DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
Status of Claims
Claims 1 and 26 have been amended.
Claims 1, 3-20, and 22-26 are currently pending in which claims 13-15 and 25 have been withdrawn.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3-12, 16, and 26 are rejected under 35 U.S.C. 103 as being unpatentable over U.S Patent Appl. Publ. No. 2021/0045711 A1 to Brattain et al. (hereinafter “BRATTAIN ’711”) and “Ultrasound-Guided Airway Procedures” by Terkawi, Abdullah Sulieman, et al. from Chapter 4 of The Ultimate Guide to Point-of-Care Ultrasound-Guided Procedures (2020): 39-61 (hereinafter referred to as Point-of-Care Ultrasound or “POCUS”) and Ma, Laifa, et al. "A novel deep learning framework for automatic recognition of thyroid gland and tissues of neck in ultrasound image." IEEE Transactions on Circuits and Systems for Video Technology 32.9 (published “8 March 2022”; see notes below Index Terms on bottom left of first page): 6113-6124 (hereinafter “MA”) and HADJERCI et al. Assistive system based on nerve detection and needle navigation in ultrasound images for regional anesthesia. Expert Systems with Applications. 2016 Nov 1;61:64-77 (hereinafter HADJERCI).
With respect to claim 1, BRATTAIN ‘711 teaches a system for guiding an interventional device in an interventional procedure of a subject (see, e.g., [0009] and claim 1 of BRATTAIN ‘711) an ultrasound probe (Id.), a guide system coupled to the ultrasound probe and configured to guide the interventional device into a field of view (FOV) of the ultrasound probe (Id.); a non-transitory memory having instructions stored thereon (Id.); a processor configured to access the non-transitory memory and execute the instructions (Id.). BRATTAIN ‘711 also teaches the processor is caused to access image data acquired from the subject using the ultrasound probe (Id.), wherein the image data include at least one image of an anatomical landmark structure of the subject ([0009], claim 1, and claim 7 in which target structure is “an airway”, and [0043]: For a “needle cricothyrotomy (to provide airway access). Portable ultrasound may be used…to detect the cricothyroid membrane and needle insertion point.” (emphasis added)) determine, from the image data and the anatomical landmark structure, a location of a target airway within the subject (Id.), determine an insertion point location for the interventional device based upon the location of the target airway and guide placement of the ultrasound probe to position the guide system at the insertion point location (Id.), and track the interventional device from the insertion point location to the target airway (Id.).
BRATTAIN ‘711 also teaches that the processor is further caused to input the image data acquired from the subject into an artificial intelligence (Al) model to identify the anatomical landmark structures in the image data. (see, e.g., [0075]): in which BRATTAIN ’711 teaches that the device may be “ultrasound guided” and “may employ machine learning or artificial intelligence for identifying a target structure for penetration and guiding penetration of the target structure.”)
BRATTAIN ‘711 does not explicitly teach wherein the anatomical landmark structure includes at least one of tracheal rings, thyroid cartilage, or cricoid cartilage and one or more landmarks to avoid including at least a thyroid gland. BRATTAIN ‘711 does not explicitly teach also determining, from the image data and the anatomical landmark structure, a location of the one or more landmarks to avoid within the subject and also determining the insertion point location based upon the location of the one or more landmarks to avoid.
However, BRATTAIN ’711 does teach that embodiments may be use to access an airway. (see, e.g., [0025], [0043], Table 1 at [0073], [0077], [0078], and claims 14 and 32). Table 1 provides non-limiting examples of cartridge configurations that can be used with the system. One of the configurations include “Cricothyrotomy (or similar methods of establishing airway access).” As discussed below, POCUS teaches how to perform ultrasound-guided procedures to provide airway access (tracheostomies). Specifically, POCUS teaches identifying anatomical structures for determining a path to the trachea and anatomical structures that should be avoided.
POCUS describes various upper airway surgical procedures that can be performed using guided ultrasound. (see, e.g., p.39, Title and Introduction). In particular, POCUS teaches how to gain access to an airway: “Percutaneous dilation tracheostomy (PDT) is a commonly performed bedside surgical procedure.” (p.44, Procedures: Percutaneous Dilation Tracheotomy (PDG)). “However, complications such as perforation of the posterior wall of the trachea, puncture of the esophagus, tracheal ring injury, and vocal cord injury can still occur. Ultrasound can help avoid some of the aforementioned complications, improve safety, and increase the success rate of the procedure especially in the challenging patient.” (pp.44-45, Id.). Notably, in one study, “[u]ltrasound guidance was associated with significantly higher first needle pass success rate and more accurate tracheal puncture site placement. Fewer complications were observed in the ultrasound group [16].” (p.47, Id.).
POCUS also describes identifying anatomical landmark structures when imaging the patient prior to PDT surgery. For example, in step 7 of the preprocedural evaluation, POCUS instructs the operator how to obtain a sagittal/longitudinal view of the relevant anatomy. “The resultant image is described as ‘pearls on a string’ and represents the hypoechoic cricoid cartilage and individual tracheal rings anterior to the hyperechoic air-mucosa interface (Fig. 4.15).” (emphasis added) (p.48, Preparation/Preprocedural Evaluation). For the surgical procedure, in step 7, POCUS instructs the surgeon to “[s]elect optimum puncture level as discussed mentioned [sic] above, ideally between the first and fourth tracheal rings, avoiding vessels or a vascular isthmus in the path of the needle (Figs. 4.20).” (emphasis added) (p.49, Id). Note that this quote refers the reader to Figure 20 (shown here). The caption of Figure 20 clarifies that the referenced isthmus is the isthmus of the thyroid and its location is determined relative to the tracheal rings. “The thyroid isthmus is seen overlying the 1st-3rd tracheal rings.” (Fig. 20, p.49). Figure 20 also shows the thyroid cartilage (TC) and the cricoid cartilage (CC) relative to the tracheal rings and the thyroid. As such, POCUS teaches identifying certain landmark structures (tracheal rings, cricoid cartilage, and thyroid cartilage) to determine the location of the target airway and to determine the location of one or more landmarks to avoid (thyroid).
To be clear, POCUS teaches determining a location of the one or more landmarks to avoid and determining the insertion point location based upon the location of the one or more landmarks to avoid. For instance, in step 7 of the surgical procedure, POCUS instructs the surgeon to “[s]elect optimum puncture level as discussed mentioned [sic] above, ideally between the first and fourth tracheal rings, avoiding vessels or a vascular isthmus in the path of the needle (Figs. 4.20).” (emphasis added) (p.49).
It would have been obvious for one having ordinary skill in the art to modify the BRATTAIN ’711 system so that a target airway could be identified based on the location of at least one of tracheal rings, thyroid cartilage, and cricoid cartilage and so that the thyroid gland could be identified based on the location of at least one of tracheal rings, thyroid cartilage, and cricoid cartilage in order to determine an insertion point location to avoid the thyroid gland. In particular, one having ordinary skill in the art would configure the processor to access image data and then input the image data into an AI model that determines a location of the airway and a location of one or more landmarks to avoid (i.e., thyroid isthmus). The AI model determines these locations relative to other anatomical structures, i.e., tracheal rings, thyroid cartilage, cricoid cartilage, and isthmus. One having ordinary skill in the art would be motivated to configure the processor to access such image data for ultrasound-guided PDT and input the image data into an AI model, because ultrasound-guided PDT “can help avoid some…complications, improve safety, and increase the success rate of the procedure especially in the challenging patient.” (pp.44-45, Id.). Moreover, at least for some surgical procedures, the optimum puncture level is “ideally between the first and fourth tracheal rings.” (p.49, Id). There would be a reasonable expectation of success because, as taught by POCUS, ultrasound-guided PDTs have already been safely performed.
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Neither POCUS nor BRATTAIN ‘711 teach wherein the AI model is pretrained on training image data including the anatomical landmark structure (i.e., at least one of tracheal rings, thyroid cartilage, or cricoid cartilage and one or more landmarks to avoid including at least a thyroid gland) .
MA teaches that an AI model can be trained to distinguish “the boundaries of different tissues” and to, in particular, identify at least the trachea, the cricoid cartilage, and the thyroid isthmus. (Abstract and at p.6115, II.B Image Preprocessing). “Therefore, the automatic segmentation and detection of the thyroid and anatomical tissues of the neck are of vital importance in promoting the screening of diseases, providing clinicians with valuable information to make the best diagnostic decisions.” (p.6113, right column). The AI model is trained using images in which the relevant tissues were labelled by humans who determine the locations of the tissues relative to one another. (e.g., MA at II.B Image Preprocessing on p.6115). Figure 6 is shown here. The caption explains that the trained model can identify “the left lobe of the thyroid, right lobe of the thyroid, muscles, trachea, carotid, cricoid cartilage, isthmus, esophagus, jugular vein, and endothyroid vessel….” (see also pp.6119-6220). Accordingly, MA teaches a trained AI model that can automatically segment and detect relevant anatomical structures of the neck, including the trachea and the thyroid. However, the AI model in MA is not employed for guiding an interventional device during a surgical procedure to avoid certain structures on a path toward the target airway.
HADJERCI teaches the automatic localization and segmentation of target anatomical structures and critical structures to avoid. (Abstract). Although in the specific context of regional anesthesia, HADJERCI is generally concerned with enabling real-time visualization of a needle during a procedure that involves a targeted anatomical structure and nearby anatomical structures. “A new method based on a machine learning algorithm with a multi-model classification process using a sliding window for localization, then an active contour was applied to delineate the localized regions.” (Abstract). In HADJERCI, the target structure is a nerve while the critical structures to avoid (called obstacles) are blood vessels. (p.65, left column, top paragraph). HADJERCI addresses two particular issues. First, the system must automatically identify the target structures and structures to avoid. Second, after identifying the target structures and critical structures to avoid, a path is generated “that the needle could follow to safely reach the target [structure].” (p.65, left column, top paragraph). HADJERCI teaches “an algorithm for path planning was also developed to obtain the optimal trajectory for needle insertion based on the result of the first stage (target and obstacle detection).” (Abstract). Moreover, HADJERCI teaches navigating the needle tip toward the target structure. (see, e.g., Figures 12 and 13).
It would have been obvious to one having ordinary skill in the art at the time of filing to pretrain the AI model using image data that shows the target structure (trachea) and the structures to avoid (e.g., thyroid), as taught in MA, in order to plan a path to the trachea that avoids critical structures, as taught in HADJERCI. One of ordinary skill in the art would have been motivated to train the AI model to identify the target and critical structures in order to quickly and reliably develop a path for performing the surgical procedure. There would have been a reasonable expectation of success as MA and HADJERCI teach that the structures can be identified to develop a surgical path.
With respect to claim 3, BRATTAIN ‘711 teaches that the processor is further caused to determine at least one of an angle for the interventional device from the insertion point location to the target airway (see, e.g., [0072]: (“base 740 contains a motor to set the angle at which the interventional device…will be inserted”), an orientation of the ultrasound probe with respect to the subject, or an insertion distance from the insertion point location to the target airway based upon the anatomical landmark structure (see also, e.g., claim 2).
With respect to claim 4 (depending from claim 3), BRATTAIN ‘711 teaches that the system also includes a display system and wherein the processor is further configured to cause the display system to show at least one of the angle for the interventional device, the insertion point location, the location of the target airway, the insertion distance, an indicator of the insertion point location projected proximate to the target airway, or an indicator of the ultrasound probe position at the insertion point location. (see, e.g., claims 3 and 4)
With respect to claim 5, BRATTAIN ‘711 teaches that the processor is further caused to provide real-time feedback to a user based on tracking the interventional device (see, e.g., claim 5).
With respect to claim 6, BRATTAIN ‘711 teaches that the processor is configured to receive a plurality of images of the anatomical landmark structure of the subject acquired in real-time to access the image data (see, e.g., claims 8 and 9).
With respect to claim 7 (depending from claim 6), BRATTAIN ‘711 teaches that the plurality of images includes a plurality of views of the target airway , and wherein the processor is configured to assess the plurality of images of the anatomical landmark structure and the plurality of views of the target airway to identify a location on the subject where the interventional device reaches the target airway from the insertion point location without penetrating a landmark to avoid in the subject (see, e.g., claims 8 and 9).
With respect to claim 8 (depending from claim 6), BRATTAIN ‘711 teaches that the landmark to avoid includes at least one of a bone, an unintended blood vessel, a non-target organ, or a nerve (see, e.g., claim 10).
With respect to claim 9 (depending from claim 7), BRATTAIN ‘711 teaches that the plurality of images includes images at a plurality of different timeframes (see, e.g., claim 11).
With respect to claim 10, BRATTAIN ‘711 teaches that the guide system includes a removable cartridge coupled to a base of the guide system, wherein the cartridge contains the interventional device (see, e.g., claim 12).
With respect to claim 11 (depending from claim 10), BRATTAIN ‘711 teaches that the interventional device is at least one of a needle, wire, dilator, blade, breathing tube, chest tube, vascular catheter, blood clotting agent, or drug (see, e.g., claim 13).
With respect to claim 12 (depending from claim 11), BRATTAIN ‘711 teaches that the interventional device is configured to perform at least one of cricothyrotomy or tracheotomy (see, e.g., [0043] and Table 1 providing examples of cartridge configurations: “Cricothyrotomy (or similar methods of establishing airway access.”)
With respect to claim 16, BRATTAIN ‘711 teaches that the guide system is configured to guide the interventional device automatically (see, e.g., claim 18).
With respect to claim 26, BRATTAIN ‘711 teaches a system for guiding an interventional device in an interventional procedure of a subject (see, e.g., [0009] and claim 1 of BRATTAIN ‘711) an ultrasound probe (Id.), a guide system coupled to the ultrasound probe and configured to guide the interventional device into a field of view (FOV) of the ultrasound probe (Id.); a non-transitory memory having instructions stored thereon (Id.); a processor configured to access the non-transitory memory and execute the instructions (Id.). BRATTAIN ‘711 also teaches the processor is caused to access image data acquired from the subject using the ultrasound probe (Id.), wherein the image data include at least one image of an anatomical landmark structure of the subject ([0009], claim 1, and claim 7 in which target structure is “an airway”, and [0043]: For a “needle cricothyrotomy (to provide airway access). Portable ultrasound may be used…to detect the cricothyroid membrane and needle insertion point.” (emphasis added)) determine, from the image data and the anatomical landmark structure, a location of a target airway within the subject (Id.), determine an insertion point location for the interventional device based upon the location of the target airway and guide placement of the ultrasound probe to position the guide system at the insertion point location (Id.), and track the interventional device from the insertion point location to the target airway (Id.).
BRATTAIN ‘711 also teaches that the processor is further caused to input the image data acquired from the subject into an artificial intelligence (Al) model to identify the anatomical landmark structure in the image data. (see, e.g., [0075]): in which BRATTAIN ’711 teaches that the device may be “ultrasound guided” and “may employ machine learning or artificial intelligence for identifying a target structure for penetration and guiding penetration of the target structure.”)
BRATTAIN ‘711 does not explicitly teach wherein the anatomical landmark structure includes at least one of tracheal rings, thyroid cartilage, or cricoid cartilage.
However, BRATTAIN ’711 does teach that surgical procedures for airway access may be performed using the system. (see, e.g., [0025], [0043], Table 1 at [0073], [0077], [0078], and claims 14 and 32). As discussed below, POCUS teaches how to perform ultrasound-guided procedures to provide airway access (tracheostomies). Specifically, POCUS teaches identifying anatomical structures for determining a path to the trachea.
POCUS describes various upper airway surgical procedures that can be performed using guided ultrasound. (see, e.g., p.39, Title and Introduction). In particular, POCUS teaches how to perform a cricothyrotomy: “Percutaneous dilation tracheostomy (PDT) is a commonly performed bedside surgical procedure.” (p.44, Procedures: Percutaneous Dilation Tracheotomy (PDG)). “However, complications such as perforation of the posterior wall of the trachea, puncture of the esophagus, tracheal ring injury, and vocal cord injury can still occur. Ultrasound can help avoid some of the aforementioned complications, improve safety, and increase the success rate of the procedure especially in the challenging patient.” (pp.44-45, Id.). Notably, in one study, “[u]ltrasound guidance was associated with significantly higher first needle pass success rate and more accurate tracheal puncture site placement. Fewer complications were observed in the ultrasound group [16].” (p.47, Id.).
POCUS also describes identifying anatomical landmark structures when imaging the patient prior to PDT surgery. For example, in step 7 of the preprocedural evaluation, POCUS instructs the operator how to obtain a sagittal/longitudinal view of the relevant anatomy. “The resultant image is described as ‘pearls on a string’ and represents the hypoechoic cricoid cartilage and individual tracheal rings anterior to the hyperechoic air-mucosa interface (Fig. 4.15).” (emphasis added) (p.48, Preparation/Preprocedural Evaluation). For the surgical procedure, in step 7, POCUS instructs the surgeon to “[s]elect optimum puncture level as discussed mentioned [sic] above, ideally between the first and fourth tracheal rings, avoiding vessels or a vascular isthmus in the path of the needle (Figs. 4.20).” (emphasis added) (p.49, Id). Note that this quote refers the reader to Figure 20 (shown here). The caption of Figure 20 clarifies that the referenced isthmus is the isthmus of the thyroid and its location is determined relative to the tracheal rings. “The thyroid isthmus is seen overlying the 1st-3rd tracheal rings.” (Fig. 20, p.49). Figure 20 also shows the thyroid cartilage (TC) and the cricoid cartilage (CC) relative to the tracheal rings and the thyroid. As such, POCUS teaches identifying certain landmark structures (tracheal rings, cricoid cartilage, and thyroid cartilage) to determine the location of the target airway and to determine the location of one or more landmarks to avoid (thyroid).
It would have been obvious for one having ordinary skill in the art to modify the BRATTAIN ’711 system so that a target airway could be identified based on the location of at least one of tracheal rings, thyroid cartilage, and cricoid cartilage. In particular, one having ordinary skill in the art would configure the processor to access image data and then input the image data into an AI model that determines a location of the airway. The AI model determines the location relative to other anatomical structures, i.e., tracheal rings, thyroid cartilage, cricoid cartilage. One having ordinary skill in the art would be motivated to configure the processor to access such image data for ultrasound-guided PDT and input the image data into an AI model, because ultrasound-guided PDT “can help avoid some…complications, improve safety, and increase the success rate of the procedure especially in the challenging patient.” (pp.44-45, Id.). Moreover, at least for some surgical procedures, the optimum puncture level is “ideally between the first and fourth tracheal rings.” (p.49, Id). There would be a reasonable expectation of success because, as taught by POCUS, ultrasound-guided PDTs have already been safely performed.
BRATTAIN ‘711 does not explicitly teach that the processor is further caused to confirm, using an AI model, insertion of the interventional device in the target airway via image segmentation of the wall of the target airway. However, BRATTAIN ‘711 does teach confirming penetration in related embodiments that involve blood vessels. For example, “[a] safety check may also be performed as part of determining an insertion point at step 450 for a needle… In some configurations, the safety check may include confirming the needle has penetrated the vessel of interest by the tracking and guidance at step 460.” (emphasis added) ([0052]). The following paragraph describes a different method but also teaches that “[t]he needle may be tracked and vessel penetration confirmed at step 499.” ([0053]).
Moreover, BRATTAIN ‘711 clearly indicates that the scope of the invention includes embodiments designed for accessing blood vessels and embodiments designed for accessing airways. “The vessels of interest may include a femoral artery, femoral vein, jugular vein, peripheral veins, subclavian vein, and or other vessels or non-vessel structures. Non-limiting example applications may include aiding a medic in performing additional emergency needle insertion procedures, such as needle decompression for tension pneumothorax (collapsed lung) and needle cricothyrotomy (to provide airway access).” ([0043]; see also claims 7 and 25 and Table 1 describing embodiments for airway access).
In addition to the above, POCUS clearly teaches that the identification of the trachea in the ultrasound images is critical when performing surgical procedures for airway access. “The success of these procedures is largely dependent on accurately identifying relevant anatomical landmarks (cricothyroid membrane, tracheal space)….” (p.39, right column). It specifically describes the trachea’s appearance in ultrasound images. (see, e.g., p.40, right column, point 4; see also Figures 4.18 and 4.19 identifying the “trachea” in each image). Furthermore, POCUS teaches that advancement of the needle is stopped “when the needle is seen to penetrate the anterior tracheal wall.” (p.49, left column, Step 9 for PDT).
MA teaches that an AI model can be trained to distinguish “the boundaries of different tissues” and to, in particular, identify at least the trachea, the cricoid cartilage, and the thyroid isthmus. (Abstract and at p.6115, II.B Image Preprocessing). “Therefore, the automatic segmentation and detection of the thyroid and anatomical tissues of the neck are of vital importance in promoting the screening of diseases, providing clinicians with valuable information to make the best diagnostic decisions.” (p.6113, right column). The AI model is trained using images in which the relevant tissues were labelled by humans who determine the locations of the tissues relative to one another. (e.g., MA at II.B Image Preprocessing on p.6115). Figure 6 is shown here. The caption explains that the trained model can identify “the left lobe of the thyroid, right lobe of the thyroid, muscles, trachea, carotid, cricoid cartilage, isthmus, esophagus, jugular vein, and endothyroid vessel….” (see also pp.6119-6220). Accordingly, MA teaches identifying anatomical structures, which are relevant to airway access surgical procedures, via image segmentation. However, the AI model in MA is not employed for guiding an interventional device during a surgical procedure and confirming that insertion of the interventional device into the target airway.
HADJERCI teaches the automatic localization and segmentation of target anatomical structures and critical structures to avoid. (Abstract). Although in the specific context of regional anesthesia, HADJERCI is generally concerned with enabling real-time visualization of a needle during a procedure that involves a targeted anatomical structure and nearby anatomical structures. “A new method based on a machine learning algorithm with a multi-model classification process using a sliding window for localization, then an active contour was applied to delineate the localized regions.” (Abstract). In HADJERCI, the target structure is a nerve while the critical structures to avoid (called obstacles) are blood vessels. (p.65, left column, top paragraph). HADJERCI addresses two particular issues. First, the system must automatically identify the target structures and structures to avoid. Second, after identifying the target structures and critical structures to avoid, a path is generated “that the needle could follow to safely reach the target [structure].” (p.65, left column, top paragraph). HADJERCI teaches “an algorithm for path planning was also developed to obtain the optimal trajectory for needle insertion based on the result of the first stage (target and obstacle detection).” (Abstract). Moreover, HADJERCI teaches navigating the needle tip toward the target structure. (see, e.g., Figures 12 and 13).
It would have been obvious to one having ordinary skill in the art to modify the system of BRATTAIN ‘711 such that the processor is configured to confirm, using an AI model, insertion of the interventional device in the target airway via image segmentation of the wall of the target airway. In order to perform the procedure safely, one would be motivated to use an AI model to segment and identify the wall of the trachea, as taught by POCUS and MA, to confirm that the needle has arrived at its target destination, as taught in HADJERCI, and that the advancement of the needle can be stopped. There would have been a reasonable expectation of success as BRATTAIN ‘711 and HADJERCI teach that the system can be configured to confirm penetration for a similar embodiment.
HADJERCI MA HADJERCI
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over U.S Patent Appl. Publ. No. 2021/0045711 A1 to Brattain et al. (hereinafter “BRATTAIN ’711”) and “Ultrasound-Guided Airway Procedures” by Terkawi, Abdullah Sulieman, et al. from Chapter 4 of The Ultimate Guide to Point-of-Care Ultrasound-Guided Procedures (2020): 39-61 (hereinafter referred to as Point-of-Care Ultrasound or “POCUS”) and Ma, Laifa, et al. "A novel deep learning framework for automatic recognition of thyroid gland and tissues of neck in ultrasound image." IEEE Transactions on Circuits and Systems for Video Technology 32.9 (published “8 March 2022”; see notes below Index Terms on bottom left of first page): 6113-6124 (hereinafter “MA”) and HADJERCI et al. Assistive system based on nerve detection and needle navigation in ultrasound images for regional anesthesia. Expert Systems with Applications. 2016 Nov 1;61:64-77 (hereinafter HADJERCI) as applied to claim 1 above, and further in view of U.S Patent Appl. Publ. No. 2020/0069900 A1 to Rabin et al. (hereinafter “RABIN”).”
With respect to claim 17, neither BRATTAIN ‘711 nor POCUS explicitly teach that the processor is further caused to determine if the target airway has been penetrated by determining the presence of CO2 using a CO2 sensor. However, BRATTAIN ’711 does teach performing a “safety check” that includes “confirming the needle has penetrated the vessel of interest by the tracking and guidance.” ([0052]).
In the same field of endeavor, Rabin teaches a “percutaneous dilation tracheostomy device.” (Title and Abstract). According to Rabin, “[e]nsuring the correct placement of a PDT device is key to minimizing complications. Correct placement can be especially challenging in patients with an abnormal tracheal anatomy.” ([0012]). To confirm airway placement of the tracheostomy tube, Rabin’s device may include a side port 108 that is “compatible with end tidal CO2 detector.” ([0076]). “An end tidal CO2 detector is a monitor generally used to confirm airway placement of an endotracheal and tracheostomy tube.” (Id). “Thus, side port 108 can allow real-time confirmation of device placement within a patient and reduce the risk of a misplaced tracheostomy tube.” (Id).
It would have been obvious for one having ordinary skill in the art at the time of filing to add a CO2 sensor to the tube of BRATTAIN ’711 (e.g., couple to a side port of the tube) and to modify the instructions for the processor of BRATTAIN ’711 to determine if the target airway has been penetrated by determining the presence of CO2 using the CO2 sensor. One having ordinary skill in the art would be motivated to add the CO2 sensor and modify the instructions because, as taught in RABIN, the “key to minimizing complications” is ensuring the correct placement of the tube and using a CO2 sensor to detect the presence of CO2 can confirm that the tube was properly placed. There would be a reasonable expectation of success because, as taught in RABIN, the tubes can be modified to have a side port that is compatible with the CO2 sensor.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over U.S Patent Appl. Publ. No. 2021/0045711 A1 to Brattain et al. (hereinafter “BRATTAIN ’711”) and “Ultrasound-Guided Airway Procedures” by Terkawi, Abdullah Sulieman, et al. from Chapter 4 of The Ultimate Guide to Point-of-Care Ultrasound-Guided Procedures (2020): 39-61 (hereinafter referred to as Point-of-Care Ultrasound or “POCUS”) and Ma, Laifa, et al. "A novel deep learning framework for automatic recognition of thyroid gland and tissues of neck in ultrasound image." IEEE Transactions on Circuits and Systems for Video Technology 32.9 (published “8 March 2022”; see notes below Index Terms on bottom left of first page): 6113-6124 (hereinafter “MA”) and HADJERCI et al. Assistive system based on nerve detection and needle navigation in ultrasound images for regional anesthesia. Expert Systems with Applications. 2016 Nov 1;61:64-77 (hereinafter HADJERCI) as applied to claim 1 above, and further in view of U.S Patent Appl. Publ. No. 2020/0179631 A1 to Sarkar et al. (hereinafter “SARKAR”).
With respect to claim 18, neither BRATTAIN ‘711 nor POCUS teach that the system also includes flexible wings to provide localization for the interventional device by grasping a surface region around the anatomical landmark structure of the subject.
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However, SARKAR teaches a system for automatic emergency airway detection. (Title and Abstract). The system includes “a frame with a carriage guide configured to be secured around a neck of a subject.” (Abstract). “[T]he frame 110 is secured around the neck 195 of the subject 194 so that the carriage guide 114 aligns with a region of the neck 195 corresponding with one or more anatomical regions of the neck (e.g. sternal notch, cricothyroid membrane, cricoid cartilage, thyroid cartilage, etc.).” ([0042]). In one example, the frame “is a collar 542 that has a central frame with side rails 512, a central opening 514, and wings 516 configured to flushly fit the collar 542 to varied neck anatomies.” ([0060]; see also Figure 5A shown here). “In the illustrated embodiment, the wings 516 are flexible to accommodate, such as by curving or bending, different patients that have various neck diameters….The wings 516 may be formed of many materials to achieve the desired degree of flexibility.” (Id).
It would have been obvious to one having ordinary skill in the art at the time of filing to attach flexible wings, such as those disclosed in SARKAR, to the guide system and ultrasound probe of BRATTAIN ’711. One having ordinary skill in the art would be motivated to attach the wings to the guide system and probe because the wings would stabilize both the guide system and the ultrasound probe with respect to the neck, thereby making it easier to align the guide system and probe with the proper anatomical region of the neck. There would have been a reasonable expectation of success as SARKAR demonstrates that the wings could be part of a guide system and help align a sensor with anatomical regions of the neck.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over U.S Patent Appl. Publ. No. 2021/0045711 A1 to Brattain et al. (hereinafter “BRATTAIN ’711”) and “Ultrasound-Guided Airway Procedures” by Terkawi, Abdullah Sulieman, et al. from Chapter 4 of The Ultimate Guide to Point-of-Care Ultrasound-Guided Procedures (2020): 39-61 (hereinafter referred to as Point-of-Care Ultrasound or “POCUS”) and Ma, Laifa, et al. "A novel deep learning framework for automatic recognition of thyroid gland and tissues of neck in ultrasound image." IEEE Transactions on Circuits and Systems for Video Technology 32.9 (published “8 March 2022”; see notes below Index Terms on bottom left of first page): 6113-6124 (hereinafter “MA”) and HADJERCI et al. Assistive system based on nerve detection and needle navigation in ultrasound images for regional anesthesia. Expert Systems with Applications. 2016 Nov 1;61:64-77 (hereinafter HADJERCI) and U.S Patent Appl. Publ. No. 2020/0179631 A1 to Sarkar et al. (hereinafter “SARKAR”) as applied to claim 18 above, and further in view of “Medical Device Solutions: Thermoplastic Polyurethane (TPU)” catalog for Lubrizol LifeSciences (2019) (hereinafter “Lubrizol LifeSciences”).
With respect to claim 19 (depending from claim 18), none of BRATTAIN ‘711, POCUS, or SARKAR teaches that the wings include a material of durometer 92A. However, SARKAR does disclose that “[t]he wings 516 may be formed of many materials to achieve the desired degree of flexibility.” ([0060]).
Lubrizol LifeSciences teaches various thermoplastic polyurethane (TPU) materials for medical devices. (Cover page). “Tecothane™ and Pellethane® medical grade polymers are known for their flexibility and offer a wide range of properties for medical and healthcare products.” (p.6, Aromatic TPU for Processing and Performance). For shaping, the polymers can be molded or extruded for shaping. (Id). Example applications include “catheters and tubing,” “wound care,” and “long-term implantables.” (Id). Pellethane® aromatic polyester 5855-92A has a durometer hardness of 92A.
It would have been obvious to one having ordinary skill in the art at the time of filing to select a material of durometer 92A for the flexible wings. In designing the flexible wings, one having ordinary skill in the art would select a material having a desired flexibility and a material that is permitted to contact patients. Pellethane® aromatic polyester 5855-92A is one such material and can be used for catheters and tubing, wound care, and long-term implantables. Within the medical industry, only a finite number of such materials are permitted to contact patients. One of ordinary skill in the art could have shaped the wings using Pellethane® aromatic polyester 5855-92A with a reasonable expectation of success because Lubrizol LifeSciences teaches that the material is a medical grade polymer that can be used for catheters, wound care, or long-term implantables.
Moreover, selecting a known material based on its suitability for its intended purpose has been held to be obvious. MPEP 2144.07. See also In re Leshin, 277 F.2d 197, 125 USPQ 416 (CCPA 1960) (selection of a known plastic to make a container of a type made of plastics prior to the invention was held to be obvious).
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over U.S Patent Appl. Publ. No. 2021/0045711 A1 to Brattain et al. (hereinafter “BRATTAIN ’711”) and “Ultrasound-Guided Airway Procedures” by Terkawi, Abdullah Sulieman, et al. from Chapter 4 of The Ultimate Guide to Point-of-Care Ultrasound-Guided Procedures (2020): 39-61 (hereinafter referred to as Point-of-Care Ultrasound or “POCUS”) and Ma, Laifa, et al. "A novel deep learning framework for automatic recognition of thyroid gland and tissues of neck in ultrasound image." IEEE Transactions on Circuits and Systems for Video Technology 32.9 (published “8 March 2022”; see notes below Index Terms on bottom left of first page): 6113-6124 (hereinafter “MA”) and HADJERCI et al. Assistive system based on nerve detection and needle navigation in ultrasound images for regional anesthesia. Expert Systems with Applications. 2016 Nov 1;61:64-77 (hereinafter HADJERCI) as applied to claim 1 above, and further in view of U.S Patent Appl. Publ. No. 2016/0000646 A1 to Scherkowski (hereinafter “SCHERKOWSKI”).
With respect to claim 20, neither BRATTAIN ‘711 nor POCUS teach that the system also includes a negative pressure barrier to isolate the interventional device from a user or the subject.
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SCHERKOWSKI teaches a handheld device that rests upon a skin region to be treated. (Abstract). A suction chamber 10 surrounds an application element chamber 8 where an application element 5 (i.e., needle) resides. (see, e.g., [0068] and Figure 1 shown here). The suction chamber can “entirely” surround the chamber where the needle resides. (see, e.g., [0029] and [0088]). “By means of the pneumatic suction force [i.e., negative pressure barrier], which in operation is coupled to the suction chamber, when the applicator is in place the skin is firmly held for the puncturing means to puncture the skin.” ([0019]). Figure 14 demonstrates how the handheld device may puncture skin. Figure 15 demonstrates that the wall may comprise an elastic material to compensate for the unevenness of the skin surface. Figure 17 demonstrates that the applicator may be sufficiently small to be applied under an eye.
It would have been obvious to one of ordinary skill in the art at the time of filing to provide a negative pressure barrier to isolate the interventional device from a user or the subject. One would have been motivated to add the suction chamber, as taught by SCHERKOWSKI, to the device of BRATTAIN ’711 because the suction chamber would hold the skin during the surgical procedure, thereby isolating the needle from the user or the subject when the needle punctures the skin. There would be a reasonable expectation of success as the handheld device of SCHERKOWSKI has a similar shape as the guide injection assembly of BRATTAIN ’711.
Claims 22-24 are rejected under 35 U.S.C. 103 as being unpatentable over U.S Patent Appl. Publ. No. 2021/0045711 A1 to Brattain et al. (hereinafter “BRATTAIN ’711”) and “Ultrasound-Guided Airway Procedures” by Terkawi, Abdullah Sulieman, et al. from Chapter 4 of The Ultimate Guide to Point-of-Care Ultrasound-Guided Procedures (2020): 39-61 (hereinafter referred to as Point-of-Care Ultrasound or “POCUS”) and Ma, Laifa, et al. "A novel deep learning framework for automatic recognition of thyroid gland and tissues of neck in ultrasound image." IEEE Transactions on Circuits and Systems for Video Technology 32.9 (published “8 March 2022”; see notes below Index Terms on bottom left of first page): 6113-6124 (hereinafter “MA”) and HADJERCI et al. Assistive system based on nerve detection and needle navigation in ultrasound images for regional anesthesia. Expert Systems with Applications. 2016 Nov 1;61:64-77 (hereinafter HADJERCI) as applied to claim 1 above, and further in view of U.S Patent Appl. Publ. No. 2021/0177373 A1 to Xie et al. (hereinafter “XIE”).
With respect to claim 22, neither BRATTAIN ‘711 nor POCUS teach that the Al model further outputs one or more bounding boxes identifying the anatomical landmark structures in the image data.
However, BRATTAIN ‘711 does describe that the device may be “ultrasound guided” and “may employ machine learning or artificial intelligence for identifying a target structure for penetration and guiding penetration of the target structure.” ([0075]). “Machine learning routines may be trained with data from multiple time periods with differences in anatomy being reflected over the different periods of time. With a temporally trained machine learning routine, vessel segmentation may be performed in a robust manner over time for a subject without misclassification and without a need to find a specific time frame or a specific probe position to identify vessels of interest.” ([0049]).
XIE teaches an ultrasound system with an artificial neural network for guided imaging. (Title and Abstract). Embodiments of XIE may “reduce operator dependence and thus improve measurement reliability” and generally enhance ultrasound systems and techniques. ([0026]).“For training a neural network according to the present disclosure, any suitable architecture, such as a VGGNet-like or ResNet-like architecture, may be used and the “blank slate” network may be trained from scratch (i.e., without any preconfiguration of the weights).” ([0054]). In one example, XIE discusses implementing a “YOLO (you only look once” network for object detection that outputs bounding boxes around the objects. “For example as shown in FIG. 11, an object detection network may be trained to recognize, in preferred examples in real-time (e.g., as each frame is received), the presence and location of any number of categories of object, for example kidney (e.g., bounding box 1110), vessel (e.g., bounding boxes 1112-1, 1112-2, and 1112-3), and other categories or classes of objects (e.g., bounding box 1114) which may be present in an image of the target anatomy.” ([0063]). It is noted that XIE teaches that the object detection network may be trained to recognize any number of categories of objects.
It would have been obvious to one of ordinary skill in the art at the time of filing to modify the processor of BRATTAIN ’711 so that the Al model outputs one or more bounding boxes identifying the anatomical landmark structures and the one or more landmarks to avoid in the image data. One would have been motivated to modify BRATTAIN ’711 to implement a YOLO network, as taught in XIE, because the YOLO network improves the workflow by automatically detecting and identifying objects with bounding boxes for the user. There would be a reasonable expectation of success because, as demonstrated by XIE, artificial intelligence models are capable of identifying objects of interest in ultrasound image data.
With respect to claim 23 (depending from claim 22), neither BRATTAIN ‘711 nor POCUS teach that the Al model is trained using a You-only-look-once (YOLO) deep learning network for outputting the one or more bounding boxes.
However, as discussed above with respect to claim 22, in one example, XIE discusses implementing a “YOLO (you only look once” network for object detection that outputs bounding boxes around the objects. “For example as shown in FIG. 11, an object detection network may be trained to recognize, in preferred examples in real-time (e.g., as each frame is received), the presence and location of any number of categories of object, for example kidney (e.g., bounding box 1110), vessel (e.g., bounding boxes 1112-1, 1112-2, and 1112-3), and other categories or classes of objects (e.g., bounding box 1114) which may be present in an image of the target anatomy.” ([0063]).
It would have been obvious to one of ordinary skill in the art at the time of filing to provide an Al model that is trained using a You-only-look-once (YOLO) deep learning network for outputting the one or more bounding boxes. One would have been motivated to modify BRATTAIN ’711 to implement the YOLO network, as taught in XIE, which improves the workflow by automatically detecting and identifying the desired objects with bounding boxes for the user. There would be a reasonable expectation of success because, as demonstrated by XIE, artificial intelligence models are capable of identifying objects of interest in ultrasound image data.
With respect to claim 24, neither BRATTAIN nor POCUS teach that the Al model is trained using a pretrained ResNet deep learning network.
However, XIE teaches embodiments that use artificial intelligence for analyzing ultrasound image data, which may “reduce operator dependence and thus improve measurement reliability” and generally enhance ultrasound systems and techniques. ([0026]). In one example, XIE teaches “[f]or training a neural network according to the present disclosure, any suitable architecture, such as a VGGNet-like or ResNet-like architecture, may be used and the “blank slate” network may be trained from scratch (i.e., without any preconfiguration of the weights). As shown in FIG. 7, to reduce the amount of training data needed, a pre-trained network, such as the Inception V3 (as shown in FIG. 7) or another network may be used as a starting point. The pre-trained network 710 may then be fine-tuned specifically for classifying medical image data with a much smaller training data set of medical images (e.g., ultrasound images 712) of the particular clinical application, in this case liver imaging. Fine-tuning may be performed, for example by feeding the penultimate layer of the Inception V3 network into a new output classifier 714 (e.g., comprised of fully connected layers) and configured to produce the desired classification (e.g., match/no match), or by providing the feature vector output by the Inception V3 network into a new classifier, which requires less training data.” ([0054]).
It would have been obvious to one of ordinary skill in the art at the time of filing to provide an Al model that is trained using a pretrained ResNet deep learning network. One would have been motivated to modify the BRATTAIN ’711 system to train the AI model using a pretrained ResNet deep learning network because, as taught in XIE, a pretrained network requires a much smaller training data set of medical images ([0054]). Reducing the amount of training data would reduce the time necessary to train the AI model. There would be a reasonable expectation of success because, as demonstrated by XIE, AI models can be trained using pretrained ResNet deep learning networks.
RESPONSE TO APPLICANT’S ARGUMENTS
Applicant’s arguments with respect to the Section 103 rejections based on BRATTAIN ’711 and POCUS 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.
More specifically, Applicant argues that the prior art does not teach a specially trained AI model. “This distinction between the instant AI model and the machine learning model of Brattain is significant. The instant claims are directed to an AI model specially trained to identify neck- and/or airway-specific anatomical targets and critical structures. A general AI model, such as the machine learning model in Brattain, receiving the imaging data of a subject's neck would not be able to perform the claimed functions of identifying both the targets and landmarks to avoid, determining their locations, and determining an insertion point for an interventional device based on their locations.” (p.10 of Response).
As explained above, however, it would have been obvious to one having ordinary skill in the art at the time of filing to pretrain the AI model using image data that shows the target structure (trachea) and the structures to avoid (e.g., thyroid), as taught in MA, in order to plan a path to the trachea, as taught in HADJERCI. One of ordinary skill in the art would have been motivated to train the AI model to identify the target and critical structures in order to quickly and reliably develop a path for performing the surgical procedure. There would have been a reasonable expectation of success as MA and HADJERCI teach that the structures can be identified to develop a surgical path.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claim 1 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Patent No. 12,193,872 (the ’872 Patent) in view of “Ultrasound-Guided Airway Procedures” by Terkawi, Abdullah Sulieman, et al. from Chapter 4 of The Ultimate Guide to Point-of-Care Ultrasound-Guided Procedures (2020): 39-61 (hereinafter referred to as Point-of-Care Ultrasound or “POCUS”) and Ma, Laifa, et al. "A novel deep learning framework for automatic recognition of thyroid gland and tissues of neck in ultrasound image." IEEE Transactions on Circuits and Systems for Video Technology 32.9 (published “8 March 2022”; see notes below Index Terms on bottom left of first page): 6113-6124 (hereinafter “MA”) and HADJERCI et al. Assistive system based on nerve detection and needle navigation in ultrasound images for regional anesthesia. Expert Systems with Applications. 2016 Nov 1;61:64-77 (hereinafter HADJERCI).
Examined Claim 1 vs. Claim 1 of the ’872 Patent (italicized)
1. A system for guiding an interventional device in an interventional procedure of a subject, comprising:
an ultrasound probe;
a guide system coupled to the ultrasound probe (claim 1: identical) and configured to guide the interventional device into a field of view (FOV) of the ultrasound probe (claim 1: motors that are “actuatable to move the interventional device into a field of view (FOV) of the ultrasound probe”);
a non-transitory memory having instructions stored thereon; (claim 1: identical)
a processor configured to access the non-transitory memory and execute the instructions, (claim 1: identical) wherein the processor is caused to:
access image data acquired from the subject using the ultrasound probe, wherein the image data include at least one image of an anatomical landmark structure of the subject (claim 1: nearly identical, except claim 1 recites “target structure” instead of “anatomical landmark structure”; there is no meaningful difference here);
input the image data acquired from the subject into an artificial intelligence (Al) model to identify the anatomical landmark structures in the image data, wherein the AI model is pretrained on training image data including the anatomical landmark structure and wherein the anatomical landmark structure include at least one of tracheal rings, thyroid cartilage, or cricoid cartilage and one or more landmarks to avoid includes at least a thyroid gland;
determine, from the image data and the anatomical landmark structure, a location of a target airway and a location of one or more landmarks to avoid within the subject (claim 1: nearly identical);
determine an insertion point location for the interventional device based upon the location of the target airway (claim 1: identical except for “target structure” instead of “target airway.”) and the location of the one or more landmarks to avoid and guide placement of the ultrasound probe to position the guide system at the insertion point location (claim 1: “communicate a guidance signal to a user, the guidance signal guiding the user in gross placement and precise placement of the interventional device relative to at least one of the insertion point location or the location of the target structure”); and
track the interventional device from the insertion point location to the target airway (claim 1: “track the interventional device as the interventional device extends into the subject from the insertion point location to the target structure”).
Examined claim 1 differs from patent claim 1 in that patent claim 1 does not recite wherein the anatomical landmark structure includes at least one of tracheal rings, thyroid cartilage, cricoid cartilage and does not recite that a location of one or more landmarks to avoid within the subject is determined, wherein the one or more landmarks to avoid includes at least a thyroid gland.
However, POCUS describes various upper airway surgical procedures that can be performed using guided ultrasound. (see, e.g., p.39, Title and Introduction). “Percutaneous dilation tracheostomy (PDT) is a commonly performed bedside surgical procedure.” (p.44, Procedures: Percutaneous Dilation Tracheotomy (PDG)). “However, complications such as perforation of the posterior wall of the trachea, puncture of the esophagus, tracheal ring injury, and vocal cord injury can still occur. Ultrasound can help avoid some of the aforementioned complications, improve safety, and increase the success rate of the procedure especially in the challenging patient.” (pp.44-45, Id.). Notably, in one study, “[u]ltrasound guidance was associated with significantly higher first needle pass success rate and more accurate tracheal puncture site placement. Fewer complications were observed in the ultrasound group [16].” (p.47, Id.).
POCUS also describes identifying anatomical landmark structures when imaging the patient prior to PDT surgery. For example, in step 7 of the preprocedural evaluation, POCUS instructs the operator how to obtain a sagittal/longitudinal view of the relevant anatomy. “The resultant image is described as ‘pearls on a string’ and represents the hypoechoic cricoid cartilage and individual tracheal rings anterior to the hyperechoic air-mucosa interface (Fig. 4.15).” (p.48, Preparation/Preprocedural Evaluation). For the surgical procedure, in step 7, POCUS instructs the surgeon to “[s]elect optimum puncture level as discussed mentioned [sic] above, ideally between the first and fourth tracheal rings, avoiding vessels or a vascular isthmus in the path of the needle (Figs. 4.20).” (emphasis added) (p.49, Id).
It would have been obvious for one having ordinary skill in the art to use the system of patent claim 1 in which the target structure was an airway (trachea), the anatomical landmark structures are tracheal rings, and the landmark to avoid is the thyroid gland. In particular, one having ordinary skill in the art would configure the processor to access image data and then input the image data into an AI model that determines a location of the airway and a location of one or more landmarks to avoid (i.e., thyroid isthmus). The AI model determines these locations relative to other anatomical structures, i.e., tracheal rings, thyroid cartilage, cricoid cartilage, and isthmus. One having ordinary skill in the art would be motivated to configure the processor to access such image data for ultrasound-guided PDT and input the image data into an AI model, because ultrasound-guided PDT “can help avoid some…complications, improve safety, and increase the success rate of the procedure especially in the challenging patient.” (pp.44-45, Id.). Moreover, at least for some surgical procedures, the optimum puncture level is “ideally between the first and fourth tracheal rings.” (p.49, Id). There would be a reasonable expectation of success because, as taught by POCUS, ultrasound-guided PDTs have already been safely performed.
Examined claim 1 differs from patent claim 1 in that patent claim 1 does not recite inputting the image data acquired from the subject into an artificial intelligence (Al) model to identify the anatomical landmark structures in the image data, wherein the AI model is pretrained on training image data including the anatomical landmark structure and wherein the anatomical landmark structure include at least one of tracheal rings, thyroid cartilage, or cricoid cartilage and one or more landmarks to avoid includes at least a thyroid gland and determining the location of the one or more landmarks to avoid and the insertion point location based on the location of the one or more landmarks to avoid.
MA teaches that an AI model can be trained to distinguish “the boundaries of different tissues” and to, in particular, identify at least the trachea, the cricoid cartilage, and the thyroid isthmus. (Abstract and at p.6115, II.B Image Preprocessing). “Therefore, the automatic segmentation and detection of the thyroid and anatomical tissues of the neck are of vital importance in promoting the screening of diseases, providing clinicians with valuable information to make the best diagnostic decisions.” (p.6113, right column). The AI model is trained using images in which the relevant tissues were labelled by humans who determine the locations of the tissues relative to one another. (e.g., MA at II.B Image Preprocessing on p.6115). Figure 6 is shown here. The caption explains that the trained model can identify “the left lobe of the thyroid, right lobe of the thyroid, muscles, trachea, carotid, cricoid cartilage, isthmus, esophagus, jugular vein, and endothyroid vessel….” (see also pp.6119-6220). Accordingly, MA teaches a trained AI model that can automatically segment and detect relevant anatomical structures of the next, including the trachea and the thyroid. However, the AI model in MA is not employed for guiding an interventional device during a surgical procedure.
HADJERCI teaches the automatic localization and segmentation of target anatomical structures and critical structures to avoid. (Abstract). Although in the specific context of regional anesthesia, HADJERCI is generally concerned with enabling real-time visualization of a needle during a procedure that involves a targeted anatomical structure and nearby anatomical structures. “A new method based on a machine learning algorithm with a multi-model classification process using a sliding window for localization, then an active contour was applied to delineate the localized regions.” (Abstract). In HADJERCI, the target structure is a nerve while the critical structures to avoid (called obstacles) are blood vessels. (p.65, left column, top paragraph). HADJERCI addresses two particular issues. First, the system must automatically identify the target structures and structures to avoid. Second, after identifying the target structures and critical structures to avoid, a path is generated “that the needle could follow to safely reach the target [structure].” (p.65, left column, top paragraph). HADJERCI teaches “an algorithm for path planning was also developed to obtain the optimal trajectory for needle insertion based on the result of the first stage (target and obstacle detection).” (Abstract). Moreover, HADJERCI teaches navigating the needle tip toward the target structure. (see, e.g., Figures 12 and 13).
It would have been obvious to modify the system of patent claim 1 to input the image data acquired from the subject into an artificial intelligence (Al) model to identify the anatomical landmark structures in the image data as taught in MA and HADJERCI. In order to perform the procedure safely, one would be motivated to use an AI model by inputting image data into the AI model to segment and identify the wall of the trachea, as taught by POCUS and MA, to confirm that the needle has arrived at its target destination, as taught in HADJERCI. There would have been a reasonable expectation of success as BRATTAIN ‘711 and HADJERCI teach that the system can be configured to confirm penetration for a similar embodiment.
Claim 3 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 2 of U.S. Patent No. 12,193,872 (the ’872 Patent) in view of “Ultrasound-Guided Airway Procedures” by Terkawi, Abdullah Sulieman, et al. from Chapter 4 of The Ultimate Guide to Point-of-Care Ultrasound-Guided Procedures (2020): 39-61 (hereinafter referred to as Point-of-Care Ultrasound or “POCUS”) and Ma, Laifa, et al. "A novel deep learning framework for automatic recognition of thyroid gland and tissues of neck in ultrasound image." IEEE Transactions on Circuits and Systems for Video Technology 32.9 (published “8 March 2022”; see notes below Index Terms on bottom left of first page): 6113-6124 (hereinafter “MA”) and HADJERCI et al. Assistive system based on nerve detection and needle navigation in ultrasound images for regional anesthesia. Expert Systems with Applications. 2016 Nov 1;61:64-77 (hereinafter HADJERCI).
Patent claim 2 recites “wherein the processor is further caused to determine at least one of…an insertion distance from the insertion point location to the target structure” and examined claim 3 recites “wherein the processor is further caused to determine at least one…an insertion distance from the insertion point location to the target airway based upon the anatomical landmark structure.”
However, when comparing examined claim 3 with patent claim 2, the scope of patent claim 2 does not necessarily include the “target structure” being an airway because patent claim 2 does not depend from patent claim 5. Nevertheless, as taught by POCUS, (see discussion above with respect to examined claim 2), it would be obvious for the target structure to be an airway and to be identified based on anatomical landmark structures (e.g., tracheal rings).
It would have been obvious for one having ordinary skill in the art to modify the instructions for the processor of patent claim 1 to determine the insertion distance from the insertion point location to the target airway would be based upon the anatomical landmark structure. In particular, one having ordinary skill in the art would modify the instructions for the processor of patent claim 1 to access image data that not only included the airway but also anatomical landmark structures (e.g., tracheal rings) and then to determine a location of the airway based on those anatomical landmark structures. If one goal is to avoid injuring the tracheal rings (i.e., the anatomical landmark structures), as taught in POCUS (pp.44), then the insertion distance would necessarily be based on the tracheal rings because the path chosen is one that is designed to avoid injuring the tracheal rings. One having ordinary skill in the art would be motivated to modify the instructions for the processor of patent claim 1 as such because “[u]ltrasound can help avoid some…complications, improve safety, and increase the success rate of the procedure especially in the challenging patient.” There would be a reasonable expectation of success because, as taught by POCUS, ultrasound-guided PDTs have already been safely performed and the relevant anatomy can be identified through ultrasound image data (see, e.g., p.53, Figure 4.24 showing the tracheal rings).
Claim 4 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 3 of U.S. Patent No. 12,193,872 (the ’872 Patent) in view of “Ultrasound-Guided Airway Procedures” by Terkawi, Abdullah Sulieman, et al. from Chapter 4 of The Ultimate Guide to Point-of-Care Ultrasound-Guided Procedures (2020): 39-61 (hereinafter referred to as Point-of-Care Ultrasound or “POCUS”) and Ma, Laifa, et al. "A novel deep learning framework for automatic recognition of thyroid gland and tissues of neck in ultrasound image." IEEE Transactions on Circuits and Systems for Video Technology 32.9 (published “8 March 2022”; see notes below Index Terms on bottom left of first page): 6113-6124 (hereinafter “MA”) and HADJERCI et al. Assistive system based on nerve detection and needle navigation in ultrasound images for regional anesthesia. Expert Systems with Applications. 2016 Nov 1;61:64-77 (hereinafter HADJERCI).
Patent claim 3 (depending from patent claim 2) recites “further comprising a display system and wherein the processor is further caused to communicate the guidance signal to the display system to show at least one…the insertion point location, the location of the target structure, or the insertion distance.” and examined claim 4 (depending from examined claim 3) recites “further comprising a display system and wherein the processor is further configured to cause the display system to show at least one of…the insertion point location, the location of the target airway, the insertion distance….”
However, when comparing examined claim 4 with patent claim 3, the scope of patent claim 3 does not necessarily include the “target structure” being an airway because patent claim 3 does not depend from patent claim 5. Nevertheless, as taught by POCUS, (see discussion above with respect to examined claim 2), it would be obvious for the target structure to be an airway and to be identified based on anatomical landmark structures (e.g., tracheal rings).
It would have been obvious for one having ordinary skill in the art to use the system of patent claim 1 in a manner that the target structure was an airway (trachea). In particular, one having ordinary skill in the art would modify the instructions for the processor of patent claim 1 to access image data that not only included the airway but also anatomical landmark structures (e.g., tracheal rings) and then to determine a location of the airway based on those anatomical landmark structures. One having ordinary skill in the art would be motivated to modify the instructions for the processor of patent claim 1 as such because “[u]ltrasound can help avoid some…complications, improve safety, and increase the success rate of the procedure especially in the challenging patient.” There would be a reasonable expectation of success because, as taught by POCUS, ultrasound-guided PDTs have already been safely performed.
Claim 5 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 4 of U.S. Patent No. 12,193,872 (the ’872 Patent) in view of “Ultrasound-Guided Airway Procedures” by Terkawi, Abdullah Sulieman, et al. from Chapter 4 of The Ultimate Guide to Point-of-Care Ultrasound-Guided Procedures (2020): 39-61 (hereinafter referred to as Point-of-Care Ultrasound or “POCUS”) and Ma, Laifa, et al. "A novel deep learning framework for automatic recognition of thyroid gland and tissues of neck in ultrasound image." IEEE Transactions on Circuits and Systems for Video Technology 32.9 (published “8 March 2022”; see notes below Index Terms on bottom left of first page): 6113-6124 (hereinafter “MA”) and HADJERCI et al. Assistive system based on nerve detection and needle navigation in ultrasound images for regional anesthesia. Expert Systems with Applications. 2016 Nov 1;61:64-77 (hereinafter HADJERCI).
Examined claim 5 recites “wherein the processor is further caused to track the interventional device from the insertion point location to the target airway and provide real-time feedback to a user based on tracking the interventional device.” Patent claim 1 recites “track the interventional device as the interventional device extends into the subject from the insertion point location to the target structure” and patent claim 4 recites “wherein the processor is further caused to provide the guidance signal as real-time feedback to the user based on tracking the interventional device.”
However, when comparing examined claim 5 with patent claim 4, the scope of patent claim 4 does not necessarily include the “target structure” being an airway because patent claim 4 does not depend from patent claim 5. Nevertheless, as taught by POCUS, (see discussion above with respect to examined claim 2), it would be obvious for the target structure to be an airway and to be identified based on anatomical landmark structures (e.g., tracheal rings). When the target structure is an airway, patent claim 4 is a species or sub-genus of claim 5.
It would have been obvious for one having ordinary skill in the art to use the system of patent claim 4 in which the target structure was an airway (trachea). In particular, one having ordinary skill in the art would modify the instructions for the processor of patent claim 4 to access image data that not only included the airway but also anatomical landmark structures (e.g., tracheal rings) and then to determine a location of the airway based on those anatomical landmark structures. One having ordinary skill in the art would be motivated to modify the instructions for the processor of patent claim 1 because “[u]ltrasound can help avoid some…complications, improve safety, and increase the success rate of the procedure especially in the challenging patient.” There would be a reasonable expectation of success because, as taught by POCUS, ultrasound-guided PDTs have already been safely performed.
Claims 6-9 rejected on the ground of nonstatutory double patenting as being unpatentable over claim 6 of U.S. Patent No. 12,193,872 (the ’872 Patent) in view of “Ultrasound-Guided Airway Procedures” by Terkawi, Abdullah Sulieman, et al. from Chapter 4 of The Ultimate Guide to Point-of-Care Ultrasound-Guided Procedures (2020): 39-61 (hereinafter referred to as Point-of-Care Ultrasound or “POCUS”) and Ma, Laifa, et al. "A novel deep learning framework for automatic recognition of thyroid gland and tissues of neck in ultrasound image." IEEE Transactions on Circuits and Systems for Video Technology 32.9 (published “8 March 2022”; see notes below Index Terms on bottom left of first page): 6113-6124 (hereinafter “MA”) and HADJERCI et al. Assistive system based on nerve detection and needle navigation in ultrasound images for regional anesthesia. Expert Systems with Applications. 2016 Nov 1;61:64-77 (hereinafter HADJERCI).
Patent claim 6 and examined claim 6 are essentially identical, except the plurality of images is of the target structure in patent claim 6 but of the anatomical landmark structure in examined claim 6. Nevertheless, when the target structure is an airway, at least some of the images will include the tracheal rings (i.e., anatomical landmark structures) because during a PDT the interventional device is intended to go between two tracheal rings before reaching the airway.
However, when comparing examined claim 6 with patent claim 6, the scope of patent claim 6 does not necessarily include the “target structure” being an airway because patent claim 6 does not depend from patent claim 5. Nevertheless, as taught by POCUS, (see discussion above with respect to examined claim 2), it would be obvious for the target structure to be an airway and to be identified based on anatomical landmark structures (e.g., tracheal rings).
It would have been obvious for one having ordinary skill in the art to use the system of patent claim 1 in which the target structure was an airway (trachea). In particular, one having ordinary skill in the art would modify the instructions for the processor of patent claim 1 to access image data that not only included the airway but also anatomical landmark structures (e.g., tracheal rings) and then to determine a location of the airway based on those anatomical landmark structures. One having ordinary skill in the art would be motivated to modify the instructions for the processor of patent claim 1because “[u]ltrasound can help avoid some…complications, improve safety, and increase the success rate of the procedure especially in the challenging patient.” There would be a reasonable expectation of success because, as taught by POCUS, ultrasound-guided PDTs have already been safely performed.
With respect to examined claim 7, patent claim 6 does not recite wherein the plurality of images includes a plurality of views of the target airway, and wherein the processor is configured to assess the plurality of images of the anatomical landmark structure and the plurality of views of the target airway to identify a location on the subject where the interventional device reaches the target airway from the insertion point location without penetrating a landmark to avoid in the subject.
Nevertheless, POCUS teaches using different views to assess the relevant anatomy. (see, e.g., p. 48, step 7 of the pre-procedure obtaining a “sagittal/longitudinal view” and, p.49, step 6 of the procedure obtaining a “transverse view” of the trachea). While POCUS does not teach or suggest a processor assessing the different views to identify a location where the interventional device reaches the target airway from the insertion point location without penetrating a landmark to avoid in the subject, POCUS not only teaches the importance of avoiding such structures but also that ultrasound can help identify the safest path. (see, e.g., pp.44-45, a tracheal ring injury is one complication that “[u]ltrasound can help avoid….”).
It would have been obvious for one having ordinary skill in the art to use the system of patent claim 6 in which the target structure was an airway (trachea) and would have been obvious to modify the instructions for the processor to use different views to identify a location on the subject where the interventional device reaches the target airway from the insertion point location without penetrating a landmark to avoid in the subject. One having ordinary skill in the art would be motivated to modify the instructions for the processor of patent claim 6 because “[u]ltrasound can help avoid some…complications, improve safety, and increase the success rate of the procedure especially in the challenging patient.” There would be a reasonable expectation of success because, as taught by POCUS, ultrasound-guided PDTs have already been safely performed and the relevant anatomy can be identified through ultrasound image data (see, e.g., p.53, Figure 4.24 showing the tracheal rings).
With respect to claim 8 (depending from claim 7), patent claim 6 does not recite that the landmark to avoid includes at least one of a bone, an unintended blood vessel, a non-target organ, or a nerve.
Nevertheless, POCUS teaches avoiding vessels in the path of the needle (see, e.g., p.48, Figure 4.19 in which the images “depict a number of ‘risk’ vessels…” and p.49, step 7 of the procedure, teaching to select an optimum puncture level “avoiding vessels or vascular isthmus in the path of the needle.”)
It would have been obvious for one having ordinary skill in the art to use the system of patent claim 6 in which the target structure was an airway (trachea) and would have been obvious to modify the instructions for the processor to use different views to identify path of the needle without penetrating vessels of the subject. One having ordinary skill in the art would be motivated to modify the instructions for the processor of patent claim 6 because “[u]ltrasound can help avoid some…complications, improve safety, and increase the success rate of the procedure especially in the challenging patient.” There would be a reasonable expectation of success because, as taught by POCUS, ultrasound-guided PDTs have already been safely performed and the relevant anatomy can be identified through ultrasound image data.
With respect to claim 9 (depending from claim 7), patent claim 6 does not recite wherein the plurality of images includes images at a plurality of different timeframes.
Nevertheless, POCUS teaches using images at different timeframes. (see, e.g., p. 48, step 7 of the pre-procedure obtaining a “sagittal/longitudinal view” and, p.49, step 6 of the subsequent PDT procedure obtaining a “transverse view” of the trachea).
It would have been obvious for one having ordinary skill in the art to use the system of patent claim 6 in which the target structure was an airway (trachea) and would have been obvious to modify the instructions for the processor to use different views from different timeframes. One having ordinary skill in the art would be motivated to use ultrasound imaging before the procedure (to assess the anatomy and identify a path for the needle) and throughout the procedure to ensure that the needle is taking the designated path to the target airway and avoiding certain structures. There would be a reasonable expectation of success because, as taught by POCUS, ultrasound-guided PDTs have already been safely performed and the relevant anatomy can be identified through ultrasound image data.
Claim 10 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 7 of U.S. Patent No. 12,193,872 (the ’872 Patent) in view of “Ultrasound-Guided Airway Procedures” by Terkawi, Abdullah Sulieman, et al. from Chapter 4 of The Ultimate Guide to Point-of-Care Ultrasound-Guided Procedures (2020): 39-61 (hereinafter referred to as Point-of-Care Ultrasound or “POCUS”) and Ma, Laifa, et al. "A novel deep learning framework for automatic recognition of thyroid gland and tissues of neck in ultrasound image." IEEE Transactions on Circuits and Systems for Video Technology 32.9 (published “8 March 2022”; see notes below Index Terms on bottom left of first page): 6113-6124 (hereinafter “MA”) and HADJERCI et al. Assistive system based on nerve detection and needle navigation in ultrasound images for regional anesthesia. Expert Systems with Applications. 2016 Nov 1;61:64-77 (hereinafter HADJERCI).
Examined claim 10 and patent claim 7 appear nearly identical in scope. There is no meaningful difference between a “removable” cartridge coupled to a base and one that is “releasably securable” to the base. However, the scope of patent claim 7 does not necessarily include the “target structure” being an airway because patent claim 7 does not depend from patent claim 5. Nevertheless, as taught by POCUS, (see discussion above with respect to examined claim 2), it would be obvious for the target structure to be an airway and to be identified based on anatomical landmark structures (e.g., tracheal rings).
It would have been obvious for one having ordinary skill in the art to use the system of patent claim 1 in which the target structure was an airway (trachea). In particular, one having ordinary skill in the art would use the system of claim 1 to access image data that not only included the airway but also anatomical landmark structures (e.g., tracheal rings) and then to determine a location of the airway based on those anatomical landmark structures. One having ordinary skill in the art would be motivated to use the ultrasound guidance system of patent claim 1 because “[u]ltrasound can help avoid some…complications, improve safety, and increase the success rate of the procedure especially in the challenging patient.” There would be a reasonable expectation of success because, as taught by POCUS, ultrasound-guided PDTs have already been safely performed.
Claims 11 and 12 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 8 of U.S. Patent No. 12,193,872 (the ’872 Patent) in view of “Ultrasound-Guided Airway Procedures” by Terkawi, Abdullah Sulieman, et al. from Chapter 4 of The Ultimate Guide to Point-of-Care Ultrasound-Guided Procedures (2020): 39-61 (hereinafter referred to as Point-of-Care Ultrasound or “POCUS”) and Ma, Laifa, et al. "A novel deep learning framework for automatic recognition of thyroid gland and tissues of neck in ultrasound image." IEEE Transactions on Circuits and Systems for Video Technology 32.9 (published “8 March 2022”; see notes below Index Terms on bottom left of first page): 6113-6124 (hereinafter “MA”) and HADJERCI et al. Assistive system based on nerve detection and needle navigation in ultrasound images for regional anesthesia. Expert Systems with Applications. 2016 Nov 1;61:64-77 (hereinafter HADJERCI).
When comparing examined claim 11 with patent claim 8, patent claim 8 appears to be narrower in scope than examined claim 11. However, the scope of patent claim 8 does not necessarily include the “target structure” being an airway because patent claim 8 does not depend from patent claim 5. Nevertheless, as taught by POCUS, (see discussion above with respect to examined claim 2), it would be obvious for the target structure to be an airway and to be identified based on anatomical landmark structures (e.g., tracheal rings).
It would have been obvious for one having ordinary skill in the art to use the system of patent claim 1 in which the target structure was an airway (trachea). In particular, one having ordinary skill in the art would use the system of patent claim 8 to access image data that not only included the airway but also anatomical landmark structures (e.g., tracheal rings) and then to determine a location of the airway based on those anatomical landmark structures. One having ordinary skill in the art would be motivated to use the ultrasound guidance system of patent claim 8 because “[u]ltrasound can help avoid some…complications, improve safety, and increase the success rate of the procedure especially in the challenging patient.” There would be a reasonable expectation of success because, as taught by POCUS, ultrasound-guided PDTs have already been safely performed.
With respect to claim 12, patent claim 8 does not recite wherein the interventional device is configured to perform at least one of cricothyrotomy or tracheotomy.
Nevertheless, POCUS describes various upper airway surgical procedures that can be performed using guided ultrasound. (see, e.g., p.39, Title and Introduction). “Percutaneous dilation tracheostomy (PDT) is a commonly performed bedside surgical procedure.” (p.44, Procedures: Percutaneous Dilation Tracheotomy (PDG)). “However, complications such as perforation of the posterior wall of the trachea, puncture of the esophagus, tracheal ring injury, and vocal cord injury can still occur. Ultrasound can help avoid some of the aforementioned complications, improve safety, and increase the success rate of the procedure especially in the challenging patient.” (pp.44-45, Id.). Notably, in one study, “[u]ltrasound guidance was associated with significantly higher first needle pass success rate and more accurate tracheal puncture site placement. Fewer complications were observed in the ultrasound group [16].” (p.47, Id.).
It would have been obvious for one having ordinary skill in the art to use the system of patent claim 8 in which the target structure was an airway (trachea) and to perform a tracheotomy. For a tracheotomy, one having ordinary skill in the art would modify the instructions for the processor of patent claim 8 to access image data that not only included the airway but also anatomical landmark structures (e.g., tracheal rings) and then to determine a location of the airway based on those anatomical landmark structures. One having ordinary skill in the art would be motivated to use the ultrasound guidance system of patent claim 8 to perform a tracheotomy because “[u]ltrasound can help avoid some…complications, improve safety, and increase the success rate of the procedure especially in the challenging patient.” There would be a reasonable expectation of success because, as taught by POCUS, ultrasound-guided PDTs have already been safely performed.
Claim 16 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 12 of U.S. Patent No. 12,193,872 (the ’872 Patent) in view of “Ultrasound-Guided Airway Procedures” by Terkawi, Abdullah Sulieman, et al. from Chapter 4 of The Ultimate Guide to Point-of-Care Ultrasound-Guided Procedures (2020): 39-61 (hereinafter referred to as Point-of-Care Ultrasound or “POCUS”) and Ma, Laifa, et al. "A novel deep learning framework for automatic recognition of thyroid gland and tissues of neck in ultrasound image." IEEE Transactions on Circuits and Systems for Video Technology 32.9 (published “8 March 2022”; see notes below Index Terms on bottom left of first page): 6113-6124 (hereinafter “MA”) and HADJERCI et al. Assistive system based on nerve detection and needle navigation in ultrasound images for regional anesthesia. Expert Systems with Applications. 2016 Nov 1;61:64-77 (hereinafter HADJERCI).
When comparing examined claim 16 with patent claim 12, patent claim 12 appears to be identical in scope than examined claim 16. However, the scope of patent claim 12 does not necessarily include the “target structure” being an airway because patent claim 12 does not depend from patent claim 5. Nevertheless, as taught by POCUS, (see discussion above with respect to examined claim 2), it would be obvious for the target structure to be an airway and to be identified based on anatomical landmark structures (e.g., tracheal rings).
It would have been obvious for one having ordinary skill in the art to use the system of patent claim 12 in which the target structure was an airway (trachea). In particular, one having ordinary skill in the art would modify the instructions of the processor of claim 12 to access image data that not only included the airway but also anatomical landmark structures (e.g., tracheal rings) and then to determine a location of the airway based on those anatomical landmark structures. One having ordinary skill in the art would be motivated to modify the processor of patent claim 12 because “[u]ltrasound can help avoid some…complications, improve safety, and increase the success rate of the procedure especially in the challenging patient.” There would be a reasonable expectation of success because, as taught by POCUS, ultrasound-guided PDTs have already been safely performed.
Claim 17 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Patent No. 12,193,872 (the ’872 Patent) in view of “Ultrasound-Guided Airway Procedures” by Terkawi, Abdullah Sulieman, et al. from Chapter 4 of The Ultimate Guide to Point-of-Care Ultrasound-Guided Procedures (2020): 39-61 (hereinafter referred to as Point-of-Care Ultrasound or “POCUS”) and Ma, Laifa, et al. "A novel deep learning framework for automatic recognition of thyroid gland and tissues of neck in ultrasound image." IEEE Transactions on Circuits and Systems for Video Technology 32.9 (published “8 March 2022”; see notes below Index Terms on bottom left of first page): 6113-6124 (hereinafter “MA”) and HADJERCI et al. Assistive system based on nerve detection and needle navigation in ultrasound images for regional anesthesia. Expert Systems with Applications. 2016 Nov 1;61:64-77 (hereinafter HADJERCI) and in view of U.S Patent Appl. Publ. No. 2020/0069900 A1 to Rabin et al. (hereinafter “RABIN”).”
Patent claim 1 does not recite wherein the processor is further caused to determine if the target airway has been penetrated by determining the presence of CO2 using a CO2 sensor.
As taught by POCUS, (see discussion above with respect to examined claim 2), it would be obvious for the target structure to be an airway and to be identified based on anatomical landmark structures (e.g., tracheal rings).
It would have been obvious for one having ordinary skill in the art to use the system of patent claim 1 in which the target structure was an airway (trachea). In particular, one having ordinary skill in the art would use the system of claim 1 to access image data that not only included the airway but also anatomical landmark structures (e.g., tracheal rings) and then to determine a location of the airway based on those anatomical landmark structures. One having ordinary skill in the art would be motivated to use the ultrasound guidance system of patent claim 1 because “[u]ltrasound can help avoid some…complications, improve safety, and increase the success rate of the procedure especially in the challenging patient.” There would be a reasonable expectation of success because, as taught by POCUS, ultrasound-guided PDTs have already been safely performed.
As to the “wherein the processor is further caused to determine if the target airway has been penetrated by determining the presence of CO2 using a CO2 sensor,” Rabin teaches a “percutaneous dilation tracheostomy device.” (Title and Abstract). According to Rabin, “[e]nsuring the correct placement of a PDT device is key to minimizing complications. Correct placement can be especially challenging in patients with an abnormal tracheal anatomy.” ([0012]). To confirm airway placement of the tracheostomy tube, Rabin’s device may include a side port 108 that is “compatible with end tidal CO2 detector.” ([0076]). “An end tidal CO2 detector is a monitor generally used to confirm airway placement of an endotracheal and tracheostomy tube.” (Id). “Thus, side port 108 can allow real-time confirmation of device placement within a patient and reduce the risk of a misplaced tracheostomy tube.” (Id).
It would have been obvious for one having ordinary skill in the art to modify the system to include a CO2 sensor (i.e., coupled to a side port of an endotracheal tube) and to modify the processor of patent claim 1 to determine if the target airway has been penetrated by determining the presence of CO2 using the CO2 sensor. One having ordinary skill in the art would be motivated to determine the presence of CO2 because, as taught in RABIN, the “key to minimizing complications” is ensuring the correct placement of the tube. Using a CO2 sensor to detect the presence of CO2 can confirm that the tube was properly placed. There would be a reasonable expectation of success because, as taught in RABIN, the tubes can be modified to have a side port that is compatible with the CO2 sensor.
Claims 21-24 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Patent No. 12,193,872 (the ’872 Patent) in view of “Ultrasound-Guided Airway Procedures” by Terkawi, Abdullah Sulieman, et al. from Chapter 4 of The Ultimate Guide to Point-of-Care Ultrasound-Guided Procedures (2020): 39-61 (hereinafter referred to as Point-of-Care Ultrasound or “POCUS”) and Ma, Laifa, et al. "A novel deep learning framework for automatic recognition of thyroid gland and tissues of neck in ultrasound image." IEEE Transactions on Circuits and Systems for Video Technology 32.9 (published “8 March 2022”; see notes below Index Terms on bottom left of first page): 6113-6124 (hereinafter “MA”) and HADJERCI et al. Assistive system based on nerve detection and needle navigation in ultrasound images for regional anesthesia. Expert Systems with Applications. 2016 Nov 1;61:64-77 (hereinafter HADJERCI) and in view of U.S Patent Appl. Publ. No. 2021/0177373 A1 to Xie et al. (hereinafter “XIE”).
With respect to claim 21, patent claim 1 does not recite wherein the processor is further caused to input the image data acquired from the subject into an artificial intelligence (Al) model to identify anatomical landmark structures in the image data.
As taught by POCUS, (see discussion above with respect to examined claim 2), it would be obvious for the target structure to be an airway and to be identified based on anatomical landmark structures (e.g., tracheal rings).
It would have been obvious for one having ordinary skill in the art to use the system of patent claim 1 in which the target structure was an airway (trachea). In particular, one having ordinary skill in the art would modify the instructions for the processor of claim 1 to access image data that not only included the airway but also anatomical landmark structures (e.g., tracheal rings) and then to determine a location of the airway based on those anatomical landmark structures. One having ordinary skill in the art would be motivated to use the ultrasound guidance system of patent claim 1 because “[u]ltrasound can help avoid some…complications, improve safety, and increase the success rate of the procedure especially in the challenging patient.” There would be a reasonable expectation of success because, as taught by POCUS, ultrasound-guided PDTs have already been safely performed.
However, XIE teaches an ultrasound system with an artificial neural network for guided imaging. (Title and Abstract). Embodiments of XIE may “reduce operator dependence and thus improve measurement reliability” and generally enhance ultrasound systems and techniques. ([0026]).“For training a neural network according to the present disclosure, any suitable architecture, such as a VGGNet-like or ResNet-like architecture, may be used and the “blank slate” network may be trained from scratch (i.e., without any preconfiguration of the weights).” ([0054]). In one example, XIE discusses implementing a “YOLO (you only look once” network for object detection that outputs bounding boxes around the objects. “For example as shown in FIG. 11, an object detection network may be trained to recognize, in preferred examples in real-time (e.g., as each frame is received), the presence and location of any number of categories of object, for example kidney (e.g., bounding box 1110), vessel (e.g., bounding boxes 1112-1, 1112-2, and 1112-3), and other categories or classes of objects (e.g., bounding box 1114) which may be present in an image of the target anatomy.” ([0063]).
It would have been obvious to one of ordinary skill in the art at the time of filing to input the image data acquired from the subject into an artificial intelligence (Al) model to identify anatomical landmark structures in the image data. One would have been motivated to modify the processor of patent claim 1 to input the image data into an AI Model (i.e., the YOLO network), because the YOLO network improves the workflow by automatically detecting and identifying the desired objects with bounding boxes for the user. There would be a reasonable expectation of success because, as demonstrated by XIE, artificial intelligence models are capable of identifying objects of interest in ultrasound image data.
With respect to claim 22 (depending from claim 21), patent claim 1 does not recite that the Al model further outputs one or more bounding boxes identifying the anatomical landmark structures in the image data.
However, XIE teaches an ultrasound system with an artificial neural network for guided imaging. (Title and Abstract). Embodiments of XIE may “reduce operator dependence and thus improve measurement reliability” and generally enhance ultrasound systems and techniques. ([0026]).“For training a neural network according to the present disclosure, any suitable architecture, such as a VGGNet-like or ResNet-like architecture, may be used and the “blank slate” network may be trained from scratch (i.e., without any preconfiguration of the weights).” ([0054]). In one example, XIE discusses implementing a “YOLO (you only look once” network for object detection that outputs bounding boxes around the objects. “For example as shown in FIG. 11, an object detection network may be trained to recognize, in preferred examples in real-time (e.g., as each frame is received), the presence and location of any number of categories of object, for example kidney (e.g., bounding box 1110), vessel (e.g., bounding boxes 1112-1, 1112-2, and 1112-3), and other categories or classes of objects (e.g., bounding box 1114) which may be present in an image of the target anatomy.” ([0063]).
It would have been obvious to one of ordinary skill in the art at the time of filing to provide an Al model that outputs one or more bounding boxes identifying the anatomical landmark structures in the image data. One would have been motivated to modify the processor of patent claim 1 to implement the YOLO network, as taught in XIE, because the YOLO network improves the workflow by automatically detecting and identifying the desired objects with bounding boxes for the user. There would be a reasonable expectation of success because, as demonstrated by XIE, artificial intelligence models are capable of identifying objects of interest in ultrasound image data.
With respect to claim 23 (depending from claim 22), patent claim 1 does not recite that the Al model is trained using a You-only-look-once (YOLO) deep learning network for outputting the one or more bounding boxes.
However, in one example, XIE discusses implementing a “YOLO (you only look once” network for object detection that outputs bounding boxes around the objects. “For example as shown in FIG. 11, an object detection network may be trained to recognize, in preferred examples in real-time (e.g., as each frame is received), the presence and location of any number of categories of object, for example kidney (e.g., bounding box 1110), vessel (e.g., bounding boxes 1112-1, 1112-2, and 1112-3), and other categories or classes of objects (e.g., bounding box 1114) which may be present in an image of the target anatomy.” ([0063]).
It would have been obvious to one of ordinary skill in the art at the time of filing to provide an Al model that is trained using a You-only-look-once (YOLO) deep learning network for outputting the one or more bounding boxes. One would have been motivated to modify the processor of patent claim 1 to implement the YOLO network, as taught in XIE, which improves the workflow by automatically detecting and identifying the desired objects with bounding boxes for the user. There would be a reasonable expectation of success because, as demonstrated by XIE, artificial intelligence models are capable of identifying objects of interest in ultrasound image data.
With respect to claim 24 (depending from claim 21), patent claim 1 does not recite that the Al model is trained using a pretrained ResNet deep learning network.
However, XIE teaches embodiments that use artificial intelligence for analyzing ultrasound image data, which may “reduce operator dependence and thus improve measurement reliability” and generally enhance ultrasound systems and techniques. ([0026]).In one example, XIE teaches “[f]or training a neural network according to the present disclosure, any suitable architecture, such as a VGGNet-like or ResNet-like architecture, may be used and the “blank slate” network may be trained from scratch (i.e., without any preconfiguration of the weights). As shown in FIG. 7, to reduce the amount of training data needed, a pre-trained network, such as the Inception V3 (as shown in FIG. 7) or another network may be used as a starting point. The pre-trained network 710 may then be fine-tuned specifically for classifying medical image data with a much smaller training data set of medical images (e.g., ultrasound images 712) of the particular clinical application, in this case liver imaging. Fine-tuning may be performed, for example by feeding the penultimate layer of the Inception V3 network into a new output classifier 714 (e.g., comprised of fully connected layers) and configured to produce the desired classification (e.g., match/no match), or by providing the feature vector output by the Inception V3 network into a new classifier, which requires less training data.” ([0054]).
It would have been obvious to one of ordinary skill in the art at the time of filing to provide an Al model that is trained using a pretrained ResNet deep learning network. One would have been motivated to modify the processor of patent claim 1 to train the AI model using a pretrained ResNet deep learning network because, as taught in XIE, a pretrained network requires a much smaller training data set of medical images ([0054]), Reducing the amount of training data would reduce the time necessary to train the AI model. There would be a reasonable expectation of success because, as demonstrated by XIE, AI models can be trained using pretrained ResNet deep learning networks.
RESPONSE TO APPLICANT’S ARGUMENTS
With respect to the double patenting rejections, Applicant’s arguments filed on February 20, 2026 have been fully considered but they are not persuasive. Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from U.S. Patent No. 12,193,872.
Conclusion
THIS ACTION IS MADE FINAL. 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 JASON P GROSS whose telephone number is (571)272-1386. The examiner can normally be reached Monday-Friday 9:00-5:00CT.
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/JASON P GROSS/ Examiner, Art Unit 3797
/SERKAN AKAR/Primary Examiner, Art Unit 3797
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