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 .
DETAILED ACTION
In the amendment filed 09 September 2025:
Claims 14-20 are cancelled
Claims 21-22 are new
Claim 1 is amended
Claims 1-13,21-22 are pending
Claim Objections
The numbering of claims is not in accordance with 37 CFR 1.126 which requires the original numbering of the claims to be preserved throughout the prosecution. When claims are canceled, the remaining claims must not be renumbered. When new claims are presented, they must be numbered consecutively beginning with the number next following the highest numbered claims previously presented (whether entered or not).
Misnumbered claim 22 been renumbered 23.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-13,21, 23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1, 21, 23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite systems, which are within a statutory category. The limitations of:
Claims 1, 21, 23 (Claim 21 being representative)
provide a surgical plan to assist a user during surgery;
determine the pose of the spine of the defined- patient relative to a pose of a surgical instrument manipulated by an operator;
obtain the spinal surgery plan and determine a target pose of the surgical instrument based on the spinal surgery plan indicating where a surgical procedure is to be performed on the spine of the defined-patient and based on the pose of the spine of the defined-patient, and generate steering information based on comparison of the target pose of the surgical instrument and the pose of the surgical instrument;
obtain postoperative feedback data provided regarding surgical outcomes for a plurality of patients;
based on the postoperative feedback data;
and obtain preoperative data characterizing a defined-patient, generate a spinal surgery plan for the defined-patient based on processing the preoperative data;
transmit images;
receiving an image of the anatomical feature;
detecting a landmark associated with the anatomical feature within the image;
determining the parameter associated with the anatomical feature based on the landmark;
and outputting an indication of the parameter associated with the anatomical feature, to display a 3D model of the spine in one portion, a quantitative listing of spinal alignment measurements in a second portion, and a coronal image and a sagittal image of a selection of spinal alignment measurements on a third portion
wherein the indication of the parameter associated with the anatomical feature is transmitted,
wherein further provides instructions to guide movement.
as drafted, is a process that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for recitation of generic computer components. That is, other than reciting a surgical system, surgical navigation system, camera tracking system, imaging system, distributed networked computers, processing circuitry, memory and robotic assisted surgery system, the claimed invention amounts to managing personal behavior or interaction between people. For example, but for the surgical system, surgical navigation system, camera tracking system, imaging system, distributed networked computers, processing circuitry, memory and robotic assisted surgery system, these claims encompass surgical planning and tracking during spinal surgical procedures in the manner described in the identified abstract idea, supra.. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
The claim further recites “training a machine learning model.” When given its broadest reasonable interpretation in light of the disclosure, the training of a machine learning model represents the creation of mathematical interrelationships between data. As such, the training of the machine learning model represents a mathematical concept that is interpreted to be part of the identified abstract idea, supra. The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional element of a (Claim 1) surgical system comprising a surgical navigation system (comprising a camera system), an imaging system and a surgical planning system (comprising a processing circuitry and memory) and a graphic user interface, (Claims 21/23) surgical system comprising a surgical navigation system, camera tracking system, navigation controller, imaging system, surgical planning system (comprising processing circuitry and memory) and a graphic user interface that implement the identified abstract idea. The (Claim 1) surgical system comprising a surgical navigation system (comprising a camera system), an imaging system and a surgical planning system (comprising a processing circuitry and memory) and a graphic user interface, (Claims 21/23) surgical system comprising a surgical navigation system, camera tracking system, navigation controller, imaging system, surgical planning system (comprising processing circuitry and memory) and a graphic user interface are not described by the applicant and are recited at a high-level of generality (i.e., a generic server performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim further recites the additional element of using a trained machine learning model to perform surgical planning and tracking. This represents mere instructions to implement the abstract idea on a generic computer. Implementing an abstract idea using a generic computer or components thereof does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. See, e.g., Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 10 (Fed. Cir. April 18, 2025) (finding that claims that do no more than apply established methods of machine learning to a new data environment are ineligible). Alternatively, or in addition, the implementation of the trained machine learning model to perform surgical planning and tracking merely confines the use of the abstract idea (i.e., the trained model) to a particular technological environment or field of use (neural network) and thus fails to add an inventive concept to the claims.
The claims further recite the additional elements of a distributed network computers, robotic assisted surgery system, surgical robot, display device, imaging device, and surgical instrument. The distributed network computers, robotic assisted surgery system, surgical robot, display device, imaging device, and surgical instrument merely generally links the abstract idea to a particular technological environment or field of use. MPEP 2106.04(d)(I) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide a practical application. Accordingly, even in combination, this additional element does not integrate the abstract idea into a practical application.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a (Claim 1) surgical system comprising a surgical navigation system (comprising a camera system), an imaging system and a surgical planning system (comprising a processing circuitry and memory) and a graphic user interface, (Claims 21/23) surgical system comprising a surgical navigation system, camera tracking system, navigation controller, imaging system, surgical planning system (comprising processing circuitry and memory) and a graphic user interface to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”).
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using the trained machine learning model to perform surgical planning and tracking was found to represent mere instructions to implement the abstract idea on a generic computer and/or confine the use of the abstract idea (i.e., the trained model) to a particular technological environment or field of use (neural network). This has been re-evaluated under the “significantly more” analysis and determined to be insufficient to provide significantly more. MPEP 2106.05(I) indicates that mere instructions to implement the abstract idea on a generic computer and/or confining the use of the abstract idea to a particular technological environment or field of use cannot provide significantly more. See also Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 17 (Fed. Cir. April 18, 2025) (finding that applying machine learning to an abstract idea does not transform a claim into something significantly more).
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a distributed network computers, robotic assisted surgery system, surgical robot, display device, imaging device, and surgical instrument were determined to generally link the abstract idea to a particular technological environment or field of use. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. MPEP 2106.05(A) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide significantly more. Accordingly, even in combination, this additional element does not provide significantly more. As such the claim is not patent eligible.
Claims 2-13 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination.
Claim(s) 2-3 merely describe(s) method of detecting landmarks, which further defines the abstract idea.
Claim(s) 4 merely describe(s) what the landmarks include, which further defines the abstract idea.
Claim(s) 5 merely describe(s) the use of a 3-d imaging modality and processing of imaging modality data, which further defines the abstract idea.
Claim(s) 6 merely describe(s) the imaging modalities that can be used, which further defines the abstract idea.
Claim(s) 7 merely describe(s) what is being displayed in the indication output, which further defines the abstract idea.
Claim(s) 8 merely describe(s) the parameters obtained from the spine, which further defines the abstract idea.
Claim(s) 9 merely describe(s) what outputting an indication includes, which further defines the abstract idea.
Claim(s) 10-11 merely describe(s) what the images include, which further defines the abstract idea.
Claim(s) 12 merely describe(s) processing the landmarks and anatomical features of the images, which further defines the abstract idea.
Claim(s) 13 merely describe(s) the perspectives of the models and images, which further defines the abstract idea.
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 Examiner notes that the rejection will reference the translated documents (attached) corresponding to any foreign documents recited in the rejection.
Claims 1-4, 6-9 is/are rejected under 35 U.S.C. 103(a) as being unpatentable over Calloway et al (US Publication No. 20210228281) in view of Forsberg et al (US Publication No. 20140323845) in view of Zhan et al (US Publication No. 20130136322).
Regarding Claim 1
Calloway teaches a surgical system configured to automatically determine a spine alignment parameter associated with an anatomical feature, the surgical system comprising:
a surgical navigation system having a camera system, and instruments having arrays configure to be tracked by the surgical navigation system [Calloway at Para. 0057 teaches FIG. 6 illustrates a block diagram view of the components of the surgical system of FIG. 5 used for the medical operation. Referring to FIG. 6, the navigation cameras 46 on the auxiliary tracking bar has a navigation field-of-view 600 in which the pose (e.g., position and orientation) of the reference array 602 attached to the patient, the reference array 604 attached to the surgical instrument, and the robot arm 20 are tracked. The navigation cameras 46 may be part of the camera tracking system component 6′ of FIGS. 3B and 3C, which includes the computer platform 910 configured to perform the operations described below. The reference arrays enable tracking by reflecting light in known patterns, which are decoded to determine their respective poses by the tracking subsystem of the surgical robot 4];
an imaging system configured to transmit images of the anatomical feature the surgical navigation system [Calloway at Para. 0075 teaches FIG. 9 illustrates a block diagram of components of a surgical system that includes imaging devices (e.g., C-Arm 104, O-Arm 106, etc.) connected to a computer platform 910 which can be operationally connected to a camera tracking system component 6 (FIG. 3A) or 6′ (FIGS. 3B, 3C) and/or to surgical robot 4 according to some embodiments of the present disclosure];
a surgical planning system comprising:
wherein the surgical planning system further provides instructions to guide movement of a surgical robot [Calloway at Para. 0060 teaches for robotic navigation, various processing components (e.g., computer platform 910) and associated software described below are provided that enable pre-operatively planning of a surgical procedure, e.g., implant placement, and electronic transfer of the plan to the surgical robot 4. The surgical robot 4 uses the plan to guide the robot arm 20 and connected end effector 26 to provide a target pose for a surgical tool relative to a patient anatomical structure for a step of the planned surgical procedure].
Calloway does not teach processing circuitry;
and memory coupled to the processing circuitry and having instructions stored therein that are executable by the processing circuitry to cause the surgical planning system to perform operations including:
receiving an image of the anatomical feature from an imaging device;
detecting a landmark associated with the anatomical feature within the image;
determining the parameter associated with the anatomical feature based on the landmark;
and outputting an indication of the parameter associated with the anatomical feature, a graphical user interface for interacting with the surgical planning system to display a 3D model of the spine in one portion, a quantitative listing of spinal alignment measurements in a second portion, and a coronal image and a sagittal image of a selection of spinal alignment measurements on a third portion
wherein the indication of the parameter associated with the anatomical feature is transmitted to a robotic assisted surgery system,
Forsberg teaches processing circuitry [Forsberg at Para. 0170 teaches in particular, the processor 300 can be commercially available or custom microprocessor, microcontroller, digital signal processor or the like];
and memory coupled to the processing circuitry and having instructions stored therein that are executable by the processing circuitry to cause the surgical planning system to perform operations including [Forsberg at Para. 0170 teaches the memory 336 may include any memory devices and/or storage media containing the software and data used to implement the functionality circuits or modules used in accordance with embodiments of the present invention]:
receiving an image of the anatomical feature from an imaging device [Forsberg at Para. 0088 teaches Embodiments may be particularly suitable for use with medical image data sets from any imaging modality including MRI and CT (MRI and CT interpreted as imaging devices)];
determining the parameter associated with the anatomical feature based on the landmark [Forsberg at Para. 0092 teaches the landmarks can be electronically attached to a location in and/or on a model or in or on a feature in patient image data and subsequently electronically registered or correlated to a corresponding position or location in a like structure in a patient image, visualization and/or model, typically using one or more defined coordinate systems];
and outputting an indication of the parameter associated with the anatomical feature [Forsberg at Para. 0030 teaches the patient images can be time-resolved images for generating visual output of curvature change over time thereby allowing monitoring of progression of a disease, change associated with a therapy or allowing visualizations and associated calculated measurements associated with changes in spinal structure between images taken in different positions of a patient], a graphical user interface for interacting with the surgical planning system to display a 3D model of the spine in one portion [Forsberg at Para. 0025 teaches the method can include allowing a user to electronically define placement of the landmarks using a GUI and display presenting the 3-D model], a quantitative listing of spinal alignment measurements in a second portion [Forsberg at Para. 0029 teaches the electronic providing of the three dimensional (3-D) model of the spine having substantially naturally shaped vertebrae with the landmarks can be carried out by providing a first landmark set for a first set of the calculated measurements and a second landmark set for a second set of calculated measurements], and a coronal image and a sagittal image of a selection of spinal alignment measurements on a third portion [Forsberg at Para. 0033 teaches each vertebra in the 3-D model comprises respective landmarks residing in each of an axial plane, a sagittal plane and a coronal plane]
wherein the indication of the parameter associated with the anatomical feature is transmitted to a robotic assisted surgery system [Forsberg at Para. 0058 teaches FIG. 7 illustrates various display screens that may be displayed on the display 34 of FIGS. 5 and 6 by the surgical robot 4 when using a navigation function of the surgical system 2. The display screens can include, without limitation, patient radiographs with overlaid graphical representations of models of instruments that are positioned in the display screens relative to the anatomical structure based on a developed surgical plan and/or based on poses of tracked reference arrays, various user selectable menus for controlling different stages of the surgical procedure and dimension parameters of a virtually projected implant (e.g. length, width, and/or diameter)],
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine camera of Calloway with the model of Forsberg with the motivation to improve GPU computational performance.
Calloway/Forsberg does not teach detecting a landmark associated with the anatomical feature within the image;
Zhan teaches detecting a landmark associated with the anatomical feature within the image [Zhan at Para. 0007 teaches described herein are systems and methods for detecting anatomical components in images. In accordance with one implementation, at least one anchor landmark is detected in an image];
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Calloway, Forsberg with the landmark detection of Zhan with the motivation to improve MR spinal analysis workflow.
Regarding Claim 2
Calloway/Forsberg/Zhan teach the surgical planning system of Claim 1,
Calloway/Forsberg/Zhan further teach wherein detecting the landmark includes:
determining a modality of the image of the anatomical feature [Zhan at Para. 0073 teaches at 602, a new image and inputs are received. Given a new image of any modality, one can run the present system to automatically detect, localize and/or label any landmark in the new image whose detector (or classifier) has been previously trained using training samples extracted from images of the same modality];
and detecting the landmark based on the modality of the image [Forsberg at Para. 0104 teaches Generally stated, in some embodiments, measurements of anatomical target, such as the spine for spinal curvature, can be generated using landmarks first attached to a substantially natural 3-D model of the organ. The model is then registered to patient data. The landmarks of the model are then transferred to the patient data according to an estimated transformation for full 3D characterization. The transferred landmarks can then be utilized for measuring the position, rotation and deformation of one or more components of the organ of the patient data (e.g., each vertebra). Altering the set of landmarks selected or used, other measures describing a deformity, e.g., a spinal deformity (including other than the ones described herein), can be derived].
Regarding Claim 3
Calloway/Forsberg/Zhan teach the surgical planning system of Claim 2,
Calloway/Forsberg/Zhan further teach wherein detecting the landmark includes:
determining a region of the anatomical feature captured in the image [Zhan at Para. 0085 teaches at 804, a region of search is determined to guide the detection of the anatomical primitive. The region of search may be determined based on the neighboring landmarks. In one implementation, a local region of search (e.g., square, cube, sphere, etc.) is heuristically defined based on a relationship of the neighboring landmarks, such as the mid-point of neighboring landmarks];
and detecting the landmark based on the modality of the image and the region of the anatomical feature captured in the image [Zhan at Para. 0078 teaches at 610, a bundle landmark detector is applied within the region of search to detect bundle landmarks (interpret to combine with image modalities of Forsberg)].
Regarding Claim 4
Calloway/Forsberg/Zhan teach the surgical planning system of Claim 3,
Calloway/Forsberg/Zhan further teach wherein the landmark includes a set of landmarks, and wherein detecting the landmark associated with the anatomical feature includes:
selecting the set of landmarks to be detected from a plurality of landmarks associated with a type of the anatomical feature based on at least one of [Forsberg at Para. 0026 teaches the electronic providing of the three dimensional (3-D) model of the spine having substantially naturally shaped vertebrae with the landmarks can be carried out by providing a menu of selectable landmark sets and allowing a user to select one or more of the landmark sets for use]:
the modality of the image; and the region of the anatomical feature captured in the image; and detecting each landmark of the set of landmarks [Forsberg at Para. 0088 (see Claim 1 for explanation)].
Regarding Claim 6
Calloway/Forsberg/Zhan teach the surgical planning system of Claim 2,
Calloway/Forsberg/Zhan further teach wherein the modality of the image includes at least one of:
computerized tomography (“CT”) imaging; magnetic resonance imaging (“MRI”) imaging; a fluroscopy image; an x-ray image; and an EOS image [Forsberg at Para. 0177 teaches in some particular embodiments, the imaging modality 95 can be any desirable modality such as, but not limited to MRI, CT (computed tomography), fluoroscopy, ultrasound, and the like. The visualization system 10 may also operate to render images using data sets from more than one of these modalities].
Regarding Claim 7
Calloway/Forsberg/Zhan teach the surgical planning system of Claim 1,
Calloway/Forsberg/Zhan further teach wherein outputting the indication of the parameter associated with the anatomical feature includes at least one of:
displaying a three-dimensional (“3D”) model of the anatomical feature labeled with the indication of the parameter; displaying a two-dimensional (“2D”) image of the anatomical feature labeled with the indication of the parameter; displaying a quantitative version of the parameter; and transmitting the indication of the parameter to a surgery navigation system [Forsberg at Para. 0104 teaches generally stated, in some embodiments, measurements of anatomical target, such as the spine for spinal curvature, can be generated using landmarks first attached to a substantially natural 3-D model of the organ. The model is then registered to patient data. The landmarks of the model are then transferred to the patient data according to an estimated transformation for full 3D characterization. The transferred landmarks can then be utilized for measuring the position, rotation and deformation of one or more components of the organ of the patient data (e.g., each vertebra). Altering the set of landmarks selected or used, other measures describing a deformity, e.g., a spinal deformity (including other than the ones described herein), can be derived].
Regarding Claim 8
Calloway/Forsberg/Zhan teach the surgical planning system of Claim 1,
Calloway/Forsberg/Zhan further teach wherein the anatomical feature includes a spine [Forsberg at Para. 0096 teaches the term “model” refers to a substantially anatomically correct 3-D model of a normal target anatomical structure of an organ, e.g., a spine model],
wherein the landmark includes a plurality of landmarks associated with the spine [Forsberg at Para. 0092 teaches the term “landmark” and derivatives thereof refer to electronic tags or markings that can be attached to structure associated with a model of an organ and/or patient image data of an organ, e.g., the spine],
wherein the parameter includes a plurality of alignment parameters associated with the spine [Forsberg at Para. 0054 teaches still other embodiments are directed to systems for evaluating spinal patient image data. The systems include: at least one processor comprising a spinal deformity analysis module; and at least one display in communication with the at least one processor. The spinal deformity analysis module is configured to: (a) provide a three dimensional (3-D) model of the spine having substantially naturally shaped vertebrae to the display, the 3-D model configured to allow a user to apply or have a plurality of landmarks electronically applied to each of the vertebrae; (b) obtain 3-D patient image data of a spine of the patient; (c) register the 3-D model and the 3-D patient image data of the spine; then (d) transfer the landmarks from the model to the 3-D patient image; then (e) calculate measurements for the vertebrae, including a plurality of the following: (i) position in x, y and z coordinates (mm), (ii) rotation θx, θy, θz in degrees, and (iii) a local deformity (lateral wedge) value (mm) using the transferred landmarks; and (f) generate a plurality of graphs of curvature of the spine to the display using the calculated measurements],
and wherein determining the parameter includes determining the plurality of anatomical parameters based on the plurality of landmarks [Forsberg at Para. 0054].
Regarding Claim 9
Calloway/Forsberg/Zhan teach the surgical planning system of Claim 1,
Calloway/Forsberg/Zhan further teach wherein outputting the indication of the parameter includes:
determining a classification associated with the anatomical feature based on the alignment parameter [Forsberg at Para. 0102 teaches Embodiments of the invention provide automated estimated measurements for quantifying the degree of scoliosis and/or an automated analysis that indicate how different measures relate to each other. These automated measurements can be used to classify various sub-types of idiopathic scoliosis and to determine suitable treatments of the different sub-types];
and outputting an indication of the classification [Zhan at Para. 0063 teaches output of these classifiers (or detectors) Ai, Bj, and Dk represents the likelihood of a voxel p belonging to an anchor landmark, bundle landmark and anatomical primitive respectively].
Claims 5, 13 rejected under 35 U.S.C. 103(a) as being unpatentable over Calloway, Forsberg, Zhan, as applied to claim 1 above, and further in view of GAWEL et al (Foreign Publication WO-2023064957-A1).
Regarding Claim 5
Calloway/Forsberg/Zhan the surgical planning system of Claim 2,
Calloway/Forsberg/Zhan further teach wherein the modality of the image includes three-dimensional (“3D”) imaging [Forsberg at Para. 0103 teaches embodiments of the invention describe registration of a highly detailed spine model M (FIGS. 2A, 2B, 4A) to image data P (FIG. 2A, 2B, 2C) from a 3-D computed tomography (CT) data set, an MRI data set or a data set that combines both CT and MRI image data (e.g., a composite image data set)],
and wherein detecting the landmark associated with the anatomical feature within the image includes:
and detecting the landmark associated with the anatomical feature within the 2D version of the image [Zhan at Para. 0007 teaches described herein are systems and methods for detecting anatomical components in images. In accordance with one implementation, at least one anchor landmark is detected in an image].
Calloway/Forsberg/Zhan do not teach processing the image to generate a two-dimensional (“2D”) version of the image;
GAWEL teaches processing the image to generate a two-dimensional (“2D”) version of the image [GAWEL at Para. 0058 teaches in some embodiments, data augmentation can be performed on 3D image data (e.g., 3D CT image data including 2D scans of a 3D volume), and the augmented 3D image data can be used to construct 2D images];
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Calloway, Forsberg, Zhan with the 2D image generation of GAWEL with the motivation to improve identifying locations of surgical sites.
Regarding Claim 13
Calloway/Forsberg/Zhan teach the surgical planning system of Claim 8,
Calloway/Forsberg/Zhan further teach wherein the first perspective is anteroposterior, wherein the second perspective is lateral [Forsberg at Para. 0098 teaches the term “rapid” means for a respective patient, the patient image data set can be automatically electronically analyzed to generate computed measurements of an entire volume in less than 10 minutes, typically between about 1-5 minutes, such as 3 minutes for 12-17 vertebrae. The computed measurements can include measurements of lateral displacement, axial rotation, sagittal rotation and anterior-posterior displacement of a target organ, e.g., each vertebrae of a spine].
Calloway/Forsberg/Zhan do not teach and wherein outputting the indication of the parameter includes displaying a 3D model based on the first 2D image and the second 2D image, the 3D model labeled with the parameter.
GAWEL teaches and wherein outputting the indication of the parameter includes displaying a 3D model based on the first 2D image and the second 2D image, the 3D model labeled with the parameter [GAWEL at Para. 0040 teaches in some embodiments, the image data generated by the imaging device 160 can include a plurality of 2D image scans that together provide image data for a 3D volume; Casey at Para. 0073 teaches The 3D model 1100 includes a set of assigned ordinal identifiers (i. e. , T12, LI, L2, L3, L4) for identifying the different levels of the spine. In some embodiments, the assigned vertebral level type or ordinal identifiers can be depicted using text (e.g., on the side of the relevant vertebrae, or overlaid on the relevant vertebrae) or other visual elements indicative of the vertebral level type or ordinal identifiers (e.g., different coloring, different shading, etc. with a suitable legend). The virtual representations of the patient’s anatomy may be visualized, e.g., on a display system of a compute device (e.g., compute device 110, 210) and/or surgical navigation system (e.g., surgical navigation system 170)].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Calloway, Forsberg, Zhan with the landmarks of GAWEL with the with the motivation to improve identifying locations of surgical sites.
Claims 10-12 rejected under 35 U.S.C. 103(a) as being unpatentable over Calloway, Forsberg, Zhan, as applied to claim 1 above, and further in view of Casey et al (US Publication 11793577).
Regarding Claim 10
Calloway/Forsberg/Zhan teach the surgical planning system of Claim 1,
Calloway/Forsberg/Zhan further teach wherein the image includes a plurality of images, wherein a first modality of a first image of the plurality of images is different than a second modality of a second image of the plurality of images, and wherein detecting the landmark includes detecting [Forsberg at Para. 0177 teaches in some particular embodiments, the imaging modality 95 can be any desirable modality such as, but not limited to MRI, CT (computed tomography), fluoroscopy, ultrasound, and the like. The visualization system 10 may also operate to render images using data sets from more than one of these modalities. That is, the visualization system 10 may be configured to render images irrespective of the imaging modality data type (i.e., a common system may render images for both CT and MRI volume image data). In some embodiments, the system 10 may optionally combine image data sets generated from different imaging modalities 95 to generate a combination image for a patient]:
Calloway/Forsberg/Zhan do not teach a first landmark associated with the anatomical feature within the first image;
and a second landmark associated with the anatomical feature within the second image, the operations further including:
registering the first image to the second image based on the first landmark and the second landmark.
Casey teaches a first landmark associated with the anatomical feature within the first image [Casey at Col. 20 Lines 40-43 teaches flowchart 450 can be used to map of common landmarks from two 2D images (e.g., anterior posterior (AP) and lateral) to 3D image for each of at least three landmarks per vertebra];
and a second landmark associated with the anatomical feature within the second image, the operations further including [Casey at Col. 20 Lines 40-43]:
registering the first image to the second image based on the first landmark and the second landmark [Casey at Col. 20 Lines 40-43 (mapping interpreted as registering)].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Calloway, Forsberg, Zhan with the landmarks of Casey with the motivation to improve an orientation of a spine and/or a position of a vertebra using 3D anatomical data in a load bearing position.
Regarding Claim 11
The surgical planning system of Claim 10, wherein the first image includes a three-dimensional ("3D") image, wherein the second image includes a two-dimensional ("2D") image, and wherein outputting the indication of the parameter includes displaying a 3D model representing the 2D image, the 3D model labeled with the parameter.
Regarding Claim 12
The surgical planning system of Claim 1, wherein the image includes a plurality of images, wherein a first image of the plurality of images includes a first two-dimensional ("2D") image of the anatomical feature from a first perspective, wherein a second image of the plurality of images includes a second 2D image of the anatomical feature from a second perspective that is different than the first perspective, and wherein detecting the landmark includes detecting: a first landmark associated with the anatomical feature within the first image; and a second landmark associated with the anatomical feature within the second image, the operations further including: registering the first image to the second image based on the first landmark and the second landmark.
Claims 21, 23 is/are rejected under 35 U.S.C. 103(a) as being unpatentable over Calloway et al (US Publication No. 20210228281 in view of KANG et al (Foreign Publication WO-2018209042-A2) in view of Paul et al (US Publication No. 20210378752) in view of Forsberg et al (US Publication No. 20140323845) in view of Zhan et al (US Publication No. 20130136322).
Regarding Claim 21
Calloway teaches a surgical system configured to automatically determine a spine alignment parameter associated with an anatomical feature, the surgical system comprising:
a surgical navigation system configured to provide a surgical plan to a display device to assist a user during surgery [Calloway at Para. 0033 teaches a surgeon reviewing the image scan(s) on a display device of the computer platform 910 (FIG. 9) generates a surgical plan defining a target pose for a surgical tool to be used during a surgical procedure on an anatomical structure of the patient.];
a navigation controller operative to obtain the spinal surgery plan from the surgical navigation system and determine a target pose of the surgical instrument based on the spinal surgery plan indicating where a surgical procedure is to be performed on the spine of the defined-patient and based on the pose of the spine of the defined-patient, and generate steering information based on comparison of the target pose of the surgical instrument and the pose of the surgical instrument [Calloway at Para. 0105 teaches the navigation controller 828 may be further configured to generate steering information based on the target pose for the surgical tool, the pose of the anatomical structure, and the pose of the surgical tool and/or the end effector, where the steering information indicates where the surgical tool and/or the end effector of a surgical robot should be moved to perform the surgical plan];
an imaging system configured to transmit images to the surgical navigation system [Calloway at Para. 0060 (see Claim 1 for explanation)];
wherein the surgical planning system further provides instructions to guide movement of a surgical robot [Calloway at Para. 0075 (see Claim 1 for explanation)].
Calloway does not teach a camera tracking system configured to determine the pose of the spine of the defined- patient relative to a pose of a surgical instrument manipulated by an operator and/or a surgical robot;
the surgical navigation system configured to obtain postoperative feedback data provided by distributed networked computers regarding surgical outcomes for a plurality of patients;
train a machine learning model based on the postoperative feedback data;
and obtain preoperative data from one of the distributed network computers characterizing a defined-patient, generate a spinal surgery plan for the defined-patient based on processing the preoperative data through the machine learning model;
and a surgical planning system comprising:
processing circuitry;
and memory coupled to the processing circuitry and having instructions stored therein that are executable by the processing circuitry to cause the surgical planning system to perform operations including:
receiving an image of the anatomical feature from an imaging device;
detecting a landmark associated with the anatomical feature within the image;
determining the parameter associated with the anatomical feature based on the landmark;
and outputting an indication of the parameter associated with the anatomical feature, a graphical user interface for interacting with the surgical planning system to display a 3D model of the spine in one portion, a quantitative listing of spinal alignment measurements in a second portion, and a coronal image and a sagittal image of a selection of spinal alignment measurements on a third portion
wherein the indication of the parameter associated with the anatomical feature is transmitted to a robotic assisted surgery system,
KANG teaches a camera tracking system configured to determine the pose of the spine of the defined- patient relative to a pose of a surgical instrument manipulated by an operator and/or a surgical robot [KANG at Para. 0036 teaches referring to Figure 3, navigation system 12 includes a plurality of tracking devices 16, also referred to herein as trackers. In the illustrated embodiment, trackers 16 are coupled to separate vertebra of the patient. In some cases, the trackers 16 are firmly affixed to sections of bone via bone screws, bone pins, or the like. In other cases, clamps on the spinous process or other portion of the spine may be used to attach the trackers 16. In further embodiments, the trackers 16 could be mounted to other tissue types or parts of the anatomy. The position of the trackers 16 relative to the anatomy to which they are attached can be determined by registration techniques, such as point-based registration in which a digitizing probe 73 (e.g., navigation pointer with its own markers) is used to touch off on bony landmarks on the bone or to touch on several points on the bone for surface-based registration. Conventional registration techniques can be employed to correlate the pose of the trackers 16 to the patient's anatomy, e.g., the vertebra V being treated; KANG at Para. 0038 teaches a base tracker 16 is also coupled to the base 22 to track the pose of the surgical tool 30];
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine robot of Calloway with the camera of KANG with the motivation to improve robotic systems for performing surgical procedures in a patient’ s spine.
Calloway/KANG do not teach the surgical navigation system configured to obtain postoperative feedback data provided by distributed networked computers regarding surgical outcomes for a plurality of patients;
train a machine learning model based on the postoperative feedback data;
and obtain preoperative data from one of the distributed network computers characterizing a defined-patient, generate a spinal surgery plan for the defined-patient based on processing the preoperative data through the machine learning model;
and a surgical planning system comprising:
processing circuitry;
and memory coupled to the processing circuitry and having instructions stored therein that are executable by the processing circuitry to cause the surgical planning system to perform operations including:
receiving an image of the anatomical feature from an imaging device;
detecting a landmark associated with the anatomical feature within the image;
determining the parameter associated with the anatomical feature based on the landmark;
and outputting an indication of the parameter associated with the anatomical feature, a graphical user interface for interacting with the surgical planning system to display a 3D model of the spine in one portion, a quantitative listing of spinal alignment measurements in a second portion, and a coronal image and a sagittal image of a selection of spinal alignment measurements on a third portion
wherein the indication of the parameter associated with the anatomical feature is transmitted to a robotic assisted surgery system,
Paul teaches the surgical navigation system configured to obtain postoperative feedback data provided by distributed networked computers regarding surgical outcomes for a plurality of patients [Paul at Para. 0122 teaches as will be explained in further detail below, the feedback training component 1228 is configured to obtain post-operative feedback data provided by distributed networked computers regarding surgical outcomes for a plurality of patients, and to train a machine learning model based on the post-operative feedback data. Although FIG. 12 shows a single computer, e.g., smart phone, providing post-operative feedback data during the post-operative stage 1204 through one or more networks 1230 (e.g., public (Internet) networks and or private networks) to the surgical guidance system 1220 for storage in the central database 1210, it is to be understood that many network computers (e.g., hundreds of computers) would provide post-operative feedback data for each of many prior patients (e.g., hundreds of patients) to the surgical guidance system 1220 (i.e., to the feedback training component 1228) for use in training the machine learning model. Moreover, as explained in further detail below, the feedback training component 1228 can further train the machine learning model based on pre-operative data (e.g., surgical plans) obtained during the pre-operative stage 1200 for the prior patients and based on intra-operative data obtained during the intra-operative stage 1202 (e.g., data indicating what surgical procedure(s) were used, what implant(s) were used, which tool(s) were used, 6 degree-of-freedom (DOF) tracked movements of the tool(s) during identified surgical procedure(s), etc.) for the prior patients.];
train a machine learning model based on the postoperative feedback data [Paul at Para. 0122];
and obtain preoperative data from one of the distributed network computers characterizing a defined-patient, generate a spinal surgery plan for the defined-patient based on processing the preoperative data through the machine learning model [Paul at Para. 0123 teaches accordingly, the pre-operative planning component 1224 of the machine learning processing circuit 1222 generates a surgical plan for the candidate patient using the machine learning model which has been trained based on the post-operative feedback data regarding surgical outcomes for the prior patients. The training of the machine learning model can be repeated as more post-operative feedback is obtained by the feedback training component 1228 so that the surgical plans that are generated will result in continuing improvement of the resulting surgical outcomes for patients];
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Calloway, KANG with the machine learning model of Paul with the motivation to provides improved surgical outcomes.
Calloway/KANG/Paul does not teach and a surgical planning system comprising:
processing circuitry;
and memory coupled to the processing circuitry and having instructions stored therein that are executable by the processing circuitry to cause the surgical planning system to perform operations including:
receiving an image of the anatomical feature from an imaging device;
detecting a landmark associated with the anatomical feature within the image;
determining the parameter associated with the anatomical feature based on the landmark;
and outputting an indication of the parameter associated with the anatomical feature, a graphical user interface for interacting with the surgical planning system to display a 3D model of the spine in one portion, a quantitative listing of spinal alignment measurements in a second portion, and a coronal image and a sagittal image of a selection of spinal alignment measurements on a third portion
wherein the indication of the parameter associated with the anatomical feature is transmitted to a robotic assisted surgery system,
Forsberg teaches and a surgical planning system comprising:
processing circuitry [Forsberg at Para. 0170 (see Claim 1 for explanation)];
and memory coupled to the processing circuitry and having instructions stored therein that are executable by the processing circuitry to cause the surgical planning system to perform operations including [Forsberg at Para. 0170 (see Claim 1 for explanation)]:
receiving an image of the anatomical feature from an imaging device [Forsberg at Para. 0088 (see Claim 1 for explanation)];
determining the parameter associated with the anatomical feature based on the landmark [Forsberg at Para. 0088 (see Claim 1 for explanation)];
and outputting an indication of the parameter associated with the anatomical feature, a graphical user interface for interacting with the surgical planning system to display a 3D model of the spine in one portion, a quantitative listing of spinal alignment measurements in a second portion, and a coronal image and a sagittal image of a selection of spinal alignment measurements on a third portion [Forsberg at Para. 0025, 0029, 0030, 0033 (see Claim 1 for explanation)]
wherein the indication of the parameter associated with the anatomical feature is transmitted to a robotic assisted surgery system [Forsberg at Para. 0058 (see Claim 1 for explanation)],
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Calloway, KANG, Paul with the image of Forsberg with the motivation to improve GPU computational performance.
Calloway/KANG/Paul/Forsberg does not teach detecting a landmark associated with the anatomical feature within the image;
Zhan teaches detecting a landmark associated with the anatomical feature within the image [Zhan at Para. 0007 (see Claim 1 for explanation)];
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Calloway, KANG, Paul, Forsberg with the landmark detection of Zhan with the motivation to improve MR spinal analysis workflow.
Regarding Claim 23
Calloway teaches a surgical system configured to automatically determine a spine alignment parameter associated with an anatomical feature, the surgical system comprising:
a surgical navigation system configured to provide a surgical plan to a display device to assist a user during surgery [Calloway at Para. 0033 (see Claim 21 for explanation)];
a navigation controller operative to obtain the spinal surgery plan from the surgical navigation system and determine a target pose of the surgical instrument based on the spinal surgery plan indicating where a surgical procedure is to be performed on the spine of the defined-patient and based on the pose of the spine of the defined-patient, and generate steering information based on comparison of the target pose of the surgical instrument and the pose of the surgical instrument [Calloway at Para. 0105 (see Claim 21 for explanation)];
an imaging system configured to transmit images to the surgical navigation system [Calloway at Para. 0075 (see Claim 21 for explanation)];
a surgical robot including a robot base [Calloway at Para. 0038 teaches robot base 10 may act as a lower support for surgical robot 4.];
a robot arm, an end effector connected to the robot base and configured to guide movement of surgical instruments and at least on one motor operatively coupled to control movement of the robot arm relative to the robot base, a surgical planning system comprising [Calloway at Para. 0074 teaches motion control subsystem 840 may be configured to physically move vertical column 16, upper arm 18, lower arm 20, or rotate end effector coupler 22. The physical movement may be conducted through the use of one or more motors 850-854. For example, motor 850 may be configured to vertically lift or lower vertical column 16. Motor 851 may be configured to laterally move upper arm 18 around a point of engagement with vertical column 16 as shown in FIG. 2. Motor 852 may be configured to laterally move lower arm 20 around a point of engagement with upper arm 18 as shown in FIG. 2. Motors 853 and 854 may be configured to move end effector coupler 22 to provide translational movement and rotation along in about three-dimensional axes]:
wherein the surgical planning system further provides instructions to guide movement of a surgical robot [Calloway at Para. 0060 (see Claim 21 for explanation)].
Calloway does not teach a camera tracking system configured to determine the pose of the spine of the defined- patient relative to a pose of a surgical instrument manipulated by an operator and/or a surgical robot;
the surgical navigation system configured to obtain postoperative feedback data provided by distributed networked computers regarding surgical outcomes for a plurality of patients;
train a machine learning model based on the postoperative feedback data;
and obtain preoperative data from one of the distributed network computers characterizing a defined-patient, generate a spinal surgery plan for the defined-patient based on processing the preoperative data through the machine learning model;
processing circuitry;
and memory coupled to the processing circuitry and having instructions stored therein that are executable by the processing circuitry to cause the surgical planning system to perform operations including:
receiving an image of the anatomical feature from an imaging device;
detecting a landmark associated with the anatomical feature within the image;
determining the parameter associated with the anatomical feature based on the landmark;
and outputting an indication of the parameter associated with the anatomical feature, a graphical user interface for interacting with the surgical planning system to display a 3D model of the spine in one portion, a quantitative listing of spinal alignment measurements in a second portion, and a coronal image and a sagittal image of a selection of spinal alignment measurements on a third portion
wherein the indication of the parameter associated with the anatomical feature is transmitted to a robotic assisted surgery system,
KANG teaches a camera tracking system configured to determine the pose of the spine of the defined- patient relative to a pose of a surgical instrument manipulated by an operator and/or a surgical robot [KANG at Para. 0036, 0038 (see Claim 21 for explanation)];
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine robot of Calloway with the camera of KANG with the motivation to improve robotic systems for performing surgical procedures in a patient’ s spine.
Calloway/KANG do not teach the surgical navigation system configured to obtain postoperative feedback data provided by distributed networked computers regarding surgical outcomes for a plurality of patients;
train a machine learning model based on the postoperative feedback data;
and obtain preoperative data from one of the distributed network computers characterizing a defined-patient, generate a spinal surgery plan for the defined-patient based on processing the preoperative data through the machine learning model;
processing circuitry;
and memory coupled to the processing circuitry and having instructions stored therein that are executable by the processing circuitry to cause the surgical planning system to perform operations including:
receiving an image of the anatomical feature from an imaging device;
detecting a landmark associated with the anatomical feature within the image;
determining the parameter associated with the anatomical feature based on the landmark;
and outputting an indication of the parameter associated with the anatomical feature, a graphical user interface for interacting with the surgical planning system to display a 3D model of the spine in one portion, a quantitative listing of spinal alignment measurements in a second portion, and a coronal image and a sagittal image of a selection of spinal alignment measurements on a third portion
wherein the indication of the parameter associated with the anatomical feature is transmitted to a robotic assisted surgery system,
Paul teaches the surgical navigation system configured to obtain postoperative feedback data provided by distributed networked computers regarding surgical outcomes for a plurality of patients [Paul at Para. 0122 (see Claim 21 for explanation)];
train a machine learning model based on the postoperative feedback data [Paul at Para. 0122 (see Claim 21 for explanation)];
and obtain preoperative data from one of the distributed network computers characterizing a defined-patient, generate a spinal surgery plan for the defined-patient based on processing the preoperative data through the machine learning model [Paul at Para. 0123 (see Claim 21 for explanation)];
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Calloway, KANG with the machine learning model of Paul with the motivation to provides improved surgical outcomes.
Calloway/KANG/Paul do not teach processing circuitry;
and memory coupled to the processing circuitry and having instructions stored therein that are executable by the processing circuitry to cause the surgical planning system to perform operations including:
receiving an image of the anatomical feature from an imaging device;
detecting a landmark associated with the anatomical feature within the image;
determining the parameter associated with the anatomical feature based on the landmark;
and outputting an indication of the parameter associated with the anatomical feature, a graphical user interface for interacting with the surgical planning system to display a 3D model of the spine in one portion, a quantitative listing of spinal alignment measurements in a second portion, and a coronal image and a sagittal image of a selection of spinal alignment measurements on a third portion
wherein the indication of the parameter associated with the anatomical feature is transmitted to a robotic assisted surgery system,
Forsberg teaches processing circuitry [Forsberg at Para. 0170 (see Claim 1 for explanation)];
and memory coupled to the processing circuitry and having instructions stored therein that are executable by the processing circuitry to cause the surgical planning system to perform operations including [Forsberg at Para. 0170 (see Claim 1 for explanation)]:
receiving an image of the anatomical feature from an imaging device [Forsberg at Para. 0088 (see Claim 1 for explanation)];
determining the parameter associated with the anatomical feature based on the landmark [Forsberg at Para. 0092 (see Claim 1 for explanation)];
and outputting an indication of the parameter associated with the anatomical feature, a graphical user interface for interacting with the surgical planning system to display a 3D model of the spine in one portion, a quantitative listing of spinal alignment measurements in a second portion, and a coronal image and a sagittal image of a selection of spinal alignment measurements on a third portion Forsberg at Para. 0025, 0029, 0030, 0033 (see Claim 1 for explanation)]
wherein the indication of the parameter associated with the anatomical feature is transmitted to a robotic assisted surgery system [Forsberg at Para. 0058 (see Claim 1 for explanation)],
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Calloway, KANG, Paul with the image of Forsberg with the motivation to improve GPU computational performance.
Calloway/KANG/Paul/Forsberg do not teach detecting a landmark associated with the anatomical feature within the image;
Zhan teaches detecting a landmark associated with the anatomical feature within the image [Zhan at Para. 0007 (see Claim 1 for explanation)];
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Calloway, KANG, Paul, Forsberg with the landmark detection of Zhan with the motivation to improve MR spinal analysis workflow.
Response to Arguments
Rejection under 35 U.S.C. § 101
Regarding the rejection of Claims 1-13,21-22, the Examiner has considered the Applicant’s arguments; however the arguments are not persuasive. Any arguments inadvertently not addressed are unpersuasive for at least the following reasons. Applicant argues:
Claim 1 is amended to more clearly recite the features of the invention and provide elements that integrate the novel method of performing robotic assisted surgery according to the claimed invention. Thus, in view of these amendments to claim 1, Applicant respectfully requests that the rejection of claims 1-13 be withdrawn.
Regarding (a), the Examiner respectfully disagrees. Applicant has not provided any evidence to support statement.
Rejection under 35 U.S.C. § 102/103
Regarding the rejection of Claims 1-13,21-22, the Examiner has considered the Applicant’s arguments; however, these arguments are moot given the new grounds of rejection as necessitated by amendment.
Conclusion
The prior art made of record and not relied upon in the present basis of rejection are noted in the attached PTO 892 and include:
DIPPEL et al (Foreign Publication WO-2023117587-A1) discloses a method for automatic registration of a patient for a surgical navigation of a surgical intervention.
Chappuis et al (US Publication No. 20210315590) discloses Systems and methods for resecting a knee joint using a navigated pin guide driver system.
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 extension fee 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 JONATHAN C EDOUARD whose telephone number is (571)270-0107. The examiner can normally be reached M-F 730 - 430.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Morgan can be reached on (571) 272 - 6773. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JONATHAN C EDOUARD/Examiner, Art Unit 3683
/JASON S TIEDEMAN/Primary Examiner, Art Unit 3683
Prosecution Insights