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 .
Claim Objections
Claims 1 and 17 are objected to because of the following informalities:
Claim 1, “identifying a medial ligament in extension value, a lateral ligament in extension value, a medial ligament in flexion value, a lateral side in flexion value associated with the joint” should read “identifying a medial ligament in extension value, a lateral ligament in extension value, a medial ligament in flexion value, and a lateral side in flexion value associated with the joint”
Claim 17, “the information including imaging data related to the at least on acquired image” should read “the information including imaging data related to the at least one acquired image”
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.
Claims 5 and 15-16 are 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.
Claim 5 recites the limitation "the amount of attenuation of the joint" in lines 8-9 and 10 There is insufficient antecedent basis for this limitation in the claim.
Claim 15 recites the limitation "the amount of attenuation of the joint" in line 10 There is insufficient antecedent basis for this limitation in the claim.
Claim 16 is rejected for inheriting the deficiency of claim 15 and failing to cure the 112(b) rejection.
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) 1-4, 7, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Publication No. 2022/0039864 to McKinnon et al. (hereinafter McKinnon), and further in view of U.S. Publication No. 2014/0093153 to Sofka et al. (hereinafter Sofka).
Regarding independent claim 1, McKinnon discloses A method of assessing a joint (abstract, “Methods and system for characterizing ligament properties using elastography”), comprising:
identifying a first bone portion in an image of the joint, wherein the first bone portion is positioned under a soft tissue (paragraph 0005, “the properties of the surrounding soft tissue” paragraph 0074, “A Tissue Navigation System 120 (not shown in FIG. 1 ) provides the surgeon with intraoperative, real-time visualization for the patient's bone, cartilage, muscle, nervous, and/or vascular tissues surrounding the surgical area.”);
identifying a medial ligament in extension value, a lateral ligament in extension value, a medial ligament in flexion value, a lateral side in flexion value associated with the joint (paragraph 0220, “ image of an anatomy of the patient (e.g., a bone or a ligament), determine a mechanical property of the anatomy based on the obtained postoperative image, and update a rehabilitation plan based on the determined mechanical property.”);
determining a lateral ligament laxity based on the lateral ligament in extension value (paragraph 0007, “used to independently tension medial and lateral compartments during flexion and extension of a joint.” paragraph 0199, “ test to assess 715 laxity of the joint”);
determining one or more adjustment parameters based on the identified (paragraph 0015, “receive the image from the imaging system, determine a mechanical property of the ligament based on the received image, and update a surgical plan based on the determined mechanical property of the ligament.” Paragraph 0028-0029, “In some embodiments, the update to the surgical plan is further based on kinematic data associated with a joint of the patient. In some embodiments, the kinematic data comprises range of motion data for the joint.”), wherein determining the one or more adjustment parameters includes applying a linear equation that receives, as input, (paragraph 0120, “a set of transfer functions are derived that simplify the mathematical operations captured by the model into one or more predictor equations”), wherein the one or more adjustment parameters include:
a predicted change in soft tissue laxity after the identified first bone portion is removed, wherein the soft tissue laxity includes the lateral ligament laxity (NOTE: not analyzed based on “and/or” the limitation is not required);
an adjustment to a planned bone resection of one or more bone cuts (paragraph 0018, “ the computer system is configured to update the surgical plan by determining an amount of bone to be resected based on the determined mechanical property.” paragraph 0120, “The term “surgical planning model” refers to software that simulates the biomechanics performance of anatomy under various scenarios to determine the optimal way to perform cutting and other surgical activities. For example, for knee replacement surgeries, the surgical planning model can measure parameters for functional activities, such as deep knee bends, gait, etc., and select cut locations on the knee to optimize implant placement.”);
an adjustment to a planned bone resection angle of the one or more bone cuts (paragraph 0195, “In some embodiments, if a first ligament is tight and an opposing ligament is loose, a plane of a resection cut for the joint may be angled with respect to the horizontal plane to account for the relative laxity between the two ligaments”); and/or
an adjustment to a planned thickness of an implant (paragraph 0017, “In some embodiments, the computer system is configured to update the surgical plan by selecting a thickness of an implant based on the determined mechanical property.”); and
outputting the one or more determined adjustment parameters to a display (paragraph 0027, “In some embodiments, the surgical system further comprises a display coupled to the computer system, wherein the computer system is further configured to display the updated surgical plan via the display.”).
McKinnon fails to explicitly disclose as further recited. However, Sofka discloses identifying a cross-sectional area of the first bone portion (paragraph 0025, “By using this learning-based algorithm, the bone segmentation is data-driven.” Paragraph 0045, “In an alternate embodiment, the segmentation results can be refined locally in a particular slice” segmenting in a 2D slice is read as a cross sectional area);
determining one or more adjustment parameters based on the identified cross-sectional area of the first bone portion (paragraph 0023, “The segmented bones and detected landmarks result in a full 3D model of the knee, which is used, together with patient information and surgical preferences, to create a patient proposal for the surgery and design patient-specific instruments, such as cutting guides and plastic blocks, for the surgery (106).”);
receives, as input, the identified cross-sectional area (paragraph 0023, “The segmented bones and detected landmarks result in a full 3D model of the knee, which is used, together with patient information and surgical preferences, to create a patient proposal for the surgery and design patient-specific instruments, such as cutting guides and plastic blocks, for the surgery (106).”).
McKinnon is directed toward, “The one or more images may be provided to a surgical planning system that identifies one or more properties of ligaments proximate to the surgical site. Musculoskeletal simulations may be performed using the identified properties to preoperatively identify a surgical plan. Preoperative identification of a surgical plan may enable a surgeon to select from more fine-tuning options for a joint replacement than conventional systems (abstract).” Sofka is directed toward, “A method and system for automatic bone segmentation and landmark detection for joint replacement surgery (abstract).” As can be easily seen by one of ordinary skill in the art before the effective filing date of the claimed invention, McKinnon and Sofka are directed toward similar methods of endeavor of analyzing surgical sites for planning surgical processes. Further, one of ordinary skill in the art would easily understand when planning surgical intervention of joints, the bones forming the joint have an impact on the surgery and the potential outcomes. Said differently, when planning a surgery it would be imperative to consider bones within the joint region to ensure there will be no adverse effects from a surgery. Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Sofka in order to ensure optimal patient outcomes for the entire region effected by surgery.
Regarding dependent claim 2, the rejection of claim 1 is incorporated herein. Additionally, Sofka further discloses wherein identifying the first bone portion and/or identifying the cross-sectional area of the first bone portion includes analyzing the image using one or more image processing techniques (paragraph 0025, “By using this learning-based algorithm, the bone segmentation is data-driven.” Paragraph 0045, “In an alternate embodiment, the segmentation results can be refined locally in a particular slice”).
Regarding dependent claim 3, the rejection of claim 1 is incorporated herein. Additionally, Sofka further discloses wherein identifying the cross-sectional area of the first bone portion includes analyzing a first dimension of the first bone portion and a second dimension of the first bone portion, and disregarding a third dimension of the first bone portion (Paragraph 0045, “In an alternate embodiment, the segmentation results can be refined locally in a particular slice” slices are read as being 2D and thus only including two dimensions).
Regarding dependent claim 4, the rejection of claim 1 is incorporated herein. Additionally, McKinnon further discloses wherein the one or more adjustment parameters include an adjustment to a planned bone resection depth of the one or more bone cuts (paragraph 0137, “The optimized parameters may depend on the portion of the patient's anatomy to be operated on. For example, for knee surgeries, the surgical parameters may include positioning information for the femoral and tibial component including, without limitation, rotational alignment (e.g., varus/valgus rotation, external rotation, flexion rotation for the femoral component, posterior slope of the tibial component), resection depths (e.g., varus knee, valgus knee), and implant type, size and position.”) and an adjustment to a planned bone resection angle of the one or more bone cuts (paragraph 0116, “ angles of planned and executed bone cuts”).
Mckinnon and Sofka in the combination fail to explicitly disclose wherein identifying the cross-sectional area of the first bone portion includes determining that the image of the joint is an image of a set of images showing a greatest extent of the first bone portion in a first dimension. However, Sofka discloses determining the segmentation on a slice level (paragraph 0045, “A segmentation algorithm (e.g., random walker segmentation) is then run locally on the selected slice based on the foreground and background seeds, resulting in a local refinement of the bone segmentation results. ”). One of ordinary skill in the art before the effective filing date of the claimed invention would easily be able to modify the teaching of Sofka in order to determine which slice contained the largest surface area in order to understand the thickest part of the bone may be of interest for calculations of surgical cutting depth.
Regarding dependent claim 7, the rejection of claim 1 is incorporated herein. Additionally, McKinnon further discloses further comprising:
identifying a second bone portion in the image of the joint that is positioned under the soft tissue (paragraph 0180, “images that depict one or more of a patient's bony anatomy and a patient's soft tissues, including the patient's ligaments.” Paragraph 0214, “bone and soft tissue may be related to the segmented volumetric data ”);
identifying a position of the first bone portion (paragraph 0180, “images that depict one or more of a patient's bony anatomy and a patient's soft tissues, including the patient's ligaments.” Paragraph 0214, “bone and soft tissue may be related to the segmented volumetric data” paragraph 0172, “a 3D model is developed during the pre-operative stage based on 2D or 3D images of the anatomical area of interest.” The images are read as containing different bone portions); and
identifying a position of the second bone portion (paragraph 0180, “images that depict one or more of a patient's bony anatomy and a patient's soft tissues, including the patient's ligaments.” Paragraph 0214, “bone and soft tissue may be related to the segmented volumetric data ” paragraph 0172, “a 3D model is developed during the pre-operative stage based on 2D or 3D images of the anatomical area of interest.” The images are read as containing different bone portions);
McKinnon fails to explicitly disclose as further recited. Sofka discloses identifying a cross-sectional area of the second bone portion (paragraph 0045, “In an alternate embodiment, the segmentation results can be refined locally in a particular slice” different slices are read as containing different bone portions);
wherein determining the one or more adjustment parameters is further based on the identified cross-sectional area of the second bone portion, the identified position of the first bone portion, and the identified position of the second bone portion (abstract, “ A plurality bone structures are automatically segmented in the target joint region of the 3D medical image and a plurality of landmarks associated with a joint replacement surgery are automatically detected in the target joint region of the 3D medical image.” Paragraph 0047, “The output bone segmentation results and landmark detection results provide an accurate 3D model of the patient's joint region, which can be used to create a personalized patient proposal for joint replacement surgery in order to manufacture patient-specific cutting guides and plastic blocks for the joint replacement surgery.” The plurality of structures are segmented and the area is identified to determine the joint surgery options ).
One of ordinary skill in the art would easily understand when planning surgical intervention of joints, the bones forming the joint have an impact on the surgery and the potential outcomes. Said differently, when planning a surgery it would be imperative to consider all bones within the joint region to ensure there will be no adverse effects from a surgery. Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Sofka in order to ensure optimal patient outcomes for the entire region effected by surgery.
Regarding independent claim 17, McKinnon discloses A system configured to assess a joint (abstract, “Methods and system for characterizing ligament properties using elastography”), comprising:
an image acquisition device configured to acquire at least one image of the joint (paragraph 0015, “In some embodiments, a surgical system for assessing a patient includes: an imaging system configured to obtain an image of an anatomy of the patient, the anatomy comprising a ligament” paragraph 0026, “ the imaging system comprises a magnetic resonance imaging (MRI) system”);
a memory configured to store information (paragraph 0228, “ internal hardware or peripheral devices, such as flash memory, equivalent non-volatile memory, or optical disk drives may be used in addition to or in place of the hardware depicted. ”), the information including imaging data related to the at least on acquired image and pose parameters (paragraph 0076, “ for storage of data or execution of computationally intensive processing tasks.”), wherein the pose parameters include a medial ligament in extension value, a lateral ligament in extension value, a medial ligament in flexion value, and a lateral ligament in flexion value (paragraph 0220, “ image of an anatomy of the patient (e.g., a bone or a ligament), determine a mechanical property of the anatomy based on the obtained postoperative image, and update a rehabilitation plan based on the determined mechanical property.”);
a controller (paragraph 0181, “the surgical planning system 510 may be a processor-based system that enables surgical planning to be performed.”) configured to:
execute a first algorithm to determine a lateral ligament laxity, wherein the first algorithm receives, as input, the at least one image and the lateral ligament in flexion (paragraph 0007, “used to independently tension medial and lateral compartments during flexion and extension of a joint.” paragraph 0199, “ test to assess 715 laxity of the joint”); and
execute a second algorithm to determine, based on the lateral ligament laxity, the at least one acquired image, and/or the stored imaging data, one or more adjustment parameters (paragraph 0015, “receive the image from the imaging system, determine a mechanical property of the ligament based on the received image, and update a surgical plan based on the determined mechanical property of the ligament.” Paragraph 0028-0029, “In some embodiments, the update to the surgical plan is further based on kinematic data associated with a joint of the patient. In some embodiments, the kinematic data comprises range of motion data for the joint.”), wherein the second algorithm applies a linear equation that receives, as input, the lateral ligament laxity a(paragraph 0120, “a set of transfer functions are derived that simplify the mathematical operations captured by the model into one or more predictor equations” paragraph 0018, “ the computer system is configured to update the surgical plan by determining an amount of bone to be resected based on the determined mechanical property.” paragraph 0120, “The term “surgical planning model” refers to software that simulates the biomechanics performance of anatomy under various scenarios to determine the optimal way to perform cutting and other surgical activities. For example, for knee replacement surgeries, the surgical planning model can measure parameters for functional activities, such as deep knee bends, gait, etc., and select cut locations on the knee to optimize implant placement.” paragraph 0017, “In some embodiments, the computer system is configured to update the surgical plan by selecting a thickness of an implant based on the determined mechanical property.”); and
a display configured to display the determined one or more adjustment parameters (paragraph 0027, “In some embodiments, the surgical system further comprises a display coupled to the computer system, wherein the computer system is further configured to display the updated surgical plan via the display.”).
McKinnon fails to explicitly disclose as further recited. However, Sofka discloses wherein the imaging data includes a cross-sectional area and position of at least one bone portion of the joint positioned under a soft tissue (paragraph 0025, “By using this learning-based algorithm, the bone segmentation is data-driven.” Paragraph 0045, “In an alternate embodiment, the segmentation results can be refined locally in a particular slice” segmenting in a 2D slice is read as a cross sectional area),
receives, as input, the cross-sectional area of the at least one bone portion (paragraph 0023, “The segmented bones and detected landmarks result in a full 3D model of the knee, which is used, together with patient information and surgical preferences, to create a patient proposal for the surgery and design patient-specific instruments, such as cutting guides and plastic blocks, for the surgery (106).”)
McKinnon is directed toward, “The one or more images may be provided to a surgical planning system that identifies one or more properties of ligaments proximate to the surgical site. Musculoskeletal simulations may be performed using the identified properties to preoperatively identify a surgical plan. Preoperative identification of a surgical plan may enable a surgeon to select from more fine-tuning options for a joint replacement than conventional systems (abstract).” Sofka is directed toward, “A method and system for automatic bone segmentation and landmark detection for joint replacement surgery (abstract).” As can be easily seen by one of ordinary skill in the art before the effective filing date of the claimed invention, McKinnon and Sofka are directed toward similar methods of endeavor of analyzing surgical sites for planning surgical processes. Further, one of ordinary skill in the art would easily understand when planning surgical intervention of joints, the bones forming the joint have an impact on the surgery and the potential outcomes. Said differently, when planning a surgery it would be imperative to consider bones within the joint region to ensure there will be no adverse effects from a surgery. Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Sofka in order to ensure optimal patient outcomes for the entire region effected by surgery.
Regarding dependent claim 18, the rejection of claim 17 is incorporated herein. Additionally, McKinnon further discloses wherein the image acquisition device is a computed tomography (CT) acquisition device, and the acquired at least one image is a CT scan (paragraph 0075, “image information collected from various modalities (e.g., CT, MRI, X-ray, fluorescent, ultrasound, etc.)”).
Regarding dependent claim 19, the rejection of claim 18 is incorporated herein. Additionally, McKinnon further discloses wherein the CT scan shows a view of the joint in a first dimension and a second dimension over which the soft tissue extends (paragraph 0075, “image information collected from various modalities (e.g., CT, MRI, X-ray, fluorescent, ultrasound, etc.)” CT is well known to generate 2D image slices ). Sofka discloses the cross-sectional area is determined using the dimension of the bone portion in the first dimension and the second dimension (paragraph 0025, “By using this learning-based algorithm, the bone segmentation is data-driven.” Paragraph 0045, “In an alternate embodiment, the segmentation results can be refined locally in a particular slice” segmenting in a 2D slice is read as a cross sectional area).
Regarding dependent claim 20, the rejection of claim 18 is incorporated herein. Additionally, McKinnon further discloses wherein the display is configured to display a graphical user interface that includes a notification based on one or more bone portions identified in the acquired image (paragraph 0075, “The Display 125 provides graphical user interfaces (GUIs) that display images collected by the Tissue Navigation System 120 as well other information relevant to the surgery. For example, in one embodiment, the Display 125 overlays image information collected from various modalities (e.g., CT, MRI, X-ray, fluorescent, ultrasound, etc.) collected pre-operatively or intra-operatively to give the surgeon various views of the patient's anatomy as well as real-time conditions. ” paragraph 0159, “For example, a GUI that provides a visual depiction of the knee ” paragraph 0190, “ displayed 570 over a portion of an image displaying the patient's anatomy to connote a characteristic.”).
Allowable Subject Matter
Claims 6 and 8-14 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim 5 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Claims 15-16 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action.
Claim 5:
The following is a statement of reasons for the indication of allowable subject matter: the closest prior arts of record teach methods of guiding surgical determinations based on bone and ligament features associated with a joint. However, none of them alone or in any combination teaches analyzing ligament measurements based on subluxation of a bone or bone cut measurements and attenuation of the ligament to determine the adjustment parameter using the linear equation to receive the ligament attenuation.
The closest prior art being McKinnon discloses, “Methods and system for characterizing ligament properties using elastography (abstract). ” Further, McKinnon discloses, “The one or more images may be provided to a surgical planning system that identifies one or more properties of ligaments proximate to the surgical site (abstract).”
However, McKinnon fails to disclose analyzing ligament measurements based on subluxation of a bone or bone cut measurements and attenuation of the ligament to determine the adjustment parameter using the linear equation to receive the ligament attenuation.
Claim 6:
The following is a statement of reasons for the indication of allowable subject matter: the closest prior arts of record teach methods of guiding surgical determinations based on bone and ligament features associated with a joint. However, none of them alone or in any combination teaches where a linear relationship is determined between a cross sectional area and either a bone resection depth or an implant thickness where the greater the cross-sectional area, the greater the decrease in resection depth or the greater the increase to the implant thickness.
The closest prior art being McKinnon discloses at paragraph 0120, “As an alternative to full execution of the surgical planning model, in some embodiments, a set of transfer functions are derived that simplify the mathematical operations captured by the model into one or more predictor equations.”
However, McKinnon fails to disclose where a linear relationship is determined between a cross sectional area and either a bone resection depth or an implant thickness where the greater the cross-sectional area, the greater the decrease in resection depth or the greater the increase to the implant thickness.
Claims 8-14:
The following is a statement of reasons for the indication of allowable subject matter: the closest prior arts of record teach methods of guiding surgical determinations based on bone and ligament features associated with a joint. However, none of them alone or in any combination teaches determining positions of two bones, one on opposite sides of a joint, and determining a difference in cross-sectional area of the two bone portions, and the linear equation determining a relationship between a difference in the cross-sectional area and the resection angle.
The closest prior art Sofka discloses at paragraph 0045, “A segmentation algorithm (e.g., random walker segmentation) is then run locally on the selected slice based on the foreground and background seeds, resulting in a local refinement of the bone segmentation results.”
However, Sofka fails to disclose determining positions of two bones, one on opposite sides of a joint, and determining a difference in cross-sectional area of the two bone portions, and the linear equation determining a relationship between a difference in the cross-sectional area and the resection angle.
Claims 15-16:
The following is a statement of reasons for the indication of allowable subject matter: the closest prior arts of record teach methods of guiding surgical determinations based on bone and ligament features associated with a joint. However, none of them alone or in any combination teaches analyzing ligament measurements and bone subluxation, then determining an amount of ligament attenuation based on the ligament measurements and subluxation or the bone, which are then used to determine adjustment parameters using a linear equation.
The closest prior art being McKinnon discloses, “Methods and system for characterizing ligament properties using elastography (abstract). ” Further, McKinnon discloses, “The one or more images may be provided to a surgical planning system that identifies one or more properties of ligaments proximate to the surgical site (abstract).”
However, McKinnon fails to disclose analyzing ligament measurements and bone subluxation, then determining an amount of ligament attenuation based on the ligament measurements and subluxation or the bone, which are then used to determine adjustment parameters using a linear equation.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
U.S. Publication No. 2015/0057756 discloses, “Improved systems, methods, and devices for performing joint arthroplasty, including patient-adapted implant components and tools, as well as intraoperative measurement and optimization of joint kinematics are disclosed herein (abstract).”
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COURTNEY J. WINDSOR
Primary Examiner
Art Unit 2661
Prosecution Insights