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 three information disclosure statements (IDS) filed on June 16, 2025, December 12, 2025, and March 4, 2026, have been considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
an ‘image capture module’ in Claims 1 and 6,
an ‘image analysis module’ in Claims 1 and 6,
a ‘model building module’ in Claims 1 and 6
a ‘novel computation module’ in Claims 1 and 6, and
an ‘image registration module’ in Claim 2.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-10 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
A claim limitation expressed in means- (or step-) plus-function language 'shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.' 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. If the specification fails to disclose sufficient corresponding structure, materials, or acts that perform the entire claimed function, then the claim limitation is indefinite because the applicant has in effect failed to particularly point out and distinctly claim the invention as required by 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. In re Donaldson Co., 16 F.3d 1189, 1195, 29 USPQ2d 1845, 1850 (Fed. Cir. 1994) (en banc). Such a limitation also lacks an adequate written description as required by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, because an indefinite, unbounded functional limitation would cover all ways of performing a function and indicate that the inventor has not provided sufficient disclosure to show possession of the invention. See also MPEP § 2181.
The Examiner has found sufficient support and description for the claimed image capture module, image analysis module, and model building module. However, there is no algorithm recited for how the ‘novel computation module’ simulates positions of the bone during exercise, and then calculates cartilage shape and wear. Instead, the specification merely recites the language of Claim 1, and then describes the outputs of the novel computation module (see pg. 10 of the Specification, where the claim language is recited, and see pg. 13, where the Specification states that cartilage thickness and volume may be obtained, but fails to teach how these values are obtained).
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 limitation “novel computation model” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. There is no algorithm recited for how the ‘novel computation module’ simulates positions of the bone during exercise, and then calculates cartilage shape and wear. Instead, the specification merely recites the language of Claim 1, and then describes the outputs of the novel computation module (see pg. 10 of the Specification, where the claim language is recited, and see pg. 13, where the Specification states that cartilage thickness and volume may be obtained, but fails to teach how these values are obtained). Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Furthermore, claims 4 and 9 recite the limitation "the XYZ axes". There is insufficient antecedent basis for this limitation in these claims.
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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 3-6, and 8-10 rejected under 35 U.S.C. 103 as being unpatentable over Mahfouz (US Pub No 20170367766), hereinafter Mahfouz.
As to Claim 1, Mahfouz teaches a measurement system for personalized bone and articular cartilage morphology applied to construct a personalized joint skeleton and cartilage model of a subject (see paragraph [0131], “The pre-operative steps include creating a virtual model of the patient-specific anatomy that will be the subject of the surgical procedure… In exemplary form, the virtual anatomical model may comprise any bone or groups of bones such as a joint including the knee, hip, shoulder, ankle and/or spine segment”),
and calculating a cartilage wear value of the subject based on the personalized joint skeleton and cartilage model, the system comprising (see paragraph [0275], “Using estimated cartilage maps along with measured joint deformity, the location of cartilage loss is determined and amount of cartilage loss can be estimated”).
a database, configured to store a dataset, wherein the dataset comprises a plurality of joint bone and cartilage images of a plurality of healthy individuals, a 3D bone and cartilage model corresponding to the plurality of joint bone and cartilage images and a plurality of shape parameters (see paragraph [0280], “There may be at least three potential paths to generate patient specific dynamic implants, all of which may rely on a database containing healthy joints' data including profiles, kinematics, and soft tissues properties”, and see paragraph [0282], “it is proposed to have a wide family of polyethylene geometry templates and corresponding femoral templates to capture statistical shape variations of the normal knee anatomy”);
an image capture module, configured to capture a plurality of bone images of the subject, each of the bone images comprising a first bone image and a second bone image (see paragraph [0241], “As part of this process, patient imaging is taken preoperatively. This can consist of 3D imaging techniques such as MRI or CT, or a plurality of 2D images, such as X-ray, ultrasound or fluoroscopy. These images are used for creation of virtual patient specific anatomical models. For the knee, it may be required that, at minimum, the knee portion of the joint be created (distal femur and proximal tibia)”, wherein the X-ray device is interpreted as the ‘image capture module’);
an image analysis module (see paragraph [0010], “It is a third aspect of the present invention to provide a surgical navigation system comprising… a processor”), coupled with the image capture module and the database, and configured to generate the 3D bone and cartilage model of the subject by applying a statistical shape model (SSM) learned from the dataset through the bone images of the subject and through principal component analysis (PCA) (see paragraph [0261], “A sequential shape and pose estimation 3D reconstruction is based on a nonlinear statistical shape model, namely kernel (KPCA)”),
to generate a plurality of first bone and cartilage shape parameters corresponding to the first bone image and a plurality of second bone and cartilage shape parameters corresponding to the second bone image based on the 3D bone and cartilage model and the shape parameters (see paragraph [0266], “It is worth mentioning that for the knee, it is required that, at minimum, the knee portion of the joint be created (distal femur and proximal tibia)”, and see paragraph [0268], “Turning to FIG. 70, as part of reconstructing the soft tissue associated with the virtual bone model, ligament locations are extracted from imaging data”, where the Examiner has interpreted the femur as the ‘first shape parameter’, and the tibia as the second shape parameter, and the ‘soft tissue’ is interpreted as cartilage),
generate a first bone and cartilage 3D model and a second bone and cartilage 3D model based on the first bone and cartilage shape parameters and the second bone and cartilage shape parameters (see Fig. 68, where a 3D model of the femur, tibia and cartilage is shown);
a neural network model, coupled with the image analysis module, and configured to input the first bone and cartilage shape parameters and the second bone and cartilage shape parameters into the neural network model and generate a plurality of first bone motion parameters and a plurality of second bone motion parameters corresponding to the first bone and cartilage shape parameters and the second bone and cartilage shape parameters (see paragraph [0276], “The deep neural network may take pathological motion input and determines the optimal healthy kinematics (see FIG. 73)”, and paragraph [0285], “For this simulation, the femoral flexion with respect to the tibia should be prescribed while the remaining five degrees of freedom (three translations, axial rotation, abduction/adduction) predicted”, wherein the translation and rotation predicted is interpreted as the first and second bone motion parameters),
wherein the neural network model is trained by a machine learning method based on the dataset (see paragraph [0276], “By way of example, determining normal healthy kinematics may be through the use of deep neural network, where the network may be trained by motions performed by healthy joints”);
a model building module (see paragraph [0010], “It is a third aspect of the present invention to provide a surgical navigation system comprising… a processor”), coupled with the image analysis module and the neural network model, and configured to generate the personalized joint skeleton and cartilage model based on the first bone and cartilage 3D model, the second bone and cartilage 3D model, the first bone motion parameters and the second bone motion parameters (see Fig. 68, where a 3D bone model with kinematic motion parameters is generated);
a novel computation module, coupled with the model building module, and configured to simulate the relative positions of the first bone and cartilage 3D model and the second bone and cartilage 3D model of the subject during exercise based on the personalized joint skeleton and cartilage model (see paragraph [0005], “imaging data is utilized to generate multiple bone models that change position with respect to one another across a range of motion”, and see paragraph [0285], “For this simulation, the femoral flexion with respect to the tibia should be prescribed while the remaining five degrees of freedom (three translations, axial rotation, abduction/adduction) predicted”)
and to calculate an actual cartilage shape and the cartilage wear value of the subject (see paragraph [00271], “FIG. 73 depicts a patient specific cartilage map (obtained from kinematic analysis, substep 21 in FIG. 53)”, and see paragraph [0275], “Using estimated cartilage maps along with measured joint deformity, the location of cartilage loss is determined and amount of cartilage loss can be estimated”).
by eliminating overlapping cartilage regions in the first bone and cartilage 3D model and the second bone and cartilage 3D model during exercise (see paragraph [0274], “The mean cartilage template is adjusted only when the femoral and tibial cartilage overlap. In this case the cartilage thickness is reduced globally by a small factor and at areas of overlap by a larger factor. This process iterates until there are no areas of overlap”).
It is recognized that the citations and evidence provided above are derived from potentially different embodiments of a single reference (see paragraph [0005], “In yet another more detailed embodiment of the first aspect, the dynamic imaging data is utilized to generate multiple bone models that change position with respect to one another across a range of motion. In yet another more detailed embodiment, the method further includes constructing virtual anatomical models using the dynamic imaging data. In a further detailed embodiment, the virtual anatomical models comprise an anatomical joint comprising at least two bones. In still a further detailed embodiment, the anatomical joint includes at least one of a shoulder joint, a knee joint…. In another more detailed embodiment, the predetermined kinematic constraint is derived from predicting normal kinematics”). Nevertheless, it 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 to employ combinations and sub-combinations consisting of kinematic analysis of these complementary embodiments in order to improve patient outcomes. Mahfouz teaches in paragraph [0252], “The process includes a kinematic assessment that may be carried out using fluoroscopic or other dynamic imaging modality (as opposed to traditional, static imaging), which is uploaded to a processing CPU to reconstruct motion and anatomical structures from the imaging data. The kinematics and anatomical structures are used as inputs to a prediction model for determining optimal reconstructive shape and normal kinematics…This data can then be utilized in a feedback loop with the predictive engine to optimize future patient implant parameters.”
As to Claim 3, Mahfouz teaches wherein each of the bone images of the subject further comprises an X-ray image (see paragraph [0131], “The virtual anatomical model may be created from a static imaging modalities, such as computed tomography (CT), magnetic resonance imaging (MRI) and/or X-ray”).
As to Claim 4, Mahfouz teaches, wherein the first bone motion parameters and the second bone motion parameters further comprise a translation parameter and a rotation parameter along the three directions of the XYZ axes (see paragraph [0285], “For this simulation, the femoral flexion with respect to the tibia should be prescribed while the remaining five degrees of freedom (three translations, axial rotation, abduction/adduction) predicted”, and wherein each ‘degree of freedom’ refers to translation and rotation along the XYZ axes).
As to Claim 5, Mahfouz teaches wherein the personalized joint skeleton and cartilage model is further visualized through a heatmap and a perspective visualization method (see Fig. 73, where a heatmap representative of cartilage thickness is shown).
As to Claim 6, Mahfouz teaches a method (see paragraph [0005], “In yet another more detailed embodiment, the method further includes constructing virtual anatomical models”), which comprises the same steps recited in Claim 1. Therefore, the rejection and rationale are analogous to that of Claim 1.
As to Claim 8, Claim 8 claims the same limitation claimed as Claim 3 and is dependent on a similarly rejected independent claim. Therefore, the rejection and rationale are similar to that of Claim 3.
As to Claim 9, Claim 7 claims the same limitation claimed as Claim 4 and is dependent on a similarly rejected independent claim. Therefore, the rejection and rationale are similar to that of Claim 4.
As to Claim 10, Claim 10 claims the same limitation claimed as Claim 5 and is dependent on a similarly rejected independent claim. Therefore, the rejection and rationale are similar to that of Claim 5.
Claims 2 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Mahfouz (US Pub No 20170367766), hereinafter Mahfouz, in view of Wu et al. (Wu, Jing; Mahfouz, Mohamed R., "Reconstruction of knee anatomy from single-plane fluoroscopic x-ray based on a nonlinear statistical shape model," Journal of Medical Imaging 8(1), Jan 2021), hereinafter Wu.
As to Claim 2, Mahfouz teaches an image registration module (see paragraph [0010], “It is a third aspect of the present invention to provide a surgical navigation system comprising… a processor”), coupled with the image analysis module and the model building module,
and compare the joint bone and cartilage images from the healthy individuals in the dataset (see paragraph [0296], “Preoperative imaging is used to extract patient specific design parameters, which are then compared against a database containing kinematic analysis of normal and implanted patients”)
by a single-plane dynamic image registration method (see paragraph [0298], “a flow diagram for an exemplary process for construction of a joint implant from dynamic data is depicted. Here a patient specific shape is constructed from analysis of single-plane digital fluoroscopy images”),
to calculate the first bone motion parameters and the second bone motion parameters of the subject (see paragraph [0300], “The process begins with a kinematic assessment using fluoroscopic or other dynamic imaging modality (as opposed to traditional, static imaging), which is uploaded to a processing CPU to reconstruct motion and anatomical structures from the imaging data”).
Mahfouz fails to teach that the image registration module configured to adjust and project the angle and position of the first bone and cartilage 3D model and the second bone and cartilage 3D model. However, in an analogous art, Wu teaches an image reconstruction technique for joints (see pg. 1, Abstract, “We present a three-dimensional (3D) reconstruction scheme that automatically and accurately reconstructs the 3D knee anatomy”),
which comprises adjusting and projecting the angle and position of a first bone 3D model and a second 3D model and comparing the joint bone images in a dataset by a single-plane dynamic image registration method (see Fig. 2, where the femur and tibia are projected and compared to joint bone images, and see pg. 3, Section 2, “The goal of this paper is to reconstruct the 3-D knee anatomy from a single-plane fluoroscopic x-ray sequence”, see pg. 11, Section 3, “The translation parameters relate the translation of the registered model relative to its starting position in mm, and the rotation parameters relate the angles along the defined coordinate system in degrees from the starting pose.”)
and see
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to calculate motion parameters (see pg. 11, Section 3, “The registration accuracy was evaluated by the motion difference with respect to the reference standard in the camera reference frame as defined in Fig. 4. Absolute mean and standard deviation of the motion differences (rotation and translation)”, wherein rotation and translation are interpreted as the motion parameters). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the model projection taught by Wu with the teachings of Mahfouz. The motivation for doing so would be to provide more accurate information motion parameter information (see pg. 18, Section 6). Thus, it would have been obvious to combine the teachings of Wu with the teachings of Mahfouz in order to obtain the invention as claimed in Claim 2.
As to Claim 7, Claim 7 claims the same limitation claimed as Claim 2 and is dependent on a similarly rejected independent claim. Therefore, the rejection and rationale are similar to that of Claim 2.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Mitra et al. (US Pub No 20210322148) teaches obtaining a 3D model of a knee joint and using the knee joint to simulate exercise stresses in order to evaluate ligament strength.
Spykerman (WO Pub No 2021030536) teaches an AI apparatus for surgery which generates a 3D model of a user joint, and then tests the model by applying stress to the 3D model.
Hu et al. (CN 113392895) teaches a method for determining cartilage damage by obtaining images and generating three dimensional model of knee joint areas.
Gardiner et al. (Gardiner, B.S., et al., “Predicting Knee Osteoarthritis”, Ann Biomed Eng 44, 222–233 (2016)), teaches a method of determining osteoarthritis in patients by building patient-specific models of knee joints, and then performing kinematic analysis on the patient specific model in order to determine damage to the cartilage.
Ciliberti et al. (Ciliberti FK, et al., “CT- and MRI-Based 3D Reconstruction of Knee Joint to Assess Cartilage and Bone. Diagnostics”, Basel, (2022)), teaches a method for creating a 3D model of a knee joint and cartilage through CT images of a knee joint, and then computing cartilage damage based on the 3D cartilage model.
Hsuan-Yu et al. (Lu HY, et al., “Three-Dimensional Subject-Specific Knee Shape Reconstruction with Asynchronous Fluoroscopy Images Using Statistical Shape Modeling”, Front Bioeng. Biotechnol. (2021)), teaches a method of reconstructing bones patient joints by using several X-ray image pairs, and statistical shape modeling and principal component analysis.
Van Dijk et al. (Van Dijck, C., et al., “Statistical shape model-based prediction of tibiofemoral cartilage”, Computer Methods in Biomechanics and Biomedical Engineering, 21(9), pp. 1-11, (2018)), teaches a method for quantifying cartilage wear by modeling cartilage using patient-specific statistical shape modeling.
Shih et al. (Shih, Kao-Shang, et al., “Patient-specific instrumentation improves functional kinematics of minimally-invasive total knee replacements as revealed by computerized 3D fluoroscopy”, Computer Methods and Programs in Biomedicine, Vol. 188, (2020)) teaches using single-plane fluoroscopy to determine 3D motion parameters of prosthetic knee joints.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SOUMYA THOMAS whose telephone number is (571)272-8639. The examiner can normally be reached M-F 8:30-5:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Mehmood can be reached at (571) 272-2976. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/S.T./Examiner, Art Unit 2664
/CHARLOTTE M BAKER/Primary Examiner, Art Unit 2664
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