SYSTEMS AND METHODS FOR SELECTING DEVICE FOR PLACEMENT WITHIN AN ANATOMICAL STRUCTURE ON AN ULTRASOUND IMAGE FEED

§101 §103 §112

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 . The IDS filed 11 October 2024 has been received and considered. Claims 1 – 20 are pending. Claims 1 – 20, all of the claims pending in this application, have been rejected. Claim Rejections - 35 USC § 112 The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. Claim 11 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 11 recites the following claim language “wherein the device is a catheter, the one of more dimensions is an internal diameter of the anatomical structure and a size of the catheter is automatically selected by the AI model, based upon a measurement gauge of an external diameter of the catheter, as compared to a best fit of the internal diameter of the anatomical structure”. It is unclear as to how the diameter of the catheter would be used over determining if the catheter actually fits in the anatomical structure. For example, even though the human anatomy is known…there are average sizes that the internal diameter of arteries, veins, trachea, etc. can be, but not a standard size due to all humans being different. Therefore, if the AI is choosing the gauge of a catheter based on the typical gauge used for a vein, how would it know that the gauge wouldn’t rupture the structure due to the structure being smaller than normal, if it’s not using a catheter that would “best fit” the abnormally small anatomical structure (in this case the internal diameter of a vein being smaller than the normal average)? 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. Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Per claim 20, based upon consideration of all of the relevant factors with respect to the claim as a whole, claim 20 held to claim a computer program product encoded on a computer readable medium that does not preclude signals or carrier waves from serving as said medium, and is therefore rejected as ineligible subject matter. Furthermore, the Applicant’s specification discloses “Program products according to the invention may be in any of a wide variety of forms. The program product may comprise, for example, non-transitory media such as magnetic data storage media including floppy diskettes, hard disk drives, optical data storage media including CD ROMs, DVDs, electronic data storage media including ROMs, flash RAM, EPROMs, hardwired or preprogrammed chips (e.g., EEPROM semiconductor chips), nanotechnology memory, or the like. The computer-readable signals on the program product may optionally be compressed or encrypted.”, Paragraph [00181]. The broadest reasonable interpretation of the claim covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer readable media. When the broadest reasonable interpretation of a claim covers a signal per se, the claim must be rejected under 35 U.S.C. 101 as covering non-statutory subject matter. See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007) (transitory embodiments are not directed to statutory subject matter) and Interim Examination Instructions for Evaluating Subject Matter Eligibility Under 35 U.S.C. 101, Aug. 24, 2009; p. 2. Claims directed toward a non-transitory computer readable medium may qualify as a manufacture and make the claim patent-eligible subject matter. MPEP 2106.03(I). Therefore, the Examiner recommends amending the claims to recite a “non-transitory computer-readable storage medium” in order to resolve this issue. 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 – 3, 5, 7 – 11, and 14 – 20 are rejected under 35 U.S.C. 103 as being unpatentable over EP Publication No. 4710888 A2 to SIEMIONOW et al. (hereinafter SIEMIONOW) in view of US Patent No. 11974881 B2 to Veronesi in further view of Non-Patent Literature “Automatic vein measurement by ultrasonography to prevent peripheral intravenous catheter failure for clinical practice using artificial intelligence: development and evaluation study of an automatic detection method based on deep learning” to Takahashi et al. (hereinafter Takahashi). Claim 1 Regarding claim 1, an independent method claim, SIEMIONOW teaches deploying an AI model to execute on a computing device communicatively connected to a medical, wherein the AI model is trained so that when the AI model is deployed, the computing device identifies and predicts one or more dimensions of the anatomical structure ("The method may further compromise processing the scan images of the anatomical structures between the identified landmarks, and determining physical dimensions of the anatomical structures in the region of interest where the medical device is intended to be placed. The method may further comprise determining preferred physical dimensions, such as size, diameter and/or length, of the medical device to be placed depending on analyzed dimensions of the anatomical structure.", Paragraph(s) [0014 - 0015]); processing, using the AI model, the new medical image to identify and predict the one or more dimensions of the anatomical structure (Rejected as applied directly above). Although it is implied, SIEMIONOW does not explicitly teach the medical device being an ultrasound scanner or the medical image being that of an ultrasound image. Furthermore, SIEMIONOW does not teach automatically selecting a device from the plurality of devices for placement therein based on the one or more dimensions. However, Veronesi teaches displaying, on a screen communicatively connected to the ultrasound scanner, the ultrasound image feed comprising the anatomical structure ("The signal processor 132 may comprise suitable logic, circuitry, interfaces and/or code that may be operable to process ultrasound scan data (i.e., summed IQ signal) for generating ultrasound images for presentation on a display system 134. The signal processor 132 is operable to perform one or more processing operations according to a plurality of selectable ultrasound modalities on the acquired ultrasound scan data. In an exemplary embodiment, the signal processor 132 may be operable to perform display processing and/or control processing, among other things. Acquired ultrasound scan data may be processed in real-time during a scanning session as the echo signals are received. Additionally or alternatively, the ultrasound scan data may be stored temporarily in the RF/IQ buffer 126 during a scanning session and processed in less than real-time in a live or off-line operation. In various embodiments, the processed image data can be presented at the display system 134 and/or may be stored at the archive 138. The archive 138 may be a local archive, a Picture Archiving and Communication System (PACS), an enterprise archive (EA), a vendor-neutral archive (VNA), or any suitable device for storing images and related information.", Column 5, lines 36 - 56…"For example, a full-size patient-specific model may be presented at display system 134 with a smaller anatomic orientation indicator comprising mocked patient anatomy models of the anatomy depicted in the patient-specific model and one or more anatomical structures nearby the anatomy depicted in the patient-specific model as illustrated, for example, in FIGS. 2-3", Column 8, lines 65 - 67; Column 9, lines 1 - 4; ); acquiring, at the computing device, a new ultrasound image during ultrasound scanning (Rejected as applied directly above). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of SIEMIONOW to incorporate the use of an ultrasound scanner that would allow for the acquisition of ultrasound images/feed with the ability to display the results on a screen or monitor, as disclosed by Veronesi. The suggestion/motivation for doing so would have been to increase the variety of medical images that can be acquired to better diagnose patient illnesses. Neither SIEMIONOW, or Veronesi, or the combination teach automatically selecting a device from the plurality of devices for placement therein based on the one or more dimensions. However, Takahashi teaches automatically selecting a device from the plurality of devices for placement therein based on the one or more dimensions ("This study aimed to develop and validate a system that automatically measures blood vessel diameters on ultrasound images using artificial intelligence (AI) and provide recommendations for selecting an implantation site. First, based on previous studies, the vessel diameter was calculated as the mean value of the maximum long diameter plus the maximum short diameter orthogonal to it. Second, the size of the PIVC to be recommended was evaluated based on previous studies. For the development and validation of an automatic detection tool, we used a fully convoluted network for automatic estimation of vein location and diameter. The agreement between manually generated correct data and automatically estimated data was assessed using Pearson’s product correlation coefficient, systematic error was identified using the Bland- Altman plot, and agreement between catheter sizes recommended by the research nurse and those recommended by the system was evaluated.", Abstract; also see "Results" section). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the teachings of SIEMIONOW, in view of Veronesi, to incorporate the AI model being able to automatically select a device from devices based on the measurements of the anatomical structure, as disclosed by Takahashi. The suggestion/motivation for doing so would have been to implement utilizing AI medical device recommendations to further reduce the percentage of human error or potential malpractice. Claim 2 Regarding claim 2, dependent on claim 1, SIEMIONOW, in view of Veronesi and Takahashi, teaches the invention as claimed in claim 1. SIEMIONOW further teaches applying the AI model to segment boundaries of the anatomical structure in the new ultrasound image (Fig(s). 2A, 2B, 2C; Paragraph [0023]). Claim 3 Regarding claim 3, dependent on claim 1, SIEMIONOW, in view of Veronesi and Takahashi, teaches the invention as claimed in claim 1. SIEMIONOW, in view of Veronesi and Takahashi, further teaches wherein the one or more dimensions is selected from the group consisting of a diameter of the anatomical structure, a length of the anatomical structure, a width of the anatomical structure, circumference of the anatomical structure, an area of the anatomical structure, and a height of the anatomical structure (Rejected as applied to claim 1), wherein it is specifically disclosed by Takahashi (Abstract; Results). Claim 5 Regarding claim 5, dependent on claim 1, SIEMIONOW, in view of Veronesi and Takahashi, teaches the invention as claimed in claim 1. SIEMIONOW, in view of Veronesi and Takahashi, further teaches applying the AI model to identify a diameter of the anatomical structure (Rejected as applied to claim 1), wherein it is specifically disclosed by Takahashi (Abstract; Results); and applying the AI model to automatically select the device for placement based on the diameter (Rejected as applied directly above). Claim 7 Regarding claim 7, dependent on claim 1, SIEMIONOW, in view of Veronesi and Takahashi, teaches the invention as claimed in claim 1. SIEMIONOW, in view of Veronesi and Takahashi, further teaches applying the AI model to select the size of the device from a plurality of devices based on at least one of i) characteristics of the anatomical structure; ii) characteristics of a patient; iii) a clinical application; iv) best practices for device placement; and v) historical records (Rejected as applied to claim 1). Claim 8 Regarding claim 8, dependent on claim 7, SIEMIONOW, in view of Veronesi and Takahashi, teaches the invention as claimed in claim 7. SIEMIONOW, in view of Veronesi and Takahashi, further teaches wherein the AI model i) identifies two devices of two different sizes from the plurality of devices, and ii) selects a smaller size from the two different sizes (Rejected as applied to claim 1), where there are different types of PIVC devices and it's implicit that the AI is trained to determine which to recommend based on the which will fit based on the diameter size of the anatomical structure, as disclosed by Takahashi. Examiner notes the chart referenced in https://pedagogyeducation.com/Resources/Infusion/Peripheral-IV-Catheter-Chart. PNG media_image1.png 939 903 media_image1.png Greyscale Claim 9 Regarding claim 9, dependent on claim 1, SIEMIONOW, in view of Veronesi and Takahashi, teaches the invention as claimed in claim 1. SIEMIONOW, in view of Veronesi and Takahashi, further teaches identifying a standardized size for the device based on the one or more dimensions of the anatomical structure (Rejected as applied to claim 1 and 8), where there are different types of PIVC devices and it's implicit that the AI is trained to determine which device size to recommend, choosing which will fit based on the diameter size of the anatomical structure, as disclosed by Takahashi; and selecting the size of the device that corresponds to the standardized size (Rejected as applied directly above). Claim 10 Regarding claim 10, dependent on claim 1, SIEMIONOW, in view of Veronesi and Takahashi, teaches the invention as claimed in claim 1. SIEMIONOW further teaches wherein the device is selected from the group consisting of a catheter, endotracheal tube and an implant (Rejected as applied to claim 1), where a type of catheter is used. Claim 11 Regarding claim 11, dependent on claim 10, SIEMIONOW, in view of Veronesi and Takahashi, teaches the invention as claimed in claim 10. SIEMIONOW, in view of Veronesi and Takahashi, (As Best Understood) further teaches wherein the device is a catheter, the one of more dimensions is an internal diameter of the anatomical structure and a size of the catheter is automatically selected by the AI model, based upon a measurement gauge of an external diameter of the catheter, as compared to a best fit of the internal diameter of the anatomical structure (As best understood, Rejected as applied to claim 1 and 8), where the catheters have standard gauge sizes used worldwide with different external diameters used for specific operations and specific anatomical structures. As stated in the 112 rejection above, the AI would be trained to recommend a PIVC based on the best gauge, but it's implicit that the system would need to know the internal diameter of the anatomical structure from which to base this recommendation. Claim 14 Regarding claim 14, dependent on claim 10, SIEMIONOW, in view of Veronesi and Takahashi, teaches the invention as claimed in claim 10. SIEMIONOW further teaches wherein the implant is selected from the group consisting of spinal implants, orthopedic implants, neurological implants, vascular implants, and cardiac implants ("The automatic implant placement method as presented herein comprises two main procedures: a training procedure and a prediction procedure…The term "medical device" is understood as a surgical implant or an instrument, for example a catheter, instrument, a cannula, a needle, a anchor, a screw, a stent, a biomechanical device. The present disclosure will be described in details with reference to an example embodiment related to a neurological surgery, wherein a screw (as an example of a medical device) is placed, i.e. inserted, to a spine (as an example of an anatomical structure). A skilled person will realize that this embodiment can be extended to other applications as well, such as guidance for a medical device (e.g., instrumentation or implant) in other natural or artificial anatomical structures, for example blood vessels, biliary ducts, subthalamic nucleus, and components of solid organs like the heart (e.g., mitral valve), kidney (e.g., renal artery), and nerves (e.g., epidural space).", Paragraphs [0020 - 0023]). Claim 15 Regarding claim 15, dependent on claim 1, SIEMIONOW, in view of Veronesi and Takahashi, teaches the invention as claimed in claim 1. Neither SIEMIONOW, or Veronesi, or the combination teach wherein the AI model is trained with a plurality of training ultrasound images comprising labelled segmented boundaries of the anatomical structure, in plurality of views, which are, one of: i) generated by one of a manual or semi automatic means; or ii) tagged from an identifier menu by one of a manual, semi automatic means or fully automatic means. However, Takahashi further teaches wherein the AI model is trained with a plurality of training ultrasound images comprising labelled segmented boundaries of the anatomical structure, in plurality of views, which are, one of: i) generated by one of a manual or semi automatic means; or ii) tagged from an identifier menu by one of a manual, semi automatic means or fully automatic means ("The FCN is trained using the ‘supervised learning’ method. A pair of US images and their structures (labelled images of vascular regions) were prepared in advance, and the parameters of each layer (image features and decision rules) were sequentially adjusted to minimize the loss function (number of misclassified pixels) using the back propagation method. Moreover, to increase the number of training samples using US images, we performed transition learning using models trained for natural image recognition and iterative learning by increasing the number of CNN layers.", Methods - Training and validation data set). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the teachings of SIEMIONOW, in view of Veronesi and Takahashi, to incorporate the AI model utilizing a supervised learning method using pre-labelled images, as disclosed by Takahashi. The suggestion/motivation for doing so would have been to train on images associated with a ground truth allowing the AI model to effectively increase accuaracy. Claim 16 Regarding claim 16, dependent on claim 1, SIEMIONOW, in view of Veronesi and Takahashi, teaches the invention as claimed in claim 1. Neither SIEMIONOW, or Veronesi, or the combination teach comprising training the AI model with one or more of the following: i) supervised learning; ii) unsupervised learning; iii) previously labelled ultrasound image datasets; and iv) cloud stored data. However, Takahashi further teaches comprising training the AI model with one or more of the following: i) supervised learning; ii) unsupervised learning; iii) previously labelled ultrasound image datasets; and iv) cloud stored data (Rejected as applied to claim 15), wherein a "supervised learning" method was used as well as previously prepared labelled images). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the teachings of SIEMIONOW, in view of Veronesi and Takahashi, to incorporate the AI model utilizing a supervised learning method using pre-labelled images, as disclosed by Takahashi. The suggestion/motivation for doing so would have been to train on images associated with a ground truth allowing the AI model to effectively increase accuaracy. Claim 17, an independent system claim, is rejected for the same reasons as applied to claim 1. Claims 18 and 19, dependent on claim 17, are rejected as applied to the above claims. Claim 20, an independent computer-readable medium claim, is rejected for the same reasons as applied to claim 1. Claims 4 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over EP Publication No. 4710888 A2 to SIEMIONOW et al. (hereinafter SIEMIONOW) in view of US Patent No. 11974881 B2 to Veronesi in further view of Non-Patent Literature “Automatic vein measurement by ultrasonography to prevent peripheral intravenous catheter failure for clinical practice using artificial intelligence: development and evaluation study of an automatic detection method based on deep learning” to Takahashi et al. (hereinafter Takahashi) in further view of US Patent Publication No. 20260041494 A1 to MICHIELS et al. (hereinafter MICHIELS). Claim 4 Regarding claim 4, dependent on claim 1, SIEMIONOW, in view of Veronesi and Takahashi, teaches the invention as claimed in claim 1. Although it is implied that a screen consisting of a visual or audio signal would be used, neither SIEMIONOW, or Veronesi, or Takahashi, or the combination explicitly teach wherein the screen is within a multi-purpose electronic device which is communicatively coupled with the ultrasound scanner and an additional step of indicating the device, which is automatically selected, is via at least one of a visual signal on the display or an audio signal. However, MICHIELS teaches wherein the screen is within a multi-purpose electronic device which is communicatively coupled with the ultrasound scanner and an additional step of indicating the device, which is automatically selected, is via at least one of a visual signal on the display or an audio signal (Figure 1, #108; "User interface 108 may be used to receive inputs from, and/or provide outputs to, a user. For example, user interface 108 may include a touchscreen, display, switches, dials, lights, etc. Accordingly, user interface 108 may display information such as 3D model reconstructions, measurements, implantable device sizing charts, and/or simulations, to facilitate implantable device selection and preoperative planning by the user, as described in further detail below. Moreover, user interface 108 may receive user input, e.g., selection of an implantable device based on the displayed information, to thereby generate virtual simulations, as well as feedback from the user based on the displayed information, e.g., corrected measurements, such that platform 100 may adjust the information accordingly. In some embodiments, user interface 108 is not present on platform 100, but is instead provided on a remote, external computing device communicatively connected to platform 100 via communication circuitry 104", Paragraph [0054]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the teachings of SIEMIONOW, in view of Veronesi and Takahashi, to incorporate a multi-purpose electronic device with an integrated screen allowing for visual and/or audio signals to be output based on received communications from a connected ultrasound device, as disclosed by MICHIELS. The suggestion/motivation for doing so would have been to display results and recommendations by the AI model in real time to be viewed by a user/medical professional, allowing for results to be compared. Claim 6 Regarding claim 6, dependent on claim 5, SIEMIONOW, in view of Veronesi and Takahashi, teaches the invention as claimed in claim 5. SIEMIONOW, in view of Veronesi and Takahashi, teaches wherein more than one device is selected by the AI model based on the diameter of the anatomical structure (Rejected as applied to claim 1), wherein it is specifically disclosed by Takahashi (Abstract; Results). Although it is implied that training an AI model would need to use clinical/medical data and Takahashi discloses the AI evaluations being based on previous studies, neither SIEMIONOW, or Veronesi, or Takahashi, or the combination explicitly teach the AI model selecting a preferred device, of the more than one device, based upon a clinical application. However, MICHIELS teaches the use of a specific application ("For this study, manually annotated or “ground truth” data was produced by trained professionals for all the above-mentioned landmarks and the 3D segmentation of the LAA, using the Materialise Mimics Innovation Suite 21 (made available by Materialise, Leuven, Belgium). Not all annotations are available for all patients as some were added at a later stage of the study. For 25 patients, three trained professionals identified the ostium and landing zone planes independently and performed the related measurements. This provides inter-operator variability data that allows to correctly interpret the accuracy of the deep learning models.", Paragraph [0122]), where the Materialise Mimics Innovation Suite 21 is a clinical application used in the anatomical analysis to help determine the correct device to be used based off of measurements. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the teachings of SIEMIONOW, in view of Veronesi and Takahashi, to incorporate utilizing a clinical application, as disclosed by MICHIELS. The suggestion/motivation for doing so would have been to provide inter-operator variability data that allows to correctly interpret the accuracy of the deep learning models, as disclosed by MICHIELS in Paragraph [0122]. Claims 12 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over EP Publication No. 4710888 A2 to SIEMIONOW et al. (hereinafter SIEMIONOW) in view of US Patent No. 11974881 B2 to Veronesi in further view of Non-Patent Literature “Automatic vein measurement by ultrasonography to prevent peripheral intravenous catheter failure for clinical practice using artificial intelligence: development and evaluation study of an automatic detection method based on deep learning” to Takahashi et al. (hereinafter Takahashi) in further view of US Patent Publication No. US 20230088056 A1 to WANG et al. (hereinafter WANG). Claim 12 Regarding claim 12, dependent on claim 10, SIEMIONOW, in view of Veronesi and Takahashi, teaches the invention as claimed in claim 10. Neither SIEMIONOW, or Veronesi, or Takahashi, or the combination teach wherein the device is an endotracheal tube, the one of more dimensions is an internal diameter of a trachea and a size of the endotracheal tube is automatically selected by the AI model, based upon a measurement gauge of an external diameter of the endotracheal tube. However, WANG teaches wherein the device is an endotracheal tube, the one of more dimensions is an internal diameter of a trachea and a size of the endotracheal tube is automatically selected by the AI model, based upon a measurement gauge of an external diameter of the endotracheal tube ("One or more models may be produced from the navigational pathways using a diameter associated with the navigated passageway.", Paragraph [0078]; " At an operation 404, computer software alone or in combination with manual input is used to convert the recorded images into a two-dimensional or three-dimensional composite representation or model of a partial or an entire anatomic organ or anatomic region. Some methods for generating a model from image data may include a segmentation process that identifies certain features of a model, such as the curvature of a bronchial passageway in the lung, extracts the curvature, and uses that to generate a centerline model representing the bronchial passageway. This segmentation relies on artificial intelligence to generate the model, such as the centerline model.", Paragraph [0068]), wherein since endoscopic tubes are not universally standardized across manufacturers, it is implicit that an AI would have had to been pre-trained on the manufacturers various tube dimensions that would be used by the system to select the tube needed based on the diameters associated with the internal navigated passageways. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the teachings of SIEMIONOW, in view of Veronesi and Takahashi, to incorporate the AI model automatically determining the internal diameter of a trachea and selecting the tube size based on those measurements, as disclosed by WANG. The suggestion/motivation for doing so would have been to allow for an AI model to be able to determine the internal diameters or measurements of anatomical structures in various regions of the body detected in ultrasound imaging and recommending medical devices based on both region and measurements. Claim 13 Regarding claim 13, dependent on claim 12, SIEMIONOW, in view of Veronesi, Takahashi, and WANG, teaches the invention as claimed in claim 12. Neither SIEMIONOW, or Veronesi, or Takahashi, or the combination teach applying the AI model to select the size of the endotracheal tube from two different sized endotracheal tubes based on at least one of: i) purpose of endotracheal tube placement; ii) characteristics of the trachea; iii) characteristics of a patient; iv) a clinical application; v) best practices for endotracheal tube placement; and vi) historical records. However, WANG further teaches applying the AI model to select the size of the endotracheal tube from two different sized endotracheal tubes based on at least one of: i) purpose of endotracheal tube placement; ii) characteristics of the trachea; iii) characteristics of a patient; iv) a clinical application; v) best practices for endotracheal tube placement; and vi) historical records (Figures 3A, 3B, 7E and 7F; "In some embodiments, medical instrument 104 may have a visualization system (discussed in more detail below), which may include a viewing scope assembly that records a concurrent or real-time image of a surgical site and provides the image to the operator or operator O through one or more displays of medical system 100, such as one or more displays of display system 110. The concurrent image may be, for example, a two or three dimensional image captured by an endoscope positioned within the surgical site. In some embodiments, the visualization system includes endoscopic components that may be integrally or removably coupled to medical instrument 104. However in some embodiments, a separate endoscope, attached to a separate manipulator assembly may be used with medical instrument 104 to image the surgical site", Paragraph [0040]; "In some embodiments, medical instrument system 200 may include a flexible bronchial instrument, such as a bronchoscope or bronchial catheter, for use in examination, diagnosis, biopsy, or treatment of a lung. Medical instrument system 200 is also suited for navigation and treatment of other tissues, via natural or surgically created connected passageways, in any of a variety of anatomic systems, including the colon, the intestines, the kidneys and kidney calices, the brain, the heart, the circulatory system including vasculature, and/or the like.", Paragraph [0059]), where various tube sizes would be used while traversing the trachea all the way down to the bronchial passageways. PNG media_image2.png 530 406 media_image2.png Greyscale PNG media_image3.png 539 344 media_image3.png Greyscale Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ronde Miller whose telephone number is (703) 756-5686 The examiner can normally be reached Monday-Friday 8:00-4:00. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor Gregory Morse can be reached on (571) 272-3838. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /RONDE LEE MILLER/Examiner, Art Unit 2663 /GREGORY A MORSE/Supervisory Patent Examiner, Art Unit 2698