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 .
Election/Restrictions
Applicant’s election without traverse of claims 1-9 and 15 in the reply filed on October 3, 2025 is acknowledged. Claims 10-13 and 16 have been non-elected. Claims 1-9, 14-15, and 17-20 are pending and have been examined.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on December 17, 2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
Claims 1-9, 15, and 17-20 are given a priority date of September 25, 2024 as they introduce new matter not present in parent application (18/334,344) that this application is a continuation-in-part of. Specifically, use of a medical waste collection system including operation of a vacuum source and a suction tube, processing audio data to determine audio representative of non-compliant equipment use, and cameras disposed of in a localizer, tablet of a sponge management system, and a light fixture mounted within the operating room.
Specification
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code. Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
Specifically, [0044] of Applicant specification recites: “As examples, a number of exemplary models are described in G. Yengera et al., “Less is More: Surgical Phase Recognition with Less Annotations through Self-Supervised Pre-training of CNN-LSTM Networks,” arXiv:1805.08569 [cs.CV], available at https://arxiv.org/abs/1805.08569.”
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 1-9, 14-15, 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 process and a machine
(claims 1-9, 14-15, and 17-20). Accordingly, claims 1-9, 14-15, 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 method Claim 1 and system Claim 14 as the claims that represents the claimed invention for analysis.
Claim 1:
A method for preventing of non-compliant use of a medical waste collection system including a vacuum source, and a suction tube configured to provide suction at a surgical site of a patient, the method comprising:
receiving, at one or more processors, signals captured by one or more devices positioned within an operating room;
providing the signals to a trained machine-learning model trained on signals representative of nominal and adverse medical events;
determining based on the signals, with the trained machine-learning model, the non- compliant use of the suction tube as being used or to be used in a manner to produce a potential adverse medical event; and
terminating or preventing, by the one or more processors, operation of the vacuum source during the non-compliant use.
These above limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity. The claim elements are directed towards “a method for preventing of non-compliant use of a medical waste collection system”, “determining…, the non- compliant use of the suction tube as being used or to be used in a manner to produce a potential adverse medical event”, and “terminating or preventing,…, operation of the vacuum source during the non-compliant use”. All of these claim limitations are directed towards monitoring and managing the personal behaviors of the surgical staff and their compliance with usage of a medical waste collection system. Under the broadest reasonable interpretation, terminating or preventing operation of the vacuum source can be performed by providing a notification to the medical staff (claim 3 and step 740 of Fig. 7). Providing feedback based on monitored activity of the surgical staff based on established protocols (how the surgical staff should behave or perform actions) is analogous to providing a teaching or instruction.
Accordingly, the claim recites at least one abstract idea.
Claim 14:
A system for preventing of non-compliant use of equipment during a surgical procedure, the system comprising:
one or more cameras positioned within an operating room;
a display;
a computer program product comprising instructions stored on non-transitory computer- readable medium and, when executed by one or more processors, being configured to cause the one or more processors to:
receive images from the one or more cameras, wherein the images include at least a surgical site of a patient;
provide the images to a trained machine-learning model trained on images representative of nominal and adverse medical events;
determine based on the images, with the trained machine-learning model, the non- compliant use of the equipment in a manner to produce a potential adverse medical event; and
cause a notification or alarm to be displayed on the display based on the determination of the non-compliant use.
These above limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity. The claim elements are directed towards “preventing of non-compliant use of equipment during a surgical procedure”, “determining…, the non- compliant use of the equipment in a manner to produce a potential adverse medical event”, and “caus[ing] a notification or alarm to be displayed on the display based on the determination of the non-compliant use”. All of these claim limitations are directed towards monitoring and managing the personal behaviors of the surgical staff and their compliance with usage of a medical waste collection system. Furthermore, providing feedback/notification based on monitored activity of the surgical staff based on established protocols (how the surgical staff should behave or perform actions) is analogous to providing a teaching or instruction.
Accordingly, the claim recites at least one abstract idea.
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 vacuum source (1,6-7,15); a suction tube (1,5,15); one or more processors (1,6-7,10,14-15,17); one or more devices (1); a trained machine-learning model (1,6,9,14,17); one or more cameras (14,18-20); a display (14); a non-transitory computer-readable storage medium (14); equipment (14); one or more microphones (17); surgical navigation system, localizer (18); a sponge management system, a tablet, moveable stand (19); a light fixture (20)
Any computing devices and their associated components (processor) that would be able to perform the method and the modules that are used within the computing environment 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. [0083] of Applicant specification recites: “Device 500 can be a client computer or a server. As shown in FIG. 5, device 500 can be any suitable type of microprocessor-based device, such as a personal computer, workstation, server or handheld computing device (portable electronic device) such as a phone or tablet.” No specific, technical improvements are being made to computing devices as generic devices are used with software to perform the abstract idea.
Machine learning is also taught at a high level of generality. [0044] of Applicant specification recites: “The one or more trained machine-learning models used herein can comprise a trained neural network model, such as a 2D CNN, 3D-CNN, temporal DNN, etc. For example, the models may comprise ResNet50, AlexNet, Yolo, I3D ResNet 50, LSTM, MSTCN, etc... As examples, a number of exemplary models are described in G. Yengera et al., “Less is More: Surgical Phase Recognition with Less Annotations through Self-Supervised Pre-training of CNN-LSTM Networks,” arXiv:1805.08569 [cs.CV], available at https://arxiv.org/abs/1805.08569.” No specific, technical improvements are being made to the field of machine learning as a variety of models are simply applied according to known methods to perform the abstract idea.
The medical waste collection system (vacuum source, suction tube, equipment) is also taught at a high level of generality. [0049] of Applicant specification recites: “Within a surgical waste collection system, the waste material is collected in a waste container connected to a vacuum source. A portable cart supports the waste container for moving throughout the health care facility. One or more suction lines extend from the waste container and are positioned near the site from which the waste material is to be collected. When the vacuum source is operating, the waste material is drawn through the suction lines into the waste container.” No specific, technical improvements are being made to medical waste collection systems as they only serve to be the object of monitoring in relation to surgical compliance, which merely ties the invention to the specific field of use of monitoring these specific types of devices.
Input devices (one or more devices, one or more cameras, one or more microphones) are taught at a high level of generality. [0029] of Applicant specification recites: “Multiple cameras may be placed in different locations in the operating room such that they can collectively capture a particular area or object of interest from different perspectives.” [0030] recites: “The microphones are configured to detect auditory outputs such as speech, alarms, and any other sound that may occur from use of medical equipment.” No specific, technical improvements are being made to the field of input devices as a variety of sensors are applied to perform the insignificant extra-solution activity of gathering data.
Devices that cameras are disposed in (surgical navigation system, localizer, tablet, moveable stand, light fixture) are also taught at a high level of generality. Claims 18-20 simply describes wherein each device houses a camera. Thus, each device is only supplied to perform the insignificant extra-solution activity of receiving image data.
Displays are also taught at a high level of generality. [0097] of Applicant specification recites: “The system may push through a notification on a display such as a tablet or surgical navigation screen.” No specific, technical improvements are being made to displays as they are only applied to perform the extra-solution activity of displaying information.
Storage mediums (non-transitory computer-readable storage medium) are also taught at a high level of generality. [0026] of Applicant specification recites: “Such a computer program may be stored in a non-transitory, computer readable storage medium, such as, but not limited to, any type of disk, including floppy disks, USB flash drives, external hard drives, optical disks, CD-ROMs, magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, application specific integrated circuits (ASICs), or any type of media suitable for storing electronic instructions, and each coupled to a computer system bus.” No specific, technical improvements are being made to displays as they are only applied to perform the extra-solution activity of storing information.
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
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:
Claim 2: This claim recites wherein the received signals are directed to at least the surgical site of the patient; which only serves to limit the signals.
Claim 3: This claim recites the method further comprising providing a notification or alarm based on the non-compliant use; which teaches an abstract idea of certain methods of organizing human activity as notifying a person of non-compliant activity.
Claim 4: This claim recites wherein the notification includes textual or graphical corrective instructions that is specific to activity implicating the potential adverse medical event; which teaches an abstract idea of certain methods of organizing human activity as teaching instructions.
Claim 5: This claim recites wherein the potential adverse medical event includes one of (i) the suction tube being coupled to a chest tube, (ii) the suction tube being coupled to a tracheal tube, and (iii) the suction tube being coupled to a closed wound drainage tube; which only serves to limit the type of medical event.
Claim 6: This claim recites the method further comprising: receiving additional signals after the determination of the non-compliant use; providing the signals to the trained machine-learning model; determining with the trained machine-learning model, the non-compliant use has been obviated; and permitting, by the one or more processors, the operation of the vacuum source; which teaches an abstract idea of certain methods of organizing human activity as monitoring the activities of surgical staff and their compliance with regards to usage of a medical waste system.
Claim 7: This claim recites the method further comprising: receiving a user input to a user interface that the non-compliant use has been obviated; and permitting, by the one or more processors, the operation of the vacuum source; which teaches an abstract idea of certain methods of organizing human activity as permitting surgical staff to use a medical waste collection system.
Claim 8: This claim recites wherein the signals include one or more of a video feed, an image, and an audio signal; which only serves to limit the signal.
Claim 9: This claim recites wherein the images and the audio signals are analyzed with the trained machine-learning model in tandem, and wherein the trained machine- learning model is trained on combined audio and images representative of the nominal and adverse medical events; which only serves to limit the data that is used to train the machine learning model. This claim further teaches training at a high level of generality such that no specific, technical improvements are made to the technology of machine learning.
Claim 15: This claim recites wherein the equipment is a medical waste collection system comprising a vacuum source, and wherein the one or more processors are configured to prevent or terminate operation of the vacuum source based on the determination of the non-compliant use of a suction tube that is coupled to the medical waste collection system; which is abstract for the same reasons as claim 1.
Claim 17: This claim recites the system further comprising one or more microphones positioned within the operating room and configured to receive audio signals, wherein the computer program product is further configured to cause the one or more processors to: receive the audio signals from the one or more microphones; provide the audio signals to the trained machine-learning model trained on audio representative of nominal and adverse medical events; and determine based on the audio signals, with the trained machine-learning model, the non-compliant use of the equipment; which teaches an abstract idea of certain methods of organizing human activity as determining non-compliant use of equipment using audio. This claim further teaches microphones at a high level of generality, such that they are only applied to perform the insignificant extra-solution activity of gathering data.
Claim 18: This claim recites the system further comprising a surgical navigation system comprising a localizer, wherein the one or more cameras is disposed on a localizer; which teaches a surgical navigation system comprising a localizer at a high level of generality, such that it is only applied to perform the insignificant extra-solution activity of images/gathering data.
Claim 19: This claim recites the system further comprising a sponge management system comprising a tablet disposed on a moveable stand, wherein the one or more cameras is disposed on the tablet; which teaches a sponge management system comprising a tablet disposed on a moveable stand at a high level of generality, such that it is only applied to perform the insignificant extra-solution activity of gathering images/data.
Claim 20: This claim recites the system further comprising a light fixture mounted within the operating room, wherein the one or more cameras is disposed on the light fixture; which teaches a light fixture at a high level of generality, such that it is only applied to perform the insignificant extra-solution activity of gathering images/data.
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 combining a camera sensing system with machine learning anomaly detection for surgical devise is known in the field of surgical device activity monitoring, as evidenced by:
Wolf (US 20200268457) recites: [0088], “For example, video/image data obtained from different view angles may be used to determine the position of the surgical instrument relative to a surface of the anatomical structure, to determine a condition of an anatomical structure, to determine pressure applied to an anatomical structure, or to determine any other information where multiple viewing angles may be beneficial. By way of another example, bleeding may be detected by one camera, and one or more other cameras may be used to identify the source of the bleeding.” [0116], “a machine learning model may be trained using training examples, each training example may include video footage known to be associated with surgical procedures, surgical phases, intraoperative events, and/or event characteristics, together with labels indicating locations within the video footage.”
Shelton (US 20220233119): [0363], “The HCPs and the environment surrounding the HCPs may also be monitored by one or more environmental sensing systems including, for example, a set of cameras 20021, a set of microphones 20022, and other sensors, etc. that may be deployed in the operating room. The surgeon sensing systems 20020 and the environmental sensing systems may be in communication with a surgical hub 20006, which in turn may be in communication with one or more cloud servers 20009 of the cloud computing system 20008, as shown in FIG. 1.” [1645], “The computing system may obtain surgical contextual data. For example, a surgical instrument may send data associated with usage of the surgical instrument. The computing system may determine whether the nurse is operating the surgical instrument. For example, the computing system may obtain the contextual data that indicates that a surgical staple gun has recently been fired and needs a reload. The computing system may determine whether the nurse is operating the surgical instrument based on the contextual data and/or measurement data associated with the nurse (e.g., measurement data associating with the nurse's hand movement). If the computing system determines that the nurse is not operating a surgical instrument and detects an elevated stress level, the computing system may generate and/or send surgical aid information to the nurse. The surgical aid information may be or may include an operation manual of the surgical instrument (e.g., reloading a staple gun).” Examiner notes that not operating a surgical instrument when a surgical instrument requires operation is non-compliant inaction.
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 2-9, 15, and 17-20 additional limitations which amount to elements that have been recognized as activities in particular fields, claims 2-9, 15, and 17-20, e.g., performing repetitive calculations, Flook, MPEP §2106.05(d)(II)(ii); claims 2-9, 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 1-9, 14-15, and 17-20 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
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.
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 14, 17, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wolf (US 20200268457).
Regarding claim 14, Wolf teaches a system for preventing of non-compliant use of equipment during a surgical procedure, the system comprising:
one or more cameras positioned within an operating room ([0085], “room 101 may include one or more microphones (e.g., audio sensor 111, as shown in FIG. 1), several cameras (e.g., overhead cameras 115, 121, and 123, and a tableside camera 125) for capturing video/image data during surgery”);
a display ([0096], “FIG. 1 includes a display screen 113 that may show views from different cameras 115, 121, 123 and 125, as well as other information.”);
a computer program product comprising instructions stored on non-transitory computer- readable medium and, when executed by one or more processors, being configured to cause the one or more processors ([0188], “a non-transitory computer readable medium may contain instructions that when executed by a processor cause the processor to perform process 800.”) to:
receive images from the one or more cameras, wherein the images include at least a surgical site of a patient ([0461], “For illustrative purposes, one of a virtual infinite number of potential inconsistencies could occur when a medical professional indicates in the report that the surgical site was closed with sutures, while the video reveals that the site was closed with staples.”);
provide the images to a trained machine-learning model trained on images representative of nominal and adverse medical events ([0080], “trained machine learning algorithms (also referred to as trained machine learning models in the present disclosure) may be used to analyze inputs and generate outputs,… the input may include an image” [0162], “image recognition may be used to note the severance of a vessel or nerve, to enable marking of that adverse event... In some embodiments, analyzing the video footage to identify the event location may include using a neural network model (such as a deep neural network, a convolutional neural network, etc.) trained using example video frames including previously-identified surgical events to thereby identify the event location.”);
determine based on the images, with the trained machine-learning model, the non- compliant use of the equipment in a manner to produce a potential adverse medical event ([0161], “The intraoperative surgical event may be a planned event, such as an incision, administration of a drug, usage of a surgical instrument, an excision, a resection, a ligation, a graft, suturing, stitching, or any other planned event associated with a surgical procedure or phase. In some embodiments, the intraoperative surgical event may include an adverse event or a complication.” [0439], “the machine-learning method may identify intraoperative events (e.g., adverse events)”); and
cause a notification or alarm to be displayed on the display based on the determination of the non-compliant use ([0615], “For example, image data may be analyzed to detect changes in a predicted outcome, and a remedial action may be communicated to a surgeon. A predicted outcome may include an outcome that may occur with an associated confidence or probability (e.g., a likelihood). For example, a predicted outcome may include a complication, a health status, a recovery period, death, disability, internal bleeding, hospital readmission after the surgery, and/or any other surgical eventuality.” [0640], “outputting a recommended remedial action. Outputting a recommended remedial action may include transmitting a recommendation to a device, causing a notification to be displayed on an interface, playing a sound, providing haptic feedback, and/or any other method of conveying a desired message, whether to an operating room, a device associated with a surgeon (e.g., a human surgeon and/or a surgical robot), and/or to any other system.”).
Regarding claim 17, Wolf teaches the system of claim 14. Wolf teaches the system further comprising one or more microphones positioned within the operating room and configured to receive audio signals ([0085], “Room 101 may include audio sensors, video/image sensors, chemical sensors, and other sensors, as well as various light sources (e.g., light source 119 is shown in FIG. 1) for facilitating the capture of video and audio data, as well as data from other sensors, during the surgical procedure.”),
wherein the computer program product is further configured to cause the one or more processors ([0188], “a non-transitory computer readable medium may contain instructions that when executed by a processor cause the processor to perform process 800.”) to:
receive the audio signals from the one or more microphones ([0085], “Room 101 may include audio sensors, video/image sensors, chemical sensors, and other sensors, as well as various light sources (e.g., light source 119 is shown in FIG. 1) for facilitating the capture of video and audio data, as well as data from other sensors, during the surgical procedure… room 101 may include one or more microphones”);
provide the audio signals to the trained machine-learning model trained on audio representative of nominal and adverse medical events ([0353], “A characteristic event (also referred to as an intraoperative surgical event) may be any event or action that occurs during a surgical procedure or phase.” [0116], “a machine learning model may be trained using training examples, each training example may include video footage known to be associated with surgical procedures, surgical phases, intraoperative events, and/or event characteristics, together with labels indicating locations within the video footage.” [0433], “a property of a phase may be non-textural data (e.g., image, audio, numerical, and/or video data) collected during a surgical procedure… a machine learning model may be trained using training examples to identify properties of surgical phases from images and/or videos. An example of such training example may include an image and/or a video of at least a portion of a surgical phase of a surgical procedure, together with a label indicating one or more properties of the surgical phase.”); and
determine based on the audio signals, with the trained machine-learning model, the non-compliant use of the equipment ([0490], “Detecting a characteristic event using a machine-learning method may be one possible approach… the characteristic event may be identified using a visual or an audio signal from the surgeon (e.g., a hand gesture, a body gesture, a visual signal produced by a light source generated by a medical instrument, a spoken word, and the like) that may be captured by one or more image sensors/audio sensors and recognized as a trigger for the characteristic event.” [0353], “A characteristic event (also referred to as an intraoperative surgical event) may be any event or action that occurs during a surgical procedure or phase… the intraoperative surgical event may include an adverse event or a complication. Some examples of intraoperative adverse events may include bleeding, mesenteric emphysema, injury, conversion to unplanned open surgery (for example, abdominal wall incision), incision significantly larger than planned, and so forth.”).
Regarding claim 20, Wolf teaches the system of claim 14. Wolf further teaches the system further comprising a light fixture mounted within the operating room, wherein the one or more cameras is disposed on the light fixture ([0298], “An image sensor may be any sensor capable of capturing image or video data. A single sensor may be used, or multiple image sensors may be positioned in a surgical operating room (e.g., the sensors may be positioned throughout the operating room). In an illustrative embodiment, an example image sensor may be positioned above a patient. The example image sensor may be above an operating table, next to the operating table, next to devices located in the operating room, or anywhere else capable of detecting information about a surgery.” [0086], “In various embodiments, camera 115 may be equipped with a laser 137 (e.g., an infrared laser) for precision tracking.”). Examiner notes that a laser is a light fixture. Thus, a camera equip with a laser encompasses a camera disposed on a light fixture.
Claim Rejections - 35 USC § 103
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.
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.
Claims 1-4, 6, 8-9, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Wolf (US 20200268457) in view of Shelton (US 20220233119).
Regarding claim 1, Wolf teaches a method for preventing of non-compliant use of surgical equipment, the method comprising:
receiving, at one or more processors, signals captured by one or more devices positioned within an operating room ([0085], “room 101 may include one or more microphones (e.g., audio sensor 111, as shown in FIG. 1), several cameras (e.g., overhead cameras 115, 121, and 123, and a tableside camera 125) for capturing video/image data during surgery”);
providing the signals to a trained machine-learning model trained on signals representative of nominal and adverse medical events ([0080], “trained machine learning algorithms (also referred to as trained machine learning models in the present disclosure) may be used to analyze inputs and generate outputs,… the input may include an image” [0158], “a machine learning model may be trained using video footage and corresponding label indicating known beginning points of an incision or other surgical events and/or procedures. The trained model may be used to identify similar procedure and/or event beginning locations within other surgical video footage.” [0699], “the audio sound from the surgeon may be captured by one or more audio sensors and recognized by a speech recognition computer-based model”);
determining based on the signals, with the trained machine-learning model, the non- compliant use of the surgical equipment as being used or to be used in a manner to produce a potential adverse medical event ([0161], “The intraoperative surgical event may be a planned event, such as an incision, administration of a drug, usage of a surgical instrument, an excision, a resection, a ligation, a graft, suturing, stitching, or any other planned event associated with a surgical procedure or phase. In some embodiments, the intraoperative surgical event may include an adverse event or a complication.”); and
terminating or preventing, by the one or more processors, operation of the surgical equipment during the non-compliant use ([0665], “a remedial action may include sending instructions to a robot. Such instructions may direct the robot to undertake an action that remediates or assists in remediating the leakage. Alternatively or additionally, the instruction may direct the robot to cease a current course of action”).
Although Wolf recites suctioning ([0100], “Instrument 301 is only one example of possible surgical instrument, and other surgical instruments such as scalpels, …, suction tips, and tubes, sealing devices, irrigation and injection needles,…” [0516], “An action (about to be performed by a healthcare professional) may be any procedure-related action. For example, the action may include suturing, …, suctioning,”), Wolf does not explicitly teach wherein the surgical equipment is a medical waste collection system including a vacuum source, and a suction tube configured to provide suction at a surgical site of a patient.
However, Shelton does teach wherein the surgical equipment is a medical waste collection system including a vacuum source, and a suction tube configured to provide suction at a surgical site of a patient ([0030], “The surgical device may include any of a powered stapler, …, a suction-irrigation device,” [1194], “Controllable device 25256 may control pressure or airflow (e.g., suction) in chest tube 25257 inserted into the chest of patient 25252 (e.g., to reduce pressure on an organ such as a lung and drain air, blood, or fluid from the pleural space around a lung).”).
Wolf in view of Shelton are considered analogous to the claimed invention because they are in the field of surgical equipment. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wolf with Shelton for the advantage of providing “suction of excess fluid” (Shelton; [0386]).
Regarding claim 2, Wolf in view of Shelton teaches the method of claim 1. Wolf further teaches wherein the received signals are directed to at least the surgical site of the patient ([0461], “For illustrative purposes, one of a virtual infinite number of potential inconsistencies could occur when a medical professional indicates in the report that the surgical site was closed with sutures, while the video reveals that the site was closed with staples.”).
Regarding claim 3, Wolf in view of Shelton teaches the method of claim 1. Wolf further teaches the method further comprising providing a notification or alarm based on the non-compliant use ([0615], “For example, image data may be analyzed to detect changes in a predicted outcome, and a remedial action may be communicated to a surgeon. A predicted outcome may include an outcome that may occur with an associated confidence or probability (e.g., a likelihood). For example, a predicted outcome may include a complication, a health status, a recovery period, death, disability, internal bleeding, hospital readmission after the surgery, and/or any other surgical eventuality.” [0640], “outputting a recommended remedial action. Outputting a recommended remedial action may include transmitting a recommendation to a device, causing a notification to be displayed on an interface, playing a sound, providing haptic feedback, and/or any other method of conveying a desired message, whether to an operating room, a device associated with a surgeon (e.g., a human surgeon and/or a surgical robot), and/or to any other system.”).
Regarding claim 4, Wolf in view of Shelton teaches the method of claims 1 and 3. Wolf further teaches wherein the notification includes textual or graphical corrective instructions that is specific to activity implicating the potential adverse medical event ([0615], “Identifying a remedial action may be based on an indication, derived at least in part from image-related data, that a remedial action may be likely to raise a predicted outcome above a threshold. For example, a data structure may contain correlations between historical remedial actions and predicted outcomes, and a remedial action may be identified based on the correlations.”).
Regarding claim 6, Wolf in view of Shelton teaches the method of claim 1. Wolf further teaches the method further comprising:
receiving additional signals after the determination of the non-compliant use ([0670], “if remedial action already began and further analysis revealed that the remedial action is unnecessary”);
providing the signals to the trained machine-learning model ([0639], “Identifying a remedial action may be based on an indication, derived at least in part from image-related data, that a remedial action may be likely to raise a predicted outcome above a threshold… identifying a remedial action may include using a machine learning model trained to identify remedial actions using historical examples of remedial actions and surgical outcomes.”);
determining with the trained machine-learning model, the non-compliant use has been obviated (0670], “if remedial action already began and further analysis revealed that the remedial action is unnecessary” [0665], “a remedial action may include sending instructions to a robot... Alternatively or additionally, the instruction may direct the robot to cease a current course of action”). Under the broadest reasonable interpretation, determining that a remedial action (such as ceasing an action) is unnecessary to encompass determining that the non-compliant use of the equipment has been obviated.
and permitting, by the one or more processors, the operation of the equipment ([0615], “if an analysis of a leakage results in a determination that no remedial action is necessary, remedial action may be forgone. Or, if remedial action already began and further analysis revealed that the remedial action is unnecessary, forgoing a remedial action may include providing an updated notification (e.g., a notification may change a recommended remedial action or otherwise present information that differs from a previous notification).”).
Wolf does not teach wherein the equipment is a vacuum source.
However, Shelton does teach wherein the equipment is a vacuum source ([0030], “The surgical device may include any of a powered stapler, …, a suction-irrigation device,” [1194], “Controllable device 25256 may control pressure or airflow (e.g., suction) in chest tube 25257 inserted into the chest of patient 25252 (e.g., to reduce pressure on an organ such as a lung and drain air, blood, or fluid from the pleural space around a lung).”).
Wolf in view of Shelton are considered analogous to the claimed invention because they are in the field of surgical equipment. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wolf with Shelton for the advantage of providing “suction of excess fluid” (Shelton; [0386]).
Regarding claim 8, Wolf in view of Shelton teaches the method of claim 1. Wolf further teaches wherein the signals include one or more of a video feed, an image, and an audio signal ([0080], “trained machine learning algorithms (also referred to as trained machine learning models in the present disclosure) may be used to analyze inputs and generate outputs,… the input may include an image” [0158], “a machine learning model may be trained using video footage and corresponding label indicating known beginning points of an incision or other surgical events and/or procedures. The trained model may be used to identify similar procedure and/or event beginning locations within other surgical video footage.” [0699], “Detecting a characteristic event using a machine learning method may be one possible approach… surgeon may identify the characteristic event using a visual or an audio signal from the surgeon (e.g., a hand gesture, a body gesture, a visual signal produced by a light source generated by a medical instrument, a spoken word, or any other signal) that may be captured by one or more image sensors/audio sensors and recognized as a trigger for the characteristic event.”).
Regarding claim 9, Wolf in view of Shelton teaches the method of claims 1 and 8. Wolf further teaches wherein the images and the audio signals are analyzed with the trained machine-learning model in tandem, and wherein the trained machine- learning model is trained on combined audio and images representative of the nominal and adverse medical events ([0353], “A characteristic event (also referred to as an intraoperative surgical event) may be any event or action that occurs during a surgical procedure or phase.” [0116], “a machine learning model may be trained using training examples, each training example may include video footage known to be associated with surgical procedures, surgical phases, intraoperative events, and/or event characteristics, together with labels indicating locations within the video footage.” [0433], “a property of a phase may be non-textural data (e.g., image, audio, numerical, and/or video data) collected during a surgical procedure… a machine learning model may be trained using training examples to identify properties of surgical phases from images and/or videos. An example of such training example may include an image and/or a video of at least a portion of a surgical phase of a surgical procedure, together with a label indicating one or more properties of the surgical phase.”).
Regarding claim 15, Wolf teaches the system of claim 14. Wolf further teaches wherein the one or more processors are configured to prevent or terminate operation of the equipment based on the determination of the non-compliant use of the equipment ([0665], “a remedial action may include sending instructions to a robot. Such instructions may direct the robot to undertake an action that remediates or assists in remediating the leakage. Alternatively or additionally, the instruction may direct the robot to cease a current course of action”).
Wolf does not teach wherein the equipment is a medical waste collection system comprising a vacuum source that is coupled with a suction tube.
However, the combination of Wolf in view of Shelton does teach wherein the equipment is a medical waste collection system comprising a vacuum source ([0030], “The surgical device may include any of a powered stapler, …, a suction-irrigation device,” [1194], “Controllable device 25256 may control pressure or airflow (e.g., suction) in chest tube 25257 inserted into the chest of patient 25252 (e.g., to reduce pressure on an organ such as a lung and drain air, blood, or fluid from the pleural space around a lung).”). Thus, it would be obvious to one of ordinary skill in the art that combining the automatic termination of an action taught by Wolf with an action such as control of a vacuum source to drain blood or fluid (waste) taught by Shelton would result in a system wherein processors would be configured to prevent or terminate operation of a vacuum source.
Wolf in view of Shelton are considered analogous to the claimed invention because they are in the field of surgical equipment. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wolf with Shelton for the advantage of providing “suction of excess fluid” (Shelton; [0386]).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Wolf (US 20200268457) in view of Shelton (US 20220233119) further in view of Ernstmeyer (Ernstmeyer; K, Nursing Skills (Open RN), 2021, Chippewa Valley Technical College, Chapter 22 Tracheostomy Care & Suctioning).
Regarding claim 5, Wolf in view of Shelton teaches the method of claims 1 and 3-4. Wolf in view of Shelton does not teach wherein the potential adverse medical event includes one of (i) the suction tube being coupled to a chest tube, (ii) the suction tube being coupled to a tracheal tube, and (iii) the suction tube being coupled to a closed wound drainage tube.
However, Ernstmeyer does teach wherein the potential adverse medical event includes one of (i) the suction tube being coupled to a chest tube, (ii) the suction tube being coupled to a tracheal tube, and (iii) the suction tube being coupled to a closed wound drainage tube (pg. 5, “While suctioning the patient, if signs of worsening respiratory distress occur, stop the procedure and request emergency assistance.”).
Wolf in view of Shelton further in view of Ernstmeyer are considered analogous to the claimed invention because they are in the field of surgical equipment. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wolf in view of Shelton with Ernstmeyer for the advantage of determining “signs of worsening respiratory distress” while suctioning (Ernstmeyer; pg. 5).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Wolf (US 20200268457) in view of Shelton (US 20220233119) further in view of Shelton2019 (US 20190201018).
Regarding claim 7, Wolf in view of Shelton teaches the method of claim 1. Wolf does not teach the method further comprising: receiving a user input to a user interface that the non-compliant use has been obviated; and permitting, by the one or more processors, the operation of the vacuum source.
However, the combination of Shelton in view of Shelton2019 does teach the method further comprising:
receiving a user input to a user interface that the non-compliant use has been obviated; and permitting, by the one or more processors, the operation of the vacuum source (Shelton2019, [0573], “Notably, in such an aspect, the surgical hub 23004 may be configured to transmit/communicate a result(s) (i.e., a warning associated with the surgical function, a reason the surgical function is prevented, etc.) associated with that determination to the user interface 23006. Further, according to various aspects, various user interfaces disclosed herein may comprise a selectable user interface feature (e.g., override element 23012) to proceed with the surgical function despite any warnings and/or reasons supporting prevention.” [0630], “the associated control circuit may permit the one or more than one functionality of the end effector if the associated control circuit determines that the surgical instrument or the component thereof and/or the positioning of the surgical instrument has been rectified (e.g., improper staple cartridge replaced, surgical instrument repositioned, etc.) or an override has been received (e.g., via a user interface on the surgical instrument, on a surgical hub coupled to the surgical instrument, in the surgical theater, etc.).” Shelton, [0030], “The surgical device may include any of a powered stapler, …, a suction-irrigation device,” [1194], “Controllable device 25256 may control pressure or airflow (e.g., suction) in chest tube 25257 inserted into the chest of patient 25252 (e.g., to reduce pressure on an organ such as a lung and drain air, blood, or fluid from the pleural space around a lung).”). It would be obvious to one of ordinary skill in the art that receiving a override command through a user interface is functionally analogous to receiving a user input to a user interface that the non-compliant use has been obviated. Thus, combining said functionality with permitting use of a surgical device based on a determination that any issues with components or positioning of a surgical instrument have been rectified encompasses the claimed subject matter.
Wolf in view of Shelton further in view of Shelton2019 are considered analogous to the claimed invention because they are in the field of surgical equipment. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wolf in view of Shelton with Shelton2019 for the advantage of “evaluat[ing] detected parameters before permitting that particular surgical function to proceed” (Shelton; [0580]).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Wolf (US 20200268457) in view of Chin (US 20210378699).
Regarding claim 18, Wolf teaches the system of claim 14. Although Wolf recites cameras on surgical tools ([0152], “An image sensor located in a surgical cavity, an organ, and/or a vasculature may include a camera included on a surgical tool inserted into the patient.” [0298], “an image sensor may be a part of a bronchoscope tube, a laparoscope, an endoscope, or any other medical instrument configured for location inside or outside a patient (e.g., for procedures such as gastroscopy, colonoscopy, hysteroscopy, cystoscopy, flexible sigmoidoscopy, wireless capsule endoscopy, and the like).”), Wolf does not explicitly teach the system further comprising a surgical navigation system comprising a localizer, wherein the one or more cameras is disposed on a localizer.
However, Chin does teach the system further comprising a surgical navigation system comprising a localizer, wherein the one or more cameras is disposed on a localizer ([0034], “the surgical robotic system 100 includes a navigation system 10.” [0021], “FIG. 3, navigation system 10 can further include a localizer 11 and one or more tracking devices 12... In the embodiment shown in FIG. 3, the localizer 1 is an optical localizer and includes a camera unit (e.g., a sensing device).” ).
Wolf in view of Chin are considered analogous to the claimed invention because they are in the field of surgical equipment. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wolf with Chin for the advantage of “track[ing] movement of various objects in the operating room (e.g., a surgery room) with respect to a target coordinate system” (Chin; [0034]).
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Wolf (US 20200268457) in view of Gorek (WO 2017075541).
Regarding claim 19, Wolf teaches the system of claim 14. Although Wolf recites sponges ([0202], “In addition to the surgical tools listed above, medical instruments may include, but are not limited to stethoscopes, gauze sponges,”), Wolf does not teach the system further comprising a sponge management system comprising a tablet disposed on a moveable stand, wherein the one or more cameras is disposed on the tablet.
However, Gorek does teach the system further comprising a sponge management system comprising a tablet disposed on a moveable stand, wherein the one or more cameras is disposed on the tablet ([0004], “During a procedure in an operating room, it can be important to accurately track usage and/or movement of various objects. In particular, it is important to accurately account for small objects such as needles and sponges, which may be at risk of accidentally being left in a patient.” [0168], “wherein the surgical procedure is performed on the patient (e.g., transplant or device placed); 1833, wherein sponges are dispensed and used to absorb blood” [0026], “The display may comprise a touch screen display, e.g., of a smart phone, tablet, or other mobile computing device... The apparatus may include a camera or microphone. A user-adjustable support may be configured to support one or more of the camera or microphone, the display and the processor in order for a user to position said camera to capture surgical images, video, or audio.”). Examiner interprets an accounting system for sponges to encompass a sponge management system.
Wolf in view of Gorek are considered analogous to the claimed invention because they are in the field of surgical equipment. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wolf with Gorek for the advantage of “recogniz[ing] instruments used during the surgical procedure, or movements or gestures of the personnel in the operating room” (Gorek; [0060]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Systems, Devices and Methods for Draining and Analyzing Bodily Fluids, Pressures and Assessing Health (US 20240099624) teaches a systems, devices and methods for draining and analyzing bodily fluids, pressures and assessing health are described where a drainage system generally comprises an elongate catheter having a first end and at least one opening in fluid communication with a catheter lumen, a drainage tube having a drainage lumen in fluid communication with a second end of the catheter, a fluid reservoir in fluid communication with the drainage lumen, and a pressure sensing membrane in communication with a pressure lumen defined through the catheter. The system also includes a pressure sensor coupled to the pressure lumen and configured to receive an intra-abdominal pressure signal from the pressure sensing membrane via the pressure lumen, and a controller in communication with the fluid reservoir and the pressure sensor. The controller is configured to determine an abdominal perfusion pressure based on the intra-abdominal pressure and a mean arterial pressure received by the controller.
Billing Method For Pump Usage (US 20080005000) teaches methods of leasing or billing for the usage of a portable suction pump adapted for use in a suction-assisted would treatment system. The pump has means for recording time units corresponding to periods of time when the pump is operating and for providing reports of usage time. The pump further has means for detecting that the wound treatment system is operating normally so as to be compliant with standards for suction wound treatment, and for recording and reporting time of normal operation or compliant usage. The methods include leasing the pump at a payment schedule that is based upon the amount of actual usage time or the amount of compliant usage time. The lease payment may include an amount of pre-paid time units such that unused time units can be credited to a new or renewed lease for a replacement pump. The billing methods can also be based in whole or in part on a planned maintenance schedule for the pum
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/D.C./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684