DETAILED ACTION
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 .
Notice to Applicant
Receipt of Applicant’s Supplemental Amendment filed December 15, 2025 is entered and has been examined.
Response to Amendment
Claims 1, 4, 6, 9-10, 13, 15, 18 have been amended. Claims 3, 5, 8, 12, 14, and 17 have not been modified. Claims 2, 7, 11, and 16 have been cancelled. Claims 19 and 20 have been added. Claims 1, 3-6, 8-10, 12-15, and 17-20 are pending and are provided to be examined upon their merits.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on November 26, 2025 has been entered.
Response to Arguments
Applicant’s arguments filed December 15, 2025 have been fully considered but they are not persuasive. A response is provided below.
Applicant argues 35 U.S.C. §101 Rejections, pg. 11 of Remarks:
Regarding Step 2A, Prong 2, Applicant argues that the claims reflect an improvement to another technology or technical field. Specifically, paragraphs [0048], [0052], and [0060] of the present application provide an improvement in the field of medical procedure/surgery.
Applicant first argues that conventional human analysis is insufficient to enumerate or track all correlations, which may involve “multiple medical devices and numerous clinical and operational factors, whose inputs, outputs, and interdependencies can be complex and dynamically coupled”. While certain embodiments of the claim language would be too complex to be performed through human means as indicated by Applicant, there is no evidence of such complexity in the particular claim language that would indicate that the tasks performed within the abstract idea could not practically be performed by human means (see MPEP § 2106.04(a)(2)(III)(A) citing SRI Int’l, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1304 (Fed. Cir. 2019)). Additionally, merely improving the speed or efficiency of an otherwise-abstract data correlation process by using computer components to implement the process does not confer patent eligibility; see MPEP 2106.05(f)(2), which recites: “"claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).”
Applicant then cites relevant passages from paragraphs [0048], [0052], and [0060]. Examiner agrees that the passages provided may provide a practical application in an improvement to the functioning of a smart bed through its correlations with an electrophysiological monitoring device. However, the claims do not reflect this level of specificity. Instead, the claims instead recite “a plurality of internet-of-things (IoT) capable medical device”, which may “include one or more of: an anesthesia machine, an electrophysiological monitoring device, an X-ray machine, a smart bed, and a drug delivery device” (new dependent claim 20). Examiner notes that a relevant consideration for evaluating whether additional elements integrate a judicial exception into a practical application includes “Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b)”; see MPEP 2106.04(d). MPEP 2106.05(b) specifically notes that a relevant consideration for a practical application regarding a particular device is “The particularity or generality of the elements of the machine or apparatus, i.e., the degree to which the machine in the claim can be specifically identified (not any and all machines).”
Examiner notes that the smart bed and anesthesia device are both described with particularity in their interactions with an electrophysiological monitoring device in [0048] and [0052], respectively. Relevant portions are provided below for Applicant convenience.
[0048]: “In an exemplary embodiment, when the correlation indicates that an increase in the delivery rate, the proportion, and the concentration of medical gases provided by the anesthesia machine 108-1 reduces the electrical activity associated with the brain, the nerve pathways, and the muscle provided by the electrophysiological monitoring device 108-2, the machine learning module 222 of the server processor 210 optimizes this correlation based on the patient data, such as the metabolism rate of the patient. For example, for a patient with high metabolism rate, the machine learning module 222 of the server processor 210 may optimize the correlation to increase the concentration of oxygen to regulate the electrical activity associated with the brain, the nerve pathways, and the muscle in the patients with high metabolism rate.”
[0052]: “For example, upon receiving the optimized correlations, the smart bed 108-4 obtains the electrical activity associated with the brain, the nerve pathways, and the muscle from the electrophysiological monitoring device 108-2 and adjusts its medical input parameters, such as, the angle based on the one or more optimized correlations and the electrical activity provided by the electrophysiological monitoring device 108-2 such that the electrical activity remains regulated or within the predefined range.”
However, Applicant specification fails to provide sufficient detail of any other type of interaction between two IoT capable medical devices, such that a person of ordinary skill in the art could replicate the invention. For example, Applicant specification does not detail how the outputs of an x-ray machine may be used to adjust the parameters of a smart bed or how the outputs of a smart bed may be used to adjust the parameters of a drug delivery device.
Regarding Step 2B, Applicant argues that the claims provide significantly more by applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, such as surgery. Examiner respectfully disagrees.
The claims fail to encompass a particular treatment as the treatment is not particular. The specific type of surgery that is performed on the patient is not specified (Factor A; MPEP 2106.04(d)(2)). The particularity of the surgery is not crucial to the claim as the claim language suggests that the invention may be applied to any live medical procedure (Factor B). As such, the surgical aspect encompass the field-of-use (Factor C; also see MPEP § 2106.05(g)). Please see Claim 2 of Example 49, which recited administration of a particular treatment (“Compound X eye drops”) to be eligible under 35 U.S.C. 101, but not administration of “an appropriate treatment” (claim 1 of Example 49).
Regarding the parallels to Desjardins, Examiner respectfully disagrees that continuously determining and optimizing previously unrecognized correlations among medical devices and other clinical factors is analogous to the decision of Desjardins. As noted on pgs. 8-9 of the prior Office Action, determining and optimizing correlations is directed to the abstract idea of certain methods of organizing human activity as determining and optimizing correlations could otherwise be performed by medical staff.
Examiner notes that Desjardins was found to be allowable as the claimed “training strategy allows the model to preserve performance on earlier tasks even as it learns new ones, directly addressing the technical problem of 'catastrophic forgetting' in continual learning systems" and provides “technical improvements over conventional systems by addressing challenges in continual learning and model efficiency by reducing storage requirements and preserving task performance across sequential training.” This analysis represents implementation of the practical application-“improvement” analysis of MPEP 2106.04(d)(I) to the facts before the Panel. Applicant’s claims do not provide such an improvement as generic machine learning models are applied to perform the abstract idea, as previously noted on pg. 11 of the prior Office Action.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-6, 8-10, 12-15, and 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Subject Matter Eligibility Criteria – Step 1:
The claims recite subject matter within a statutory category as a system and a method (1, 3-6, 8-10, 12-15, and 17-20). Accordingly, claims 1, 3-6, 8-10, 12-15, and 17-20 are all within at least one of the four statutory categories.
Subject Matter Eligibility Criteria – Step 2A – Prong One:
Regarding Prong One of Step 2A of the Alice/Mayo test, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. MPEP §2106.04(II)(A)(1). An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and /or c) mathematical concepts. MPEP §2106.04(a).
The Examiner has identified system Claim 1 as the claim that represents a claimed invention for analysis and is similar to method claim 10.
Claim 1:
A system for continuously optimizing a medical procedure, the system comprising:
a plurality of internet-of-things (IoT) capable medical devices, each IoT capable medical device of the plurality of IoT capable medical devices configured to provide medical output data associated with the medical procedure;
a server communicatively coupled to the plurality of IoT capable medical devices, the server is configured to:
receive, by a server transceiver, the medical output data associated with the medical procedure from the plurality of IoT capable medical devices;
determine, by a server processor, one or more correlations between the medical output data of at least one IoT capable medical device of the plurality of IoT capable medical devices and at least one another IoT capable medical device of the plurality of IoT capable medical devices using one or more machine learning models;
obtain, by the server processor, patient data associated with a patient undergoing the medical procedure;
optimize, by the server processor, the one or more correlations between the medical output data of the at least one IoT capable medical device and the at least one another IoT capable medical device to obtain one or more optimized correlations based on the patient data using the one or more machine learning models;
provide, by the server transceiver, the one or more optimized correlations to the at least one IoT capable medical device; and
repeat the receiving, the determining, the obtaining, the optimizing, and the providing steps during the medical procedure to continuously optimize the medical procedure,
wherein, upon receiving the one or more optimized correlations, the at least one IoT capable medical device is configured to:
determine a predefined range corresponding to the medical output data of the at least one IoT capable medical device, the another IoT capable medical device, or a combination thereof;
determine the medical output data of the at least one another IoT capable medical device based on the one or more received optimized correlations; and
adjust the medical input parameters of the at least one IoT capable medical device based on the determined predefined range, the determined medical output data of the at least one another IoT capable medical device, and the one or more received optimized correlations such that the medical output data of the at least one IoT capable device, the another IoT capable medical device, or the combination thereof is maintained within the determined predefined range.
These above limitations, not in bold, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activities. The claims are directed towards the management of medical staff who could otherwise determine correlations, optimize the correlations, and adjust the medical input parameters of medical devices based on predefined ranges and optimized correlations to manage the interaction between the patient and an interfacing medical device to ensure the patient’s condition is maintained within a desired range. It is important to note that the examples provided by the MPEP such as social activities, teaching, and following rules or instructions are provided as examples and not an exclusive listing.
Accordingly, the claim recites an abstract idea.
Claim 10 is also abstract for similar reasons.
Subject Matter Eligibility Criteria – Step 2A – Prong Two:
Regarding Prong Two of Step 2A of the Alice/Mayo test, it must be determined whether the claim as a whole integrates the idea into a practical application. As noted at MPEP §2106.04 (ID)(A)(2), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” MPEP §2106.05(I)(A).
Additional elements cited in the Claims:
A system (1,3-6,8-9,19); a plurality of internet-of-things capable medical devices (1,6,8,,10,15,17,19-20); a server (1,4,9-10,13,18); a server transceiver (1,4); a server processor (1,4,9,); one or more machine learning models (1,4,10,13); an image capturing device (4,13); a user device (4,13); an anesthesia machine (19-20); an electrophysiological monitoring device (19-20); an x-ray machine (19-20); a smart bed (19-20); a drug delivery device (19-20)
Any computing systems that would be able to perform the method (server, server transceiver, server processor) are taught at a high level of generality such that the claim elements amounts to no more than mere instructions to apply the exception using any generic component capable of performing the claim limitations. [0019] of Applicant specification recites: “It will further be appreciated by those of ordinary skill in the art that the server 102 is a personal computer, a desktop computer, a tablet, an augmented reality device, a smartphone, a wearable device (wrist worn, eye worn), or any other server now known or in the future developed.” [0028] of Applicant specification further recites: “The server processor 210 is a hardware device for executing software instructions. In an embodiment, the server processor 210 is any custom made or commercially available processor, a central processing unit (CPU), an auxiliary processor among several processors associated with the server processor 210, a semiconductor-based microprocessor, or generally any device for executing software instructions now known or in the future developed.” No specific, technical improvements are being made to the technology of computing devices as any generic server may be applied to perform the abstract idea.
Transceiver devices are also taught at a high level of generality. [0021] of Applicant specification recites: “It will be appreciated by those of ordinary skill in the art that the server 102 includes a single server transceiver 202 as shown, or alternatively separate transmitting and receiving components, for example, but not limited to, a transmitter, a transmitting antenna, a receiver, and a receiving antenna and/or any combination thereof.” No specific, technical improvements are being made to the technology of data transmission as any generic wireless receiver device may be applied to perform the insignificant extra-solution activity of receiving data.
Machine learning is also taught at a high level of generality. [0029] of Applicant specification recites: “The server processor 210 includes a machine learning module 222 having one or more machine learning models configured to determine one or more correlations between the medical output data of at least one IoT capable medical device of the plurality of IoT capable medical devices and at least one another IoT capable medical device of the plurality of IoT capable medical devices… The machine learning module 222 is configured to implement one or more machine learning algorithms to continuously optimize the medical procedure. In accordance with some embodiments of the invention, the machine learning algorithm utilizes any machine learning methodology, now known or in the future developed, for classification. For example, the machine learning methodology utilized includes one or a combination of: Linear Classifiers (Logistic Regression, Naive Bayes Classifier); Nearest Neighbor; Support Vector Machines; Decision Trees; Boosted Trees; Random Forest; and/or Neural Networks.” No specific, technical improvements are being made to the technology of machine learning as any generic type of model may be applied to perform the abstract idea.
Image capturing devices also taught at a high level of generality. [0015] of Applicant specification recites: “For example, the imaging device 104 includes one or more of a low-angle camera, a wall-mounted camera, an augmented reality device, or any device, now known or in the future developed, which can capture video/image frames and/or record or generate videos/images of the one or more of the medical item(s), the medical practitioner(s), the robotic system(s), and the medical procedure room.” No specific, technical improvements are being made to the technology of image capturing devices as any generic camera device may be applied to perform the insignificant extra-solution activity of taking images.
User devices are also taught at a high level of generality. [0031] of Applicant specification recites: “FIG. 3 is a block diagram of one exemplary embodiment of the user device 106 for use within the system 100 of FIG. 1. In accordance with some embodiments, the user device 106 is a tablet, a smartphone, an augmented reality or wearable device, or any other user device now known or in the future developed.” No specific, technical improvements are being made to the technology of user devices as any generic device may be applied to perform the insignificant extra-solution activity of receiving user inputs.
The anesthesia machine is claimed at a high level of generality, such that it is applied at to serve as an application of the abstract idea. Examiner notes that Applicant specification provides sufficient detail concerning the particularity of the anesthesia machine in relation to the electrophysiological monitoring device, which may assist in overcoming the 35 U.S.C. 101 rejection. Specifically, [0048] of Applicant specification recites: “In an exemplary embodiment, when the correlation indicates that an increase in the delivery rate, the proportion, and the concentration of medical gases provided by the anesthesia machine 108-1 reduces the electrical activity associated with the brain, the nerve pathways, and the muscle provided by the electrophysiological monitoring device 108-2, the machine learning module 222 of the server processor 210 optimizes this correlation based on the patient data, such as the metabolism rate of the patient. For example, for a patient with high metabolism rate, the machine learning module 222 of the server processor 210 may optimize the correlation to increase the concentration of oxygen to regulate the electrical activity associated with the brain, the nerve pathways, and the muscle in the patients with high metabolism rate.”
The X-ray machine is taught at a high level of generality. [0014] of Applicant specification recites: “The IoT capable medical devices 108 include, but are not limited to, an anesthesia machine 108-1, an electrophysiological monitoring device 108-2, an X-ray machine 108-3, a smart bed 108-4, a drug delivery device 108-n, and various other IoT capable medical devices 108 now known or in the future developed.” No specific, technical improvements are made to x-ray machines as Applicant specification does not describe any particularities with how they may be adjusted based on correlations. Instead, the X-ray machine is applied such that it serves as an application of the abstract idea.
The smart bed is claimed at a high level of generality, such that it is applied at to serve as an application of the abstract idea. Examiner notes that Applicant specification provides sufficient detail concerning the particularity of the smart bed in relation to the electrophysiological monitoring device, which may assist in overcoming the 35 U.S.C. 101 rejection. Specifically, [0052] of Applicant specification recites: “upon receiving the optimized correlations, the smart bed 108-4 obtains the electrical activity associated with the brain, the nerve pathways, and the muscle from the electrophysiological monitoring device 108-2 and adjusts its medical input parameters, such as, the angle based on the one or more optimized correlations and the electrical activity provided by the electrophysiological monitoring device 108-2 such that the electrical activity remains regulated or within the predefined range.”
The electrophysiological monitoring device is claimed at a high level of generality, such that it is applied at to serve as an application of the abstract idea. Examiner notes that Applicant specification provides more information concerning the particularity of the electrophysiological monitoring device in relation to the anesthesia machine and smart bed, which may assist in overcoming the 35 U.S.C. 101 rejection, specifically paragraphs [0048] and [0052] for the reasons cited above.
The drug delivery device is taught at a high level of generality. [0014] of Applicant specification recites: “The IoT capable medical devices 108 include, but are not limited to, an anesthesia machine 108-1, an electrophysiological monitoring device 108-2, an X-ray machine 108-3, a smart bed 108-4, a drug delivery device 108-n, and various other IoT capable medical devices 108 now known or in the future developed.” No specific, technical improvements are made to drug delivery devices as Applicant specification does not describe any particularities with how they may be adjusted based on correlations. Instead, the drug delivery device is applied such that it serves as an application of the abstract idea.
Looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole with the limitations reciting the at least one abstract idea, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above -noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole does not integrate the abstract idea into a practical application of the abstract idea. MPEP §2106.05(I)(A) and §2106.04(IID)(A)(2).
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
Claims 3 and 12: These claims recite wherein a value of a correlation coefficient for each of the one or more correlations is greater than a predetermined threshold value; which teaches an abstract idea of mathematical processes, by determining and comparing correlation coefficients to threshold values.
Claims 4 and 13: These claims recite wherein the server is further configured to: receive, by the server transceiver, one or more inputs associated with the live medical procedure, wherein the one or more inputs includes at least an image captured by an image capturing device, a voice command provided during the live medical procedure, or a user input received via a user device; and analyze, by the server processor, the one or more inputs to optimize the one or more correlations or the optimized one or more correlations based on the one or more inputs using the one or more machine learning models; which serves to limit the type of inputs that are used for the abstract idea of optimizing the correlations. This claim further teaches the server transceiver at a high level of generality, such that it is only applied to perform the insignificant extra-solution activities of receiving inputs and selecting a source or type of data for manipulation. This claim further teaches image capturing devices and user devices at a high level of generality, such that they are only applied to teach an insignificant extra-solution activity of providing data.
Claims 5 and 14: These claims recite wherein the patient data includes one or more of current health parameters and previous health parameters of the patient; which serves to limit the patient data that is received to perform the abstract idea.
Claims 6 and 15: These claims recite wherein the one or more correlations includes correlations between the medical output data, from a plurality of IoT capable medical devices associated with one or more medical procedures performed prior to the live medical procedure; which serves to limit the type of correlations.
Claims 8 and 17: These claims recite wherein the at least one IoT capable medical device is configured to adjust the one or more medical input parameters by: providing a notification on a user device associated with a medical practitioner to approve adjustment of the one or more medical input parameters; and adjusting the one or more medical input parameters upon receiving the approval; which teaches an abstract idea certain methods of organizing human activity by receiving approval from a user.
Claims 9 and 18: These claims recite wherein the server is further configured to: associate, by the server processor, the one or more correlations or the one or more optimized correlations with one or more of a medical practitioner identifier identifying a medical practitioner performing the live medical procedure and a medical room identifier identifying a medical procedure room for performing the live medical procedure; which teaches an abstract idea of associating a correlation to a person or location.
Claims 19 and 20: These claims recite wherein the IoT capable medical devices include one or more of: an anesthesia machine, an electrophysiological monitoring device, an X-ray machine, a smart bed, and a drug delivery device; which serves to narrow the type of IoT capable medical device without describing how they may specifically interface/affect each other through the correlations of described in the independent claim.
Subject Matter Eligibility Criteria – Step 2B:
Regarding Step 2B of the Alice/Mayo test, representative independent claims do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
These claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field use. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
Amount to elements that have been recognized as activities in particular fields (such as Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), MPEP §2106.05(d)(II)(i);storing and retrieving information in memory, Versata Dev. Group, MPEP §2106.05(d)(II)(iv)).
Examiner notes that it is known to combine computing components, machine learning, and IoT devices for medical data analysis/correlation, as evidenced by:
Roh (US 11471227): col. 10, lines 58-62, “In some embodiments, the imaging system 136 includes X-ray medical imaging instruments that use X-ray radiation (i.e., X-ray range in the electromagnetic radiation spectrum) for the creation of images of the interior of the human body for diagnostic and treatment purposes.” Col. 34, lines 10-19, “the imaging device 614 comprises a real time imaging device used in surgery, such as an endoscope or other surgical imaging device. In such an embodiment, the real time imaging data from imaging device 614 can be used to update the virtual model of the at least a portion of the patient's body. The updated model can be used to adjust the location of implant components 618, the path of the surgical tool 154 or implant 616, the speed at which the surgical tool 154 moves, the orientation of an implant component 618, or the axial forces to be applied.” Col. , lines “The ML system 200 can use supervised ML to train the ML model 216, with feature vectors of the positive training set and the negative training set serving as the inputs. In some embodiments, different ML techniques, such as linear support vector machine (linear SVM), boosting for other algorithms (e.g., AdaBoost), logistic regression, naïve Bayes, memory-based learning, random forests, bagged trees, decision trees, boosted trees, boosted stumps, neural networks, CNNs, etc., are used.”
Shelton (US 20190125455): [1056], “the situational awareness system includes a pattern recognition system, or machine learning system (e.g., an artificial neural network), that has been trained on training data to correlate various inputs (e.g., data from databases 5122, patient monitoring devices 5124, and/or modular devices 5102) to corresponding contextual information regarding a surgical procedure. In other words, a machine learning system can be trained to accurately derive contextual information regarding a surgical procedure from the provided inputs.” [1808], “The robotic surgical tools 12426 and 12526 can be used in connection with a hub, such as the robotic hub 122 or the robotic hub 222, for example. In one aspect, the robotic hubs can include a situational awareness module, as described herein. The situational awareness module can be configured to determine and/or confirm a step in a surgical procedure and/or suggest a particular surgical action based on information received from various sources, including one or more robotic surgical tool(s) and/or a generator module… the processor can be communicatively coupled to a memory that stores instructions executable by the processor to adjust a pumping rate of the pump based on data from the situational awareness module which can indicate, for example, the type of surgical procedure and/or the step in the surgical procedure. For example, situational awareness can indicate that insufflation is necessary for at least a portion of a particular surgical procedure. In such instances, a pump, such as the blower 12546 (FIG. 219) can be activated and/or maintained at a level to maintain a sufficient insufflation.”
Shelton (US 20240215800): [0619], “Further, in such an aspect, the imaging module 238 may analyze the snapshots themselves to detect evidence of an improper/insufficient sealing temperature (e.g., charring, oozing/bleeding). In one alternative aspect, the surgical hub 206 may communicate the snapshots to the cloud-based system 205, and a component of the cloud-based system 205 may perform the various imaging module functions described above to detect evidence of an improper/insufficient sealing temperature and to report the detection to the surgical hub 206. According to the various aspects described above, in response to the detected and/or identified failure event, the surgical hub 206 may download a program from the cloud-based system 205 for execution by the surgical device/instrument 235 that corrects the detected issue (i.e., program that alters surgical device/instrument parameters to prevent misfired staples, program that alters surgical device/instrument parameters to ensure correct sealing temperature).” [0885], “the situational awareness system includes a pattern recognition system, or machine learning system (e.g., an artificial neural network), that has been trained on training data to correlate various inputs (e.g., data from databases 5122, patient monitoring devices 5124, and/or modular devices 5102) to corresponding contextual information regarding a surgical procedure. In other words, a machine learning system can be trained to accurately derive contextual information regarding a surgical procedure from the provided inputs… the situational awareness system includes a further machine learning system, lookup table, or other such system, which generates or retrieves one or more control adjustments for one or more modular devices 5102 when provided the contextual information as input.”
Roh (US 20190262084): [0031], “systems and methods can monitor a patient's brain activity during surgery to determine a level of consciousness, patient response during a procedure, or the like. For example, using of a wireless EEG system during surgery can provide a basis for determining the amount of medication to give a patient. The EEG can track the amount of discomfort the patient is experiencing, and more medication (i.e., anesthesia) can be administered if the amount of discomfort exceeds a threshold. The system can include an artificial intelligence unit that receive monitored brain activity data (e.g., brain activity patterns, brain activity spikes, etc.) and identify correlations with anesthesia based adverse events. Pain, discomfort, and other patient parameters can be monitored and evaluated to determine whether to modify the treatment plan, administer anesthesia, etc. The AI/machine learning can be used to analyze brain activity, patient feedback, or other patient parameters to, for example, improve safety, comfort, or the like.”
Dong (US 20250222218): [0120], “the physiological parameter, which is detected by an anesthesia machine, includes but is not limited to a sedative muscle relaxation parameter, such as an EEG signal, a BIS (bi-spectral index) signal, and a neuromuscular electrical signal” [0157], "the monitor is connected with the anesthesia machine and the infusion pump, so as to obtain data for a physiological parameter which is acquired by the anesthesia machine, and transmitting an adjustment instruction for a control parameter to the anesthesia machine and the infusion pump.”
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 3-6, 8-9, 12-15, and 17-20, additional limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, claims 3-6, 8-9, 12-15, and 17-20, e.g., performing repetitive calculations, Flook, MPEP §2106.05(d)(II)(ii); claims 3-6, 8-9, 12-15, and 17-20, e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP §2106.05(d)(II)(iv). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, claims 1, 3-6, 8-10, 12-15, and 17-20 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Conclusion
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/D.C./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684