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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/20/2023 and 06/07/2023 has been considered by the examiner.
Claim Objections
Claim 17 is objected to because of the following informalities:
In claim 17, “for a second point of the tool path successive to the one point;” should be “for a second point of the tool path successive to the one point:”, replacing the semicolon with a colon for clarity.
Appropriate correction is required.
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)(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 1-3, 13, and 19-21 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Eskesen et al. (US 20180263697 A1, published September 20, 2018), from IDS, hereinafter referred to as Eskesen.
Regarding claim 1, and similarly for claims 20 and 30, Eskesen teaches a computer-implemented surgical planning method (Fig. 4) comprising:
obtaining anatomical data comprising a geometry and density values of an anatomical volume (Fig. 4, “Bone model with density values” 406; see para. 0029 – “In one embodiment, the planning computer may use the uploaded pre-operative imaging data to create a 3-D model of the patient's bone. The 3-D model of the bone 406 includes the patient's bone topology as well as normalized and/or relative bone density information of the bone…”);
obtaining path data comprising a tool path along which a robotic manipulator will move a tool to interact with the anatomical volume, the tool path defined by points between which the tool will successively pass (see para. 0028 – “Available cutting strategies illustratively include cut-paths [tool path] to create cut volumes for different prosthesis sizes, geometries, shapes, function, and combinations thereof.”);
obtaining tool data including a geometry of the tool that will interact with the tool path (see para. 0028 – “Additionally, cut-paths available to create a cut volume illustratively include options for different cutter positions, orientations, cutter speeds, cutter types…”);
merging the path data and the anatomical data (Fig. 4, “Registration” 414 as merging; see para. 0006 – “Registration techniques well known in the art such as point to surface registration can align the coordinate frames of a patient's bone to the coordinate frames of a 3-D model of a patient's bone and to the coordinate frame of the surgical device.”); and
for at least one point of the tool path: identifying a location of the point relative to the anatomical data (see para. 0030 – “For the illustrative example of total hip arthroplasty, the densities would be a correlation between forces and different densities of bone. The experimental data or theoretical models can be analyzed to make a correlation between HU values or density values and cutter speeds and cutter engagements.”),
loading, from the tool data, the geometry of the tool at the identified location, at the identified location (see para. 0030 – “…the planning computer can automatically determine a further sub-set of cutting strategies based on the normalized and/or relative voxel density information to determine cutting speeds, cutter positions, cutter engagement speeds, and orientations that can reduce operating times or provide a least path of resistance while maintaining a cut volume that will precisely fit the prosthesis as specified for the user.”),
identifying an intersection between the geometry of the tool and the anatomical volume, determining density values of the anatomical data within the intersection (see para. 0038 – “In the case of using relative bone density values, once the cutter comes into contact with the bone, the force experienced by the cutter can be correlated to the relative density value at that initial position [intersection of tool and anatomy].”),
computing a tool contact factor related to an interaction between the geometry of the tool and the anatomical volume (see para. 0038 – “In the case of using relative bone density values, once the cutter comes into contact with the bone, the force experienced by the cutter [tool contact factor] can be correlated to the relative density value at that initial position. Once the force of the cutter is correlated to at least one relative bone density value, the bias of any subsequent relative bone density value can be correlated with the force experienced by the cutter in real time.”),
setting a planned feed rate factor for the tool based on the determined density values and the computed tool contact factor, and associating the planned feed rate factor with the at least one point (Fig. 4, “Speed and engagement parameters” 407 as planned feed rate factor; see para. 0030 - “This is referred to as the speed and engagement parameters 407, which uses the normalized and/or relative density values and the bone topology to reference the experimental or theoretical data that estimate cutting speeds, cutter positions, cutter engagements, and cutter orientations before cutting and during cutting within the bone that can provide a least path of resistance or reduce operating times while maintaining a cut volume to precisely receive a prosthesis.”); and
outputting cut plan data comprising the tool path including the planned feed rate factor associated with the at least one point of the tool path (Fig. 4; see para. 0038 – “Once the optimal cutting strategy 307 for the patient 403 is determined [outputting cut plan data], the instructions are executed by the computer-assisted surgical device 304. In one embodiment, the 3-D bone model with the normalized and/or relative density values can be used and compared with forces measured by the cutter in real-time.”).
Furthermore, regarding claims 2 and 21, Eskesen further teaches wherein obtaining anatomical data further includes obtaining a bone model associated with the anatomical volume (Fig. 4, “Bone model with density values” 406).
Furthermore, regarding claim 3, Eskesen further teaches wherein obtaining path data further includes obtaining the tool path being predetermined based on one or more of: a planned resection volume of the bone model of the anatomical volume; and geometry of an implant model selected for the bone model (Fig. 4; see para. 0029 – “A prosthesis model 404 of the chosen implant is shown and can be manipulated by the user with the planning computer to optimally place the implant within the 3-D model of the bone 406…Each size, geometry, shape, and function of a prosthetic within the prosthesis module has an associated cut volume or volume model 405 that further defines a sub-set of cutting strategies 303 that can be used to cut a volume of bone to receive a desired prosthesis.”).
Furthermore, regarding claim 19, Eskesen further teaches
for each point of the tool path: identifying the location of the point relative to the anatomical data (see para. 0030 – “For the illustrative example of total hip arthroplasty, the densities would be a correlation between forces and different densities of bone. The experimental data or theoretical models can be analyzed to make a correlation between HU values or density values and cutter speeds and cutter engagements.”),
loading, from the tool data, the geometry of the tool at the identified location, at the identified location (see para. 0030 – “…the planning computer can automatically determine a further sub-set of cutting strategies based on the normalized and/or relative voxel density information to determine cutting speeds, cutter positions, cutter engagement speeds, and orientations that can reduce operating times or provide a least path of resistance while maintaining a cut volume that will precisely fit the prosthesis as specified for the user.”),
identifying the intersection between the geometry of the tool and the anatomical volume (see para. 0038 – “In the case of using relative bone density values, once the cutter comes into contact with the bone, the force experienced by the cutter can be correlated to the relative density value at that initial position.”),
determining density values of the anatomical data within the intersection (see para. 0030 – “For the illustrative example of total hip arthroplasty, the densities would be a correlation between forces and different densities of bone. The experimental data or theoretical models can be analyzed to make a correlation between HU values or density values and cutter speeds and cutter engagements.”),
setting the planned feed rate factor for the tool based on the determined density values, and associating the planned feed rate factor with the point (Fig. 4, “Speed and engagement parameters” 407 as planned feed rate factor; see para. 0030 - “This is referred to as the speed and engagement parameters 407, which uses the normalized and/or relative density values and the bone topology to reference the experimental or theoretical data that estimate cutting speeds, cutter positions, cutter engagements, and cutter orientations before cutting and during cutting within the bone that can provide a least path of resistance or reduce operating times while maintaining a cut volume to precisely receive a prosthesis.”); and
wherein outputting the cut plan data further comprises the tool path including the planned feed rate factor associated with each point of the tool path (Fig. 4; see para. 0038 – “Once the optimal cutting strategy 307 for the patient 403 is determined [outputting cut plan data], the instructions are executed by the computer-assisted surgical device 304. In one embodiment, the 3-D bone model with the normalized and/or relative density values can be used and compared with forces measured by the cutter in real-time.”).
Claims 4-5 are rejected under 35 U.S.C. 103 as being unpatentable over Eskesen in view of T. Weglinkski et al, “The Concept of Image Processing Algorithms and Diagnosis of Hydrocephalus in Children”, Proceedings of Electrotechnical Institute, vol. 251, pp. 156-177, 2011, hereinafter referred to as Weglinkski.
Regarding claim 4, Eskesen teaches all of the elements disclosed in claim 1 above.
Eskesen teaches obtaining data of an anatomical volume, and it is known in the art to stack 2D images to generate a 3D image, but does not explicitly teach obtaining slices of the anatomical volume.
Whereas, Weglinkski, in an analogous field of endeavor, teaches wherein obtaining anatomical data further includes obtaining imaging data including slices of the anatomical volume (Fig. 3, cross-sections (slices) of anatomical volume (brain)).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified obtaining data, as disclosed in Eskesen, by obtaining slices of data, as disclosed in Weglinski. One of ordinary skill in the art would have been motivated to make this modification in order to easily process information from CT and MRI digital images, as taught in Weglinski (see pg. 158, para. 1).
Furthermore, regarding claim 5, Weglinkski further teaches wherein obtaining imaging data further includes obtaining DICOM data comprising intercept and slope values, slice thickness, and patient position at a time of imaging (Table 1, DICOM data includes intercept value, slope value, and slice thickness; see pg. 163, para. 2 – “The [DICOM] header contains significant information about manufacturer of the scanner, type of the study, the patient's position and distance from the imaging device, the detailed properties of produced images, etc.”).
The motivation for claim 5 was shown previously in claim 4.
Claims 6-7 and 22-23 are rejected under 35 U.S.C. 103 as being unpatentable over Eskesen in view of Weglinkski, as applied to claim 4 and 21 above, and in further view of Yildirim et al. (US 20200170604 A1, published June 4, 2020), hereinafter referred to as Yildirim.
Regarding claims 6 and 22, Eskesen in view of Weglinkski teaches all of the elements disclosed in claim 4 and 21 above.
Eskesen in view of Weglinkski teaches identifying the intersection between the geometry of the tool and the anatomical volume, but does not explicitly teach identifying one or more slices of the imaging data exhibiting the intersection between the geometry of the tool and the anatomical volume.
Whereas, Yildirim, in an analogous field of endeavor, teaches wherein at the identified location, identifying the intersection between the geometry of the tool and the anatomical volume further comprises: identifying one or more slices of the imaging data exhibiting the intersection between the geometry of the tool and the anatomical volume (see para. 0050 – “FIG. 4C illustrates an exemplary slice 400 of the scan shown in FIG. 4B. For the axial slice example, certain ratios of bone density (as measured, for example, in Hounsfield units) of the femur 305 may be determined.”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified identifying the intersection between the geometry of the tool and the anatomical volume, as disclosed in Eskesen in view of Weglinkski, by also identifying one or more slices of the imaging data exhibiting the intersection between the geometry of the tool and the anatomical volume, as disclosed in Yildirim. One of ordinary skill in the art would have been motivated to make this modification in order to have performing the imaging along the anatomical axis of the femur, and for the results of the imaging may more easily be compared to data obtained from imaging along the anatomical axis of femurs of a healthy population, as taught in Yildirim (see para. 0049).
Furthermore, regarding claims 7 and 23, Yildirim further teaches wherein determining density values of the anatomical data within the intersection further comprises: for each identified slice, determining radiodensity values of the imaging data located within the intersection; and collecting radiodensity values located within the intersections of each of the one or more identified slices (Fig. 4B-4C; see para. 0051 – “A ratio of interest HU1/HU2 [radiodensity] may be determined for each slice 400 in the scan of the patient's femur 305 to determine a particular density ratio profile.”).
The motivation for claims 7 and 23 was shown previously in claim 6 and 22.
Claims 8-16 and 24-29 are rejected under 35 U.S.C. 103 as being unpatentable over Eskesen in view of Weglinkski and Yildirim, as applied to claims 7, 20, and 23, and in further view of de la Barrera et al. (US 20170000572 A1, published January 5, 2017), from IDS, hereinafter referred to as Barrera.
Regarding claims 8, 14, 24, and 28, Eskesen in view of Weglinkski and Yildirim teaches all of the elements disclosed in claims 7, 20, and 28.
Eskesen in view of Weglinkski and Yildirim teaches calculating bone density, but does not explicitly teach calculating bone mineral density factor based on collected radiodensity values.
Whereas, Barrera, in an analogous field of endeavor, teaches wherein setting the planned feed rate factor for the tool based on the determined density values further comprises: calculating a bone mineral density (BMD) factor of the anatomical volume relative to the identified location based on the collected radiodensity values (see para. 0073 – “The densities [radiodensity values] for different clusters of adjacent voxels may also be averaged to determine a common BMD coefficient for each of the different clusters.”); and
setting the planned feed rate factor for the tool based on the calculated BMD factor (see para. 0081 – “The associated BMD coefficient is then retrieved from the look up table and provided as an input to the feed rate calculator 132.”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified calculating bone density, as disclosed in Eskesen in view of Weglinkski and Yildirim, by also calculating bone mineral density factor based on collected radiodensity values, as disclosed in Barrera. One of ordinary skill in the art would have been motivated to make this modification in order for the tool feed rate to be changed between portions of bone or other materials having different densities, which reduces the amount of time it takes to perform the procedure on the patient to further the goal of minimizing the time it takes to perform the procedure on the patient, as taught in Barrera (see para. 0074).
Furthermore, regarding claims 9 and 25, Yildirim further teaches wherein:
obtaining imaging data further comprises obtaining CT slices of the anatomical volume (see para. 0050 – “FIG. 4C illustrates an exemplary slice 400 of the scan shown in FIG. 4B. For the axial slice example, certain ratios of bone density (as measured, for example, in Hounsfield units) of the femur 305 may be determined.”); and
the geometry of the tool comprises a 3-D geometry of the tool (see para. 0022 – “If desired, surgeon 116 may be presented with a representation of the anatomy being operated on and/or a virtual representation of surgical tool 112 and/or haptic object 110 on display 30.”), and
for the at least one point of the tool path: loading the 3-D geometry of the tool at the identified location, and at the identified location, identifying CT slices for which there is a cross-sectional intersection between the 3-D geometry of the tool and the anatomical volume (see para. 0019 – “Display device 30 may be any suitable device for displaying two-dimensional and/or three-dimensional images…”; see para. 0050 – “FIG. 4C illustrates an exemplary slice 400 of the scan shown in FIG. 4B. For the axial slice example, certain ratios of bone density (as measured, for example, in Hounsfield units) of the femur 305 may be determined.” known in the art for surgical tools to have very high Hounsfield units (dense));
for each identified CT slice, determining a Hounsfield unit for each pixel within the cross-sectional intersection; and collecting the Hounsfield units from the pixels within the cross-sectional intersections of each of the identified CT slices (see para. 0050 – “FIG. 4C illustrates an exemplary slice 400 of the scan shown in FIG. 4B. For the axial slice example, certain ratios of bone density (as measured, for example, in Hounsfield units) of the femur 305 may be determined.”).
Furthermore, regarding claims 10 and 26, Barrera further teaches wherein: collecting the radiodensity values located within the intersection of each of the one or more identified slices further includes identifying one or more of: an average, a median, and a maximum radiodensity value from the collected radiodensity values (see para. 0073 – “The densities for different clusters of adjacent voxels may also be averaged [average radiodensity] to determine a common BMD coefficient for each of the different clusters.”); and
calculating the BMD factor further includes converting the one or more of: the average, the median, and the maximum radiodensity value into the BMD factor (see para. 0073 – “The densities for different clusters of adjacent voxels may also be averaged to determine a common BMD coefficient [average BMD factor] for each of the different clusters.”).
One of ordinary skill in the art would have been motivated to make this modification in order to determine a common BMD coefficient for a range of densities, as taught in Barrera (see para. 0073).
Furthermore, regarding claims 11 and 27, Barrera further teaches for the at least one point of the tool path, and after identifying slices of the imaging data exhibiting the intersection between the geometry of the tool and the anatomical volume, for each identified slice, computing the tool contact factor by computing an intersection ratio related to the geometry of the tool within the intersection relative to the geometry of the tool outside of the intersection (see para. 0090 – “The optimal speed of the bur cutting teeth is a factor of cutter rotational speed and cutter diameter, which are optimized based on the tooth geometry and the type of material being removed.”). One of ordinary skill in the art would have been motivated to make this modification in order to improve the accuracy of the tissue removal and to minimize heat generation at the tissue, as taught in Barrera (see para. 0090).
Furthermore, regarding claim 12, Barrera further teaches wherein converting the one or more of: the average, the median, and the maximum radiodensity value into the BMD factor further comprises multiplying the one or more of: the average, the median, and the maximum radiodensity value with the intersection ratio (see para. 0090 – “In cases where the material being cut is increasing or decreasing in density, the rotational speed of the surgical tool 22 may be adjusted to reduce heat generation while the surgical tool 22 is moving relative to the material being removed based on the desired feed rate. The rotational speed may also be adjusted in proportion to the feed rate adjustment.”).
Furthermore, regarding claim 13, Eskesen further teaches wherein, for each identified slice, determining a quantity of pixels within the geometry of the tool having Hounsfield units exceeding a predetermined threshold (see para. 0023 – “The different densities can be representative of different materials ranging from air to stainless steel. The Hounsfield unit Units (HU) has commonly been used as a measure of bone density with plenty of literature reporting a range of values that indicate a particular structure.” known in the art for surgical tools to have very high Hounsfield units (dense)).
Furthermore, regarding claims 15 and 29, Barrera further teaches
accessing a look-up table defining associations between predefined BMD factors and predefined feed rate factors (Fig. 7; see para. 0080 – “The BMD coefficients are then stored in a look up table for access by the feed rate calculator 132 in step 306.”); and
wherein, for the at least one point of the tool path, setting the planned feed rate factor based on the calculated BMD factor further comprises: identifying, in the look-up table, the predefined BMD factor that is closest to the calculated BMD factor (see para. 0081 - “The associated BMD coefficient is then retrieved from the look up table and provided as an input to the feed rate calculator 132.”); and
setting the planned feed rate factor based on the predefined feed rate factor associated with the closest identified predefined BMD factor in the look-up table (Fig. 8; see para. 0082 – “In step 310, the associated BMD coefficient is then applied to the defined feed rate (e.g., a default feed rate for the particular procedure) by the feed rate calculator 132 to produce a tool feed rate by multiplying the defined feed rate by the BMD coefficient (other coefficients may or may not be used in this embodiment).”).
Furthermore, regarding claim 16, Barrera further teaches wherein, for the at least one point of the tool path, setting the planned feed rate factor based on the calculated BMD factor further comprises: setting the planned feed rate factor to be a maximum feed rate factor in response to determining that the calculated BMD factor is below a minimum threshold; and/or setting the planned feed rate factor to be a minimum feed rate factor in response to determining that the calculated BMD factor is above a maximum threshold (Fig. 8; see para. 0080 – “The BMD coefficients may range anywhere from 0.0 to 1.0…”; see para. 0082 – “In step 310, the associated BMD coefficient is then applied to the defined feed rate (e.g., a default feed rate for the particular procedure) by the feed rate calculator 132 to produce a tool feed rate by multiplying the defined feed rate by the BMD coefficient (other coefficients may or may not be used in this embodiment).” Where setting minimum and maximum feed rate is defined by multiplying the BMD coefficients by the default feed rate).
Claims 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Eskesen in view of Atria et al. (US 20170200271 A1, published July 13, 2017), hereinafter referred to as Atria.
Furthermore, regarding claim 17, Eskesen teaches all of the elements disclosed in claim 1 above, and
Eskesen further teaches
for one point of the tool path, storing interaction coordinates obtained from the intersection between the geometry of the tool and the anatomical volume at the location of the one point, the interaction coordinates indicative of locations of simulated interaction between the geometry of the tool and the anatomical volume at the location of the one point ((see para. 0030 – “For the illustrative example of total hip arthroplasty, the densities would be a correlation between forces and different densities of bone. The experimental data or theoretical models can be analyzed to make a correlation between HU values or density values and cutter speeds and cutter engagements.”); and
for a second point of the tool path successive to the one point: identifying a location of the second point relative to the anatomical data (see para. 0030 – “For the illustrative example of total hip arthroplasty, the densities would be a correlation between forces and different densities of bone. The experimental data or theoretical models can be analyzed to make a correlation between HU values or density values and cutter speeds and cutter engagements.”),
loading, from the tool data, the geometry of the tool at the identified location of the second point, at the identified location of the second point (see para. 0030 – “…the planning computer can automatically determine a further sub-set of cutting strategies based on the normalized and/or relative voxel density information to determine cutting speeds, cutter positions, cutter engagement speeds, and orientations that can reduce operating times or provide a least path of resistance while maintaining a cut volume that will precisely fit the prosthesis as specified for the user.”),
identifying an intersection between the geometry of the tool and the anatomical volume, determining density values of the anatomical data within the intersection at the location of the second point (see para. 0038 – “In the case of using relative bone density values, once the cutter comes into contact with the bone, the force experienced by the cutter can be correlated to the relative density value at that initial position [intersection of tool and anatomy].”).
Eskesen teaches determining density values at coordinates of the anatomical data, but does not explicitly teach disregarding density values.
Whereas, Atria, in an analogous field of endeavor, teaches comparing coordinates of the determined density values to the interaction coordinates; and disregarding any density values having coordinates that are identical to the interaction coordinates (see para. 0024 – “In some such embodiments, the target object may comprise a patient, and the constraint of zero density may be applied to a region outside of the at least a portion of the surface of the target object and outside of at least a portion of a surface of a surgical instrument.”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified determining density values at coordinates of the anatomical data, as disclosed in Eskesen, by also disregarding density values, as disclosed in Atria. One of ordinary skill in the art would have been motivated to make this modification in order to improve image reconstruction, as taught in Atria (see para. 0195).
Furthermore, regarding claim 18, Atria further teaches for the at least one point of the tool path: disregarding any density values located beyond the intersection between the geometry of the tool and the anatomical volume (see para. 0024 – “In some such embodiments, the target object may comprise a patient, and the constraint of zero density may be applied to a region outside of the at least a portion of the Surface of the target object and outside of at least a portion of a surface of a surgical instrument.”).
The motivation for claim 18 was shown previously in claim 17.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Avinash et al. (US 20040101104 A1, published May 27, 2004) discloses determining and displaying the position of the surgical instrument relative to the volumetric soft tissue and bone images with standard image slices (Fig. 8).
Hamauzu (US 20210251583 A1, published August 19, 2021) discloses a processor that detects a region of the surgical tool from the confirmation radiographic image using a discriminator that detects the region of the surgical tool included in the radiographic image.
Mittelstadt et al. (US 20130035690 A1, published February 7, 2013) discloses generating a tool path based upon the intersection of the pre-determined cutting pattern and the bone so as to minimize soft tissue trauma by leaving a thin perimeter of bone at the boundary of the bone cut when the boundary of the bone cut is adjacent to a bone surface.
R. Katherine et al, “CT scan image segmentation based on hounsfield unit values using Otsu thresholding method”, The 10th International Conference on Theoretical and Applied Physics (ICTAP2020), vol. 1816, pp. 1-7, Nov. 2020 discloses the Otsu Thresholding method based on the threshold value, which is combined with the Hounsfield unit (HU) value which will be the input for the segmentation process.
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/N.C./Examiner, Art Unit 3798
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