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 .
Status of Claims
Claims 1-19 are pending in this application. Claims 15-19 are withdrawn, and Claims 1-14 have
been examined on the merits.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 4, 6-9, and 12-13 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Blau (US20220044440A1, cited in Applicant IDS).
Regarding Claim 1,
Blau teaches a computer-implemented method of assisting positioning of a tool (5) with respect to a specific body part (202) of a patient (200) (corresponding disclosure in at least [0008], where there is a method for assisting positioning of a tool (the surgical tool is visible in the image with a coordinate system, assisting in the positioning “object of the invention to provide a device and/or a method enabling a 3D representation and localization of at least one object at least partially visible in an X-ray projection image with respect to a coordinate system”), the method comprising:
a) receiving, by a computing device (10), intraoperative imaging data (ID) from an imaging device (20) arranged in a proximity of the patient (200) (corresponding disclosure in at least [0080], where intraoperative image data is used “use preoperative imaging to generate information about the 3D shape of the patient's specific bone, instead of working with general statistical models that describe the variability of bones”),
the intraoperative imaging data (ID) comprising 2D images, a plurality of the 2D images capturing the specific body part (202) of the patient (200) from a plurality of different perspectives with respect to the specific body part (202) of the patient (200) (corresponding disclosure in at least [0083], where there are multiple 2D images that makes up the total image, at different parts of the body part “Deep Morphing may be used to detect the contour of Object F and label points on its contour in each 2D X-ray image.”) and one or more of the plurality of the 2D images capturing at least a part of the tool (5) from at least one perspective (corresponding disclosure in at least disclosure in at least [0084], where the images are taken in different perspectives (angles) “taking into account the 3D angle between the imaging directions, which may be determined using two different procedures. The more precisely this angle can be determined, the more precise the 3D registration may be”);
b) reconstructing, by the computing device (10), an anatomical 3D shape (AS) of the specific body part (202) using an artificial-intelligence based algorithm corresponding to the specific body part (202) based on the intraoperative imaging data (ID) and data indicative of perspectives corresponding to the plurality of the 2D images (corresponding disclosure in at least [0040], where reconstruction is performed using artificial intelligence (ML) at different perspectives “ f multiple X-ray images are acquired, the information from them may be fused (or registered) to increase the accuracy of 3D reconstruction and/or determination of spatial positions or orientations. It is preferable if these X-ray images are acquired from different imaging directions because this may help resolve ambiguities. The more different the imaging directions are (e.g., AP and ML images), the more helpful the images may be in terms of a determination of 3D information” and further in [0028], where artificial intelligence (DNN) is used corresponding disclosure in at least to a body part “ a deep neural net (DNN) may be utilized for a classification of an object in an X-ray projection image”);
c) estimating, by the computing device (10), a current position (5c) of the tool (5) with respect to the anatomical 3D shape (AS) of the specific body part (202) based on the intraoperative imaging data (ID) (corresponding disclosure in at least [0135], where steps are outlined to determine a position of the tool (nail) based on the image “ A deep-morphing approach detects the outline of the nail assembly (nail, aiming device, and interface) in the X-ray image, taking into account the viewing direction (AP or ML).”); and
d) generating, by the computing device (10), positioning guidance data (GD) comprising a visual representation of the estimated current position (5c) of the tool (5) with respect to the anatomical 3D shape (AS) of the specific body part (202) (corresponding disclosure in at least [0142], where a visualization is generated in a 3D model “A visualization of the 3D model of the nail assembly and/or the proximal femur may be shown in the X-ray projection image. This could be the outline of their 2D projection or a rendering of the 3D model”).
Regarding Claim 4,
Blau further teaches wherein the step of reconstructing the anatomical 3D shape (AS) comprises: a) segmenting the intraoperative imaging data (ID) in order to identify the specific body part (202) of the patient (200) (corresponding disclosure in at least [0029], where there is a first step of segmenting the data to determine the body part (object) “ The technique consists of a two-stage approach (called deep morphing), where in the first stage a neural segmentation network detects the contour (outline) of the bone or other object”); and
b) reconstructing the anatomical 3D shape (AS) further using the segmented intraoperative imaging data (ID) (corresponding disclosure in at least [0029], where there is a further step of fitting the model to a shape to then be used for reconstruction “then in the second stage a statistical shape model is fit to this contour using a variant of an Active Shape Model algorithm (but other algorithms can be used as well for the second stage)” and further in [0076] “Given a 3D statistical shape or appearance model of the same anatomical structure, this model can then be deformed in a way that its virtual projection matches the actual projection in the X-ray image, hence leading to a 3D reconstruction of the anatomical structure and allowing a localization of the object and determination of the imaging direction”).
Regarding Claim 6,
Blau further teaches estimating, by the computing device (10), the perspectives corresponding to the intraoperative imaging data (ID) (corresponding disclosure in at least [0076], where the perspectives for the imaging data are estimated (the views/orientation are localized to determine the direction of the projection) “Given a 3D statistical shape or appearance model of the same anatomical structure, this model can then be deformed in a way that its virtual projection matches the actual projection in the X-ray image, hence leading to a 3D reconstruction of the anatomical structure and allowing a localization of the object and determination of the imaging direction” and further in [0077], where each individual perspective (2D image) is used to estimate (localize) “ the present invention is also able to localize an implant or instrument relative to anatomy based on one X-ray image”),
wherein estimating the perspectives comprises identifying a location and/or location orientation of the imaging device relative to the specific body part (corresponding disclosure in at least [0082]-[0083], where multiple images are taken of the specific body part (object) with the location being determined of the imaging device “The invention allows to register multiple X-ray images of at least one common object taken from different directions. This is important because 3D registration allows a determination of relative 3D positions between multiple objects without an explicit determination of the imaging depth” and further “This procedure does not need a priori information about the imaging directions because it implicitly determines the imaging direction for each X-ray image”, where the directions are just determined).
Regarding Claim 7,
Blau further teaches wherein estimating perspectives corresponding to the intraoperative imaging data (ID) is performed using a tool geometrical model indicative of a geometry of the tool (5) captured at least partially by one or more of the plurality of the 2D images (corresponding disclosure in at least [0077], where a part of the instrument is captured by the image “Being able to provide a 3D reconstruction of anatomy based on one X-ray image only is an advantage over the state-of-the-art, which requires the acquisition of at least two images from different viewing directions (typically an AP and an ML image). Moreover, the present invention is also able to localize an implant or instrument relative to anatomy based on one X-ray image”), wherein the estimation comprises:
a) computing a plurality of projections of the tool geometrical model from a plurality of candidate perspectives (corresponding disclosure in at least [0077], where the tool (instrument) has multiple projections “Being able to provide a 3D reconstruction of anatomy based on one X-ray image only is an advantage over the state-of-the-art, which requires the acquisition of at least two images from different viewing directions (typically an AP and an ML image). Moreover, the present invention is also able to localize an implant or instrument relative to anatomy based on one X-ray image” and further in [0147] ,where it’s taught that multiple perspectives (imaging direction) are computed “As mentioned before, the present invention and using the invention disclosed in Blau 917 allow a determination of the imaging direction based on independent approaches utilizing different information”) ; and
b) identifying the perspectives corresponding to the intraoperative imaging data (ID) by comparing the at least part of the tool (5) as captured by the respective 2D image of the intraoperative imaging data (ID) with the plurality of projections from the plurality of candidate perspectives (corresponding disclosure in at least [0039], where the plurality of projections (the imaging direction) is compared with the projections (the imaging direction of the object) for determining projection direction “Based on one image of an anatomical object, the model is deformed in such a way that its virtual projection matches the actual projection of the object in the X-ray image. Doing so allows a computation of an imaging direction (which describes the direction in which the X-ray beam passes through the object). As an additional plausibility check, the computed imaging direction may then be compared with the imaging direction for the same object that is determined”).
Regarding Claim 8,
Blau further teaches wherein estimating the current position (Sc) of the tool (5) is performed using a tool geometrical model indicative of a geometry of the tool (5), the step of estimating the current position (Sc) of the tool (5) comprising at least one of the following steps: a) comparing a projection of the tool geometrical model - onto the plane of one or more of the 2D images of the intraoperative imaging data (ID) - with the at least part of the tool (5) as captured by the respective 2D image of the intraoperative imaging data (ID) (corresponding disclosure in at least [0085], where the tool (the nail) being captured in the image and being compared to a plane of the 2D image (projection) “One way of determining this angle would be to determine the imaging directions as disclosed in Blau 917 for each X-ray image and to compute their difference. Another way may be to utilize another object in the X-ray image (called “Object G”) whose model is deterministic (e.g., a nail connected to an aiming device). By matching the virtual projection of Object G to its actual projection in each X-ray image, the imaging directions for Object G may be determined”);
And b) determining a position of the tool geometrical model that produces a projection onto the planes of the 2D images of the intraoperative imaging data (ID) that matches the at least part of the tool (5) as captured by the respective 2D image of the intraoperative imaging data (ID).
Regarding Claim 9,
Blau further teaches wherein generating positioning guidance data (GD) comprises overlaying the visual representation of the estimated current position (Sc) of the tool (5) onto a visual representation of the reconstructed anatomical 3D shape (AS) (corresponding disclosure in at least [0036], where the tool (imaged object) is overlaid onto the image “When displaying the X-ray projection image, geometrical aspects and/or dimensions may be shown as an overlay in the projection image. Alternatively and/or additionally, at least a portion of the model may be shown in the X-ray image, for example as a transparent visualization or 3D rendering, which may facilitate an identification of structural aspects of the model and thus of the imaged object by a user”).
Regarding Claim 12,
Blau further teaches at least one of the following steps: a) providing a tool (5) in accordance with a tool geometrical mode indicative of a geometry of the tool captured at least partially by one or more of the plurality of the 2D images (corresponding disclosure in at least [0077], where a part of the instrument is captured by the image “Being able to provide a 3D reconstruction of anatomy based on one X-ray image only is an advantage over the state-of-the-art, which requires the acquisition of at least two images from different viewing directions (typically an AP and an ML image). Moreover, the present invention is also able to localize an implant or instrument relative to anatomy based on one X-ray image”) ; and b) capturing, using an imaging device (20), intraoperative imaging data (ID) capturing at least a body part of the patient (200) and at least a part of the tool (5); and c) controlling, by the computing device (10), a display device (30) to display at least part of the guidance data (GD) (corresponding disclosure in at least [0041], where there is a tool that is being provided (in accordance with step a)) “ Another way may be to utilize another object (e.g. “Object D”), also shown in both images, whose model is deterministic (e.g., a nail connected to an aiming device). By matching the virtual projection of Object D to its actual projection in each X-ray image”).
Regarding Claim 13,
Blau further teaches wherein the intraoperative imaging data (ID) comprises one or more of: a) radiation-based images, in particular X-ray image(s) of the specific body part (202) of the patient (200) respectively a part of the tool (5);b) ultrasound image(s) of the specific body part (202) of the patient (200) respectively a part of the tool (5);c) arthroscopic image(s) of the specific body part (202) of the patient (200) respectively a part of the tool (5);d) optical imagery of the specific body part (202) of the patient (200) respectively a part of the tool (5); and e) cross-sectional imaging of the patient (200) respectively a part of the tool (5) (corresponding disclosure in at least [0041], where there is an x-ray image of a specific body part wit a part of the tool (object) “for each X-ray image and to compute their difference. Another way may be to utilize another object (e.g. “Object D”), also shown in both images, whose model is deterministic (e.g., a nail connected to an aiming device). By matching the virtual projection of Object D to its actual projection in each X-ray image, the imaging directions for Object D may be determined”).
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 2 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Blau (US20220044440A1 cited in Applicant IDS) in view of Pheiffer (US20170084036A1).
Regarding Claim 2,
Blau further teaches wherein the step of reconstructing, by the computing device (10), an anatomical 3D shape (AS) of the specific body part (202) comprises training the artificial-intelligence based algorithm using a multitude of annotated imaging data sets capturing body parts corresponding to the specific body part (202) of the patient (200) (corresponding disclosure in at least [0027], where multiple annotated datasets are used from other persons that are not the patient “ a statistical atlas is constructed by annotating the salient features in a large set of images or volumes from many patients whom are representative of the population undergoing the intervention”), wherein the annotations comprise data identifying and/or describing properties of the body part (corresponding disclosure in at least [0032], where there are multiple 2D images of the body part (bone of interest) “A neural net may be trained based on a multiplicity of data that is comparable to the data on which it will be applied. In case of an assessment of bone structures in images, a neural net should be trained on the basis of a multiplicity of X-ray images of bones of interest. It will be understood that the neural net may also be trained on the basis of simulated X-ray images”, further in [0033], where the algorithm determines the structure “ a first neural net may be trained to evaluate X-ray image data so as to classify an anatomical structure in the 2D projection image”).
Blau does not teach using a multitude of annotated imaging data sets capturing body parts of persons other than the patient corresponding to the specific body part (202) of the patient.
Pheiffer, in a similar field of endeavor, teaches a similar concept () of using a multitude of annotated imaging data sets capturing body parts of persons other than the patient corresponding to the specific body part (202) of the patient (corresponding disclosure in at least [0027], where multiple annotated datasets are used from other persons that are not the patient “ a statistical atlas is constructed by annotating the salient features in a large set of images or volumes from many patients whom are representative of the population undergoing the intervention”)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have incorporated the annotated imaging data sets from body parts of persons other than the patient as taught by Pheiffer. One of the ordinary skill in the art would have been motivated to incorporate this because it is common to use multiple datasets from other patients for broader data coverage and robustness for the machine learning algorithm.
Regarding Claim 14, Blau teaches the limitations of Claim 1 and further teaches wherein the steps of: receiving intraoperative imaging data (ID); estimating a current position (Sc) of the tool (5); and generating guidance data (GD) are carried out repeatedly (corresponding disclosure in at least [0143], where steps including estimating a position of the tool (object) and generating guidance data are carried out repeatedly “Step 11: Steps 2 through 10 may be repeated. When processing a new image, the system considers any information (in particular, about image and object characteristics) it has gathered from previously processed images” and further in [0132]-[0142], where the steps are described), but does not teach repeating steps at set intervals and/or triggered by certain events and/or manually triggered.
Pheiffer, in a similar field of endeavor, teaches a similar concept (determining alignment of images) of repeating steps at set intervals and/or triggered by certain events and/or manually triggered (corresponding disclosure in at least [0054], where the steps are repeated when triggered by certain events (e.g., triggered by the additional features being located) “ As additional features are located, the imaging of act 22, receipt of indication of act 24 and registering of act 26 are performed again or repeated. The repetition continues until all features are identified and/or until a metric or measure of sufficient registration is met”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have incorporated repeating steps triggered by certain events as taught by Pheiffer. One of the ordinary skill in the art would have been motivated to incorporate this because repeating steps when triggered by certain events ensures that necessary information is acquired with each update.
Claims 3 is rejected under 35 U.S.C. 103 as being unpatentable over Blau (US20220044440A1 cited in Applicant IDS) and Pheiffer (US20170084036A1) as applied in Claim 2, and in further view of Siemionow (US20190142519A1, cited in Applicant IDS).
Regarding Claim 3, Blau and Pheiffer teach the limitations of Claim 2, and Blaufurther teaches generating a multitude of annotated imaging data sets - comprising 2D image(s) capturing body parts corresponding to the specific body part (202) of the patient (200) from a plurality of different perspectives (corresponding disclosure in at least [0032] and [0033]), but does not teach annotated 3D imaging data, in particular computed tomography CT scans, capturing body parts corresponding to the specific body part (202) of the patient (200).
Siemionow, in a similar field of endeavor, teaches a similar concept (surgical planning) of annotated 3D imaging data, in particular computed tomography CT scans, capturing body parts corresponding to the specific body part (202) of the patient (200) (corresponding disclosure in at least [0161], where CT scans are captured in regards to a specific body part of the patient “a set of samples are generated first, wherein LDCT images and HDCT images of the same object (such as an artificial phantom or a lumbar spine) are captured using the computed tomography device” and further in [0029], where the annotated images (labeled) are used “receives segmentation learning data comprising a plurality of batches of labeled anatomical image sets, each image set comprising image data representative of a series of slices of a three-dimensional bony structure of the anatomy”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have incorporated the CT scans capturing body parts corresponding disclosure in at least to the specific body part as taught by Siemionow. One of the ordinary skill in the art would have been motivated to incorporate this because CT scans provide high resolution anatomical data, which is advantageous for use when training neural networks.
Claims 5, and 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Blau (US20220044440A1 cited in Applicant IDS) in view of Siemionow (US20190142519A1, cited in Applicant IDS).
Regarding Claim 5, Blau teaches the limitations of Claim 4 and further teaches wherein the step of segmenting the intraoperative imaging data (ID) comprises: a) identifying region(s) of interest within the intraoperative imaging data (ID) containing the specific body part (202) of the patient (200) using at least one of an artificial-intelligence based detection and segmentation models and a convolutional neural network based detection and segmentation model (corresponding disclosure in at least [0083], where a regio of interest (Object F, which refers to the specific body part, thus the region of interest is identified in each image through the detection network “by a statistical shape or appearance model and called “Object F” in this section) based on two or more X-ray images, the procedure outlined above for one image may be extended to two or more images. That is, Deep Morphing may be used to detect the contour of Object F and label points on its contour in each 2D X-ray image. Given a 3D statistical shape model of Object F, this model can then be deformed in a way that its virtual projections simultaneously match the actual projections of Object F in two or more X-ray images as closely as possible”); and
b) segmenting the region(s) of interest using the artificial- intelligence based detection and segmentation model to thereby generate the segmented intraoperative imaging data (ID) (corresponding disclosure in at least [0076], where AI-based detection is used for segmentation of the image, specifically the region of interest (femur) “he outline/contour of a bone and label points on the contour. For instance, in the segmentation of a femur, the technique is able to determine which points on the contour in the 2D X-ray projection image correspond to the lesser trochanter, and which points correspond to the femoral neck, etc”), but does not teach semantically segmenting the regions of interest.
Siemionow, in a similar field of endeavor, teaches a similar concept of semantically segmenting the regions of interest (corresponding disclosure in at least [0172], where the regions of interest (part of anatomy) are semantically segmented using AI-based detection “The final layer for binary segmentation recognizes two classes (bone and no-bone). The semantic segmentation is capable of recognizing multiple classes, each representing a part of the anatomy”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have incorporated semantically segmenting the regions of interest as taught by Siemionow. One of the ordinary skill in the art would have been motivated to incorporate this because semantic segmentation provides high precision segmentation as each pixel is classified, which is beneficial in training data.
Regarding Claim 10, Blau teaches the limitations of Claim 1, but does not teach identifying, by the computing device (10), a prescribed position (Sp) of the tool (5) with respect to the anatomical 3D shape (AS) of the specific body part (202); and b) overlaying a visual representation of the prescribed position (5p) of the tool (5) onto a visual representation of the estimated current position (Sc) of the tool (5).
Siemionow, in a similar field of endeavor, teaches a similar concept of a) identifying, by the computing device (10), a prescribed position (Sp) of the tool (5) with respect to the anatomical 3D shape (AS) of the specific body part (202); and b) overlaying a visual representation of the prescribed position (5p) of the tool (5) onto a visual representation of the estimated current position (Sc) of the tool (5) (corresponding disclosure in at least [0125], where there is a prescribed position of the tool (suggested position of the instrument according to the preoperative plan) as well as an overlaid representation including where the real instrument is located “may demonstrate a mismatch between a supposed/suggested position of the instrument according to the pre-operative plan 161, displayed as a first virtual image of the instrument 164A located at its supposed/suggested position, and an actual position of the instrument, visible either as the real instrument via the see-through display and/or a second virtual image of the instrument 164B overlaid on the current position of the instrument” and Figure 3E further highlighting the overlaid image).
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Figure 3E of Siemionow
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have incorporated identifying a prescribed position with respect to the anatomical shape and overlaying the prescribed position over the estimated current position as taught by Siemionow. One of the ordinary skill in the art would have been motivated to incorporate this because determining the proper trajectory of a device is essential to ensure accuracy and safety during a procedure.
Regarding Claim 11, Blau and Siemionow teach the limitations of Claim 10 and Siemionow further teaches at least one of the following steps: a) retrieving, by the computing device (10), the prescribed position (5p) of the tool (5) from a datastore comprised by or communicatively connected to the computing device (10); and b) computing the prescribed position (5p) of the tool (5) by the computing device (10), the prescribed position (5p) of the tool (5) being determined by an optimization function based on the anatomical 3D shape (AS) of the body part as well as data indicative of a surgical procedure (corresponding disclosure in at least [0107], where the prescribed position (surgical navigation image contains the prescribed position) is stored in a database “ It generates a surgical navigation image 142A comprising data of at least one of: the pre-operative plan 161 (which are generated and stored in a database before the operation), data of the intra-operative plan 162 (which can be generated live during the operation), data of the patient anatomy scan 163 (which can be generated before the operation or live during the operation) and virtual images 164 of surgical instruments used during the operation (which are stored as 3D models in a database)” and further in [0113], where the surgical navigation image comprises the prescribed position (suggested placement) “, the surgical navigation image may further comprise a 3D image 171 representing at least one of: the virtual image of the instrument 164 or surgical guidance indicating suggested (ideal) trajectory and placement of surgical instruments”).
Response to Arguments
Applicant requested a telephonic interview in the arguments after the examiner has an opportunity to consider the amendment, but before the issuance of the next office action. MPEP 713 lays out the requirements for interviews. In particular section IV) states that the applicant should provide "a brief description of the issues to be discussed" and "the unexpected appearance of an attorney or applicant requesting an interview without any previous notice may well justify the examiner’s refusal of the interview at that time”. In this particular case, no brief description of the issues to be discussed have been sent and there was no notification made by the applicant ahead of time (e.g., a phone call or a letter) requesting an interview. Therefore, an interview could not be scheduled at this time.
Applicant's arguments filed 05/19/2026 regarding the Claim objections have been fully considered and the objections are withdrawn in light of amendments.
Applicant's arguments filed 05/19/2026 regarding the 35 U.S.C. 112b rejections have been fully considered and the rejection is withdrawn in light of amendments.
Applicant's arguments filed 05/19/2026 regarding the 35 U.S.C. 102(a)(1) and 35 U.S.C. 103 rejections have been fully considered but they are not persuasive.
Applicant argues that Claim 1 of the instant application differs from the prior art Blau based on the feature b), where reconstruction is based directly on intraoperative imaging data and data indicative of the imaging perspectives. However, in both [0079] and further in [0080] of Blau, images of different viewing angles, or perspectives are obtained “The accuracy of the 3D reconstruction of an anatomical object may be improved upon by a registration of multiple images acquired from different viewing angles”. [0079] further goes on to describe that using a single image is unsatisfactory “ because such information may not be determined with sufficient precision in typical ML images, it may be extracted from AP images that are routinely acquired earlier in the course of the surgery on the proximal femur. The more information is used from earlier images, the more accurate a 3D reconstruction may be”, hence the use of multiple viewing angles.
In regards to the intraoperative imaging data, [0018] of Blau recites the images being taken during procedure “ a 3D representation of an anatomical structure as generated for example during a diagnostic procedure may be taken as a model of a real anatomical object” and further in [0080], where the images are acquired during surgery (routinely during surgery) “Depending on the bone shape there may be a remaining ambiguity or matching error in the 3D reconstruction based on one image only. This may be alleviated by acquiring a series of images, potentially from different viewing directions, which may be routinely acquired during surgery anyway (e.g. when repositioning the fracture)”
Further, Applicant argues that Blau does not provide an entire surgical navigation suite. However, this is also not disclosed in the instant application. Ultimately, Claim 1 as it is currently written is anticipated by Blau. However, Examiner notes that further detail regarding the surgical navigation aspect or the AI algorithm (e.g., the inputs to the training models) would overcome the current prior art rejection.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., the improvements of patient specificity, enabled by the capability to retain individual features captured by the intraoperative images, retaining or reproducing unique anatomical deviations present in the individual patients) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. Such prior arts include Mahfouz (US20190133693A1) in regards to surgical navigation, image reconstruction, and intraoperative procedures; Zimmermann (US20220125517A1) in regards to surgical navigation, reconstruction via machine learning; and Ben-Yishai (US20240180629A1) in regards to assistance in surgical procedures and determining positions of a tool in surgical treatments.
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/K.E.K./Examiner, Art Unit 3797
/SHAHDEEP MOHAMMED/Primary Examiner, Art Unit 3797
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