the patient's clinical outcome with clinical outcomes of other patients receiving a different drug than the drug delivered to the patient via the drug administration device, comparing side effects experienced by the patient with side effects experienced by other patients receiving a different drug than the drug delivered to the patient, determining whether the drug was delivered to the patient in compliance with the patient's treatment plan, identifying a malfunction in the administration of the drug, determining that additional data is needed from the drug administration device and triggering a request for the additional data to be wirelessly transmitted from the other device to the drug administration device, and predictive modeling of the patient's clinical outcome.” One with ordinary skill in the art would appreciate that one would use standard mathematical correlations to understand these features. This would include the correlation methods stated.);
Regarding claim 14 Shelton/Goncalves teaches The non-transitory computer-readable storage medium of claim 8, wherein the computer instructions cause the one or more computer processors to determine a physical condition of the patient by analyzing diagnostic images of the patient taken using one or more imaging devices. (Shelton [0119] reads “In various embodiments, a sensor includes an image capturing device such as a camera, and a processor is configured to analyze image(s) and/or video(s) captured by the image capturing device,” and [0008] reads “a sensor configured to sense information relating to a condition regarding at least one of the patient, the drug, and the drug administration device,”);
Regarding claim 15 Shelton teaches determine that sensor data received during the surgical procedure indicates an adverse patient condition; (Shelton [0012] reads “in another aspect, a drug administration method is provided that in one embodiment includes delivering a drug from a drug administration device to a patient, sensing information, using a sensor, relating to a condition regarding at least one of the patient, … to at least one of analyzing an outcome of a surgical procedure performed on the patient,”);
generate, using a machine learning module executed on the particular computer, instructions for a surgical robot to modify an amount of medication administered to a patient or adjust anesthesiology parameters based on the sensor data; (Shelton [0138] reads “The artificial intelligence and machine learning system configured within the system 700 can include data processing components, each associated with a data processor, to monitor the effectiveness of the drug that is delivered via the drug administration device 500 (and/or additional drug administration device(s) 500 and/or drug housing(s) 630). In at least some embodiments, the system 700 can be configured to process the drug administration device usage and drug delivery data that has been aggregated with the clinical outcome data to determine how well the drug provides a therapeutic benefit and if the drug causes the patient to experience any side effects which may be reported via the clinical outcome data, including post-operative data. For example, the system 700 may determine a correlation between a particular drug (or a particular drug delivery schedule) and self-reported and/or sensed symptoms of nausea. The system 700 may further process data associated with an individual patient's medical history to determine a suitable dosage or delivery schedule which is less likely to cause nausea. In this way, new drugs or drug delivery regimens can be determined which produce a desired clinical outcome for a patient population. For another example, the system 700 may determine that patients receiving a different drug than the drug delivered to the patient did not experience a side effect experience by the patient receiving the drug and/or experienced the side effect less severely than the patient receiving the drug. The system 700 may thus determine that the drug received by the other patients would be a good alternative to suggest for the patient receiving the drug in an effort to stop the patient from experiencing the side effect or to reduce the side effect's severity.”);
and execute, by the surgical robot, the instructions during the surgical procedure to stabilize the sensor data and avoid the adverse patient condition. (Shelton [0139] reads “In some embodiments, the system 700 can be configured to electronically transmit an instruction, which is based on the system's analysis of previously received data, to the drug administration device 500. The drug administration device 500 can be configured to execute the received instruction on board the drug administration device 500 to change at least one aspect of the device's/housing's functionality. The system 700 can thus be configured to remotely control the drug administration device 500.”);
Shelton does not teach A surgical system comprising: one or more computer processors; and a non-transitory computer-readable storage medium storing computer instructions, which when executed by the one or more computer processors, cause the surgical system to: identify a particular computer based on a complexity of a surgical procedure and a computation time of the particular computer for providing the real- time surgical assistance;
Goncalves in analogous art, teaches A surgical system comprising: one or more computer processors; and a non-transitory computer-readable storage medium storing computer instructions, which when executed by the one or more computer processors, cause the surgical system to: identify a particular computer based on a complexity of a surgical procedure and a computation time of the particular computer for providing the real- time surgical assistance; (Goncalves [0047] reads “Each computing task and/or sub-task is preferably associated with a corresponding type 306 that may be used, in some embodiments, for determining which of a plurality of computer resources to which to assign the sub-task. … For example, a set of available sub-task types may include descriptions of different types of tasks, such as, without limitation: “Arithmetic,” “Linear regression,” and “Volume calculation,” (e.g., in the context of computing tasks related to mathematics); or “Simple injury,” “Complex injury,” and “Surgical procedure” (e.g., in the context of computing tasks related to medical care processes and/or the automated processing of medical records).” And [0082] reads “In another example, a computer resource allocation rule may comprise a formula for determining, based on a respective current computing workload for a networked computer resource, whether a respective maximum computing workload for that networked computer resource would be exceeded for an associated predetermined period of time if at least one of the first sub-task and the second sub-task were assigned to that networked computer resource.”);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Shelton with that of Goncalves to provide a system that allows for the dynamic allocation of computer resources. (Goncalves [0002] reads “Some types of computing systems are configured to receive and process a plurality of computing tasks (e.g., from different sources). Despite the importance of managing computing tasks in an efficient manner, known methods for allocating computer resources to handle requests to process computing tasks fail to take into account the suitability of different computer resources to handle a given computing task and/or fail to efficiently allocate sub-tasks of a given computing task.”);
Regarding claim 17 Shelton/Goncalves teaches The surgical system of claim 15, wherein avoiding the adverse condition comprises: reducing a recovery time and post-operative pain of the patient. (Shelton [0107] reads “Applying cloud computer data processing techniques on the data collected by the devices 1 a-1 n/2 a-2 m the surgical data network may provide improved surgical outcomes, reduced costs, and/or improved patient satisfaction.” One with ordinary skill in the art would appreciate that the improved surgical outcomes and patient satisfaction comprises a reduction in recover time and a decrease in the pain felt by the patient.);
Regarding claim 18 Shelton/Goncalves teaches The surgical system of claim 15, wherein determining that the sensor data indicates the adverse patient condition comprises: performing at least one of a linear correlation, a parabolic correlation, or a logarithmic regression correlation. (Shelton [0118] reads “The computer system that receives the data from the drug administration device (and/or other computer system that receives the data therefrom) can be configured to analyze the data received from the drug administration device in a variety of ways to help achieve one or more of these and/or other goals, such as by any one or more of correlating the patient's use of the drug with the patient's clinical outcome, performing a cost analysis that includes comparing the patient's clinical outcome with clinical outcomes of other patients receiving a different drug than the drug delivered to the patient via the drug administration device, comparing side effects experienced by the patient with side effects experienced by other patients receiving a different drug than the drug delivered to the patient, determining whether the drug was delivered to the patient in compliance with the patient's treatment plan, identifying a malfunction in the administration of the drug, determining that additional data is needed from the drug administration device and triggering a request for the additional data to be wirelessly transmitted from the other device to the drug administration device, and predictive modeling of the patient's clinical outcome.” One with ordinary skill in the art would appreciate that one would use standard mathematical correlations to understand these features. This would include the correlation methods stated.);
Claim(s) 2, 6, 9, 13, 16, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over as applied to Shelton/Goncalves, in further view of Wolf (US 20200273561 A1).
Regarding claim 2 Shelton/Goncalves teaches The method of claim 1.
Shelton/Goncalves does not teach wherein identifying the particular computer comprises: simulating the surgical procedure to determine the complexity; and determining that the complexity exceeds a threshold.
Wolf in analogous art, teaches wherein identifying the particular computer comprises: simulating the surgical procedure to determine the complexity; (Wolf [0250] reads “For example, a machine learning algorithm (such as a Generative Adversarial Network) may be used to train a machine learning model (such as an artificial neural network, a deep learning model, a convolutional neural network, etc.) using training examples to generate simulations of surgical procedures based on groups of intraoperative events and/or frames of surgical video footage, and the trained machine learning model may be used to analyze the identified group of intraoperative events likely to be encountered and/or the identified specific frames in specific sets of the plurality of sets of surgical video footage corresponding to the identified group of intraoperative events and generate the simulated surgical procedure.”);
and determining that the complexity exceeds a threshold. (Wolf [0294] reads “Embodiments of the present disclosure may further include comparing the first and/or second surgical complexity levels to a selected threshold.”);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Shelton/Goncalves with that of Wolf to provide a surgical system that is equipped with cameras which is designed to assist the surgeon during the surgical task. (Wolf [0003] reads “When preparing for a surgical procedure, it may be beneficial for a surgeon to view video footage depicting certain surgical events, including events that may have certain characteristics. In addition, during a surgical procedure, it may be helpful to capture and analyze videos to provide various types of decision support to surgeons. Further, it may be helpful analyze surgical videos to facilitate postoperative activity.”);
Regarding claim 6 Shelton/Goncalves teaches The method of claim 1.
Shelton/Goncalves does not teach comprising identify a surgical site based on real-time images during the surgical procedure. (Wolf [0008] reads “Consistent with disclosed embodiments, systems, methods, and computer readable media related to video indexing are disclosed. The video indexing may include accessing video footage to be indexed, including footage of a particular surgical procedure, which may be analyzed to identify a video footage location associated with a surgical phase of the particular surgical procedure. A phase tag may be generated and may be associated with the video footage location. The video indexing may include analyzing the video footage to identify an event location of a particular intraoperative surgical event within the surgical phase and associating an event tag with the event location of the particular intraoperative surgical event. Further, an event characteristic associated with the particular intraoperative surgical event may be stored.”);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Shelton/Goncalves with that of Wolf to provide a surgical system that is equipped with cameras which is designed to assist the surgeon during the surgical task. (Wolf [0003] reads “When preparing for a surgical procedure, it may be beneficial for a surgeon to view video footage depicting certain surgical events, including events that may have certain characteristics. In addition, during a surgical procedure, it may be helpful to capture and analyze videos to provide various types of decision support to surgeons. Further, it may be helpful analyze surgical videos to facilitate postoperative activity.”);
Regarding claim 9 Shelton/Goncalves teaches The storage medium of claim 8.
Shelton/Goncalves does not teach wherein the computer instructions to identify the particular computer cause the one or more computer processors to: simulate the surgical procedure to determine the complexity; and determine that the complexity exceeds a threshold.
Wolf in analogous art, teaches wherein the computer instructions to identify the particular computer cause the one or more computer processors to: simulate the surgical procedure to determine the complexity; (Wolf [0250] reads “For example, a machine learning algorithm (such as a Generative Adversarial Network) may be used to train a machine learning model (such as an artificial neural network, a deep learning model, a convolutional neural network, etc.) using training examples to generate simulations of surgical procedures based on groups of intraoperative events and/or frames of surgical video footage, and the trained machine learning model may be used to analyze the identified group of intraoperative events likely to be encountered and/or the identified specific frames in specific sets of the plurality of sets of surgical video footage corresponding to the identified group of intraoperative events and generate the simulated surgical procedure.”);
and determine that the complexity exceeds a threshold. (Wolf [0294] reads “Embodiments of the present disclosure may further include comparing the first and/or second surgical complexity levels to a selected threshold.”);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Shelton/Goncalves with that of Wolf to provide a surgical system that is equipped with cameras which is designed to assist the surgeon during the surgical task. (Wolf [0003] reads “When preparing for a surgical procedure, it may be beneficial for a surgeon to view video footage depicting certain surgical events, including events that may have certain characteristics. In addition, during a surgical procedure, it may be helpful to capture and analyze videos to provide various types of decision support to surgeons. Further, it may be helpful analyze surgical videos to facilitate postoperative activity.”);
Regarding claim 13 Shelton/Goncalves teaches The non-transitory computer-readable storage medium of claim 8.
Shelton/Goncalves does not teach wherein the computer instructions cause the one or more computer processors to identify a surgical site based on real-time images during the surgical procedure.
Wolf in analogous art, teaches wherein the computer instructions cause the one or more computer processors to identify a surgical site based on real-time images during the surgical procedure. (Wolf [0008] reads “Consistent with disclosed embodiments, systems, methods, and computer readable media related to video indexing are disclosed. The video indexing may include accessing video footage to be indexed, including footage of a particular surgical procedure, which may be analyzed to identify a video footage location associated with a surgical phase of the particular surgical procedure. A phase tag may be generated and may be associated with the video footage location. The video indexing may include analyzing the video footage to identify an event location of a particular intraoperative surgical event within the surgical phase and associating an event tag with the event location of the particular intraoperative surgical event. Further, an event characteristic associated with the particular intraoperative surgical event may be stored.”);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Shelton/Goncalves with that of Wolf to provide a surgical system that is equipped with cameras which is designed to assist the surgeon during the surgical task. (Wolf [0003] reads “When preparing for a surgical procedure, it may be beneficial for a surgeon to view video footage depicting certain surgical events, including events that may have certain characteristics. In addition, during a surgical procedure, it may be helpful to capture and analyze videos to provide various types of decision support to surgeons. Further, it may be helpful analyze surgical videos to facilitate postoperative activity.”);
Regarding claim 16 Shelton/Goncalves teaches The surgical system of claim 15.
Shelton/Goncalves does not teach wherein the computer instructions to identify the particular computer cause the surgical system to: simulate the surgical procedure to determine the complexity; and determine that the complexity exceeds a threshold.
Wolf in analogous art, teaches The surgical system of claim 15, wherein the computer instructions to identify the particular computer cause the surgical system to: simulate the surgical procedure to determine the complexity; (Wolf [0250] reads “For example, a machine learning algorithm (such as a Generative Adversarial Network) may be used to train a machine learning model (such as an artificial neural network, a deep learning model, a convolutional neural network, etc.) using training examples to generate simulations of surgical procedures based on groups of intraoperative events and/or frames of surgical video footage, and the trained machine learning model may be used to analyze the identified group of intraoperative events likely to be encountered and/or the identified specific frames in specific sets of the plurality of sets of surgical video footage corresponding to the identified group of intraoperative events and generate the simulated surgical procedure.”);
and determine that the complexity exceeds a threshold. (Wolf [0294] reads “Embodiments of the present disclosure may further include comparing the first and/or second surgical complexity levels to a selected threshold.”);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Shelton/Goncalves with that of Wolf to provide a surgical system that is equipped with cameras which is designed to assist the surgeon during the surgical task. (Wolf [0003] reads “When preparing for a surgical procedure, it may be beneficial for a surgeon to view video footage depicting certain surgical events, including events that may have certain characteristics. In addition, during a surgical procedure, it may be helpful to capture and analyze videos to provide various types of decision support to surgeons. Further, it may be helpful analyze surgical videos to facilitate postoperative activity.”);
Regarding claim 20 Shelton/Goncalves teaches The surgical system of claim 15.
Shelton/Goncalves does not teach wherein the computer instructions cause the surgical system to identify a surgical site based on real-time images during the surgical procedure.
Wolf in analogous art, teaches wherein the computer instructions cause the surgical system to identify a surgical site based on real-time images during the surgical procedure. (Wolf [0008] reads “Consistent with disclosed embodiments, systems, methods, and computer readable media related to video indexing are disclosed. The video indexing may include accessing video footage to be indexed, including footage of a particular surgical procedure, which may be analyzed to identify a video footage location associated with a surgical phase of the particular surgical procedure. A phase tag may be generated and may be associated with the video footage location. The video indexing may include analyzing the video footage to identify an event location of a particular intraoperative surgical event within the surgical phase and associating an event tag with the event location of the particular intraoperative surgical event. Further, an event characteristic associated with the particular intraoperative surgical event may be stored.”);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Shelton/Goncalves with that of Wolf to provide a surgical system that is equipped with cameras which is designed to assist the surgeon during the surgical task. (Wolf [0003] reads “When preparing for a surgical procedure, it may be beneficial for a surgeon to view video footage depicting certain surgical events, including events that may have certain characteristics. In addition, during a surgical procedure, it may be helpful to capture and analyze videos to provide various types of decision support to surgeons. Further, it may be helpful analyze surgical videos to facilitate postoperative activity.”);
Claim(s) 5, 12, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over as applied to Shelton/Goncalves in further view of Satish (US 20130303870 A1).
Regarding claim 5 Shelton/Goncalves teaches The method of claim 1,
Shelton/Goncalves does not teach comprising generating a recommendation that the surgical procedure be delayed to avoid the adverse condition.
Satish in analogous art, teaches comprising generating a recommendation that the surgical procedure be delayed to avoid the adverse condition. (Satish [0066] reads “Block S192 can additionally or alternatively provide suggestions related to patient care, such as to start or stop intravenous transfusion or infusion, to set a particular volume or volume flow rate of transfused or infused fluid, to respond to a hemorrhage, to delay a surgical operation until a patient condition reaches an acceptable status, to speed up a surgical operation before a patient condition reaches a certain status, or any other suitable suggestion.”);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Shelton/Goncalves with that of Satish to provide a system that accurately monitors the patient to assess their condition for surgical events. (Satish [0004] reads “Overestimation and underestimation of patient blood loss is a significant contributor to high operating and surgical costs for hospitals, clinics and other medical facilities. Specifically, overestimation of patient blood loss results in wasted transfusion-grade blood and higher operating costs for medical institutions and can lead to blood shortages. Underestimation of patient blood loss is a key contributor of delayed resuscitation and transfusion in the event of hemorrhage and has been associated with billions of dollars in avoidable patient infections, re-hospitalizations, and lawsuits annually. Uninformed estimation of varying patient hematocrit during hemorrhage, blood transfusion, and intravenous saline infusion further exacerbates inaccurate estimation of patient red blood cell loss and negatively impacts the timing and quantity of fluids supplied to a patient intravenously. Thus, there is a need in the surgical field for a new and useful system and method for managing blood loss of a patient. This invention provides such a new and useful system and method.”);
Regarding claim 12 Shelton/Goncalves The non-transitory computer-readable storage medium of claim 8.
Shelton/Goncalves does not teach wherein the computer instructions cause the one or more computer processors to generate a recommendation that the surgical procedure be delayed to avoid the adverse condition.
Satish in analogous art, teaches wherein the computer instructions cause the one or more computer processors to generate a recommendation that the surgical procedure be delayed to avoid the adverse condition. (Satish [0066] reads “Block S192 can additionally or alternatively provide suggestions related to patient care, such as to start or stop intravenous transfusion or infusion, to set a particular volume or volume flow rate of transfused or infused fluid, to respond to a hemorrhage, to delay a surgical operation until a patient condition reaches an acceptable status, to speed up a surgical operation before a patient condition reaches a certain status, or any other suitable suggestion.”);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Shelton/Goncalves with that of Satish to provide a system that accurately monitors the patient to assess their condition for surgical events. (Satish [0004] reads “Overestimation and underestimation of patient blood loss is a significant contributor to high operating and surgical costs for hospitals, clinics and other medical facilities. Specifically, overestimation of patient blood loss results in wasted transfusion-grade blood and higher operating costs for medical institutions and can lead to blood shortages. Underestimation of patient blood loss is a key contributor of delayed resuscitation and transfusion in the event of hemorrhage and has been associated with billions of dollars in avoidable patient infections, re-hospitalizations, and lawsuits annually. Uninformed estimation of varying patient hematocrit during hemorrhage, blood transfusion, and intravenous saline infusion further exacerbates inaccurate estimation of patient red blood cell loss and negatively impacts the timing and quantity of fluids supplied to a patient intravenously. Thus, there is a need in the surgical field for a new and useful system and method for managing blood loss of a patient. This invention provides such a new and useful system and method.”);
Regarding claim 19 Shelton/Goncalves teaches The surgical system of claim 15.
Shelton/Goncalves does not teach wherein the computer instructions cause the surgical system to generate a recommendation that the surgical procedure be delayed to avoid the adverse condition.
Satish in analogous art, teaches wherein the computer instructions cause the surgical system to generate a recommendation that the surgical procedure be delayed to avoid the adverse condition. (Satish [0066] reads “Block S192 can additionally or alternatively provide suggestions related to patient care, such as to start or stop intravenous transfusion or infusion, to set a particular volume or volume flow rate of transfused or infused fluid, to respond to a hemorrhage, to delay a surgical operation until a patient condition reaches an acceptable status, to speed up a surgical operation before a patient condition reaches a certain status, or any other suitable suggestion.”);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Shelton/Goncalves with that of Satish to provide a system that accurately monitors the patient to assess their condition for surgical events. (Satish [0004] reads “Overestimation and underestimation of patient blood loss is a significant contributor to high operating and surgical costs for hospitals, clinics and other medical facilities. Specifically, overestimation of patient blood loss results in wasted transfusion-grade blood and higher operating costs for medical institutions and can lead to blood shortages. Underestimation of patient blood loss is a key contributor of delayed resuscitation and transfusion in the event of hemorrhage and has been associated with billions of dollars in avoidable patient infections, re-hospitalizations, and lawsuits annually. Uninformed estimation of varying patient hematocrit during hemorrhage, blood transfusion, and intravenous saline infusion further exacerbates inaccurate estimation of patient red blood cell loss and negatively impacts the timing and quantity of fluids supplied to a patient intravenously. Thus, there is a need in the surgical field for a new and useful system and method for managing blood loss of a patient. This invention provides such a new and useful system and method.”);
Other references not Cited
Throughout examination other references were found that could read onto the prior art. Though these references were not used in this examination they could be used in future examination and could read on the contents of the current disclosure. These references are, Abri (US 8452615 B2); Asselmann (US 20210313051 A1); Makrinich (US 20210313052 A1).
Conclusion
Any inquiry concerning