DETAILED ACTION
Acknowledgements
This office action is in response to the claims filed March 4, 2026.
Claims 1-2, 4-15, and 18-21 are pending.
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 .
Response to Amendment(s)
Claims 1-2, 4-15, and 18-21 are pending.
Information Disclosure Statement(s)
The information disclosure statement (IDS) submitted on 03/04/2026 was considered by the examiner.
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 1-2, 4-15, and 18-21 are rejected to under 35 U.S.C 101 as not being directed to eligible subject matter based on the grounds set out in detail below:
Independent Claims 1, 19, and 20:
Eligibility Step 1 (does the subject matter fall within a statutory category?):
Independent claim 1 falls within the statutory category of method.
Independent claim 19 falls within the statutory category of machine.
Independent claim 20 falls within the statutory category of article or manufacture
Eligibility Step 2A-1 (does the claim recite an abstract idea, law of nature, or natural phenomenon?): Independent claims 1, 19, and 20 (claim 1 being representative) claimed invention is directed to an abstract idea without significantly more.
The claim elements which set forth the abstract idea in the independent claims (claim 1 being representative) is:
A method for generating an alert to close a door of an operating room, comprising:
determining, a status of a surgical procedure in the operating room
detecting at least one object in the operating room
tracking movement of the at least one object to determine the status of the surgical procedure;
in accordance with a determination that the surgical procedure is in progress:
determining, a status of the door of the operating room by:
receiving one or more images of the door; and inputting the one or more images to obtain the status of the door, wherein using a plurality of training images depicting open or closed doors;
determining, whether the door of the operating room is a door to a sterile room that is acceptable to open at least once while the surgical procedure is in progress or a door to a non-sterile corridor that is not acceptable to open while the surgical procedure is in progress
receiving, one or more signals in the operating room;
determining, based on the one or more signals, whether an alert threshold is reached wherein the alert threshold is based on a type of the surgical procedure; in accordance with a determination that i) the alert threshold is reached ii) the status of the door is open, and (iii) the door of the operating room is a door to a sterile room,
generating, the alert to close the door of the operating room; and in accordance with a determination that (i) the alert threshold is not reached, (ii) the status of the door is open, and (iii) the door of the operating room is a door to a sterile room, foregoing generating the alert to close the door of the operating room;
determining a correlation between the status of the door and an incidence of a surgical site infection;
and automatically formulating a surgical protocol for a future surgical procedure based on the determined correlation, wherein the surgical protocol comprises a requirement for the door to remain closed for a predetermined amount of time during the future surgical procedure
This abstract idea is “mental process” as it is merely making an observation, determination or judgment on whether to alert that a door be opened or closed in an operating room based on received data MPEP § 2106.04(a)(2), subsection III)
Eligibility Step 2A-2 (does the claim recite additional elements that integrate the judicial exception into a practical application?): For Independent claims 1, 19, and 20 judicial exception is not integrated into a practical application.
Independent claim 1 recites the additional claim elements below:
one or more processors of one or more electronic devices
a video stream of operating room
an object detection algorithm perform segmentation of one or more image frames of the video stream;
cameras
a trained machine-learning model
one or more sensors
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional element, one or more processors of one or more electronic devices, is performing the abstract idea.
The additional element, a video stream, is merely “apply-it” as the cameras are used as a tool to gather data
The additional element, an object detection algorithm perform segmentation of one or more image frames of the video stream, is merely “apply-it” as the cameras are used as a tool to gather data merely generally linking the abstract idea to the technological field of machine learning
The additional element, a trained machine learning model, is merely generally linking the abstract idea to the technological field of machine learning
The additional element, cameras, is merely “apply-it” as the cameras are used as a tool to gather data (e.g. “captured by”)
The additional element, one or more sensors, is merely “apply-it” as the sensors are used as a tool to gather data
Accordingly, independent claims 1, 19, and 20 as a whole do not integrate the recited abstract idea into a practical application (MPEP 2106.05(f) and 2106.04(d)(1).
Eligibility Step 2B (Does the claim amount to significantly more?): The independent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computer element as analyzed above in step 2A prong 2, is merely generally linking or applying the abstract idea and therefore, does not amount to significantly more. The claims are patent ineligible
Dependent Claims 2, 4-15, 18, and 21:
Eligibility Step 1 (does the subject matter fall within a statutory category?):The dependent claims 2, 4-15, 18, and 21 fall within the statutory category of method.
Eligibility Step 2A-1 (does the claim recite an abstract idea, law of nature, or natural phenomenon?): Dependent claims 2, 4-15, 18, and 21 claimed invention is directed to an abstract idea without significantly more. The claims continue to limit the independent claim 1 abstract idea by (1) further limiting the types of data (2) further limiting when to determine the status of the door, (3) further limiting the machine learning model, (4) further limiting when the surgery is in progress and (5) further limiting the infection prevention protocol. Therefore, the dependent claims inherit the same abstract idea which is “mental process” as it is merely making an observation, determination or judgment on whether to alert that a door be opened or closed in an operating room based on received data MPEP § 2106.04(a)(2), subsection III)
Eligibility Step 2A-2 (does the claim recite additional elements that integrate the judicial exception into a practical application?): For claims 2, 4-15, 18, and 21 this judicial exception is not integrated into a practical application.
The dependent claims recite the below additional elements not already recited in the independent claims
One or more objects (e.g. stretcher)
Neural network
Sensor (e.g. temperature)
Surgical light
Display
Mobile device
Electronic medical record
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional elements, (b) neural network, is recited in the manner of generally linking to the technological environment of deep learning
The additional elements, (a) one or more objects (e.g. stretcher), (c) sensors (e.g. temperature), (d) surgical light, , (e) display, (f) mobile device, and (g) electronic medical record, are merely “apply-it” as they are used as tools to manipulate data, data gather, and data output.
Accordingly, the dependent claims as a whole do not integrate the recited abstract idea into a practical application (MPEP 2106.05(f) and 2106.04(d)(1).
Eligibility Step 2B (Does the claim amount to significantly more?): The dependent claims do not include additional elements that amount to significantly more for the same reasons given in Prong 2. The claims are patent ineligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, 4, 5, 9, 10, 11, 13, 14, 15, 18, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Block et. al (hereinafter Block) (US20220399105A1) in view of Egan (GB2458118A)
As per claim 1, Block teaches:
A method for generating an alert to close a door of an operating room, comprising: receiving, by one or more processors of one or more electronic devices, a video stream of the operating room; determining, by the one or more processors of the one or more electronic devices, a status of a surgical procedure in the operating room by: detecting at least one object in the operating room using an object detection algorithm, ([0026] discloses, “The monitoring server ( 80 ) may include one or more servers ( e.g. , local physical servers , virtual servers , cloud servers , or other computing environments ) that are configured to provide the processor ( 100 ) with data and other computing resources to assist in the analysis of data produced by the sensor array ( 60 ) ; to receive , store , and directly process sensor data ; or both . As an example , sensor data may include images and / or video captured from within the operating room that is analyzed using machine vision to identify certain objects , people , or other characteristics ( e.g. , whether a surgeon is wearing a mask and gloves ) . Analysis for machine vision may be performed by the processor ( 100 ) , the monitoring server ( 80 ) , or both.” And see [0030] discloses, “While the sensor array ( 60 ) of ORMS ( 50 ) is distributed around the operating room ( 10 ) , it should be understood that some implementations of the sensor array ( 60 ) may be integrated into a single unit such as a mobile cart or equipment cabinet . As shown in FIG . 2 , the sensor array ( 60 ) includes a set of cameras ( 62 ) , a set of proximity sensors ( 64 ) , a set of air velocity sensors ( 66 ) , a set of door position sensors ( 68 ) , a temperature sensor ( 70 ) and a relative humidity sensor ( 72 ) . However , it will be appreciated that in various implementations of the ORMS ( 50 ) the number and type of sensors may vary , and certain sensors may be omitted while other alternative sensors may be provided ( e.g. , a particle counter , a microbial detector ) in order to suit particular application. “ and see [0034] discloses, “The set of door position sensors ( 68 ) are placed on doors ( 32 ) in order to determine whether the equipped door is open or closed . Open doors may influence infection risks within the operating room ( e.g. , by disturbing normal air flow and potentially introducing contaminants ) . Data produced by the door position sensors ( 68 ) may be used to determine how many times a door is opened , the length of time that the door remains open , and whether the door is completely shut ( e.g. , firmly seated within the frame with a mechanical or magnetic latch or other mechanism engaged ) . Door position sensors ( 68 ) may also include other features such as an automatic door opener , automatic door closer , automatic locking mechanism , and door status indicator ( e.g. , a light indicator or audio indicator ) to provide information related to the door . Such features may be used by the ORMS ( 50 ) to enforce guidelines and minimize the extent and frequency of door use , automatically close doors that are partially open , or otherwise gather information related to the doors ( 32 ).”)
wherein the object detection algorithm is configured to perform segmentation of one or more image frames of the video stream; ([0057] discloses, “As has been described , the ORMS ( 50 ) may include specialized machine vision configurations ( e.g. , the room analysis engine ( 56 ) and / or data from the monitoring server ( 80 ) ) to aid in object recognition , which may include an event identifier for identifying particular events that occur in association with the identified objects , an artificially intelligent engine ( such as a machine learning engine that is trained upon suitable video segments ) for determining whether each particular event or combination of events affects compliance with an infection - reduction protocol developed for the hospital operating room . ORMS ( 50 ) may also include an alarm indicator for issuing an alert ( e.g. , via the alert indicator ( 106 ) or remote devices ( 20 ) ) when any particular event or combination of events is determined to be non - compliant with said infection - reduction protocol . This method of determining compliance may be used in conjunction with other detection methods using other sensors , or it may operate based on data from a single detection method.” And see [0058] discloses, “Captured images and video usable in machine vision processes may combine the different views from one or more of the set of cameras ( 62 ) so as to generate a single concatenated video information feed , or it may correlate certain areas of each video information feed with one another , such as where multiple cameras cover the same area . Image capture may also include pre - processing of the incoming image data , such as to correct brightness , contrast , or the like . Image processing may also include masking of known background objects , which can reduce the processing needed in subsequent steps and aid in the identification of material objects within the video . Such a process can be performed efficiently where the ORMS ( 50 ) has been con figured with certain details about a unique operating room ( 10 ) since such details largely remain static . Examples of static features that the ORMS may be configured to account for and mask may include walls , lights , doors , switches , cabinets , and other permanent or generally static features of operating room ( 10 ).” And see [0059] discloses, “In some implementations , image processing may also include masking out or otherwise anonymizing facial features or patient anatomy , and may also mask out confidential information ( e.g. , such as patient information dis played on a video monitor ) . As with prior video processing examples , known locations of video monitors or other information sources may be used as inputs to aid in identifying and masking such data . Similarly , known identities of patients , practitioners , and others may be used as inputs to aid in identifying and masking facial features , identity data , or patient anatomy.” / examiner notes as one of ordinary skill in the art would understand under BRI that machine vision is a type of image segmentation application)
tracking movement of the at least one object using an object tracking algorithm to determine the status of the surgical procedure; ([0050] discloses, “The set of proximity sensors ( 64 ) as shown are located near high - risk areas within the operating room ( 10 ) as well as near the doors ( 32 ) and scrub - in station . These sensors provide data that may be used in assessing compli ance with one or more of the rules outlined in the infection reduction protocol , such as tracking movement of equipment and personnel between zones and monitoring compliance with washing procedures and other requirements . For example , the proximity sensors ( 64 ) may produce data usable to ensure compliance with other rules , such as scrub - in requirements upon entering the operating room and / or the maximum number of hospital staff permitted within the operating room . In some implementations , the proximity sensors ( 64 ) each emit an electromagnetic field or a beam of electromagnetic radiation ( infrared , for instance ) , and monitor the different elements for changes in the field or return signal.” And see [0060] discloses, “During machine vision analysis , the processor ( 100 ) , the monitoring server ( 80 ) , or both ( e.g. , using the room analysis engine ) can then subsequently process the image information using an object recognizer to recognize foreground objects present in the video information . In some implementations , the machine vision engine utilizes a pre dictive modeling engine , such as indoor air quality ( IAD ) analytics engines like those described in Jagriti Saini , et al . ( Saini , J. , Dutta , M. , & Marques , G. ( 2020 ) . A Comprehen sive Review on Indoor Air Quality Monitoring Systems for Enhanced Public Health , Sustainable Environment Research , 30 ( 6 ) , https://doi.org/10.1186/s42834-020-0047 y ) , incorporated herein by reference , which measured 2.5 micrometer - size particles to control airflow for optimum public health in buildings . Objects identified may include a surgical table , a surgeon , an equipment stack , an anesthesia cart , an anesthesiologist , a back table , a mobile base cart , a Mayo stand , a spec cart , a nurse , a workstation , any sub component thereof , any surgical tool , implant , or the like lying thereon , or any other medical equipment.” And see [0061] discloses, “During machine vision analysis , the room analysis engine ( 56 ) may include a machine learning function that focuses on characteristics or events associated with identified objects , while a machine vision analysis focuses on the identification of various foreground objects . For example , the machine learning process may be trained on images of a surgeon in properly and improperly worn surgical gowns , including headwear and a mask , to improve subsequent recognition of proper and improper technique . Still further , the machine learning process may be trained on images of a human having a mask properly and improperly positioned over their mouth and nose , thereby enabling the machine learning process to detect the improper lowering of a face mask during an operation , or the entry into the room of a hospital staff member who is either not wearing a face mask or is wearing the face mask in an improper position ( e.g. , around the neck ).”)
in accordance with a determination that the surgical procedure is in progress: determining, by the one or more processors, a status of the door of the operating room by: (see [0034] discloses, “The set of door position sensors ( 68 ) are placed on doors ( 32 ) in order to determine whether the equipped door is open or closed . Open doors may influence infection risks within the operating room ( e.g. , by disturbing normal air flow and potentially introducing contaminants ) . Data produced by the door position sensors ( 68 ) may be used to determine how many times a door is opened , the length of time that the door remains open , and whether the door is completely shut ( e.g. , firmly seated within the frame with a mechanical or magnetic latch or other mechanism engaged ) . Door position sensors ( 68 ) may also include other features such as an automatic door opener , automatic door closer , automatic locking mechanism , and door status indicator ( e.g. , a light indicator or audio indicator ) to provide information related to the door . Such features may be used by the ORMS ( 50 ) to enforce guidelines and minimize the extent and frequency of door use , automatically close doors that are partially open , or otherwise gather information related to the doors ( 32 ).”)
receiving one or more images of the door captured by one or more cameras; (see fig. 2 and see [0025] disclose, “The sensor array ( 60 ) detects one or more conditions , events , and information about the operating room and may include sensors such as temperature sensors , relative humidity sensors , CO2 sensors , proximity sensors , motion sensors , vibration sensors , image capture devices , sound capture devices , door position sensors , differential pressure sensors , air flow / velocity and air quality and toxicity sensors , near real - time biological aerosol pathogenic organism detectors , patient physiological sensors , and other types of sensors , depending upon a particular implementation . In various embodiments , one or more of these sensors capture video , and in other embodiments one or more of these sensors will not capture video .” and see [0030] discloses, “While the sensor array ( 60 ) of ORMS ( 50 ) is distributed around the operating room ( 10 ) , it should be understood that some implementations of the sensor array ( 60 ) may be integrated into a single unit such as a mobile cart or equipment cabinet . As shown in FIG . 2 , the sensor array ( 60 ) includes a set of cameras ( 62 ) , a set of proximity sensors ( 64 ) , a set of air velocity sensors ( 66 ) , a set of door position sensors ( 68 ) , a temperature sensor ( 70 ) and a relative humidity sensor ( 72 ) . However , it will be appreciated that in various implementations of the ORMS ( 50 ) the number and type of sensors may vary , and certain sensors may be omitted while other alternative sensors may be provided ( e.g. , a particle counter , a microbial detector ) in order to suit particular applications of the technology or implementation environments. And see [0031] discloses, “A set of one or more cameras ( 62 ) is positioned around the operating room ( 10 ) to provide various views despite the likelihood of obstruction by equipment or per sonnel at times during a procedure . As such , the set of cameras ( 62 ) may be mounted near walls , on the ceiling away from a wall , or on ( e.g. , on the mobile arm ( 31 ) ).” And see [0034] discloses, “The set of door position sensors ( 68 ) are placed on doors ( 32 ) in order to determine whether the equipped door is open or closed . Open doors may influence infection risks within the operating room ( e.g. , by disturbing normal air flow and potentially introducing contaminants ) . Data produced by the door position sensors ( 68 ) may be used to determine how many times a door is opened , the length of time that the door remains open , and whether the door is completely shut ( e.g. , firmly seated within the frame with a mechanical or magnetic latch or other mechanism engaged ) . Door position sensors ( 68 ) may also include other features such as an automatic door opener , automatic door closer , automatic locking mechanism , and door status indicator ( e.g. , a light indicator or audio indicator ) to provide information related to the door . Such features may be used by the ORMS ( 50 ) to enforce guidelines and minimize the extent and frequency of door use , automatically close doors that are partially open , or otherwise gather information related to the doors ( 32 ).”)
and inputting the one or more images into a trained machine-learning model to obtain the status of the door, wherein the machine-learning model is trained using a plurality of training images depicting open or closed doors; ([0026] discloses, “As an example , sensor data may include images and / or video captured from within the operating room that is analyzed using machine vision to identify certain objects , people , or other characteristics ( e.g. , whether a surgeon is wearing a mask and gloves ) . Analysis for machine vision may be performed by the processor ( 100 ) , the monitoring server ( 80 ) , or both . Where machine vision includes aspects of machine learning , the monitoring server ( 80 ) may store , update , and maintain data related to the training of recognition , processing , and interface features of the ORMS ( 50 ) , and the monitoring server ( 80 ) may distribute such data and the output of such processing to a plurality of ORMSS ( 50 ) so that analysis may be performed locally to each ORMS . In some implementations , the processor ( 100 ) , the monitoring server ( 80 ) , or both may be configured to execute and provide a room analysis engine ( 56 ) that is configured to receive sensor data and perform specialized analysis thereon ( e.g. , specialized video and image processing , specialized numerical data processing ) , as will be described in more detail below . As has been described , varying implementations of the room analysis engine ( 56 ) may include features such as machine learning , machine vision , statically configured rule evaluation , glob ally shared datasets ( e.g. , machine vision datasets and training datasets ) , and other features.” And see [0047] discloses, “The infection - reduction protocol may also include other rules which are more general or global in their application . For example , the infection - reduction protocol may be configured to mimic a set of one or more operating room guidelines regardless of risk area , such as : all hospital staff must scrub in upon entering the operating room ; all hospital staff must wear proper gowns during the operation ; all hospital staff must wear a facemask and maintain it in the proper position ; all hospital staff must wear shoe covers ; all hospital staff must wear hair coverings ; doors to the operating room must remain closed ; the temperature must be maintained within a specified range ; the relative humidity must be maintained within a specified range ; the airflow rate ( at one or more locations ) must be maintained within a specified range ; the number of occupants in the operating room must fall between a minimum number and a maximum number ; diathermy or electrocautery devices must be used with a vacuum ; and other guidelines , as will be apparent to those of ordinary skill in the art in light of this disclosure . As has been described , sensor data collected from the sensor array ( 60 ) may be evaluated in view of each such global guidelines of the infection - reduction protocol in order to identify aberrations , update the risk picture ( 51 ) , provide alerts , and take other actions” and see [0061] discloses, “During machine vision analysis , the room analysis engine ( 56 ) may include a machine learning function that focuses on characteristics or events associated with identified objects , while a machine vision analysis focuses on the identification of various foreground objects . For example , the machine learning process may be trained on images of a surgeon in properly and improperly worn surgical gowns , including headwear and a mask , to improve subsequent recognition of proper and improper technique . Still further , the machine learning process may be trained on images of a human having a mask properly and improperly positioned over their mouth and nose , thereby enabling the machine learning process to detect the improper lowering of a face mask during an operation , or the entry into the room of a hospital staff member who is either not wearing a face mask or is wearing the face mask in an improper position ( e.g. , around the neck ).” / examiner notes that the disclosed teaching states the events can be any events associated with infection reduction from the infection reduction protocol therefore the training example applies also to door opening and closing which is an example of infection reduction)
determining, by the one or more processors, whether the door of the operating room is a door to a sterile room that is acceptable to open at least once while the surgical procedure is in progress or a door to a non-sterile corridor that is not acceptable to open while the surgical procedure is in progress; (see [0072] discloses, “While various circumstances can be detected as violations and trigger notification and / or other action ( e.g. , such as incursions ( 408 ) , guideline violations ( 412 ) , air quality violations ( 416 ) , and door violations ( 420 ) ) , the ORMS ( 50 ) may also be configured to only take action after detection when the circumstance exceeds a certain threshold or magnitude.” And see [0034] discloses, “The set of door position sensors ( 68 ) are placed on doors ( 32 ) in order to determine whether the equipped door is open or closed . Open doors may influence infection risks within the operating room ( e.g. , by disturbing normal air flow and potentially introducing contaminants ) . Data produced by the door position sensors ( 68 ) may be used to determine how many times a door is opened , the length of time that the door remains open , and whether the door is completely shut ( e.g. , firmly seated within the frame with a mechanical or magnetic latch or other mechanism engaged ) . Door position sensors ( 68 ) may also include other features such as an automatic door opener , automatic door closer , automatic locking mechanism , and door status indicator ( e.g. , a light indicator or audio indicator ) to provide information related to the door . Such features may be used by the ORMS ( 50 ) to enforce guidelines and minimize the extent and frequency of door use , automatically close doors that are partially open , or otherwise gather information related to the doors ( 32 ).” / examiner notes that under BRI one of ordinary skill in the art would understand that an operating room is considered to be a sterile room and that the only time an alert for closing door is considered is when exceeding a threshold as disclosed thus necessarily there are times when the doors open and its considered okay)
receiving, by the one or more processors, one or more signals from one or more sensors in the operating room; ([0034] discloses, “The set of door position sensors ( 68 ) are placed on doors ( 32 ) in order to determine whether the equipped door is open or closed . Open doors may influence infection risks within the operating room ( e.g. , by disturbing normal air flow and potentially introducing contaminants ) . Data produced by the door position sensors ( 68 ) may be used to determine how many times a door is opened , the length of time that the door remains open , and whether the door is completely shut ( e.g. , firmly seated within the frame with a mechanical or magnetic latch or other mechanism engaged ) . Door position sensors ( 68 ) may also include other features such as an automatic door opener , automatic door closer , automatic locking mechanism , and door status indicator ( e.g. , a light indicator or audio indicator ) to provide information related to the door . Such features may be used by the ORMS ( 50 ) to enforce guidelines and minimize the extent and frequency of door use , automatically close doors that are partially open , or otherwise gather information related to the doors ( 32 )” and see [0055 ] As has been described , the ORMS ( 50 ) evaluates data from the sensor array ( 60 ) with one or more rules provided by the infection - reduction protocol . For example , ORMS ( 50 ) may trigger an alert if door position sensor ( 68 ) signals that the door is opened for greater than a specified number of seconds ( e.g. , 10 seconds ) .” and see [0072] discloses, “While various circumstances can be detected as violations and trigger notification and / or other action ( e.g. , such as incursions ( 408 ) , guideline violations ( 412 ) , air quality violations ( 416 ) , and door violations ( 420 ) ) , the ORMS ( 50 ) may also be configured to only take action after detection when the circumstance exceeds a certain threshold or magnitude . Technical violations that do not exceed the threshold may be ignored or may be prioritized for processing of future analysis tasks ( e.g. , data associated with the technical violation may be received and processed at an increased priority relative to other tasks of the processor ( 100 ) , the monitoring server ( 80 ) , or other processors ).” / examiner notes the processor is receiving data such as the sensor data of open or closed doors)
determining, by the one or more processors, based on the one or more signals, whether an alert threshold is reached, wherein the alert threshold is based on a type of the surgical procedure; in accordance with a determination that j)the alert threshold is reached (ii) (see fig. 7 and fig. 8 and see [0034] discloses, “Door position sensors ( 68 ) may also include other features such as an automatic door opener , automatic door closer , automatic locking mechanism , and door status indicator ( e.g. , a light indicator or audio indicator ) to provide information related to the door . Such features may be used by the ORMS ( 50 ) to enforce guidelines and minimize the extent and frequency of door use , automatically close doors that are partially open” and see [0055] discloses “As has been described , the ORMS ( 50 ) evaluates data from the sensor array ( 60 ) with one or more rules provided by the infection - reduction protocol . For example , ORMS ( 50 ) may trigger an alert if door position sensor ( 68 ) signals that the door is opened for greater than a specified number of seconds ( e.g. , 10 seconds ) . In some implementations , an alert may be issued if the door is opened more than a specified number of times during a given operation.” And see [0072] discloses, “[ 0072 ] While various circumstances can be detected as violations and trigger notification and / or other action ( e.g. , such as incursions ( 408 ) , guideline violations ( 412 ) , air quality violations ( 416 ) , and door violations ( 420 ) ) , the ORMS ( 50 ) may also be configured to only take action after detection when the circumstance exceeds a certain threshold or magnitude . Technical violations that do not exceed the threshold may be ignored or may be prioritized for process ing of future analysis tasks ( e.g. , data associated with the technical violation may be received and processed at an increased priority relative to other tasks of the processor ( 100 ) , the monitoring server ( 80 ) , or other processors ) . Such threshold configurations may be defined at the level of the infection - reduction protocol ( e.g. , one person improperly wearing a mask may not be defined as an aberration , while two or more people improperly wearing masks is an aber ration ) , but may also be configured separately from the infection - reduction protocol ( e.g. , such as by manual user configuration specific to a particular operating room , pro cedure , or facility ) . Such thresholds may additionally be used to determine varying levels of response to aberrations . For example , an occurrence that is technically an aberration but is of low risk may trigger actions related to daily report generation ( 512 ) , case summaries ( 516 ) , and analytics ( 518 ) , but may not generate general alerts ( 424 ) . A higher - risk aberration may trigger various administrative actions , and may additionally trigger tailored responses ( e.g. , individual notifications ( 414 ) ) and general alerts ( 424 ) ( e.g. , visual or auditory feedback from the alert indicator ( 106 ) ) . As with prior examples , such configurations may be defined as part of the infection - reduction protocol or may be manually configured for a particular operating room , procedure , or facility , for example .” and see [0027] discloses, “The HIS ( 90 ) may include one or more servers that are configured to provide the processor ( 100 ) and / or the monitoring server ( 80 ) with hospital records , patient records , personnel records , and other information usable during analysis of sensor data to monitor for , detect , qualify , quantify , and analyze infection risks . Information provided by the HIS ( 90 ) may include , for example , HVAC mainte nance records for an operating room , personnel records indicating training , certification , or adherence to operating room guidelines , historic infection rates associated with certain types of procedures or procedures performed in certain operating rooms , scheduling information ( such as which patients are to be operated on in which operating rooms at what times ) , patients ' electronic medical records , and other information.” / examiner notes the ORMS system which controls the alerting and is based on information from the HIS which stores information on procedure type)
And in accordance with a determination that (i) the alert threshold is not reached, (ii) the status of the door is open, and (iii) the door of the operating room is a door to a sterile room, foregoing generating the alert to close the door of the operating room. (see [0072] discloses, “While various circumstances can be detected as violations and trigger notification and / or other action ( e.g. , such as incursions ( 408 ) , guideline violations ( 412 ) , air quality violations ( 416 ) , and door violations ( 420 ) ) , the ORMS ( 50 ) may also be configured to only take action after detection when the circumstance exceeds a certain threshold or magnitude.” And see [0034] discloses, “The set of door position sensors ( 68 ) are placed on doors ( 32 ) in order to determine whether the equipped door is open or closed . Open doors may influence infection risks within the operating room ( e.g. , by disturbing normal air flow and potentially introducing contaminants ) . Data produced by the door position sensors ( 68 ) may be used to determine how many times a door is opened , the length of time that the door remains open , and whether the door is completely shut ( e.g. , firmly seated within the frame with a mechanical or magnetic latch or other mechanism engaged ) . Door position sensors ( 68 ) may also include other features such as an automatic door opener , automatic door closer , automatic locking mechanism , and door status indicator ( e.g. , a light indicator or audio indicator ) to provide information related to the door . Such features may be used by the ORMS ( 50 ) to enforce guidelines and minimize the extent and frequency of door use , automatically close doors that are partially open , or otherwise gather information related to the doors ( 32 ).” / examiner notes that under BRI one of ordinary skill in the art would understand that an operating room is considered to be a sterile room and that the only time an alert for closing door is considered is when exceeding a threshold as disclosed thus necessarily there are times when the doors open and its considered okay or threshold not reached. Furthermore the ORMS is disclosed as knowing when the status of the door is open or closed and not generating an alert again if the threshold is not met)
determining a correlation between the status of the door and an incidence of a surgical site infection; ([0068] discloses, “a When such a risk zone incursion is detected ( 408 ) , the ORMS ( 50 ) may determine and provide ( 410 ) a safe path or other corrective action that may be performed to reduce or mitigate the risk of infection related to the incursion . This may include displaying via one or more local or remote devices ( 20 ) a textual description or a representation of the operating room ( 10 ) ; an identification of the person , equip ment , or object associated with the risk ; and identifying a path to a destination that the person , equipment , or object should follow to reverse or mitigate the effect of the incur sion . The safe path may be provided ( 410 ) to each remote device ( 20 ) or other display or may only be provided to an individual responsible for mitigating the incursion . For example , where a surgeon places an instrument in an elevated risk area , a notification may be provided ( 410 ) to a user device associated with a nurse tech that identifies the object and describes where the object should be placed or moved to and how it should be transported . As an additional example , where a nurse tech moves into a lower risk area and causes an incursion ( 408 ) , a notification may be pro vided ( 410 ) to a remote device ( 20 ) associated with that nurse tech.” and see [0069] discloses, “Another general category of action taken by the ORMS ( 50 ) may be triggered by violation of guidelines ( 412 ) provided by an infection - reduction protocol , such as a failure to wash hands , failure to wear a mask , placement of an uncovered or unsealed instrument in a contaminated area , excessive personnel within the operating room , or other examples that have been described herein or will be apparent to those of ordinary skill in the art in light of this disclosure . Such violations may be detected in various ways as has been described ( e.g. , image analysis to identify improper mask placement , image analysis or wireless triangulation to locate instruments , proximity tracking at doorways and wash areas , etc. ) . When detected ( 412 ) , the ORMS ( 50 ) may provide ( 414 ) a notification of the violation to each remote device ( 20 ) or to a subset of remote devices ( 20 ) that identifies the violation and any actions that should be performed to mitigate the violation . [ 0070 ] Another general category of action taken by the ORMS ( 50 ) may be triggered by some aspect of air quality or climate within the operating room ( 10 ) falling ( 416 ) outside of desirable or sufficient ranges . This may include deviations in temperature , humidity , pressure , magnitude and direction of air flow , particle count , microbial count , or other characteristics , as may be detected by the sensor array ( 60 ) . When detected ( 416 ) , the ORMS ( 50 ) may provide notifications to responsible parties as has been described , or it may automatically adjust ( 418 ) or operate HVAC equip ment or other systems ( e.g. , through direct communication or communication via the HIS ( 90 ) to correct the air quality issue . This may include adjusting ACH rates , increasing or decreasing temperature or humidity , activating additional exhaust vents or air filtration systems , or other actions . [ 0071 ] Another general category of action taken by the ORMS ( 50 ) may be triggered based upon violation ( 420 ) of protocol related to doors ( e.g. , room entry doors , cabinet or equipment doors , or other fixtures that may be equipped with a sensor such as the door position sensor ( 68 ) . This may include frequent or prolonged opening of the door ( 32 ) , failures to completely close the door ( 32 ) , failures to close doors or other seals on equipment ( e.g. , such as closing a cabinet door where sterile instruments are stored ) , and other scenarios related to the infection - reduction protocol that may be detected by the sensor array ( 60 ) . When detected ( 420 ) , the ORMS ( 50 ) may provide ( 422 ) a door warning to one or more remote devices ( 20 ) ( e.g. , such as a device mounted near the door ( 32 ) ) indicating that the door should not be opened or that opening of the door should be delayed for some period of time ( e.g. , to allow air quality to be corrected after prior openings ) . In some implementations this may also include operating a door closer to close the door or performing other automated actions to comply with the infection - reduction protocol.”)
However, Block does not teach explicitly:
and automatically formulating a surgical protocol for a future surgical procedure based on the determined correlation, wherein the surgical protocol comprises a requirement for the door to remain closed for a predetermined amount of time during the future surgical procedure.
However, Egan teaches:
and automatically formulating a surgical protocol for a future surgical procedure based on the determined correlation, wherein the surgical protocol comprises a requirement for the door to remain closed for a predetermined amount of time during the future surgical procedure. (page 18 example 4 discloses, “As for Example 2, a proximity switch is fitted into/is already present in the dispenser. Operation of the soap dispenser therefore causes the doors to unlock, after a pre-set period for thorough washing of hands. This system is again preferred for the control of C. difficile mediated infections, because there are some reports that the use of soap and water is more effective in controlling such infection than alcohol gel based systems. Once C. difficile has spread it is notoriously difficult to eradicate. Thus it is particularly advantageous here to control access both to and from a hospital ward This system can be of course also be used to control a wide range of other infections.” And see page 8 para. 5-9 discloses, “There are many other applications where the apparatus can be utilised and the examples here are not exhaustive. In some instances it may be desired for the apparatus to provide a time delay before access to and/or exit from a given location is allowed. For example, it may be determined that a time delay of at least I 0, 20, 30, 40, 50 or 60 etc. seconds should be provided to allow sufficient time for washing of hands. Longer time delays may be utilised in certain environments ( e.g. when surgical staff are required to scrub up thoroughly by intensive washing prior to surgery). An adjustable time delay may even be provided so that the owner/ client can pre-program a desired time delay.”)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Block’s teachings of utilizing machine learning algorithms which are trained to determine whether events trigger a door to open or close as previously cited with Egan’s teachings of as previously, the motivation being Block’s teaches the rising cost of healthcare and the need for improved outcomes (see [0003]-[0005]) and Egan teaches the need to control or prevent infections (e.g. page 1) therefore it would be predictable to combine automation of door closing in regards to infection prevention in Block with the infection prevention door closing in Egan to increase positive outcomes for patients and reduce resources.
As per claim 2, Block teaches:
The method of claim 1, further comprising: in accordance with a determination that the surgery is not in progress, foregoing determining the status of the door of the operating room. (see fig. 7 and fig. 8 and see [0071] discloses, “Another general category of action taken by the ORMS ( 50 ) may be triggered based upon violation ( 420 ) of protocol related to doors ( e.g. , room entry doors , cabinet or equipment doors , or other fixtures that may be equipped with a sensor such as the door position sensor ( 68 ) ) . This may include frequent or prolonged opening of the door ( 32 ) , failures to completely close the door ( 32 ) , failures to close doors or other seals on equipment ( e.g. , such as closing a cabinet door where sterile instruments are stored ) , and other scenarios related to the infection - reduction protocol that may be detected by the sensor array ( 60 ) . When detected ( 420 ) , the ORMS ( 50 ) may provide ( 422 ) a door warning to one or more remote devices ( 20 ) ( e.g. , such as a device mounted near the door ( 32 ) ) indicating that the door should not be opened or that opening of the door should be delayed for some period of time ( e.g. , to allow air quality to be corrected after prior openings ) . In some implementations this may also include operating a door closer to close the door or performing other automated actions to comply with the infection - reduction protocol.” And see [0045] discloses, “The low - risk zone ( 16 ) may be associated with a still different set of procedures , rules , and requirements in a third infection - reduction protocol for low - risk areas . Such areas may have the most permissive requirements , and thus may be defined within the third infection - reduction protocol as the only location ( s ) where the surgical table ( 37 ) , the back instrument tables ( 40 ) , the Mayo stands ( 48 ) , and uncovered instruments and / or implants and other critical equipment may be placed . As can be seen , defining and configuring the ORMS ( 50 ) with an infection - reduction protocol ( e.g. , one including a different sub - protocol specific to each risk area type ) provides a set of rules that may be evaluated and applied to each area based on data received from the sensor array ( 60 ) . Thus , while the placement of sensors and the configuration of a particular operating room ( 10 ) as illustrated in FIGS . 2-5 may vary depending on each unique room , the infection - reduction protocol may stay largely constant between rooms and may be applied based upon the static or dynamic determination of areas within a particular room .” also see [0072] /examiner notes that the second element of this limitation is a negative limitation and the disclosed art triggers based on violation of protocol during a surgery to ensure infection prevention someone of ordinary skill in the art would understand based on the cited disclosure that if the situation is low risk or no risk the triggering does not occur thus status not taken)
As per claim 4, Block teaches:
The method of claim 1, wherein the atleast one object includes: a stretcher, a patient, a surgical mask, an intubation mask, an anesthesia cart, a cleaning cart, an operating table, an X-Ray device, an imaging device, a surgeon, the surgeon’s hand, a scalpel, an endoscope, a trocar, an oxygen mask, a light in the operating room, the door, a surgical drape, a case cart, a surgical robot, or any combination thereof. ([0038] discloses, “Monitoring and enforcement of guidelines may include configuring the ORMS ( 50 ) to define optimal positions for various equipment in the room . With reference to FIG . 3 , a set of patterned circles are shown overlaid upon the operating room ( 10 ) to indicate optimal areas within which certain equipment should be placed . For example , the mobile case cart ( 36 ) can be seen as substantially contained by an optimal zone ( 39 ) , which is represented as a patterned circle . Defined areas may be configured manually for the ORMS ( 50 ) when the operating room ( 10 ) is initially configured , or they may be automatically configured based upon known characteristics of the room ( e.g. , a single value or pair / set of coordinates indicating the position and orientation of the surgical table ( 37 ) within the operating room ( 10 ) may provide an anchor for relative positions where each other piece of equipment should be ideally placed ) . While FIG . 3 illustrates a configuration associated with the ORMS ( 50 ) and the operating room ( 10 ) , it may also be displayed as a user interface via the local display ( 21 ) , a remote device ( 20 ) , or both . Such an interface may aid in positioning equipment prior to a procedure , positioning equipment dur ing a procedure in response to an alert from the ORMS ( 50 ) , and in other tasks . For example , where a wireless triangulation or other position determination is made for the mobile case cart ( 36 ) , its current position may be displayed within the operating room ( 10 ) relative to the optimal zone ( 39 ).”)
As per claim 5, Block teaches:
The method of claim 4, wherein determining whether the surgery is in progress is based on: whether the stretcher is brought into the operating room, whether the surgeon is masked, whether the patient is masked, whether the patient is draped, whether the surgeon is donning a gown, whether the patient is intubated, whether the patient is on the operating table, whether an incision is made, whether the surgical light is in use, whether the X-Ray device is in use, whether the anesthesia cart is in use, whether the imaging device is in use or within a predefined proximity to the patient, whether the case cart has been brought into the operating room, whether one or more instruments from the case cart are unwrapped, whether the cleaning cart is in use, or any combination thereof. ([0047] discloses, “The infection - reduction protocol may also include other rules which are more general or global in their appli cation . For example , the infection - reduction protocol may be configured to mimic a set of one or more operating room guidelines regardless of risk area , such as : all hospital staff must scrub in upon entering the operating room ; all hospital staff must wear proper gowns during the operation ; all hospital staff must wear a facemask and maintain it in the proper position ; all hospital staff must wear shoe covers ; all hospital staff must wear hair coverings ; doors to the operating room must remain closed ; the temperature must be maintained within a specified range ; the relative humidity must be maintained within a specified range ; the airflow rate ( at one or more locations ) must be maintained within a specified range ; the number of occupants in the operating room must fall between a minimum number and a maximum number ; diathermy or electrocautery devices must be used with a vacuum ; and other guidelines , as will be apparent to those of ordinary skill in the art in light of this disclosure . As has been described , sensor data collected from the sensor array ( 60 ) may be evaluated in view of each such global guidelines of the infection - reduction protocol in order to identify aberrations , update the risk picture ( 51 ) , provide alerts , and take other actions.” And see [0069] Another general category of action taken by the ORMS ( 50 ) may be triggered by violation of guidelines ( 412 ) provided by an infection - reduction protocol , such as a failure to wash hands , failure to wear a mask , placement of an uncovered or unsealed instrument in a contaminated area , excessive personnel within the operating room , or other examples that have been described herein or will be apparent to those of ordinary skill in the art in light of this disclosure . Such violations may be detected in various ways as has been described ( e.g. , image analysis to identify improper mask placement , image analysis or wireless triangulation to locate instruments , proximity tracking at doorways and wash areas , etc. ) . When detected ( 412 ) , the ORMS ( 50 ) may provide ( 414 ) a notification of the violation to each remote device ( 20 ) or to a subset of remote devices ( 20 ) that identifies the violation and any actions that should be performed to mitigate the violation.” / examiner notes surgery is in progress as previously cited therefore determined)
As per claim 9, Block teaches:
The method of claim 1, wherein the one or more sensors include: a temperature sensor, a humidity sensor, a pressure sensor, an air quality sensor, a gas sensor, or any combination there. ([0025] discloses, “The sensor array ( 60 ) detects one or more conditions , events , and information about the operating room and may include sensors such as temperature sensors , relative humidity sensors , CO2 sensors , proximity sensors , motion sensors , vibra tion sensors , image capture devices , sound capture devices , door position sensors , differential pressure sensors , air flow / velocity and air quality and toxicity sensors , near real - time biological aerosol pathogenic organism detectors , patient physiological sensors , and other types of sensors , depending upon a particular implementation.”)
As per claim 10, Block teaches:
The method of claim 1, wherein the one or more sensors are placed within a predefined distance from a surgery table. ([0038] discloses, “Monitoring and enforcement of guidelines may include configuring the ORMS ( 50 ) to define optimal positions for various equipment in the room . With reference to FIG . 3 , a set of patterned circles are shown overlaid upon the operating room ( 10 ) to indicate optimal areas within which certain equipment should be placed . For example , the mobile case cart ( 36 ) can be seen as substantially contained by an optimal zone ( 39 ) , which is represented as a patterned circle . Defined areas may be configured manually for the ORMS ( 50 ) when the operating room ( 10 ) is initially configured , or they may be automatically configured based upon known characteristics of the room ( e.g. , a single value or pair / set of coordinates indicating the position and orien tation of the surgical table ( 37 ) within the operating room ( 10 ) may provide an anchor for relative positions where each other piece of equipment should be ideally placed ) . While FIG . 3 illustrates a configuration associated with the ORMS ( 50 ) and the operating room ( 10 ) , it may also be displayed as a user interface via the local display ( 21 ) , a remote device ( 20 ) , or both . Such an interface may aid in positioning equipment prior to a procedure , positioning equipment dur ing a procedure in response to an alert from the ORMS ( 50 ) , and in other tasks . For example , where a wireless triangulation or other position determination is made for the mobile case cart ( 36 ) , its current position may be displayed within the operating room ( 10 ) relative to the optimal zone ( 39 ). And see [0039] discloses, “The ORMS ( 50 ) may also be configured to define optimal positions or areas where certain personnel should move within the operating room ( 10 ) . With reference to FIG . 4 , several such areas are overlaid upon the diagram of the operating room ( 10 ) as dotted circles or ovals . A surgeon area ( 200 ) is positioned proximately to the surgical table ( 37 ) and defines an area in which a surgeon should ideally remain in order to reduce the spread of bacteria and limit the chance of infection . An anesthetist area ( 202 ) is located proximately to the anesthesia cart ( 33 ) and defines a similar area for an anesthetist ….[…]…Personnel areas may be used to determine the spread of bacteria and calculate resulting infection risk , to provide alerts or other indications when personnel have left a designated area ( e.g. , based on an alert from a proximity sensor ( 64 ) ) , or to take other actions .” and see [0032] discloses, “As another example , a proximity sensor ( 64 ) positioned near anesthesiology equipment may detect that a person ( e.g. , the anesthesiologist ) has moved from their area and may risk spreading contamination into other areas.” / examiner notes proximity sensors are placed in optimal locations to determine specific events such as movement of anesthesiology equipment or personnel which the proximity is predetermined based on the location of the surgical table for e.g. to optimize layout of the room for infection prevention protocols.)
As per claim 11, Block teaches:
The method of claim 1, wherein the alert threshold includes a temperature threshold, a humidity threshold, a pressure threshold, an air quality threshold, or any combination thereof. ([0047] discloses, “the relative humidity must be maintained within a specified range ; the airflow rate ( at one or more locations ) must be maintained within a specified range ; the number of occupants in the operating room must fall between a minimum number and a maximum number ; diathermy or electrocautery devices must be used with a vacuum ; and other guidelines , as will be apparent to those of ordinary skill in the art in light of this disclosure . As has been described , sensor data collected from the sensor array ( 60 ) may be evaluated in view of each such global guidelines of the infection - reduction protocol in order to identify aberrations , update the risk picture ( 51 ) , provide alerts , and take other actions .” and see [0055] discloses, “As another example , humidity sensor ( 72 ) may periodically or continuously monitor the relative humidity in the operating room ( 10 ) . In the event the humidity falls below or rises above a level or range provided in the infection reduction protocol , an alert may be issued , or other corrective action may be automatically implemented .”)
As per claim 13, Block teaches:
The method of claim 1, further comprising: displaying the generated alert on a display in the operating room and/or a display in a monitoring area. ([0024] discloses, “A local display ( 21 ) may be proximate to the ORMS ( 50 ) and operable by the processor ( 100 ) to display interfaces and information to users of the ORMS ( 50 ) . A user interface device ( 108 ) may be proximate to the ORMS ( 50 ) and operable by a user to provide user inputs to the ORMS ( 50 ) . The user interface device ( 108 ) may be , for example , a mouse , keyboard , touchscreen display ( e.g. , the local display ( 21 ) ) , voice activation , or one or more other inter face devices . User - facing portions of the ORMS ( 50 ) that may be present within an operating room ( e.g. , the local display ( 21 ) , the user interface device ( 108 ) ) may be adapted for use in sterile environments , which may include sterile coverings or coatings , specialized interface devices that are usable with gloves or voice , or other features . An alert indicator ( 106 ) may be one or more of a visual display ( e.g. , the local display ( 21 ) ) , a light indicator , a sound indicator , or another device operable to provide a human - perceptible notification that can alert personnel to the detected circumstances.” And see [ 0028 ] The remote device ( 20 ) may include one or more display devices ( e.g. , LED display ) or users ' devices ( e.g. , computers , tablets , mobile devices that include a display and that are configured to interact with the ORMS ( 50 ) via a web browser interface , software application , or other interface ) located within and remotely from an operating room where the ORMS ( 50 ) is configured . As an example , the remote device ( 20 ) may include a flat panel display mounted near an operating table or on a wall within the operating room that is configured to display the same information as the local display ( 21 ) or a different set of information , as will be described in more detail below . As another example , the remote device ( 20 ) may include a web browser interface or software application interface in possession of a hospital administrator and configured to provide high - level information and alerts associated with one or more ORMSs ( 50 ) . As another example , each person involved in a procedure may possess a remote device ( 20 ) ( e.g. , a smartphone , head mounted display , wearable device , or proprietary device ) so that alerts and information can be individualized to particular users and / or devices . Implementations of the ORMS ( 50 ) may have more or fewer components than those shown in FIG . 1 , with such other variations being apparent to those of ordinary skill in the art in light of this disclosure.”)
As per claim 14, Block teaches:
The method of claim 1, further comprising: displaying the alert as a message on a mobile device. ([0024] discloses, “An alert indicator ( 106 ) may be one or more of a visual display ( e.g. , the local display ( 21 ) ) , a light indicator , a sound indicator , or another device operable to provide a human - perceptible notification that can alert personnel to the detected circumstances .” and see [0028] discloses, “The remote device ( 20 ) may include one or more display devices ( e.g. , LED display ) or users ' devices ( e.g. , computers , tablets , mobile devices that include a display and that are configured to interact with the ORMS ( 50 ) via a web browser interface , software application , or other interface ) located within and remotely from an operating room where the ORMS ( 50 ) is configured . As an example , the remote device ( 20 ) may include a flat panel display mounted near an operating table or on a wall within the operating room that is configured to display the same information as the local display ( 21 ) or a different set of information , as will be described in more detail below.” / examiner notes the instant application disclosure gives e.g. of a message as a notification in para. [0006])
As per claim 15, Block teaches:
The method of claim 1, further comprising: storing the determined door status, an amount of time that the door is open during a surgery, a number of times that the door is open during the surgery, an average duration the door is open, a number of times a threshold breach occurred during a surgery, and/or the one or more signals as part of an electronic medical record. ([0063] discloses, “A door indicator ( 304 ) may be configured to dis play and provide an indication of a number of times that doors have been opened , an average length of time that they have remained open , and other information . A door status ( 306 ) may be configured to display and provide an indication of whether the door is currently open . Other indicators beyond the traffic indicator (302) and the door indicator ( 304 ) may be provided or configured by a user as may be desired to present information via the dashboard ( 58 ) , as will be apparent to those of ordinary skill in the art in light of this disclosure.” And see [0027] discloses, “The HIS ( 90 ) may include one or more servers that are configured to provide the processor ( 100 ) and / or the monitoring server ( 80 ) with hospital records , patient records , personnel records , and other information usable during analysis of sensor data to monitor for , detect , qualify , quantify , and analyze infection risks . Information provided by the HIS ( 90 ) may include , for example , HVAC maintenance records for an operating room , personnel records indicating training , certification , or adherence to operating room guidelines , historic infection rates associated with certain types of procedures or procedures performed in certain operating rooms , scheduling information ( such as which patients are to be operated on in which operating rooms at what times ).” / examiner notes that the HIS stores electronic medical records which inform the creation of the infection reduction protocol and suggestions can be made based on prior procedures subsequently and also stores any data collected)
As per claim 18, Block teaches:
The method of claim 1, further comprising: if the alert threshold is reached and the status of the door is closed: foregoing generating the alert to close the door of the operating room and generating an environmental alert. ([0070 ] discloses, “Another general category of action taken by the ORMS ( 50 ) may be triggered by some aspect of air quality or climate within the operating room ( 10 ) falling ( 416 ) outside of desirable or sufficient ranges . This may include deviations in temperature , humidity , pressure , magnitude and direction of air flow , particle count ,microbial count , or other characteristics , as may be detected by the sensor array ( 60 ) . When detected ( 416 ) , the ORMS ( 50 ) may provide notifications to responsible parties as has been described , or it may automatically adjust ( 418 ) or operate HVAC equipment or other systems ( e.g. , through direct communication or communication via the HIS ( 90 ) ) to correct the air quality issue . This may include adjusting ACH rates , increasing or decreasing temperature or humidity , activating additional exhaust vents or air filtration systems , or other actions.” And see [0072] discloses, “While various circumstances can be detected as violations and trigger notification and / or other action ( e.g. , such as incursions ( 408 ) , guideline violations ( 412 ) , air quality violations ( 416 ) , and door violations ( 420 ) ) , the ORMS ( 50 ) may also be configured to only take action after detection when the circumstance exceeds a certain threshold or magnitude/ examiner notes if the door is closed” / examiner notes that the disclosed art no matter door open or closed addresses the violation of environmental issues with its own alert.”)
As per claim 19, it is a system claim which repeat the same limitations of claim 1, the corresponding method claim, as a collection of elements as opposed to a series of process steps. Since the teachings of Block and Egan and motivations to combine disclose the underlying process steps that constitute the methods of claim 1, it is respectfully submitted that they provide the underlying structural elements that perform the steps as well. As such, the limitations of claim 19 is rejected for the same reasons given above for claim 1.
As per claim 20, it is an article of manufacture claim which repeats the same limitations of claim 1, the corresponding method claim, as a collection of executable instructions stored on machine readable media as opposed to a series of process steps. Since the teachings of Block and Egan and motivations to combine disclose the underlying process steps that constitute the method of claim 1, it is respectfully submitted that they likewise disclose the executable instructions that perform the steps as well as. As such, the limitations of claim 20 is rejected for the same reasons given above for claim 1.
Claims 6, 7, and 8 are rejected to under 35 U.S.C. 103 as being unpatentable over Block et. al (hereinafter Block) (US20220399105A1) in view of Egan (GB2458118A) and in further view of Mao et. al (hereinafter Mao) (US2023/0099920Al)
As per claim 6, Block and Egan do not teach:
The method of claim 1, wherein the trained machine-learning model is an object detection algorithm.
However, Mao does teach:
The method of claim 1, wherein the trained machine-learning model is an object detection algorithm. ([0090] In other words, the system can train a classifier that is configured to receive an input sensor sample that characterizes a particular type of object and is generated from sensor data captured by one or more sensors of an autonomous vehicle and process the input sensor sample to generate a particular state score that represents a predicted likelihood that the object is in the particular state.” And see [0043] In some cases, the open door classifier is a convolutional neural network that receives the sensor sample 155, i.e., a three-dimensional tensor generated from the sensor data, and processes the sensor sample 155 through multiple layers that include one or more convolutional layers to generate an open door prediction. Thus, the on-board classifier subsystem 134 includes one or more computing devices having software or hardware modules that implement the respective operations of each layer of the neural network” / examiner notes a CNN is the object detection algorithm)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Block’s teachings of utilizing machine learning algorithms which are trained to determine whether events trigger a door to open or close as previously cited and Egan’s teachings as previously cited with Mao’s teachings of object detection algorithm in machine learning to determine open or closed doors as previously cited, the motivation being Block’s teaches the rising cost of healthcare and the need for improved outcomes (see [0003]-[0005]) and Mao teaches the improved classification and data analysis to predict events (e.g. [0028]) therefore it would be predictable to combine the machine learning in Block with the machine learning in Mao to increase accuracy in prediction capability of particular events which may negatively and positively effect infection prevention and decrease the resources need to ensure patient outcomes are successful.
As per claim 7, Block and Egan do not teach:
The method of claim 6, wherein the trained machine-learning model is a neural network model.
However, Mao does teach:
The method of claim 6, wherein the trained machine-learning model is a neural network model. ([0043] discloses, “In some cases, the open door classifier is a convolutional neural network that receives the sensor sample 155, i.e., a three-dimensional tensor generated from the sensor data, and processes the sensor sample 155 through multiple layers that include one or more convolutional layers to generate an open door prediction.”)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Block’s teachings and Egan’s teachings with Mao’s teachings for the same reasons given above in claim 6.
As per claim 8, Block and Egan do not teach:
The method of claim 6, wherein the machine-learning model is trained using a plurality of annotated images.
However, Mao does teach:
The method of claim 6, wherein the machine-learning model is trained using a plurality of annotated images. ([0014] discloses, “To train the classifier, a system obtains a plurality of initial training examples, each initial training example comprising (i) a sensor sample from a collection of sensor samples and (ii) label data classifying the sensor sample as characterizing an object that is in the particular state;” and see [0040] discloses, “Once a group of one or more raw sensor measurements has been classified as being a measure of another vehicle, the sensor subsystems 132 or the other components of the vehicle 122 generate a sensor sample 155 from the sensor measurements that measure the vehicle. In some cases, the sensor sample 155 is a three-dimensional tensor that represents measurements from one or more of the sensors and that each characterize the same vehicle at the same time. For example, the three-dimensional tensor can include a patch of an image captured by the camera sensor of the region of the environment…[…]…”)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Block’s teachings and Egan’s teachings with Mao’s teachings for the same reasons given above in claim 6.
Claim 12 is rejected to under 35 U.S.C. 103 as being unpatentable over Block et. al (hereinafter Block) (US20220399105A1) in view of Egan (GB2458118A) and in further view of Mangiardi et. al (hereinafter Mangiardi) (US11707397B2)
As per claim 12, Block and Egan do not teach:
The method of claim 1, wherein the one or more cameras include a camera integrated into a surgical light.
However, Mangiardi does teach:
The method of claim 1, wherein the one or more cameras include a camera integrated into a surgical light. (Col. 6 lines 63-67 and 7 lines 20-31 discloses, “In one aspect, the present invention is directed to an integrated air and lighting plenum comprising: a first (e.g., outermost) ring-shaped unit comprising general illumination lighting with translucent cover panels, wherein the first unit is modular; a second ring-shaped unit (e.g., interior to the…[….]… wherein the interior surgical lights are equally spaced in the third unit forming a second arrangement concentric to the second unit (e.g., the interior surgical lights in each group are spaced apart by 120 degrees relative to the center of the units (e.g., the first unit, the second unit, and the third unit)); and one or more accessories removably mounted to the plenum (e.g., webcams, cameras, microphones, speakers, sensors), wherein the one or more accessories are for monitoring a procedure and/or providing feedback and are mounted to the plenum using a removable mounting component ( e.g., a removable mounting plate, a removable housing).”)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Block’s teachings of the infection prevention protocols and events in relation to a surgical operation utilizing images and cameras in the operating room as previously cited and Egan’s teachings as previously cited with Mangiardi’s teachings of the camera being integrated into the surgical light as previously cited, the motivation being Block discloses the cameras being mounted throughout the operating room even on the mobile arm to ensure unobstructed views (see [0031]) therefore it would be predictable to one or ordinary skill in the art to combine the various ways Block mounts cameras with the explicit way Mangiardi mounts a camera within a surgical light as this would improve the field of view of an operation and increase the diversity of views within the room further allowing for proper alerts if a particular event is triggered.
Claim 21 is rejected to under 35 U.S.C. 103 as being unpatentable over Block et. al (hereinafter Block) (US20220399105A1) in view of Egan (GB2458118A) and in further view of Grant et. al (hereinafter Grant) (US20210043311A1)
As per claim 21, Block teaches:
The method of claim 1, further comprising: obtaining a surgery type for the surgical procedure; obtaining a current infection prevention protocol for the surgical procedure; and automatically identifying a modification to the current infection prevention protocol based on the surgery type of the surgical procedure, a duration of the surgical procedure, and/or the alert threshold, ([0042] discloses, “One or more configurations , such as those described above and others , may be used to define an infection - reduction protocol for each unique operating room such as the operating room ( 10 ) . As an example , FIG . 6 shows a screenshot of an exemplary interface that may be displayed via the local display ( 21 ) or any remote device ( 20 ) to provide an indication of zones for which the infection - reduction protocol provides certain requirements . The a interface includes a contamination risk picture ( 51 ) overlaid upon the operating room ( 10 ) . The interface also includes an air change indicator ( 11 ) , which provides information relating to the flow , filtration , and changing of air within the operating room ( 10 ) , and a risk level key ( 13 ) which provides information usable to visually distinguish and interpret zones in contamination risk picture ( 51 ) . The contamination risk picture ( 51 ) depicted in FIG . 6 shows a large portion of the operating room ( 10 ) designated as a high - risk area ( 12 ) , a smaller area right - of - center designated as a medium - risk area ( 14 ) , and a smaller area left - of - center designated as a low - risk area ( 16 ).” And see [0043] discloses, “The high - risk area ( 12 ) may be an area where air quality is low compared to the rest of the operating room ( 10 ) , as may be determined based upon configured information such as that shown in FIG . 5. As such , the infection reduction protocol may require that any surgical instrument or surgical device present within this area must be covered or sealed within a sterile space or must be sterilized prior to leaving the high - risk area ( 12 ) . The protocol may similarly require that any personnel within the high - risk area ( 12 ) scrub and re - sterilize before leaving the high - risk area ( 12 ) . A multitude of other requirements may be predetermined and associated with the high - risk area ( 12 ) in order to provide a set of rules and criteria that data gathered from the sensor array ( 60 ) may be evaluated against , as will occur to those skilled in the art.” And see [0044] discloses, “The medium - risk area ( 14 ) may be associated with a different set of procedures , rules , and requirements in a second infection - reduction protocol for medium - risk areas . Such requirements may allow hospital staff and covered instruments to be freely placed within or to pass through the area without violating the second infection - reduction protocol . However , such instruments and personnel passing into the high - risk area ( 12 ) may violate procedures associated with the infection - reduction protocol for that area.” And see [0045] discloses, “The low - risk zone ( 16 ) may be associated with a still different set of procedures , rules , and requirements in a third infection - reduction protocol for low - risk areas . Such areas may have the most permissive requirements , and thus may be defined within the third infection - reduction protocol as the only location ( s ) where the surgical table ( 37 ) , the back instrument tables ( 40 ) , the Mayo stands ( 48 ) , and uncovered instruments and / or implants and other critical equipment may be placed . As can be seen , defining and configuring the ORMS ( 50 ) with an infection - reduction protocol ( e.g. , one including a different sub - protocol specific to each risk area type ) provides a set of rules that may be evaluated and applied to each area based on data received from the sensor array ( 60 ) . Thus , while the placement of sensors and the configuration of a particular operating room ( 10 ) as illustrated in FIGS . 2-5 may vary depending on each unique room , the infection - reduction protocol may stay largely constant between rooms and may be applied based upon the static or dynamic determination of areas within a particular room.” And see [0071] discloses, “Another general category of action taken by the ORMS ( 50 ) may be triggered based upon violation ( 420 ) of protocol related to doors ( e.g. , room entry doors , cabinet or equipment doors , or other fixtures that may be equipped with a sensor such as the door position sensor ( 68 ) . This may include frequent or prolonged opening of the door ( 32 ) , failures to completely close the door ( 32 ) , failures to close doors or other seals on equipment ( e.g. , such as closing a cabinet door where sterile instruments are stored ) , and other scenarios related to the infection - reduction protocol that may be detected by the sensor array ( 60 ) . When detected ( 420 ) , the ORMS ( 50 ) may provide ( 422 ) a door warning to one or more remote devices ( 20 ) ( e.g. , such as a device mounted near the door ( 32 ) ) indicating that the door should not be opened or that opening of the door should be delayed for some period of time ( e.g. , to allow air quality to be corrected after prior openings ) . In some implementations this may also include operating a door closer to close the door or performing other automated actions to comply with the infection - reduction protocol.” And see [0072] and see [0027] discloses, “The HIS ( 90 ) may include one or more servers that are configured to provide the processor ( 100 ) and / or the monitoring server ( 80 ) with hospital records , patient records , personnel records , and other information usable during analysis of sensor data to monitor for , detect , qualify , quantify , and analyze infection risks . Information provided by the HIS ( 90 ) may include , for example , HVAC mainte nance records for an operating room , personnel records indicating training , certification , or adherence to operating room guidelines , historic infection rates associated with certain types of procedures or procedures performed in certain operating rooms , scheduling information ( such as which patients are to be operated on in which operating rooms at what times ) , patients ' electronic medical records , and other information.” / examiner notes procedure type is stored in the HIS under BRI as one of ordinary skill would understand and alert threshold is elected as the claim recites “or” between the series of elements. Per MPEP § 2143.03, Language that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art. See, e.g., Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298, 92 USPQ2d 1163, 1171 (Fed. Cir. 2009). )
However, Block does not teach:
wherein the modification to the current infection prevention protocol comprises an increased antibiotic dose, a longer course of antibiotic treatment, use of a different antibiotic, or use of a supplemental antibiotic.
However, Grant does teach:
wherein the modification to the current infection prevention protocol comprises an increased antibiotic dose, a longer course of antibiotic treatment, use of a different antibiotic, or use of a supplemental antibiotic. ([0036] discloses, “If the patient's HAI risk profile is determined to be increasing during her / his admission to the healthcare institution , the system can , for example , outputting an alert to at least one authenticated user of such increase and / or output a recommendation for one or more interventions , for example , a clinical intervention ( e.g. , administration of prophylactic course of antibiotics , a dietary change , the change of one or more infection prevention products ( i.e. , a disinfecting cap ) used in caring for the patient on a more frequent basis than is called for in the manufacturer's use instructions or the corresponding infection prevention protocol , etc. ) , designed to reduce the patient's HAI risk profile.” / examiner interprets the clinical intervention of a prophylactic course of antibiotics as a supplemental antibiotic)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Block’s teachings of utilizing machine learning algorithms which are trained to determine whether events trigger a door to open or close as previously cited with Grant’s teachings of as previously, the motivation being Block’s teaches the rising cost of healthcare and the need for improved outcomes (see [0003]-[0005]) and Grant teaches the need to control or prevent infections (e.g. [0004]-[0006]) therefore it would be predictable to combine automation of door closing in regards to infection prevention in Block with the infection prevention antibiotics in Grant to increase positive outcomes for patients and reduce resources.
Response to Arguments Regarding 35 U.S.C § 101 Rejection and Affidavit under 1.132
The applicant argues on pages 1-6 of the submitted remarks that the rejection of claims under 35 U.S.C § 101 should be withdrawn in light of the below arguments. Amended claim 1 requires determining a correlation between door status and a surgical site infection and automatically formulating a surgical protocol for a future procedure based on the correlation. As an initial matter, as explained in the Subject Matter Eligibility Declaration (SMED) of inventor Gaurav Bhardwaj being submitted concurrently herewith (hereinafter the "SMED"), "[a] person of skill in the art would have understood that executing object detection and object tracking algorithms on a video stream includes performing large-scale numerical computations on digital image data, which is far beyond the ability of a human mind to practically perform. Similarly, a person of skill in the art would understand that the human mind cannot practically obtain a door status by 'inputting the one or more images into a trained machine-learning model' that is 'trained using a plurality of training images depicting open and closed doors' because the human mind cannot perform the extensive computational processing at the speed and precision achieved by a trained machine learning model." (SMED, p. 3-4.)
The SMED affidavit under 37 CFR 1.132 filed 03/04/2026 is insufficient to overcome the rejection of claims 1-2, 4-15, and 18-21 because: Examiner does not find persuasive the affidavit arguments for the following reasons.
The MPEP states The Alice/Mayo two-part test is the only test that should be used to evaluate the eligibility of claims under examination. While the machine-or-transformation test is an important clue to eligibility, it should not be used as a separate test for eligibility. Instead it should be considered as part of the "integration" determination or "significantly more" determination articulated in the Alice/Mayo test. Bilski v. Kappos, 561 U.S. 593, 605, 95 USPQ2d 1001, 1007 (2010). See MPEP § 2106.04(d) for more information about evaluating whether a claim reciting a judicial exception is integrated into a practical application and MPEP § 2106.05(b) and MPEP § 2106.05(c) for more information about how the machine-or-transformation test fits into the Alice/Mayo two-part framework.
The enumerated groupings of abstract ideas are defined as:
1) Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I); (Mathematical Calculations - A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.)
2) Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (see MPEP § 2106.04(a)(2), subsection II); and
3) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).
Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures "can be carried out in existing computers long in use, no new machinery being necessary." 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of "anonymous loan shopping" recited in a computer system claim is an abstract idea because it could be "performed by humans without a computer"). [AltContent: rect]
In evaluating whether a claim that requires a computer recites a mental process, examiners should carefully consider the broadest reasonable interpretation of the claim in light of the specification. For instance, examiners should review the specification to determine if the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept. In these situations, the claim is considered to recite a mental process.
1. Performing a mental process on a generic computer. An example of a case identifying a mental process performed on a generic computer as an abstract idea is Voter Verified, Inc. v. Election Systems & Software, LLC, 887 F.3d 1376, 1385, 126 USPQ2d 1498, 1504 (Fed. Cir. 2018). In this case, the Federal Circuit relied upon the specification in explaining that the claimed steps of voting, verifying the vote, and submitting the vote for tabulation are "human cognitive actions" that humans have performed for hundreds of years. The claims therefore recited an abstract idea, despite the fact that the claimed voting steps were performed on a computer. 887 F.3d at 1385, 126 USPQ2d at 1504. Another example is Versata, in which the patentee claimed a system and method for determining a price of a product offered to a purchasing organization that was implemented using general purpose computer hardware. 793 F.3d at 1312-13, 1331, 115 USPQ2d at 1685, 1699. The Federal Circuit acknowledged that the claims were performed on a generic computer, but still described the claims as "directed to the abstract idea of determining a price, using organizational and product group hierarchies, in the same way that the claims in Alice were directed to the abstract idea of intermediated settlement, and the claims in Bilski were directed to the abstract idea of risk hedging." 793 F.3d at 1333; 115 USPQ2d at 1700-01.
2. Performing a mental process in a computer environment. An example of a case identifying a mental process performed in a computer environment as an abstract idea is Symantec Corp., 838 F.3d at 1316-18, 120 USPQ2d at 1360. In this case, the Federal Circuit relied upon the specification when explaining that the claimed electronic post office, which recited limitations describing how the system would receive, screen and distribute email on a computer network, was analogous to how a person decides whether to read or dispose of a particular piece of mail and that "with the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper". 838 F.3d at 1318, 120 USPQ2d at 1360. Another example is FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 120 USPQ2d 1293 (Fed. Cir. 2016). The patentee in FairWarning claimed a system and method of detecting fraud and/or misuse in a computer environment, in which information regarding accesses of a patient’s personal health information was analyzed according to one of several rules (i.e., related to accesses in excess of a specific volume, accesses during a pre-determined time interval, or accesses by a specific user) to determine if the activity indicates improper access. 839 F.3d. at 1092, 120 USPQ2d at 1294. The court determined that these claims were directed to a mental process of detecting misuse, and that the claimed rules here were "the same questions (though perhaps phrased with different words) that humans in analogous situations detecting fraud have asked for decades, if not centuries." 839 F.3d. at 1094-95, 120 USPQ2d at 1296.
3. Using a computer as a tool to perform a mental process. An example of a case in which a computer was used as a tool to perform a mental process is Mortgage Grader, 811 F.3d. at 1324, 117 USPQ2d at 1699. The patentee in Mortgage Grader claimed a computer-implemented system for enabling borrowers to anonymously shop for loan packages offered by a plurality of lenders, comprising a database that stores loan package data from the lenders, and a computer system providing an interface and a grading module. The interface prompts a borrower to enter personal information, which the grading module uses to calculate the borrower’s credit grading, and allows the borrower to identify and compare loan packages in the database using the credit grading. 811 F.3d. at 1318, 117 USPQ2d at 1695. The Federal Circuit determined that these claims were directed to the concept of "anonymous loan shopping", which was a concept that could be "performed by humans without a computer." 811 F.3d. at 1324, 117 USPQ2d at 1699. Another example is Berkheimer v. HP, Inc., 881 F.3d 1360, 125 USPQ2d 1649 (Fed. Cir. 2018), in which the patentee claimed methods for parsing and evaluating data using a computer processing system. The Federal Circuit determined that these claims were directed to mental processes of parsing and comparing data, because the steps were recited at a high level of generality and merely used computers as a tool to perform the processes. 881 F.3d at 1366, 125 USPQ2d at 1652-53.
Examiners should determine whether a claim recites an abstract idea by (1) identifying the specific limitation(s) in the claim under examination that the examiner believes recites an abstract idea, and (2) determining whether the identified limitations(s) fall within at least one of the groupings of abstract ideas listed above. Furthermore, the MPEP state in 2106.04(d), “Examiners evaluate integration into a practical application by: (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical applications.”
Therefore, examiner notes that applicants remarks in the affidavit filed 03/04/2026 section 1-8 and 17 are mere statements on the status or information in the prosecution history. Examiner notes that argument in section 9 states, “Based on my knowledge of the invention and the relevant technical arts, as of the effective filing date of the '336 application, a person of skill in the art would understand that the human mind could not practically determine a status of a surgical procedure in an operating room by "detecting at least one object in the operating room using an object detection algorithm "that is "configured to perform segmentation of one or more image frames of the video stream "and "tracking movement of the at least one object using an object tracking algorithm," as recited in representative claim 1. A person of skill in the art would have understood that executing object detection and object tracking algorithms on a video stream includes performing largescale numerical computations on digital image data, which is far beyond the ability of a human mind to practically perform. Similarly, a person of skill in the art would understand that the human mind cannot practically obtain a door status by "inputting the one or more images into a trained machine-learning model" that is "trained using a plurality of training images depicting open and closed doors" because the human mind cannot perform the extensive computational processing at the speed and precision achieved by a trained machine learning model.”
Examiner does not find this argument persuasive. A claim being performed on a computer or in a computer environment with a single or multiple people involved does not make it dispositive of being directed to an abstract idea. Examiner must review the claim as a whole and analyze the elements which are considered abstract and elements which are considered additional. Examiner must review the abstraction of the claims and determine if the claims are apart of one or more of the enumerated subgroupings. In the case of the instant application the claims as a whole is determining protocol for the opening or closing of doors based on correlations for surgical site infections. This is not a question of would a human perform but rather could a human perform. This could be performed mentally by a human and has been performed by humans prior to the computer environment or applied additional elements within the claim. If inventor’s sworn beliefs were true no correlation could be deduced currently today without these additional elements of when to open or close a door based on information correlated with surgical site infections with or without the aid of pen and paper or computer which would be false as decisions are made historically and today of SSI risk and protocols for a surgery inclusive of open and closing doors, etc. during a procedure. Examiner maintains the claims are directed to an abstract idea of “mental process”.
Examiner notes that argument in section 10 states, “Cairn 1 recites "detecting at least one object in the operating room using an object detection algorithm" that is "configured to perform segmentation of one or more image frames of the video stream." For example, as explained in the specification, the object detection algorithm can be "a 20 convolutional neural network (CNN) or 30-CNN (e.g., MobileNetV2, ResNet, MobileNetV3, CustomCNN)." (Specification, ,i [0087].) The object detection algorithm "can include an instance segmentation algorithm, which can be configured to simultaneously perform classification (e.g., determining what type of object an image depicts), semantic segmentation (e.g., determining what pixels in the image belong to the object), and instance association (e.g., identifying individual instances of the same class; for example, person1 and person2)." (Id.) A person of skill in the art would have understood that performing segmentation of one or more image frames using an object detection algorithm involves iteratively performing large-scale numerical computations on digital image data, which cannot practically be performed mentally. Processing even a single image can require thousands or millions of repeated calculations, such as matrix multiplications, nonlinear transformations, back propagation, loss functions, bounding box regressions, and/or iterative comparisons. The human mind cannot practically store and apply the large amount of data required to perform these operations, much less actually execute the iterative operations required for object detection across full-resolution image frames.”
Examiner does not find this argument persuasive. A claim being performed on a computer or in a computer environment with a single or multiple people involved does not make it dispositive of being directed to an abstract idea. Examiner must review the claim as a whole and analyze the elements which are considered abstract and elements which are considered additional. Examiner must review the abstraction of the claims and determine if the claims are a part of one or more of the enumerated subgroupings. Further the specification is not read in to the claims rather the broadest reasonable interpretation of the claims is taken when analyzing by examiner. In the case of the instant application the claims as a whole is determining protocol for the opening or closing of doors based on correlations for surgical site infections. This is not a question of would a human perform but rather could a human perform. There is no claimed limitation of a specific “large amount of data” being analyzed nor is there specific technical object detection operations rather it is broadly applied to the abstract scope of the claim therefore, this could be performed mentally by a human and has been performed by humans prior to the computer environment or applied additional elements within the claim. If inventor’s sworn beliefs were true no correlation could be deduced currently today without these additional elements of when to open or close a door based on information correlated with surgical site infections with or without the aid of pen and paper or computer which would be false as decisions are made historically and today of SSI risk and protocols for a surgery inclusive of open and closing doors, etc. during a procedure. Furthermore, the elements noted by inventor are additional elements which would be further analyzed under practical application step of eligibility analysis. Examiner maintains the claims are directed to an abstract idea of “mental process”.
Examiner notes that argument in section 11 states, “Claim 1 recites "tracking movement of the at least one object using an object tracking algorithm" to determine the status of a surgical procedure. For example, object tracking algorithms such as computer-vision algorithms can be used to track the movement of detected objects. (Specification, ,i [0087].) A person of skill in the art would have understood that object tracking algorithms can work by repeatedly calculating an object's position and movement across video frames using continuous numerical state estimation techniques. For example, an object tracking algorithm can involve predicting object states via matrix multiplication, updating covariance matrices, computing matrix inversions, applying probabilistic weighting, and/or solving combinatorial assignment problems, all at video frame rates (e.g. 30-60 Hz). These computations are far too numerous and exact for a human to carry out mentally, whether or not aided by pen and paper.
Examiner does not find this argument persuasive. A claim being performed on a computer or in a computer environment with a single or multiple people involved does not make it dispositive of being directed to an abstract idea. Examiner must review the claim as a whole and analyze the elements which are considered abstract and elements which are considered additional. Examiner must review the abstraction of the claims and determine if the claims are a part of one or more of the enumerated subgroupings. Further the specification is not read in to the claims rather the broadest reasonable interpretation of the claims is taken when analyzing by examiner. In the case of the instant application the claims as a whole is determining protocol for the opening or closing of doors based on correlations for surgical site infections. This is not a question of would a human perform but rather could a human perform. The claim limitations broadly apply the additional element object detection algorithm with aid of a computer therefore aforementioned arguments still apply and the claim is still directed to an abstract idea. Examiner maintains the claims are directed to an abstract idea of “mental process”.
Examiner notes that argument in section 12 states, “Claim 1 recites "inputting the one or more images into a trained machine-learning model to obtain the status of the door, wherein the machine-learning model is trained using a plurality of training images depicting open or closed doors." A person of skill in the art would have understood that obtaining a door status using a machine learning model cannot be performed mentally because training and executing a machine learning model involves storing and iteratively manipulating enormous quantities of numerical values. For example, training alone involves performing iterative optimization procedures in which numerical weights are repeatedly adjusted based on calculated error gradients across numerous training examples. The human mind cannot practically store the amount of data required for this process, much less perform the complex computations required by a machine learning model trained to identify a door status with the speed and accuracy needed for use in an operating room monitoring and alert system. Accordingly, a person of ordinary skill in the art would understand that both training and executing the recited machine learning model are computational processes not practically capable of being performed in the human mind.”
Examiner does not find this argument persuasive. A claim being performed on a computer or in a computer environment with a single or multiple people involved does not make it dispositive of being directed to an abstract idea. Examiner must review the claim as a whole and analyze the elements which are considered abstract and elements which are considered additional. Examiner must review the abstraction of the claims and determine if the claims are a part of one or more of the enumerated subgroupings. Further the specification is not read in to the claims rather the broadest reasonable interpretation of the claims is taken when analyzing by examiner. In the case of the instant application the claims as a whole is determining protocol for the opening or closing of doors based on correlations for surgical site infections. This is not a question of would a human perform but rather could a human perform. The claim limitations broadly apply the additional element of machine learning with aid of a computer therefore aforementioned arguments still apply and the claim is still directed to an abstract idea. Examiner maintains the claims are directed to an abstract idea of “mental process”.
Examiner notes that argument in section 13 states, “Based on my knowledge of the technical field of computer-assisted surgery as of the effective filing date of the '336application, conventional operating room monitoring and alert systems did not provide real-time alerts to operating room personnel to close doors in the operating room during a surgical procedure. This is a problem because excessive or prolonged door openings can disrupt environmental controls within the operating room and can increase the risk of surgical site infection (SSI), thereby negatively affecting patient safety. During surgery, operating room personnel are often focused on clinical tasks and may not be aware that a door has remained open for an extended period of time or has been opened too frequently. Accordingly, as of the effective filing date of the '336 application, there was a need for improved operating room monitoring and alert systems capable of generating real-time alerts to prompt personnel to close doors during surgical procedures.”
Examiner does not find this argument persuasive. MPEP 2106.05 states novelty of any element or steps is of no relevance when determining subject matter eligibility. Furthermore, the problem of patient safety and infection risk is not a problem understood to be arising from technology but rather a problem arising in infection prevention. Further the claims do not recite improvements to an operating room monitoring and alert system but rather the claims are confined to a general purpose computer (see [0071]) and applying additional elements to implement the abstract idea more efficiently. Examiner maintains the claims are directed to an abstract idea of “mental process” and not integrated into a practical application.
Examiner notes that argument in section 14 states, “Claim 1 recites "determining, by the one or more processors, whether the door of the operating room is a door to a sterile room that is acceptable to open at least once while the surgical procedure is in progress" and generating or foregoing generating an alert to close the door based at least in part on this determination. A person skilled in the art would understand this to improve the field of computer-assisted surgery by ensuring that the alerts provided by the system are necessary alerts and not unnecessary alerts that could distract operating room personnel from an ongoing procedure. As explained in the specification, while opening a door to a non-sterile corridor may never be acceptable during a surgical procedure and may warrant an alert every time this occurs, "it may be common for a door to a sterile room to open a few times during surgery and thus may not warrant an alert unless some other condition is met." (Specification, ,i [0103].) A person of skill in the art would have recognized that limiting alerts based on the degree of risk of the door opening (as determined based on the type of door that has been opened) would improve focus and efficiency in the operating room, thus improving the field of computer-assisted surgery.
Examiner does not find this argument persuasive. The claims do not reflect or recite an improvement to the computer technology of assisted surgery rather the claims are confined to a general purpose computer (see [0071]) and apply additional elements to implement the abstract idea more efficiently. The determination to alert and whether the room is sterile is improvement to the abstract idea and the abstract idea cannot provide the practical application. Examiner maintains the claims are directed to an abstract idea of “mental process” and not integrated into a practical application.
Examiner notes that argument in section 15 states, “Claim 1 recites "the alert threshold is based on a type of the surgical procedure." A person of skill in the art would have recognized this as an improvement to computer-assisted surgery because, like the door type-specific alerts discussed above, surgery type-specific alert thresholds can prevent unnecessary alerts from being issued, thus improving operating room efficiency. The specification explains that "[s]ome types of procedures (e.g., cardiac and orthopedic cases) are more concerned about SSis, and thus may be associated with a different alert threshold than other surgeries (e.g., abdominal surgical cases)." (Specification, ,i [0097].) Therefore, for a procedure where SSis are less of a concern, the alert threshold can be set such that alerts are not generated as frequently as they would be for a procedure where SSis are a paramount concern. A skilled person would recognize that this improves the technical field of computer-assisted surgery because it ensures that operating room personnel receive alerts about potential SSI risks while simultaneously preventing operating room personnel from receiving too many of these alerts when they may not be relevant, thus reducing potential distractions in the operating room and consequently improving surgical outcomes.”
Examiner does not find this argument persuasive. The claims do not reflect or recite an improvement to the computer technology of assisted surgery rather the claims are confined to a general purpose computer (see [0071]) and apply additional elements to implement the abstract idea more efficiently. The determination to alert and reducing distractions is improvement to the abstract idea and the abstract idea cannot provide the practical application. Examiner maintains the claims are directed to an abstract idea of “mental process” and not integrated into a practical application
Examiner notes that argument in section 16 states, “In the Amendment being submitted concurrently with this Declaration, claim 1 is being amended to recite "determining a correlation between the status of the door and an incidence of a surgical site infection" and "automatically formulating a surgical protocol for a future surgical procedure based on the determined correlation, wherein the surgical protocol comprises a requirement for the door to remain closed for a predetermined amount of time during the future surgical procedure." A person of ordinary skill in the art would understand these limitations to reflect a further technical improvement to the field of computer-assisted surgery. As explained in the specification, the operating room monitoring and alert system can "identify correlations between any of the data points above [e.g., door status data] with the occurrence or severity of [a] complication" and "determine a recommended protocol change for future surgeries." (Specification, ,i [0102].) The system therefore performs a structured comparison between recorded door status information and documented post-operative outcomes. By analyzing this data, the system can determine statistically meaningful relationships between these variables that may not be apparent from isolated review of a medical record. Based on this analysis, the system automatically formulates a protocol for a future surgical procedure, such as "a new protocol requiring that the door remain closed for a certain period of time." (Id.) A person skilled in the art would understand that automatically formulating a surgical safety protocol based on a recognized correlation between door status and surgical site infections reduces the risk of surgical site infections in future surgical procedures, thereby improving the field of computer-assisted surgery.”
Examiner does not find this argument persuasive. The claims do not reflect or recite an improvement to the computer technology of assisted surgery rather the claims are confined to a general purpose computer (see [0071]) and apply additional elements to implement the abstract idea more efficiently. Formulating protocols and statistical analysis (which no statistical steps or analysis is recited in the claims or reflected in the claims) is improvement to the abstract idea and the abstract idea cannot provide the practical application. Examiner maintains the claims are directed to an abstract idea of “mental process” and not integrated into a practical application.
Further applicant argues on remarks from pages 3-6 that these new limitations recite additional elements that integrate any alleged abstract ideas into a practical application thereof because they represent improvements to the technical field of computer-assisted surgery. Claims that recite additional elements that improve another technology or technical field integrate any abstract ideas in those claims into a practical application, rendering the claims subject matter-eligible. MPEP § 2106(d)(1). Amended claim 1 recites "determining a correlation between the status of the door and an incidence of a surgical site infection; and automatically formulating a surgical protocol for a future surgical procedure based on the determined correlation, wherein the surgical protocol comprises a requirement for the door to remain closed for a predetermined amount of time during the future surgical procedure." These limitations represent an improvement in the technical field of computer-assisted surgery. As explained in the specification, a goal of the present invention is to "ensure surgical safety and prevent/reduce SSIs [surgical site infections] by monitoring various aspects of the operating room and providing alerts to take appropriate actions during a surgical procedure." (Specification, [0009].) One way in which surgical site infections can be reduced is by monitoring the door status throughout the procedure and using that information to develop appropriate protocols to improve surgical safety. A system can "identify correlations between any of the data points above [e.g., door status data] with the occurrence or severity of [a] complication" and "determine a recommended protocol change for future surgeries." ( [0102].) For instance, "if the system determines a correlation between the duration of door opening and a post-surgery complication, the system may automatically formulate a new protocol requiring that the door remain closed for a certain period of time." (Id.)
Analyzing correlations between door status information and surgical site infections to "automatically formulat[e] a surgical protocol for a future surgical procedure" that requires the door to remain closed for a predetermined amount of time reduces the risk of surgical site infections in future surgical procedures. This improves the technical field of computer-assisted surgery. As explained in the SMED, a "person skilled in the art would understand that automatically formulating a surgical safety protocol based on a recognized correlation between door status and surgical site infections reduces the risk of surgical site infections in future surgical procedures, thereby improving the field of computer-assisted surgery." (SMED, p. 7.) Accordingly, at least because amended claim 1 recites an improvement to the technical field of computer-assisted surgery, amended claim 1 is patent-eligible.
For at least these reasons, the rejection of amended claim 1 should be withdrawn. Independent claims 19 and 20 have been amended to recite limitations analogous to those discussed above and, thus, the rejection of claims 19 and 20 should be withdrawn for at least the same reasons. The rejection of the remaining claims should be withdrawn at least for the respective dependencies of the claims. By virtue of this response, new claim 21 has been added.
Claim 21 is subject matter- eligible at least for its dependency from claim 1, which is believed to be eligible for the reasons discussed above. Claim 21 is further eligible because the claim integrates any allegedly abstract ideas into a practical application. New claim 21 recites "obtaining a surgery type for the surgical procedure; obtaining a current infection prevention protocol for the surgical procedure; and automatically identifying a modification to the current infection prevention protocol based on the surgery type of the surgical procedure, a duration of the surgical procedure, and/or the alert threshold, wherein the modification to the current infection prevention protocol comprises an increased antibiotic dose, a longer course of antibiotic treatment, use of a different antibiotic, or use of a supplemental antibiotic." These limitations integrate any allegedly abstract ideas into a practical application because they improve the field of computer-assisted surgery. Automatically identifying modifications to infection prevention protocols to include "an increased antibiotic dose, a longer course of antibiotic treatment, use of a different antibiotic, or use of a supplemental antibiotic" and tailoring those modification decisions to a surgery type, duration, and/or alert threshold can ensure that a patient receives an optimal infection prevention treatment. Providing a patient with an appropriate infection prevention treatment can decrease the likelihood of a surgical site infection. Accordingly, new claim 21 recites an improvement in the technical field of computer- assisted surgery and is subject matter-eligible.
Examiner does not find applicant’s arguments persuasive and will not repeat aforementioned arguments in reply to the SMED which also reiterate why the claims do not reflect or recite an improvement to computer assisted surgery. All aforementioned arguments apply here and are maintained by examiner.
In regards to claim 21 arguments examiner does not find the arguments persuasive. In order to qualify as a "treatment" or "prophylaxis" limitation for purposes of this consideration, the claim limitation in question must affirmatively recite an action that effects a particular treatment or prophylaxis for a disease or medical condition. An example of such a limitation is a step of "administering amazonic acid to a patient" or a step of "administering a course of plasmapheresis to a patient." If the limitation does not actually provide a treatment or prophylaxis, e.g., it is merely an intended use of the claimed invention or a field of use limitation, then it cannot integrate a judicial exception under the "treatment or prophylaxis" consideration. For example, a step of "prescribing a topical steroid to a patient with eczema" is not a positive limitation because it does not require that the steroid actually be used by or on the patient, and a recitation that a claimed product is a "pharmaceutical composition" or that a "feed dispenser is operable to dispense a mineral supplement" are not affirmative limitations because they are merely indicating how the claimed invention might be used. To modify an infection prevention protocol is not an affirmative administration of a prophylaxis or treatment. Further, use of a general purpose computer to more efficiently do a human task is not enough to integrate the abstract idea into a practical application or provide significantly more.
Examiner maintains the 35 USC § 101 rejection.
Response to Arguments Regarding 35 U.S.C § 102/103 Rejections
Applicant argues on pages 6-9 of the remarks that claims rejected under 35 U.S.C § 102 and /or 103 should be withdrawn for the following arguments. The cited references fail to disclose or suggest "determining, by the one or more processors, based on the one or more signals, whether an alert threshold is reached, wherein the alert threshold is based on a type of the surgical procedure," as recited in claim 1. First, the cited references fail to disclose or suggest "determining, by the one or more processors, based on the one or more signals, whether an alert threshold is reached, wherein the alert threshold is based on a type of the surgical procedure," as recited in claim 1. In the Office Action, the Examiner asserts that Block teaches this limitation because "the ORMS [operating room monitoring system] system which controls the alerting is disclosed as based on information from the HIS which stores information on procedure type thus one of ordinary skill would understand this is considered in the analyzed information to control the door in the ORMS and other elements of the system." (Action, p. 57.) This is an improper conclusion to draw from Block. At most, Block discloses generally that its system can be connected to a hospital information system (HIS) and that the HIS can store procedure-related information. (Block,[0027], [0066].) However, Block fails to disclose or suggest that the procedure information from the HIS is used to configure surgery-specific sensor alert thresholds for the ORMS system. The remaining references fail to cure this deficiency of Block. Accordingly, the cited references fail to disclose or suggest "determining, by the one or more processors, based on the one or more signals, whether an alert threshold is reached, wherein the alert threshold is based on a type of the surgical procedure," as recited in claim 1.
The cited references fail to disclose or suggest "determining, by the one or more processors, whether the door of the operating room is a door to a sterile room that is acceptable to open at least once while the surgical procedure is in progress or a door to a non-sterile corridor that is not acceptable to open while the surgical procedure is in progress," as recited in claim 1.
Further, the cited references fail to disclose or suggest "determining, by the one or more processors, whether the door of the operating room is a door to a sterile room that is acceptable to open at least once while the surgical procedure is in progress or a door to a non-sterile corridor that is not acceptable to open while the surgical procedure is in progress," as recited in claim 1. The Examiner relies on Block as teaching this limitation. Specifically, the Examiner asserts that "Block is teaching an operating room system which one of ordinary skill would understand is considered to be a sterile room thus the door is considered a sterile room door and that the only time an alert for closing door is considered is when exceeding a threshold as disclosed thus necessarily there are times when the doors open and its considered okay or threshold not reached." (Action, p. 57.) However, that Block allegedly "teach[es] an operating room system" does not mean that a person of ordinary skill in the art would understand Block's system to determine whether a door is a door to a sterile room or non-sterile corridor, as required by claim 1. Operating rooms can have doors to both sterile and non-sterile spaces, which may be appropriate to open with differing levels of frequency during a surgical procedure. Block does not even acknowledge this possibility. As a result, Block cannot teach determining whether a given door is a door to a sterile room or a door to a non-sterile corridor. The remaining references fail to cure this deficiency of Block. Thus, the cited references fail to disclose or suggest "determining, by the one or more processors, whether the door of the operating room is a door to a sterile room that is acceptable to open at least once while the surgical procedure is in progress or a door to a non-sterile corridor that is not acceptable to open while the surgical procedure is in progress," as recited in claim 1.
Accordingly, the rejection of independent claim 1 should be withdrawn. Independent claims 19 and 20 recite limitations analogous to those discussed above and, thus, the rejection of claims 19 and 20 should be withdrawn for at least the same reasons. The rejections of the remaining claims should be withdrawn at least for the respective dependencies of the claims. IV.
New claim 21 recites "automatically identifying a modification to the current infection prevention protocol based on the surgery type of the surgical procedure, a duration of the surgical procedure, and/or the alert threshold, wherein the modification to the current infection prevention protocol comprises an increased antibiotic dose, a longer course of antibiotic treatment, use of a different antibiotic, or use of a supplemental antibiotic." The cited references, whether considered alone or in combination, fail to disclose or suggest this limitation. For example, Block is entirely silent on automatically identifying modifications to an existing infection prevention protocol and says nothing about modifying a protocol to adjust a dosage, type, or administration schedule of an antibiotic. Mao and Mangiardi fail to cure this deficiency of Block. Therefore, the cited references cannot disclose or suggest "automatically identifying a modification to the current infection prevention protocol based on the surgery type of the surgical procedure, a duration of the surgical procedure, and/or the alert threshold, wherein the modification to the current infection prevention protocol comprises an increased antibiotic dose, a longer course of antibiotic treatment, use of a different antibiotic, or use of a supplemental antibiotic," as recited in claim 21.
Examiner appreciates applicant’s arguments but does not find them persuasive. As previously cited for claim 1 within this office action Block recites some portion here stating “As with prior examples , such configurations may be defined as part of the infection - reduction protocol or may be manually configured for a particular operating room , procedure , or facility , for example .” and see [0027] discloses, “The HIS ( 90 ) may include one or more servers that are configured to provide the processor ( 100 ) and / or the monitoring server ( 80 ) with hospital records , patient records , personnel records , and other information usable during analysis of sensor data to monitor for , detect , qualify , quantify , and analyze infection risks . Information provided by the HIS ( 90 ) may include , for example , HVAC maintenance records for an operating room , personnel records indicating training , certification , or adherence to operating room guidelines , historic infection rates associated with certain types of procedures or procedures performed in certain operating rooms , scheduling information ( such as which patients are to be operated on in which operating rooms at what times ) , patients ' electronic medical records , and other information.” Examiner notes the ORMS system which controls the alerting is based on information from the HIS which stores information on procedure type and procedures in certain operating rooms and examiner notes this is enough for the recited claim language as it is broadly recited as the alert threshold being based on the procedure type there is not recited in the claim limitation it is used for configuration of anything particular but rather that when an alert threshold is reached due to infection risk reasoning that this can be based on procedure type and Block clearly teaches that information from the HIS such as procedure type is stored for signal analysis of infection risk. Therefore, examiner maintains the 103 rejection.
In response to applicant’s argument that Block does not teach "determining, by the one or more processors, whether the door of the operating room is a door to a sterile room that is acceptable to open at least once while the surgical procedure is in progress or a door to a non-sterile corridor that is not acceptable to open while the surgical procedure is in progress," the examiner does not find applicant’s arguments persuasive. The claims are given their broadest reasonable interpretation in light of the specification but not reading the specification into the claims and taking the claims for face construction. The art is applied as one of reasonable and ordinary skill would understand in regards to the claim construction and words recited in the claims. If no clear definition is given in the specification then the plain definition is taken. Examiner notes the recitation of the claim merely recites “whether the door of the operating room is a door to a sterile room that is acceptable to open at least once while the surgical procedure is in progress” and Block teaches as previously cited in this office action for claim 1 that the door sensors are positioned within an operating room and are detect whether a door Is opened or closed to keep out contaminants and infection risk within that operating room. Further examiner notes the claim recites “or” between the series of elements. Per MPEP § 2143.03, Language that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art. See, e.g., Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298, 92 USPQ2d 1163, 1171 (Fed. Cir. 2009). Examiner notes that the claim does not recite whether the claim is to determine whether an area is unsterile or sterile but whether the operating room door is a door to a sterile room that is acceptable to open during a surgical procedure and examiner notes it is understood under BRI by one of ordinary skill that an operating room itself is a sterile room. There may be unsterile items or areas within the room that may not be considered sterile but the claim did not recite this rather ultimately the goal of an operating room as stated in both instant application and Block to keep infection risk limited in these rooms as one of ordinary skill would understand the operating room is to be a sterile environment as surgery on human beings is performed here. Therefore, examiner maintains the 103 rejection.
Applicant’s arguments with respect to claim 21 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Prior Art not cited but made of record
US20230358629 Al – Kirschman et. al
A system for detecting the opening of a door to a pressurized room, such as a hospital operating room. A pressure sensor within the room produces a historical record of barometric pressures at the sensors location. The opening of the door produces a characteristic variation in the barometric pressure. The invention searches through the record, looking for the characteristic variation. When it is found, the invention issues a door-open signal.
Terry (US20210125444A1)
A perioperative safety system embodying tools to assess , monitor and document traffic in the perioperative operating room setting to reduce risk of infection for a surgical patient by enforcing the reduction and elimination of unnecessary traffic in the operating room setting .
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ashley Elizabeth Evans whose telephone number is (571) 270-0110. The examiner can normally be reached Monday – Friday 8:00 AM – 5:00 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid can be reached on (571) 270-1813. The fax phone number for the organization where this application or proceeding is assigned 571-273-8300.
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/ASHLEY ELIZABETH EVANS/Examiner, Art Unit 3687
/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687
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