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 .
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Application No. 62/851,602; 62/894,818; 62/960,149; 16/878,533; 17/976,785; 63/443,380; and 18/419,547, fail to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. These documents do not provide support for the following limitations from independent claim 1, “selecting a plurality of bone spatial segments from the plurality of spatial segments, each of the plurality of spatial segments depicting at least one part of bone anatomy; identifying one or more anatomical areas in each spatial segment; applying a ruleset to at least one spatial segment comprising at least one selected anatomical area to determine optimal hardware placement for each selected anatomical area; and outputting the determined optimal hardware placement.” These documents do not provide support for the following limitations from independent claim 10, “determining which spatial segments depict bone anatomy; and placing a given number of screws in each spatial segment according to an algorithm that optimizes for placement objectives”.
Claim Objections
Claim 1 is objected to because of the following informalities: the excerpt, “each of the plurality of spatial segments” should be corrected to instead recite, “each of the plurality of bone spatial segments” for consistency, such that the entire clause will be recited as, “selecting a plurality of bone spatial segments from the plurality of spatial segments, each of the plurality of bone spatial segments depicting at least one part of bone anatomy” (emphasis added). Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 14 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 14, the phrase "such as" renders the claim indefinite because it is unclear whether the limitations following the phrase are part of the claimed invention. See MPEP § 2173.05(d).
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 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to mental process and/or recite math abstract idea without significantly more.
Independent claim 1 recite(s):
"method for analyzing an image to determine hardware placement in a patient", which can be reasonably interpreted as a human observer mentally making this determination via visual perception;
"selection comprising one or more anatomical areas for hardware placement"; "dividing the image into a plurality of spatial segments"; "determining which spatial segments depict bone anatomy; "selecting a plurality of bone spatial segments from the plurality of spatial segments, each of the plurality of spatial segments depicting at least one part of bone anatomy"; "identifying one or more anatomical areas in each spatial segment", which can be reasonably interpreted as a human observer viewing displayed image(s) and mentally performing these image-region selection, dividing, determining, selecting, and identifying actions via visual perception; and
"applying a ruleset to at least one spatial segment comprising at least one selected anatomical area to determine optimal hardware placement for each selected anatomical area", which can be reasonably interpreted as a human observer viewing displayed image(s) and mentally applying a mental-model ruleset in mentally determining the optimal hardware placement for each mentally selected anatomical area.
This judicial exception is not integrated into a practical application because additional elements of:
"computer implemented" are generically recited computer elements that do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer;
"receiving a digital image depicting at least one bone " are generically recited insignificant pre-solution activity of data gathering;
"receiving a user selection" are generically recited insignificant pre-solution activity of data gathering; and
"outputting the determined optimal hardware placement" are generically recited insignificant post-solution activity of data outputting.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because additional elements of:
"computer implemented" are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f);
"receiving a digital image depicting at least one bone " are insignificant pre-solution activity of data gathering;
"receiving a user selection" are insignificant pre-solution activity of data gathering; and
"outputting the determined optimal hardware placement" are insignificant post-solution activity of data outputting.
Depending claims do not remedy these deficiencies:
Claims 3-9 further recite limitations that can be reasonably be interpreted as being performed by a human observer viewing displayed image(s) – via visual perception. See analysis provided above for independent claim 1.
Claim 2 further recites, “the algorithm optimizes for placement objectives”, which is recited math. Claim 2 also recites, “the algorithm… is a neural network”, which are additional elements that are generically recited and are well-understood, routine, conventional.
Independent claim 10 recite(s):
“method for placing pedicle screws”, which can be reasonably interpreted as a human observer mentally determining screw placement;
“dividing the bone image into spatial segments” and “determining which spatial segments depict bone anatomy”, which can be reasonably interpreted as a human observer viewing displayed image(s) and mentally performing these dividing and determining actions via visual perception;
“according to an algorithm that optimizes for placement objectives”, is recited math.
This judicial exception is not integrated into a practical application because additional elements of:
“computer-implemented” and “an algorithm” are generically recited computer elements that do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer;
“inputting a medical image; the medical image comprising at least one bone image” are generically recited insignificant pre-solution activity of data gathering; and
“placing a given number of screws in each spatial segment” are generically recited insignificant post-solution activity akin to adding the words “apply it”.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because additional elements of:
“computer-implemented” and “an algorithm” are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f);
“inputting a medical image; the medical image comprising at least one bone image” are insignificant pre-solution activity of data gathering; and
“placing a given number of screws in each spatial segment” are insignificant post-solution activity akin to adding the words “apply it”.
Depending claims do not remedy these deficiencies:
Claim 11 further recites, “the entry point, angle, and maximal screw length is predicted”, which can also be reasonably be interpreted as being performed by a human observer viewing displayed image(s) – via visual perception. Claim 11 also recites, “based on a neural network”, which are additional elements that are generically recited and are well-understood, routine, conventional.
Claim 12 further recites limitations that characterize the received images and is reasonably interpreted as insignificant pre-solution activity of data gathering.
Claims 13-16 further recite limitations that can be reasonably be interpreted as being performed by a human observer viewing displayed image(s) – via visual perception. See analysis provided above for independent claim 1.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-6 and 8-16 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US 20240216067 A1 (O’Connor).
As per claim 1, O’Connor teaches a computer implemented method for analyzing an image to determine hardware placement in a patient, the method comprising:
receiving a digital image depicting at least one bone (O’Connor: abstract: “A scan of a spine may be received, and positions of one or more vertebra”;
para 5: “generating custom pedicle screw trajectories involves receiving a scan of a spine”)
receiving a user selection comprising one or more anatomical areas for hardware placement (O’Connor: para 25: “the surgeon S selects a section of the spine that the surgeon will perform surgery on using the input device 112. For example, the pedicle screw planning system 110 determines one or more vertebral levels (e.g., receiving a selection of vertebral levels via input from the surgeon S) and/or selects one or more vertebra associated with a surgery. The pedicle screw planning system 110 determines the screw trajectory plan for the selected area in response to the surgeon's S input.”);
dividing the image into a plurality of spatial segments; determining which spatial segments depict bone anatomy; selecting a plurality of bone spatial segments from the plurality of spatial segments, each of the plurality of spatial segments depicting at least one part of bone anatomy; identifying one or more anatomical areas in each spatial segment (O’Connor: para 25: “the surgeon S selects a section of the spine that the surgeon will perform surgery on using the input device 112. For example, the pedicle screw planning system 110 determines one or more vertebral levels (e.g., receiving a selection of vertebral levels via input from the surgeon S) and/or selects one or more vertebra associated with a surgery. The pedicle screw planning system 110 determines the screw trajectory plan for the selected area in response to the surgeon's S input.”;
abstract: “one or more components of the one or more vertebra in the scan may be identified”;
para 5: “identifying positions of one or more vertebra and one or more components of the one or more vertebra in the scan, including any one of (i) an endplate, (ii) a pedicle, (iii) laminae, (iv) facets, and (v) a combination of (i)-(iv)”;
para 16: “A pre-operative or intraoperative scan of the patient can be segmented to localize and segment individual vertebrae and vertebral components in the scan. The exterior surface meshes of each vertebral segmentation volume are computed and its properties can be analyzed.”;
para 17: “The segmentation (e.g., using raw machine learning model or manual annotations) is used to identify parts of the vertebra, such as vertebral bodies, endplates (and distinguishing between upper and lower endplates), and pedicle channels, spinous processes, transverse processes, facets, laminae, orientation of vertebral level (e.g. body-centered coordinates, cranial/caudal, patient's left/right, patient's anterior/posterior), geometric properties of identified regions (e.g., centroid location, minimum location, maximum location), midline of spine (e.g., to separate patient's left side from right side), other locations, or combinations thereof.”;
para 53: “The input instructions 204 can also cause the identification instructions 200 to operate to identify vertebra and/or components of the vertebra in the image associated with the pedicle screw trajectory plan request.”;
para 56: “The pedicle screw planning system 110 can determine a 3D model of the spine using the multiple 2D images. In some embodiments, the pedicle screw planning system 110 receives the scan of the spine from an imaging device, such as the imaging device 120. Following operation 302, the flow of the method can move to operation 304”;
para 57: “In operation 304, positions of one or more vertebra and one or more components of the one or more vertebra in the scan of the spine are identified. For example, the pedicle screw planning system 110 determines the positions of the vertebra and/or the components of the vertebra. The components of the vertebra can include endplates, bodies, and pedicles of the vertebra. This operation 304 can include performing segmentation, such as using techniques described elsewhere herein.”;
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applying a ruleset to at least one spatial segment comprising at least one selected anatomical area to determine optimal hardware placement for each selected anatomical area; and outputting the determined optimal hardware placement (O’Connor: abstract: “automatically determining pedicle screw trajectories for surgery may be provided…
a screw trajectory planning algorithm may determine an initial screw trajectory plan using the positions of the one or more vertebra and the one or more components. The screw trajectory planning algorithm may then determine a revised screw trajectory plan by revising the initial screw trajectory plan according to weighted factors”;
para 5: “determining, by a screw trajectory planning algorithm, an initial screw trajectory plan using the positions of the one or more vertebra and the one or more components; and determining, by the screw trajectory planning algorithm, a revised screw trajectory plan by revising the initial screw trajectory plan according to weighted factors. Identifying positions of the one or more vertebra and components may include using a vertebral segmentation machine learning model. The initial screw trajectory plan may include an initial position and initial orientation for pedicle screws having a predetermined pedicle screw length and a predetermined pedicle screw diameter. Determining the initial screw trajectory plan may include performing an atlas technique. The weighted factors may comprise weights assigned to parameters associated with any one of (a) a screw length, (b) a screw width, (c) a medial entry angle, (d) a cranial entry angle, (e) an entry point, and (f) any combination of (a)-(f). The method may further include receiving a selection, from a user, of the weighted factors. The weighted factors may be associated with a pedicle screw insertion technique…
The method may further include causing a robotic system to position a robotic component based on the revised screw trajectory plan.”;
Para 6: “render one or more guide-lines on an image of the spine for screw insertion using the revised screw trajectory plan; and cause the display to display the image of the spine with one or more guide-lines. The initial screw trajectory plan may include an initial position and initial orientation for pedicle screws; and the revised screw trajectory plan may include a screw inventory that includes one or more amounts of screws of one or more screw dimensions for performing the revised screw trajectory plan. The weighted factors may comprise weights assigned to parameters associated with any of (a) a screw length, (b) a screw width, (c) a medial entry angle, (d) a cranial entry angle, (e) an entry point, and (f) any combination of (a)-(e). The weighted factors may be associated with a pedicle screw insertion technique.”;
Para 16: “Based on at least the scan of the patient, an initial screw trajectory plan is determined. That initial plan is then modified to form an optimized pedicle screw plan. For instance, the initial screw plan is modified according to various constraints and optimization parameters.”;
Para 18: “The initial screw trajectory can be determined relative to the position of the segmented parts. The initial screw trajectory plan can include initial screw trajectories with initial positions and initial orientations, initial screw lengths, and/or initial screw widths. In some examples, the initial screw plan is determined manually, such as by receiving input from a clinician. In addition or instead, the initial screw trajectory plan is determined automatically, such as by using an atlas technique or a machine learning model. In an example implementation, the machine learning model is a Deep Neural Network (DNN) that a pedicle screw planning system uses to determine the initial screw trajectory plan.”;
Para 19: “The initial screw trajectory plan can then be automatically optimized based on factors. The factors can include a length of the screw, a width of the screw, a medial entry angle, a cranial entry angle, an optimal entry point, and/or the like. In some embodiments, the factors are weighted, such as using a lookup table or an algorithm. For example, the algorithm quantifies points along the surface of the vertebral body and determines the weight for the optimal entry point factor based on the ease of placing a screw at a given location using the points. The weighted factors can be predetermined for commonly used strategies for pedicle screw placement. For example, the screw trajectory planning system predetermines weights for various screw placement techniques, such as the Magerl technique, the Roy-Camile technique, the Anderson technique, the Ann technique, an anatomical technique, a modified technique, other techniques, or combinations thereof…
The revised screw trajectory plan includes revised screw trajectories with revised positions and revised orientations, revised screw lengths, and/or revised screw widths. In an example implementation, the algorithm can be updated to better determine the weights based on produced revised screw trajectories, specifically to emphasize a stable entry point to avoid screw skiving for example. For example, the algorithm may receive successful or otherwise optimal revised screw trajectories the algorithm previously produced or optimal revised screw trajectories that represent improvements on screw trajectories the algorithm previously produced. The algorithm then can be configured such that the generation of subsequent revised screw trajectory plans are based on the successful revised screw trajectories.”;
Para 20: “A user, such as a surgeon, can use the revised screw trajectory plan for manually inserting screws during surgery. In some embodiments, the surgeon uses surgical navigation systems that display the algorithm's output trajectory as a guide-line or graphic for the surgeon to follow during placement. A user, such as a surgeon, can also use the revised screw plan for inserting screws during surgery assisted with robotic surgery. The robot can be instructed or otherwise caused to hold and/or guide the path of the screw to match the revised screw plan's output trajectory.”;
Para 24: “the surgical robot 130 is a robotic system that may assist the surgeon S with surgery. The surgical robot 130 can align screws, align instruments, and/or the like. The pedicle screw planning system 110 can communicate with surgical robot 130, so the surgical robot 130 can assist with the surgery based on the screw trajectory plan the pedicle screw planning system 110 determines.”
Para 26: “The surgeon S can view a surgical image of the patient P with rendered screw trajectories on the display device 114 based on a screw trajectory plan the pedicle screw planning system 110 determines. The pedicle screw planning system 110 can also render the instrument T on the display device 114. In some embodiments, the surgical robot 130 receives the screw trajectory plan from the pedicle screw planning system 110 and assists the surgeon S with the surgery. For example, the surgical robot 130 uses the screw trajectory plan to position a robotic component to align screws according to the plan, and the surgeon S can implant the aligned screws.”;
Para 38: “operations performed by the trajectory instructions 202 or other aspects described herein can be described using various symbols or variables, including those described in Table 1 below”:
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Para 39: “parameters can be values to be determined by an algorithm or process. Parameters can physically describe a screw plan ( e.g. a length, width, orientation, and entry position)”;
Para 40: “The cost function can enable comparing the value of different screw plans (either using each candidate plan's set of parameters, or information derived from the parameters). The cost function can allow optimizing one or more objectives (e.g. maximizing the length, width or entry point stability, or minimizing number of breaches, difference with an preferred angle or invasiveness, etc., or any combination thereof) together (e.g. using a weighted sum of factors) or independently (e.g. computing the Pareto front).”;
Para 60: “In operation 306, an initial screw trajectory plan is determined using the positions of the one or more vertebra and the one or more components. For example, the pedicle screw planning system 110 determines the initial screw trajectory plan using any of a variety of techniques. This can include, for example, receiving over a user interface an initial screw plan from a user, receiving the initial screw plan from an atlas-based technique, or receiving the initial screw plan from an artificial-intelligence-based technique, other techniques or combinations thereof.”
Para 87: “Operation 316 includes determining whether absolute constraints are violated by the trajectory defined by operation 314. For example, absolute constraints can include breaching the pedicle, breaching the vertebral body, sticking out too much, entering no-go areas, other constraints, or combinations thereof. If the constraints are violated, the flow of the method can move to operation 312 or operation 314.”;
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As per claim 2, O’Connor teaches the method of claim 1 wherein the algorithm that optimizes for placement objectives is a neural network (O’Connor: See arguments and citations offered in rejecting claim 1 above;
Para 63: “Another strategy is the use of machine learning (e.g., a DNN) to identify a center of mass of the pedicle channel and a fixed trajectory is determined that passes through that center of mass. Another strategy uses a determined center of mass of the pedicle channel and then an arbitrary screw trajectory is chosen that passes through that point. In an example, the arbitrary trajectory has some fixed angle relative to the anatomy (e.g., every single scan will get a screw passing through the pedicle center, at a cranial angle of 35 degrees). The trajectory can be based on a simple predetermined constant. In other examples, the trajectory is pseudorandom within a range (e.g., a range based on constraints).”).
As per claim 3, O’Connor teaches the method of claim 1 wherein the anatomical areas are vertebral levels (O’Connor: See arguments and citations offered in rejecting claim 1 above).
As per claim 4, O’Connor teaches the method of claim 1 wherein the hardware is pedicle screws (O’Connor: See arguments and citations offered in rejecting claim 1 above).
As per claim 5, O’Connor teaches the method of claim 1 wherein optimal hardware placement includes entry point coordinates (O’Connor: See arguments and citations offered in rejecting claim 1 above).
As per claim 6, O’Connor teaches the method of claim 1 wherein optimal hardware placement includes angle of insertion (O’Connor: See arguments and citations offered in rejecting claim 1 above).
As per claim 8, O’Connor teaches the method of claim 1 wherein optimal hardware placement is constrained by user specified requirements (O’Connor: See arguments and citations offered in rejecting claim 1 above;
Para 5: “The method may further include receiving a selection, from a user, of the weighted factors. The weighted factors may be associated with a pedicle screw insertion technique, wherein the pedicle screw insertion technique is any one of a Magerl technique, a Roy-Camile technique, an Anderson technique, an Ann technique, an anatomical technique, or a modified technique…
A user or system can adjust weights to determine a screw trajectory plan or can take a screw trajectory plan and calculate the weights needed to recreate that plan. The method may further include providing the initial screw trajectory plan to a user; and receiving one or more modifications to the initial screw trajectory plan, wherein determining the revised screw trajectory plan includes using the one or more modifications. The revised screw trajectory plan may include a screw inventory that includes one or more amounts of screws of one or more screw dimensions for performing the revised screw trajectory plan.”;
Para 18: “the initial screw plan is determined manually, such as by receiving input from a clinician”;
Para 25: “a user, such as a surgeon, uses the input device 112 to cause the pedicle screw planning system 110 to generate pedicle screw trajectory plans and/or cause the pedicle screw planning system 110 to adjust pedicle screw trajectory plans. The surgeon can use the input device 112 to select a technique for pedicle screw trajectory plans to be based on, such as a Magerl technique, a Roy-Camile technique, an Anderson technique, an Ann technique, an anatomical technique, a modified technique, a custom technique based on the surgeon's S previous surgeries, and/or the like”;
Para 69: “The determining of the one or more sets of factors to apply can include the system receiving a user selection of the set to apply. For instance, the system can receive actuation of a user interface element (e.g., a drop down menu) that specifies a factor. In addition or instead, the operation 312 can include determining the set by loading a set from a preferences file. In addition or instead, the operation 312 can include determining the set by algorithmic or artificial intelligent selection based on one or more criteria. For example, certain sets of factors can be associated with particular benefits or risks and the set can be selected to increase one or more benefits while reducing one or more risks. Example criteria affecting risks or benefits include: risk of screw pullout (e.g., which can be an increased or decreased factor based on patient bone density, the nature of the planned spinal construct, other factors, or combinations thereof), ease of screw insertion, margin of error (e.g., which can be an increased or decreased factor depending on insertion technique, such as freehand, navigated, or robotic), risk of skiving (e.g., which can be an increased or decreased factor depending on insertion technique), risk of pedicle breach, nature of the surgery (e.g., spinal fusion or scoliosis correction), construct stability, risk of screw breakage, risk of pedicle fracture, risk of pedicle breach, risk of toggling, other circumstances, or combinations thereof. These criteria can be determined based on AI or manual (e.g., by the user) determination. For example, an AI can analyzed patient imaging or patient records to determine these circumstances (e.g., the risk thereof) and facilitate selection of one or more sets of weighted factors to apply in view of such circumstances.”).
As per claim 9, O’Connor teaches the method of claim 1 wherein optimal hardware placement is constrained by a catalog of available hardware (O’Connor: para 5: “The revised screw trajectory plan may include a screw inventory that includes one or more amounts of screws of one or more screw dimensions for performing the revised screw trajectory plan.”;
Para 21: “A user, such as a surgeon or surgical staff, can use the revised trajectory plan for inventory planning, because the revised trajectory plan provides estimates of the screw dimensions (e.g., length and width) required for each vertebral level in example implementations. Thus, the user can determine the screws needed for surgeries and order the inventory determined for the procedure. The user may thereby reduce shipping and logistics costs because the user does not need to order unnecessary parts.”).
As per claim 10, O’Connor teaches a computer-implemented method for placing pedicle screws, the method comprising:
inputting a medical image; the medical image comprising at least one bone image (O’Connor: abstract: “A scan of a spine may be received, and positions of one or more vertebra”;
para 5: “generating custom pedicle screw trajectories involves receiving a scan of a spine”);
dividing the bone image into spatial segments; determining which spatial segments depict bone anatomy (O’Connor: abstract: “one or more components of the one or more vertebra in the scan may be identified”;
para 5: “identifying positions of one or more vertebra and one or more components of the one or more vertebra in the scan, including any one of (i) an endplate, (ii) a pedicle, (iii) laminae, (iv) facets, and (v) a combination of (i)-(iv)”;
para 16: “A pre-operative or intraoperative scan of the patient can be segmented to localize and segment individual vertebrae and vertebral components in the scan. The exterior surface meshes of each vertebral segmentation volume are computed and its properties can be analyzed.”;
para 17: “The segmentation (e.g., using raw machine learning model or manual annotations) is used to identify parts of the vertebra, such as vertebral bodies, endplates (and distinguishing between upper and lower endplates), and pedicle channels, spinous processes, transverse processes, facets, laminae, orientation of vertebral level (e.g. body-centered coordinates, cranial/caudal, patient's left/right, patient's anterior/posterior), geometric properties of identified regions (e.g., centroid location, minimum location, maximum location), midline of spine (e.g., to separate patient's left side from right side), other locations, or combinations thereof.”;
para 53: “The input instructions 204 can also cause the identification instructions 200 to operate to identify vertebra and/or components of the vertebra in the image associated with the pedicle screw trajectory plan request.”;
para 56: “The pedicle screw planning system 110 can determine a 3D model of the spine using the multiple 2D images. In some embodiments, the pedicle screw planning system 110 receives the scan of the spine from an imaging device, such as the imaging device 120. Following operation 302, the flow of the method can move to operation 304”;
para 57: “In operation 304, positions of one or more vertebra and one or more components of the one or more vertebra in the scan of the spine are identified. For example, the pedicle screw planning system 110 determines the positions of the vertebra and/or the components of the vertebra. The components of the vertebra can include endplates, bodies, and pedicles of the vertebra. This operation 304 can include performing segmentation, such as using techniques described elsewhere herein.”;
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placing a given number of screws in each spatial segment according to an algorithm that optimizes for placement objectives (O’Connor: abstract: “automatically determining pedicle screw trajectories for surgery may be provided…
a screw trajectory planning algorithm may determine an initial screw trajectory plan using the positions of the one or more vertebra and the one or more components. The screw trajectory planning algorithm may then determine a revised screw trajectory plan by revising the initial screw trajectory plan according to weighted factors”;
para 5: “determining, by a screw trajectory planning algorithm, an initial screw trajectory plan using the positions of the one or more vertebra and the one or more components; and determining, by the screw trajectory planning algorithm, a revised screw trajectory plan by revising the initial screw trajectory plan according to weighted factors. Identifying positions of the one or more vertebra and components may include using a vertebral segmentation machine learning model. The initial screw trajectory plan may include an initial position and initial orientation for pedicle screws having a predetermined pedicle screw length and a predetermined pedicle screw diameter. Determining the initial screw trajectory plan may include performing an atlas technique. The weighted factors may comprise weights assigned to parameters associated with any one of (a) a screw length, (b) a screw width, (c) a medial entry angle, (d) a cranial entry angle, (e) an entry point, and (f) any combination of (a)-(f). The method may further include receiving a selection, from a user, of the weighted factors. The weighted factors may be associated with a pedicle screw insertion technique…
The method may further include causing a robotic system to position a robotic component based on the revised screw trajectory plan.”;
Para 6: “render one or more guide-lines on an image of the spine for screw insertion using the revised screw trajectory plan; and cause the display to display the image of the spine with one or more guide-lines. The initial screw trajectory plan may include an initial position and initial orientation for pedicle screws; and the revised screw trajectory plan may include a screw inventory that includes one or more amounts of screws of one or more screw dimensions for performing the revised screw trajectory plan. The weighted factors may comprise weights assigned to parameters associated with any of (a) a screw length, (b) a screw width, (c) a medial entry angle, (d) a cranial entry angle, (e) an entry point, and (f) any combination of (a)-(e). The weighted factors may be associated with a pedicle screw insertion technique.”;
Para 16: “Based on at least the scan of the patient, an initial screw trajectory plan is determined. That initial plan is then modified to form an optimized pedicle screw plan. For instance, the initial screw plan is modified according to various constraints and optimization parameters.”;
Para 18: “The initial screw trajectory can be determined relative to the position of the segmented parts. The initial screw trajectory plan can include initial screw trajectories with initial positions and initial orientations, initial screw lengths, and/or initial screw widths. In some examples, the initial screw plan is determined manually, such as by receiving input from a clinician. In addition or instead, the initial screw trajectory plan is determined automatically, such as by using an atlas technique or a machine learning model. In an example implementation, the machine learning model is a Deep Neural Network (DNN) that a pedicle screw planning system uses to determine the initial screw trajectory plan.”;
Para 19: “The initial screw trajectory plan can then be automatically optimized based on factors. The factors can include a length of the screw, a width of the screw, a medial entry angle, a cranial entry angle, an optimal entry point, and/or the like. In some embodiments, the factors are weighted, such as using a lookup table or an algorithm. For example, the algorithm quantifies points along the surface of the vertebral body and determines the weight for the optimal entry point factor based on the ease of placing a screw at a given location using the points. The weighted factors can be predetermined for commonly used strategies for pedicle screw placement. For example, the screw trajectory planning system predetermines weights for various screw placement techniques, such as the Magerl technique, the Roy-Camile technique, the Anderson technique, the Ann technique, an anatomical technique, a modified technique, other techniques, or combinations thereof…
The revised screw trajectory plan includes revised screw trajectories with revised positions and revised orientations, revised screw lengths, and/or revised screw widths. In an example implementation, the algorithm can be updated to better determine the weights based on produced revised screw trajectories, specifically to emphasize a stable entry point to avoid screw skiving for example. For example, the algorithm may receive successful or otherwise optimal revised screw trajectories the algorithm previously produced or optimal revised screw trajectories that represent improvements on screw trajectories the algorithm previously produced. The algorithm then can be configured such that the generation of subsequent revised screw trajectory plans are based on the successful revised screw trajectories.”;
Para 20: “A user, such as a surgeon, can use the revised screw trajectory plan for manually inserting screws during surgery. In some embodiments, the surgeon uses surgical navigation systems that display the algorithm's output trajectory as a guide-line or graphic for the surgeon to follow during placement. A user, such as a surgeon, can also use the revised screw plan for inserting screws during surgery assisted with robotic surgery. The robot can be instructed or otherwise caused to hold and/or guide the path of the screw to match the revised screw plan's output trajectory.”;
Para 24: “the surgical robot 130 is a robotic system that may assist the surgeon S with surgery. The surgical robot 130 can align screws, align instruments, and/or the like. The pedicle screw planning system 110 can communicate with surgical robot 130, so the surgical robot 130 can assist with the surgery based on the screw trajectory plan the pedicle screw planning system 110 determines.”
Para 26: “The surgeon S can view a surgical image of the patient P with rendered screw trajectories on the display device 114 based on a screw trajectory plan the pedicle screw planning system 110 determines. The pedicle screw planning system 110 can also render the instrument T on the display device 114. In some embodiments, the surgical robot 130 receives the screw trajectory plan from the pedicle screw planning system 110 and assists the surgeon S with the surgery. For example, the surgical robot 130 uses the screw trajectory plan to position a robotic component to align screws according to the plan, and the surgeon S can implant the aligned screws.”;
Para 38: “operations performed by the trajectory instructions 202 or other aspects described herein can be described using various symbols or variables, including those described in Table 1 below”:
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Para 39: “parameters can be values to be determined by an algorithm or process. Parameters can physically describe a screw plan ( e.g. a length, width, orientation, and entry position)”;
Para 40: “The cost function can enable comparing the value of different screw plans (either using each candidate plan's set of parameters, or information derived from the parameters). The cost function can allow optimizing one or more objectives (e.g. maximizing the length, width or entry point stability, or minimizing number of breaches, difference with an preferred angle or invasiveness, etc., or any combination thereof) together (e.g. using a weighted sum of factors) or independently (e.g. computing the Pareto front).”;
Para 60: “In operation 306, an initial screw trajectory plan is determined using the positions of the one or more vertebra and the one or more components. For example, the pedicle screw planning system 110 determines the initial screw trajectory plan using any of a variety of techniques. This can include, for example, receiving over a user interface an initial screw plan from a user, receiving the initial screw plan from an atlas-based technique, or receiving the initial screw plan from an artificial-intelligence-based technique, other techniques or combinations thereof.”
Para 87: “Operation 316 includes determining whether absolute constraints are violated by the trajectory defined by operation 314. For example, absolute constraints can include breaching the pedicle, breaching the vertebral body, sticking out too much, entering no-go areas, other constraints, or combinations thereof. If the constraints are violated, the flow of the method can move to operation 312 or operation 314.”;
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).
As per claim 11, O’Connor teaches the method of claim 11, wherein the entry point, angle, and maximal screw length is predicted based on a neural network (O’Connor: See arguments and citations offered in rejecting claim 11 above).
As per claim 12, O’Connor teaches the method of claim 11, wherein the medical image is an MRI, Xray, CT, or ultrasound image (O’Connor: para 22: “The images can be two dimensional (2D) or 3D images of a spine, such as x-rays, Computed Tomography (CT) scans, Magnetic Resonance Imaging (MRI) images, and the like. When the images are 2D, the pedicle screw planning system 110 uses multiple 2D images to determine a 3D rendering of the imaged spine in example implementations.”).
As per claim 13, O’Connor teaches the method of claim 11, wherein the screw is predicted based on a ruleset (O’Connor: See arguments and citations offered in rejecting claim 11 above;
Para 61: “Using points identified in an atlas, finding similar key points in a new scan, and then placing the screw in the same relative position. In an example, an ideal trajectory for a given idealized vertebra is determined by morphing fit screw trajectories from many different surgeons on many different vertebra to correspond to a single model vertebra. In an example, the trajectory can be found by determining a trajectory on an average vertebra from many different scans of placed pedicle screws. Mapping of the trajectory to the vertebra can be achieved using a registration technique. For instance, the patient scan reference space can be registered to specific patient space for annotation. An example atlas technique is performed using generalized Procrustes analysis.”;
Para 62: “Another strategy is a skeleton technique where the morphology of a given vertebra is assessed to identify the likely location of the pedicle channel and a screw trajectory is chosen that passes through that channel. Here, skeleton refers to a topological skeleton of the vertebra. The topological skeleton is then used to find a path through the pedicle. The location of the channel can be identified based on a segmentation algorithm. This technique can choose a screw trajectory that passes through the center of the topological skeleton of the pedicle as close as possible.”).
As per claim 14, O’Connor teaches the method of claim 11, wherein the screw entry point is calculated according to one or more key features such as (the following limitations are not required) distance from bone edge or trajectory (O’Connor: See arguments and citations offered in rejecting claim 11 above).
As per claim 15, O’Connor teaches the method of claim 11, wherein the screw is predicted based on a catalog of available screws (O’Connor: para 5: “The revised screw trajectory plan may include a screw inventory that includes one or more amounts of screws of one or more screw dimensions for performing the revised screw trajectory plan.”;
Para 21: “A user, such as a surgeon or surgical staff, can use the revised trajectory plan for inventory planning, because the revised trajectory plan provides estimates of the screw dimensions (e.g., length and width) required for each vertebral level in example implementations. Thus, the user can determine the screws needed for surgeries and order the inventory determined for the procedure. The user may thereby reduce shipping and logistics costs because the user does not need to order unnecessary parts.”).
As per claim 16, O’Connor teaches the method of claim 11, wherein the screw is predicted based on the patient's medical data (O’Connor: See arguments and citations offered in rejecting claim 11 above).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 7 is rejected under 35 U.S.C. 103 as being unpatentable over O’Connor as applied to claim 1 above, and further in view of US 20110087233 A1 (Wu).
As per claim 7, O’Connor teaches the method of claim 1. O’Connor does not teach optimal hardware placement ensures symmetry within a tolerance between two or more hardware placements.
Wu teaches these limitations (Wu: para 9: “The present invention provides a method for guiding symmetric implantation of bone screws, embodies the notion of computer-aided surgery, and applies to a surgical guidance system for use with pedicle screw placement. The method involves generating a second implanting information that is based on and symmetric to a first implanting information of a first bone screw, so that bone screws implanted in two sides of a spinous process are precisely aligned with each other in the same transverse section of the spinous process.”;
Para 26: “Since the second implanting information mirrors the first implanting information, the first bone screw 20a and the second bone screw 20b have corresponding implantation depths and implantation angles, i.e., the implantation depths and implantation angles of the two bone screws 20a and 20b are bilaterally symmetrical with respect to the spinous process section 40.”;
Para 19: “refer to FIG. 3 for a flowchart of a method for guiding symmetric implantation of bone screws according to an embodiment of the present invention.”;
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Abstract: “A method for guiding symmetric implantation of bone screws is provided. The method includes the steps of: providing a spinal image of a spine, wherein the spine has a first bone screw implanted in one side of a spinous process of the spine; obtaining a first implanting information of the first bone screw; setting a reference line in the spinal image; generating a second implanting information by mirroring the first implanting information with respect to the reference line; and guiding a second bone screw to be implanted in the other side of the spinous process according to the second implanting information. The method generates a second implanting information that is based on and symmetric to the first implanting information, thus allowing the bone screws to be precisely implanted in both sides of the spinous process and aligned with each other in the same transverse section of the spinous process.”;
Para 12: “The method disclosed herein embodies the notion of computer-aided surgery and uses a spinal image as a reference interface so as to generate a second implanting information that is based on and symmetric to a first implanting information of a first bone screw and is subsequently used to guide the implantation of a second bone screw.”).
Thus, it would have been obvious for one of ordinary skill in the art, prior to filing, to implement the teachings of Wu into O’Connor since both O’Connor and Wu suggest a practical solution and field of endeavor of a guidance system for screw placement in vertebra in general and Wu additionally provides teachings that can be incorporated into O’Connor in that symmetry is ensured between the two or more hardware placements “so that bone screws implanted in two sides of a spinous process are precisely aligned with each other in the same transverse section of the spinous process. Thus, the forces exerted on the two sides of the spinous process are equal, the bone screws are prevented from loosening, dislocating, or breaking, and as a consequence, the success rate of pedicle screw placement is significantly improved.” (Wu: para 9). The teachings of Wu can be incorporated into O’Connor in that symmetry is ensured between the two or more hardware placements. Furthermore, one of ordinary skill in the art could have combined the elements as claimed by known methods and, in combination, each component functions the same as it does separately. One of ordinary skill in the art would have recognized that the results of the combination would be predictable.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Atiba Fitzpatrick whose telephone number is (571) 270-5255. The examiner can normally be reached on M-F 10:00am-6pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Bee can be reached on (571) 270-5183. The fax phone number for Atiba Fitzpatrick is (571) 270-6255.
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Atiba Fitzpatrick
/ATIBA O FITZPATRICK/
Primary Examiner, Art Unit 2677
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