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 .
Response to Amendment
Claims 1-20 remain pending in the application in response to the applicant’s amendments to the rejections previously set forth in the Non-Final Office Action mailed 04/08/2026.
Response to Arguments
Applicant’s arguments, see pg. 6-8, filed 07/01/2026, with respect to the rejection(s) of claim(s) 1, 10, and 15 under 35 U.S.C. 102(a) (Salgaonkar) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Salgaonkar in view of Chunduru (claim 1), Salgaonkar in view of Vaan and Li (claim 10), and Salgaonkar in view of Li (claim 15).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3 and 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Salgaonkar (US 20200315572A1, published October 8, 2020) in view of Chunduru et al. (US 20250117928 A1, published April 10, 2025 with a priority date of October 4, 2023), hereinafter referred to as Salgaonkar and Chunduru, respectively.
Regarding claim 1, Salgaonkar teaches a method for live anatomical mapping of a left atrium with robot assisted intra-cardiac echocardiography (ICE) (Fig. 2 and 4), the method comprising:
inserting an ICE catheter into a patient, wherein catheter steering controls of the ICE catheter are controlled at least in part by a machine trained agent (see para. 0036 " the classifier [machine trained agent] is further trained to provide a recommendation of the next one or more maneuvers to steer the probe [inserted ICE catheter] to the next location as required by a navigation protocol. The one or more maneuvers may be represented by navigational instructions.");
automatically maneuvering the ICE catheter, using instructions from the machine trained agent, from a first position in a patient's anatomy to a second position in the patient's anatomy while acquiring image data of the patient's anatomy (see para. 0036 - "Alternatively, the navigational instructions may be in the form of machine instructions that are executable by a robotic controller to automatically steer the probe 111 to a desired position [from a first position to a second position].”).
Salgaonkar teaches acquiring image data, but does not explicitly teach generating a model of the patient's anatomy from the acquired image data.
Whereas, Chunduru, in an analogous field of endeavor, teaches generating a model of the patient's anatomy from the acquired image data (Fig. 8; see para. 0123 – “With continued reference to FIG. 8, method 800 includes a step 810 of generating, by the at least a processor, an 3D data structure representing the cardiac anatomy as a function of the set of images.”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified acquiring image data, as disclosed in Salgaonkar, by also generating a model of the patient's anatomy from the acquired image data, as disclosed in Chunduru. One of ordinary skill in the art would have been motivated to make this modification in order to generating a three-dimensional (3D) model of cardiac anatomy via machine-learning, as taught in Chunduru (see para. 0122).
Furthermore, regarding claim 2, Salgaonkar further teaches tracking a position and orientation of an ICE catheter tip of the ICE catheter using the acquired image data of the patient's anatomy as the ICE catheter is maneuvered from the first position to the second position (see para. 0041 "The graphical representation may be, for example, a catheter tip overlaid on a plane projection of a three-dimensional rendering of the structure of interest or an image-derived model of the structure of interest. The graphical representation provides a visual guide of where the probe 111 is predicted to be currently located.").
Furthermore, regarding claim 3, Salgaonkar further teaches wherein the robot assisted ICE is performed without an additional positioning system (see para. 0018 "The framework relies on images acquired by the probe, and avoids the use of sensors and additional secondary imaging modalities.").
Furthermore, regarding claim 8, Salgaonkar further teaches wherein the ICE catheter is controlled with an external joystick providing a digital input directly mapped to one or more standard knob controls of the catheter (see para. 0042 "Alternatively, the navigational instructions may be in the form of machine instructions that are executable by a robotic controller (or processor) to automatically steer the probe 111 to the desired position.").
Furthermore, regarding claim 9 Salgaonkar further teaches wherein the automatically maneuvering of the ICE catheter is used for image stabilization to limit misalignment issues, automatic cardiac gating, and/or respiratory gating (see para. 0014 " "registering" "aligning" may refer to the actions and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (e.g., electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices.").
Claims 4 and 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Salgaonkar in view of Chunduru, as applied to claim 1 above, and in further view of K. Li et al, "Autonomous Navigation of an Ultrasound Probe Towards Standard Scan Planes with Deep Reinforcement Learning", IEEE International Conference on Robotics and Automation (ICRA 2021), pp. 8302-8308, May 2021, hereinafter referred to as Li.
Regarding claim 4, Salgaonkar in view of Chunduru teaches all of the elements disclosed in claim 1 above.
Salgaonkar in view of Chunduru teaches a machine learning model, but does not explicitly teach where the machine learning model is a deep reinforcement learning (DRL) model.
Whereas, Li, in an analogous field of endeavor, teaches wherein the machine trained agent is a Deep Reinforcement Learning (DRL) agent that interacts with the catheter steering controls, the DRL agent trained to move the ICE catheter to one or more key positions through a simulated environment (see Abstract "In this work, we propose a deep reinforcement learning framework to autonomously control the 6-D pose of a virtual US probe based on real-time image feedback to navigate towards the standard scan planes under the restrictions in real-world US scans.").
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a machine learning model, as disclosed in Salgaonkar in view of Chunduru, by having the machine learning model as a deep reinforcement learning (DRL) model, as disclosed in Li. One of ordinary skill in the art would have been motivated to make this modification in order to mimic the decision-making process of sonographers to autonomously navigate towards the standard scan planes, as taught in Li (see pg. 8303, col. 1, para. 1).
Furthermore, regarding claim 6, Li further teaches wherein the key positions and one or more anatomical landmarks are annotated in the simulated environment, wherein the machine trained agent is placed virtually in selected key positions and navigates within the simulated environment (see pg. 8303, col. 2, para. 2 "In this work, we build a simulation environment using 3D US volumes reconstructed from real world US data covering the region of interest in the patient.").
Furthermore, regarding claim 7, Li further teaches wherein along a navigation pathway, synthetic ICE images are generated from local views of the simulated environment (see pg. 8306, col. 1, para. 3 - "We built a simulation environment in Python for US probe navigation with the SonoRL algorithm. At each time step, the agent observes an image of size 150 X 150 and stacks 4 recent frames as the state.").
The motivation for claims 6-7 was shown previously in claim 4.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Salgaonkar in view of Chunduru and Li, as applied to claim 4 above, and in further view of de Vaan et al. (US 20200170617 A1, published June 4, 2020), hereinafter referred to as Vaan.
Regarding claim 5, Salgaonkar in view of Chunduru and Li teaches all of the elements disclosed in claim 4 above.
Salgaonkar in view of Chunduru and Li teaches generating a simulated environment, but does not explicitly teach the simulated environment is constructed from a plurality of pre-operative cardiac CT volumes.
Whereas, Vaan, in an analogous field of endeavor, teaches wherein the simulated environment is constructed from a plurality of pre-operative cardiac CT volumes (Fig. 19; see para .0046 – “The present application is particularly advantageous in pre-operated planning of intracavity imaging (such as TEE or intracardiac imaging) during transcatheter heart procedures based on patient specific CT image dataset as acquired with a CT system…”; see para. 0250 – “…while the volumetric imaging acquisition subsystem 1900 can be configured to acquire the imaging dataset of the patient using, for example, a volumetric imaging modality selected from the group consisting of X-ray CT imaging, rotational angiography, MRI, SPECT, PET, three-dimensional ultrasound, and the like”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified generating a simulated environment, as disclosed in Salgaonkar in view of Chunduru and Li, by having the simulated environment constructed from a plurality of pre-operative cardiac CT volumes, as disclosed in Vaan. One of ordinary skill in the art would have been motivated to make this modification in order to provide the physician with accurate three-dimensional information of the heart structure and possible surrounding structures, as taught in Vaan (see para. 0004).
Claims 10-13 are rejected under 35 U.S.C. 103 as being unpatentable over Salgaonkar in view of Vaan and Li.
Regarding claim 10, Salgaonkar teaches a method for training an agent to operate catheter steering controls of a ICE catheter (see para. 0036 – “In some implementations, the classifier is further trained to provide a recommendation of the next one or more maneuvers to steer the probe to the next location as required by a navigation protocol. The one or more maneuvers may be represented by navigational instructions…Alternatively, the navigational instructions may be in the form of machine instructions that are executable by a robotic controller to automatically steer the probe to a desired position.”), but does not explicitly teach acquiring a plurality of pre-operative cardiac CT volumes.
Whereas, Vaan, in an analogous field of endeavor, teaches acquiring a plurality of pre-operative cardiac CT volumes (Fig. 19; see para .0046 – “The present application is particularly advantageous in pre-operated planning of intracavity imaging (such as TEE or intracardiac imaging) during transcatheter heart procedures based on patient specific CT image dataset as acquired with a CT system…”; see para. 0250 – “…while the volumetric imaging acquisition subsystem 1900 can be configured to acquire the imaging dataset of the patient using, for example, a volumetric imaging modality selected from the group consisting of X-ray CT imaging, rotational angiography, MRI, SPECT, PET, three-dimensional ultrasound, and the like”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified steering an ICE catheter, as disclosed in Salgaonkar, by also acquiring a plurality of pre-operative cardiac CT volumes. One of ordinary skill in the art would have been motivated to make this modification in order to provide the physician with accurate three-dimensional information of the heart structure and possible surrounding structures, as taught in Vaan (see para. 0004).
Salgaonkar in view of Vaan teaches training an agent to maneuver the ICE catheter from a first position to a second position, but does not explicitly teach training an agent to maneuver a probe from a first position in the simulated cardiac environment to a second position in the simulated cardiac environment using deep reinforcement learning.
Whereas, Li, in an analogous field of endeavor, teaches
generating a simulated cardiac environment from the plurality of pre-operative cardiac images (see pg. 8308, col. 1, para. 3 to pg. 8308, col. 2, para. 1 "We built a simulation environment in Python for US probe navigation with the SonoRL algorithm The design of this setting is motivated by the real-world scenario where more than one US acquisition of the same patient is required, such as pre- and post-operative ultrasonography."); and
training an agent to maneuver the probe from a first position in the simulated cardiac environment to a second position in the simulated cardiac environment using deep reinforcement learning (see Abstract "In this work, we propose a deep reinforcement learning framework to autonomously control the 6-D pose of a virtual US probe based on real-time image feedback to navigate towards the standard scan planes under the restrictions in real-world US scans.").
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified training an agent to maneuver the ICE catheter from a first position to a second position, as disclosed in Salgaonkar in view of Vaan, by training an agent to maneuver a probe from a first position in the simulated cardiac environment to a second position in the simulated cardiac environment using deep reinforcement learning, as disclosed in Li. One of ordinary skill in the art would have been motivated to make this modification in order to mimic the decision-making process of sonographers to autonomously navigate towards the standard scan planes, as taught in Li (see pg. 8303, col. 1, para. 1).
Furthermore, regarding claim 11, Li further teaches generating synthetic ICE images from image local views of the simulated cardiac environment, wherein the synthetic ICE images are used in the deep reinforcement learning to define states, observations, and/or rewards (see pg. 8306, col. 1, para. 3 "We built a simulation environment in Python for US probe navigation with the SonoRL algorithm. At each time step, the agent observes an image of size 150 X 150 and stacks 4 recent frames as the state.").
Furthermore, regarding claim 12, Li further teaches wherein the agent is further trained to identify landmarks and key positions in the simulated cardiac environment (see pg. 8303, col. 2, para. 2 "In this work, we build a simulation environment using 3D US volumes reconstructed from real world US data covering the region of interest in the patient.").
Furthermore, regarding claim 13, Li further teaches wherein the catheter steering controls comprises twelve degrees of freedom (aka six degrees of freedom) of the catheter represents all possible actions for moving the catheter: positive and negative translation in a X, Y, Z plane and clockwise and counterclockwise rotation in a yaw, pitch, and roll axes (see pg. 8303, col. 1, para. 1 "We present the first deep RL framework to control the 6-D [six degrees of freedom] pose of a virtual US probe based on real-time US image feedback...").
The motivation for claims 11-13 was shown previously in claim 10.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Salgaonkar in view of Vaan and Li, as applied to claim 10 above, and in further view of C. DeSimone et al, “ICE Imaging of the Left Atrial Appendage”, J Cardiovasc Electrophysiol., vol. 25, no. 11, pp. 1-7, Nov. 2014, hereinafter referred to as DeSimone.
Regarding claim 14, Salgaonkar in view of Vaan and Li teaches all of the elements disclosed in claim 10 above.
Salgaonkar in view of Vaan and Li teaches positioning an ICE catheter, but does not explicitly teach positioning the ICE catheter at a septum wall and an outflow tract.
Whereas, DeSimone, in an analogous field of endeavor, teaches wherein
the first position comprises a right atrium septal wall observing a left atrium left veins ostia (see pg. 2, para. 2 – “Thus, when ICE is used from the RA to guide transseptal puncture, the probe is positioned in the anterior RA and the imaging plane directed posteriorly. Excellent inter-atrial septal images are obtained, and viewing through the septum in the same planar orientation, the LA, and posteriorly located pulmonary veins are typically well-visualized.”) and
the second position comprises a right ventricle outflow tract observing a left atrial appendage (see pg. 4, para. 1 – “The RVOT, main pulmonary artery (PA), and left pulmonary artery (LPA) are excellent vantage points to image the LAA once the complex relationship between these structures is appreciated.”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified positioning an ICE catheter, as disclosed in Salgaonkar in view of Vaan and Li, by positioning the ICE catheter from the right atrium septal wall and the right ventricle outflow tract, as disclosed in DeSimone. One of ordinary skill in the art would have been motivated to make this modification in order to obtain detailed and comprehensive images of the LAA from the RVOT, as taught in DeSimone (see pg. 4, para. 2).
Claims 15 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Salgaonkar in view of Li.
Regarding claim 15, Salgaonkar teaches a system for live anatomical mapping of a left atrium of a patient with robot assisted intra-cardiac echocardiography (ICE) (Fig. 2 and 4), the system comprising:
a machine trained agent (see para. 0036 " the classifier [machine trained agent] is further trained to provide a recommendation of the next one or more maneuvers to steer the probe [inserted ICE catheter] to the next location as required by a navigation protocol. The one or more maneuvers may be represented by navigational instructions."); and
an ICE catheter comprising steering controls that are controlled at least in part by the machine trained agent (see para. 0036 " the classifier [machine trained agent] is further trained to provide a recommendation of the next one or more maneuvers to steer the probe [inserted ICE catheter] to the next location as required by a navigation protocol. The one or more maneuvers may be represented by navigational instructions.");
wherein the machine trained agent controls movement of the ICE catheter from a first position to a second position while acquiring imaging data of at least the left atrium (see para. 0036 - "Alternatively, the navigational instructions may be in the form of machine instructions that are executable by a robotic controller to automatically steer the probe 111 to a desired position [from a first position to a second position].”).
Salgaonkar teaches a machine trained agent, but does not explicitly teach deep reinforcement learning using a simulated volumetric environment.
Whereas, Li, in an analogous field of endeavor, teaches the machine trained agent trained using deep reinforcement learning using a simulated volumetric environment (see Abstract "In this work, we propose a deep reinforcement learning framework to autonomously control the 6-D pose of a virtual US probe based on real-time image feedback to navigate towards the standard scan planes under the restrictions in real-world US scans.").
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a machine trained agent, as disclosed in Salgaonkar, by having the machine trained agent as a deep reinforcement learning using a simulated volumetric environment, as disclosed in Li. One of ordinary skill in the art would have been motivated to make this modification in order to mimic the decision-making process of sonographers to autonomously navigate towards the standard scan planes, as taught in Li (see pg. 8303, col. 1, para. 1).
Furthermore, regarding claim 19, Salgaonkar further teaches wherein the robot assisted ICE is performed without an additional positioning system (see para. 0018 "The framework relies on images acquired by the probe, and avoids the use of sensors and additional secondary imaging modalities.").
Claims 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Salgaonkar in view of Li, as applied to claim 15 above, and in further view of Chunduru.
Regarding claim 16, Salgaonkar in view of Li teaches all of the elements disclosed in claim 15 above.
Salgaonkar in view of Li teaches acquiring imaging data, but does not explicitly teach generating a model of the patient's anatomy from the acquired image data.
Whereas, Chunduru, in an analogous field of endeavor, teaches a processor configured to generate a model of the anatomy of the patient from the acquired imaging data (Fig. 8; see para. 0123 – “With continued reference to FIG. 8, method 800 includes a step 810 of generating, by the at least a processor, an 3D data structure representing the cardiac anatomy as a function of the set of images.”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified acquiring image data, as disclosed in Salgaonkar in view of Li, by also generating a model of the patient's anatomy from the acquired image data, as disclosed in Chunduru. One of ordinary skill in the art would have been motivated to make this modification in order to generating a three-dimensional (3D) model of cardiac anatomy via machine-learning, as taught in Chunduru (see para. 0122).
Furthermore, regarding claim 17, Chunduru further teaches wherein the model is used as feedback for controlling the movement of the ICE catheter (see para. 0091 – “In such embodiment, iterative feedback loop may allow synthetic ICE data generator to adapt to the user's needs and performance requirements, enabling one or more generative machine learning models described herein to learn and update based on user responses and generated feedbacks.”).
Furthermore, regarding claim 18, Salgaonkar further teaches wherein the processor is configured to track a position and orientation of an ICE catheter tip of the ICE catheter using the acquired image data of a patient's anatomy as the ICE catheter is maneuvered from the first position to the second position (see para. 0041 "The graphical representation may be, for example, a catheter tip overlaid on a plane projection of a three-dimensional rendering of the structure of interest or an image-derived model of the structure of interest. The graphical representation provides a visual guide of where the probe 111 is predicted to be currently located.").
The motivation for claim 17 was shown previously in claim 16.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Salgaonkar in view of Li, as applied to claim 15 above, and in further view of DeSimone.
Regarding claim 20, Salgaonkar in view of Li teaches all of the elements disclosed in claim 15 above.
Salgaonkar in view of Li teaches positioning an ICE catheter, but does not explicitly teach positioning the ICE catheter at a septum wall and an outflow tract.
Whereas, DeSimone, in an analogous field of endeavor, teaches wherein
the first position comprises a right atrium septal wall observing a left atrium left veins ostia (see pg. 2, para. 2 – “Thus, when ICE is used from the RA to guide transseptal puncture, the probe is positioned in the anterior RA and the imaging plane directed posteriorly. Excellent inter-atrial septal images are obtained, and viewing through the septum in the same planar orientation, the LA, and posteriorly located pulmonary veins are typically well-visualized.”) and
the second position comprises a right ventricle outflow tract observing a left atrial appendage (see pg. 4, para. 1 – “The RVOT, main pulmonary artery (PA), and left pulmonary artery (LPA) are excellent vantage points to image the LAA once the complex relationship between these structures is appreciated.”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified positioning an ICE catheter, as disclosed in Salgaonkar in view of Li, by positioning the ICE catheter from the right atrium septal wall and the right ventricle outflow tract, as disclosed in DeSimone. One of ordinary skill in the art would have been motivated to make this modification in order to obtain detailed and comprehensive images of the LAA from the RVOT, as taught in DeSimone (see pg. 4, para. 2).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Kezurer et al. (US 20190354856 A1, published November 21, 2019) discloses converting the transformation into orientation instructions for a user of the probe and to provide and display the orientation instructions to the user to change the position and rotation of the probe.
Camus et al. (US 20190090951 A1, published March 28, 2019) discloses a 3D anatomy model of the LAA of the patient is generated from the ultrasound data representing the heart volume.
Shlomi et al. (US 20240105328 A1, March 28, 2024) discloses a generative neural network to receive ultrasound probe position and orientation and to generate at least one simulated ultrasound image or clip of a body part of a subject, where the generative neural network is trained on a multiplicity of 2D ultrasound images or clips of said body part taken from a plurality of ultrasound probe positions and orientations.
Constantine et al. (US 20240293071 A1, published September 5, 2024) discloses displaying, on the GUI, a real-time rendering of the anatomical model and/or a point of interest of the anatomical model from an ICE catheter.
Chunduru et al. (US 12217361 B1, published February 4, 2025 with a priority date of January 30, 2024) discloses receiving, by a processor, a set of ICE images of a cardiac anatomy pertaining to a subject; and generating, by the processor, a 3D model of the cardiac anatomy based on the set of ICE images.
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/N.C./Examiner, Art Unit 3798
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