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 .
Response to Amendment
Claims 1-21 remain pending in the application in response to the applicant’s amendments to the rejections previously set forth in the Non-Final Office Action mailed 07/25/2025.
Response to Arguments
The affidavit under 37 CFR 1.132 filed 01/23/2026 is sufficient to overcome the rejection of claims 1-21 under 35 U.S.C. 101 (abstract idea without significantly more).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-21 are rejected under 35 U.S.C. 103 as being unpatentable over Lang (US 20160045317 A1, published February 18, 2016) in view of Imhauser et al. (US 20170162078 A1, published June 8, 2017), hereinafter referred to as Lang and Imhauser, respectively.
Regarding claim 1, and similarly for claims 20 and 21, Lang teaches a method (Fig. 1) comprising:
acquiring three-dimensional (3D) data of a joint of a subject (see para. 0029 – “As illustrated in FIG. 1, a method of generating a model of a patient's joint or other biological feature can include one or more of the steps of obtaining image data of a patient's biological structure 910…”; see para. 0033 – “Moreover, as shown in FIG. 2B, the segmented and selected data from multiple images can be combined to create a 3D representation of the biological structure.”);
determining a tissue quality on articular contact geometry based on the 3D data (see para. 0187 — “The finite element data can be augmented with patient specific data (e.g., data obtained from the patient's scan including also for example bone mineral density or structure [tissue quality and geometry]) or any of the parameters mentioned above and throughout the specification.”); and
determining a motion signature of the joint based on the reconstructed articular contact geometry and the tissue quality by applying a predetermined threshold and ranking the joint poses of the movement capacity based on the determined tissue quality (see para. 0567 – “The kinematics of each joint and the force-generating parameters for each muscle can be derived from any combinations of actual patient-specific data, experimental data, databases of relevant patients and/or mathematical approximations. The various models can estimate muscle-tendon lengths and movement arms for each of the muscles over a wide range of postures, movements and/or degrees of freedom. Given a modeled pattern of muscle activations, the hybrid kinematic model can estimate muscle forces, joint movements and surface/subsurface forces and stresses experienced by joint support structures and/or articulating surfaces (including implant component designs therefor).”),
wherein the movement capacity is a subject-specific set of physically permissible joint poses based on the reconstructed articular geometry (see para. 0105 – “In certain embodiments, all patients below a certain age, for example, all patients below 40 years of age can be scanned to collect one or more images of the patient's joint [physically permissible joint poses]. The images and data collected from the patient can be banked or stored in a patient-specific database.”), and
wherein the motion signature is a subject-specific set of joint poses that is a subset of the movement capacity and that corresponds to the subject's habitual movement and is ranked based on the determined tissue quality (see para. 0568 – “In various embodiments, the various levels of stress and/or strain in a muscle and/or muscle group modeled may indicate relevant information for the model, such as a value that exceeds a specified threshold and indicates the potential for injury and/or pain generation in a given muscle based upon a certain implant design and/or procedure, which may be important information to a clinician seeking to avoid such an occurrence in a patient during and after surgical recovery.”).
Lang teaches acquiring imaging data of a joint, including articular contact geometry (see para. 0054 – “In certain embodiments, imaging data collected from the patient, for example, imaging data from one or more of x-ray imaging, digital tomosynthesis, cone beam CT, non-spiral or spiral CT, non-isotropic or isotropic MRI, SPECT, PET, ultrasound, laser imaging, and/or photo-acoustic imaging, is used to qualitatively and/or quantitatively measure one or more of a patient's biological features, one or more of normal cartilage, diseased cartilage, a cartilage defect, an area of denuded cartilage, subchondral bone, cortical bone, endosteal bone, bone marrow, a ligament, a ligament attachment or origin, menisci, labrum, a joint capsule, articular structures, and/or voids or spaces between or within any of these structures.”), but does not explicitly teach determining joint poses from the sets of contact points of joint structures from the reconstructed articular contact geometry.
Whereas, Imhauser, in an analogous field of endeavor, teaches
reconstructing an articular contact geometry of the joint based on the 3D data (Fig. 2; see para. 0094 – “The knee joint model [3D data] can further be constructed with a meniscus model 106 [articular contact geometry]…”);
determining a movement capacity of the joint based on the reconstructed articular contact geometry by determining sets of contact points of joint structures from the reconstructed articular contact geometry (Fig. 2; see para. 0094 – “The knee joint model can further be constructed with a meniscus model 106 [articular contact geometry] and a coronary ligament having seven fibers [sets of contact points] constraining a medial menisci 106A of the meniscus and the tibial bone…”),
determining joint poses from the sets of contact points (Fig. 2, viewing (determining) joint poses from knee ligament fibers (shown as lines connecting sets of contact points)), and
applying a predetermined threshold to the determined joint poses, and including only the joint poses which satisfy the predetermined threshold (see para. 0148 – “The computer model 700 was constructed to model passive knee flexion through a large functionally important range of motion i.e., from 0° to 130° flexion [joint poses of predetermined threshold], since this is a common peri- and intra-operative clinical examination.”).
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 acquiring imaging data of a joint, including articular contact geometry, as disclosed in Lang, by also determining joint poses from the sets of contact points of joint structures from the reconstructed articular contact geometry, as disclosed in Imhauser. One of ordinary skill in the art would have been motivated to make this modification in order to make accurate predictions of instability, which is a common symptom of an ACL-deficient knee, as taught in Imhauser (see para. 0211).
Furthermore, regarding claim 2, Lang further teaches wherein the joint is a musculoskeletal joint (see para. 0064 — “In certain embodiments, a computer program simulating biomotion of one or more joints, such as, for example, a knee joint, or a knee and ankle joint, or a hip, knee and/or ankle joint [musculoskeletal joints], can be utilized.”).
Furthermore, regarding claim 3, Lang further teaches wherein the joint is an artificial joint (see para. 0093 — “If desired, an implant design [artificial joint] can alter the kinematics of the patient knee as desired, such as, for example, by altering a condyle location and/or surface to alter the implant motion and ultimately the kinematics of the patient's limb.”).
Furthermore, regarding claim 4, Imhauser further teaches wherein reconstructing the articular contact geometry comprises: segmenting bones of the joint based on the 3D data; determining subchondral regions of bone surface geometry of the joint based on the segmented bones and the 3D data; and determining articular contact surfaces based on the subchondral regions (see para. 0187 – “Referring to FIG. 7, a 3D computational computer model of a knee joint 700 was constructed based on geometries of the bones 702, 704, articular cartilage, menisci 706, and ligaments 708…”).
Furthermore, regarding claim 5, Imhauser further teaches determining the movement capacity of the joint, wherein determining the movement capacity of the joint comprises determining combinations of contact points between opposing articular contact surfaces of the reconstructed articular contact geometry, wherein each determined combination of contact points corresponds to a pose and orientation of the joint (Fig. 2, viewing (determining) joint poses from ligament fibers (shown as lines connecting sets of contact points) between tibia and femur).
Furthermore, regarding claim 6, Imhauser further teaches wherein determining the movement capacity or the motion signature of the joint comprises excluding determined combinations of contact points in which opposing articular contact surfaces penetrate each other beyond a predetermined threshold (Fig. 2, knee ligament fibers shown as lines connecting sets of contact points; see para. 0148 – “The computer model 700 was constructed to model passive knee flexion through a large functionally important range of motion i.e., from 0° to 130° flexion [joint poses of predetermined threshold], since this is a common peri- and intra-operative clinical examination.” So modelling the knee out of the range of motion is inherently excluded).
Furthermore, regarding claim 7, Imhauser further teaches wherein each of the opposing articular contact surfaces have corresponding contact points, and wherein alignment of the corresponding contact points to each other is constrained based on geometric relationships between contact points of opposing articular surfaces (Fig. 2; see para. 0094 – “The knee joint model can further be constructed with a meniscus model 106 and a coronary ligament having seven fibers [sets of contact points] constraining a medial menisci 106A of the meniscus and the tibial bone…”).
Furthermore, regarding claim 8, Lang further teaches wherein determining the motion signature comprises: mapping the determined tissue quality on the articular contact geometry (see para. 0032 — “As shown, the distinctive transition in color intensity or grayscale 19000 at the surface of the structure can be used to identify pixels, voxels, corresponding data points, a continuous line, and/or surface data representing the surface or other feature of the biological structure.”); and
estimating joint rotations and translations based on contact point sets of opposing articular contact surfaces (see para. 0193 — “Joint motion that can be measured can include, but is not limited to: translation of one articular surface relative to the other; rotation of one articular surface relative to the other during: Flexion; Extension; Abduction; Adduction; Elevation; Internal rotation; External rotation; and other joint movements.”).
Furthermore, regarding claim 9, Lang further teaches excluding estimated joint rotations and translations based on thresholds of the determined tissue quality mapped to the articular contact geometry at the contact points (see para. 0568 – “In various embodiments, the various levels of stress and/or strain in a muscle and/or muscle group modeled may indicate relevant information for the model, such as a value that exceeds a specified threshold and indicates the potential for injury and/or pain generation in a given muscle based upon a certain implant design and/or procedure, which may be important information to a clinician seeking to avoid such an occurrence in a patient during and after surgical recovery.”).
Furthermore, regarding claim 10, Lang further teaches wherein the joint rotations and translations are estimated based on locations of local peaks of the determined tissue quality mapped to the articular geometry as contact point candidates, and based on a cumulative tissue quality across contact points of each joint rotation and translation (see para. 0187 — “The finite element data can be augmented with patient specific data (e.g., data obtained from the patient's scan including also for example bone mineral density or structure) or any of the parameters mentioned above and throughout the specification.”).
Furthermore, regarding claim 11, Imhauser further teaches segmenting cartilage of the joint based on the 3D data; and generating surface geometries of cartilage based on the segmented cartilage, wherein reconstructing the articular contact geometry comprises generating articular surfaces based on articulating portions of cartilage determined from the segmented cartilage and generated surface geometries of the cartilage (see para. 0187 – “Referring to FIG. 7, a 3D computational computer model of a knee joint 700 was constructed based on geometries of the bones 702, 704, articular cartilage, menisci 706, and ligaments 708…”).
Furthermore, regarding claim 12, Imhauser further teaches wherein determining the movement capacity or motion signature comprises: estimating joint rotations and translations based on contact point sets of opposing articular contact surfaces; and excluding joint rotations and translations in which cartilage surfaces penetrate each other beyond a predetermined threshold (Fig. 2, knee ligament fibers shown as lines connecting sets of contact points; see para. 0148 – “The computer model 700 was constructed to model passive knee flexion through a large functionally important range of motion i.e., from 0° to 130° flexion [joint poses of predetermined threshold], since this is a common peri- and intra-operative clinical examination.” So modelling the knee out of the range of motion is inherently excluded).
Furthermore, regarding claim 13, Imhauser further teaches wherein the joint is a knee and the method further comprises: segmenting menisci tissue of the knee based on the 3D data; and generating surface geometry of menisci based on the segmented menisci, wherein reconstructing the articular contact geometry comprises generating articular surfaces based on the segmented menisci and generated surface geometries of the menisci (see para. 0187 – “Referring to FIG. 7, a 3D computational computer model of a knee joint 700 was constructed based on geometries of the bones 702, 704, articular cartilage, menisci 706, and ligaments 708…”).
Furthermore, regarding claim 14, Imhauser further teaches wherein determining the movement capacity or the motion signature comprises: estimating joint rotations and translations based on contact point sets of opposing articular contact surfaces; and excluding joint rotations and translations in which opposing articular contact surfaces and menisci penetrate each other beyond a predetermined threshold (Fig. 2, knee ligament fibers shown as lines connecting sets of contact points; see para. 0148 – “The computer model 700 was constructed to model passive knee flexion through a large functionally important range of motion i.e., from 0° to 130° flexion [joint poses of predetermined threshold], since this is a common peri- and intra-operative clinical examination.” So modelling the knee out of the range of motion is inherently excluded).
Furthermore, regarding claim 15, Imhauser further teaches wherein the method further comprises determining ligament insertions by: segmenting ligament insertion footprints on joint bones based on the 3D data; and identifying a centroid of ligament insertion based on the segmented ligament insertion footprints, or identifying elevated tissue intensity regions on the joint bones and estimating ligament insertion footprints based on the identified elevated tissue intensity regions (see para. 0187 – “Referring to FIG. 7, a 3D computational computer model of a knee joint 700 was constructed based on geometries of the bones 702, 704, articular cartilage, menisci 706, and ligaments 708…”).
Furthermore, regarding claim 16, Imhauser further teaches wherein determining the movement capacity or motion signature comprises: estimating joint rotations and translations based on contact point sets of opposing articular contact surfaces; determining a length of a ligament for the estimated joint rotations and translations based on the estimated ligament insertion footprints; determining a statistical distribution of the determined ligament lengths across a plurality of joint rotations and translations; and excluding joint rotations and translations in which ligament lengths exceed a predetermined threshold (see para. 0218 – “In sum, the computational computer model 700 and method of constructing the computational computer model of a knee joint included identifying ligaments having a load at full extension, and identifying a ligament slack length of each ligament and incorporating said ligaments and slack lengths in a computational computer knee model. The computer model was capable of predicting knee kinematics and forces on a subject knee joint at low ligament loads and coupled anterior translation and internal rotation through a large range of passive flexion of the subject's knee joint.”).
Furthermore, regarding claim 17, Lang further teaches wherein the 3D data of the joint is of an unloaded configuration of the joint (see para. 0122 — “It may also be desirable to model various of the patient measurements (especially non-load-bearing measurements as described above) to simulate the targeted joint and surrounding anatomy virtually.”).
Furthermore, regarding claim 18, Lang further teaches wherein the 3D data of the joint is of a loaded configuration of the joint (see para. 0121 — “Such load-bearing measurements can include imaging of the patient standing, kneeling, walking and/or carrying loads of varying sizes and/or weights.”).
Furthermore, regarding claim 19, Lang further teaches generating a report of the determined movement capacity and/or the determined motion signature; outputting the report (see para. 0082 – “Once one or more desired models has been created using the various techniques described above, the models (optionally with information from other data sources) can be utilized to select and/or design appropriate implant components and/or surgical tools, as well as to plan the surgical procedure.”; see para. 0118 — “The models (as well as the raw anatomical information) can be used to simulate biomotion of one or more joints and/or extremities, such as a knee joint, or a knee and ankle joint, or a hip, knee and/or ankle joint.”); and
diagnosing or treating a patient based on the determined movement capacity or the determined motion signature (see para. 0088 — “In certain embodiments, the computer software program can have a user interface that includes, for example, one or more of the components including a 3D render canvas, a data path selector, an ID listbox, a report views selection, a scan selection, a generate report button, a generate views button, an image display, and an image slice slider.”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Metcalfe et al. (US 20230080229 A1, published March 26, 2023 with a priority date of September 14, 2021) discloses the contact points may be associated with one or more landmarks or other surface features along the bone model 30 and/or other portions of the patient anatomy, where each contact point may be established along an articular surface or non-articular surface of a joint (Fig. 2; see para. 0068), and the range of motion database 68 may store range of motion data derived from range of motion simulations performed by the computing device 40 for each surgical plan (see para. 0078).
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/N.C./Examiner, Art Unit 3798