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 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.
Claim 23 is 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.
Claim 23 recites “determining the first shape model comprises determining the first shape model based on the first image data and without the second image data”. It is noted this is a negative limitation. Applicant’s remarks cite paragraph 76 for support, but paragraph 76 does not indicate that the first shape model is generated without the second image data. As discussed in MPEP 2173.05(i), silence is generally not sufficient to support a negative limitation, and Applicant’s disclosure does not discuss determining the first shape model without the second image data. Therefore this claim is rejected due to reciting 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.
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 6, 16, and 23 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.
Claims 6 and 16 have been amended to recite “wherein the first image data includes more complete image data of the first anatomical structure than the second image data does of the second anatomical structure”. Applicant cites to paragraph 55 as indicating examples wherein the first anatomical structure is not injured or diseased, or less injured or diseased in comparison to second anatomical structure, however paragraph 55 is simply providing examples and not providing a clear definition for the use of “more complete image data” as recited in the claims. That is, the relative term “more complete image data” is subjective because there is no apparent objective basis or metric for evaluating how “complete” each image data is for comparison, leaving the scope of the claims indefinite.
For purposes of applying prior art, claims 6 and 16 will be interpreted similar to claim 5, i.e. “wherein is non-pathological and includes a pathological portion”.
As discussed in the 112(a) written description rejection above, claim 23 recites “determining the first shape model comprises determining the first shape model based on the first image data and without the second image data”. It was noted this is a negative limitation. Applicant’s remarks cite paragraph 76 for support, but paragraph 76 does not indicate that the first shape model is generated without the second image data. Further, the scope of the negative limitation is indefinite, i.e. claim 1 requires determining the first shape model, including a first estimated shape of the second anatomical structure, but claim 23 requires determining the first shape model without the second image data, leaving the basis for estimating the shape of the second anatomical structure in the second image data undefined. That is, determining the first estimated shape of the second anatomical structure in the second image data inherently requires image data of the second anatomical shape, i.e. the second image data. In other words, without using the second image data of the second anatomical structure, the estimated shape is, at most, an estimated shape of a generic structure rather than the one in the image data, i.e. a generic human scapula/humerus estimate rather than an estimate of the scapula/humerus in the second image data as in the claim. Further, as noted, Applicant’s disclosure does not discuss performing such an operation, i.e. there is no description of estimating the shape of an anatomical structure without image data of said anatomical structure. Therefore the scope of claim 23 is indefinite because it is contradicted by the requirements of the independent claim limitations.
It is noted that because there is no definite alternative limitation supported by the disclosure apparent to the examiner corresponding to this limitation, claim 23 will not be addressed in view of the prior art.
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.
Claims 1, 4, 5, 6, 8, 9, 11, 14, 15, 16, 18, 19, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over “Automated quantification of glenoid bone defects using 3-dimensional measurements” by Katrien Plessers, et al. (hereinafter Plessers) in view of “Articulated Statistical Shape Modeling of the Shoulder Joint” by Tewodros Alemneh (hereinafter Alemneh).
Regarding claim 1, the limitations “A method comprising: receiving first image data of a first anatomical structure and second image data of a second anatomical structure, the first and second anatomical structures being different bones; determining a first shape model based on the … image data and a … statistical shape model (SSM), the first shape model including … a first estimated shape of the second anatomical structure … determining a second shape model based on the first shape model … image data, … the second shape model including a second estimated shape of the first anatomical structure; and generating anatomical information indicative of a pre-morbid shape of at least the second anatomical structure based on the second shape model” are taught by Plessers (Plessers, e.g. abstract, pages 1051-1057, describes a system for reconstructing the native shape of the glenoid by fitting a statistical shape model of the scapula. Plessers, e.g. section Data, indicates that the SSM is built on 66 3D surface models segmented from CT scan data of healthy scapulae, and evaluated using 34 CT scans of scapulae having defects, where the defect CT scans were manually segmented and converted to 3D meshes of the scapulae and humeri, i.e. the defect CT scans correspond to the claimed received image data of first and second anatomical structures which are different bones, i.e. the first anatomical structure is the humerus and the second anatomical structure is the scapula. Plessers, e.g. section SSM-based reconstruction, indicates that the SSM is fit to the target scapula shape by iteratively performing the 4 steps of 1) registering the SSM to the target scapula shape using an iterative closest point (ICP) algorithm, 2) performing a corresponding point search, 3) predicting missing corresponding points, 4) updating shape coefficients by projecting corresponding points onto the SSM space, where the result of the first step corresponds to the claimed determining a first estimated shape model based on the first image data and a joint SSM, i.e. the registered SSM model is the first estimated shape model, and the result of the fourth step corresponds to the claimed determining a second shape model based on the first shape model and the image data, i.e. the updated shape coefficients change the shape of the first/registered estimated shape model to generate the second shape model. Finally, Plessers, e.g. sections Anatomic parameters, Defect quantification, Visual representation, Evaluation, Results, teaches that after fitting the SSM and reconstructing the original, i.e. pre-morbid, shape of the scapula, anatomical information is generated quantifying the defects, including presenting a visual representation of the pre-morbid shape of the scapula, e.g. as shown in figure 4c, the red dots represent the reconstructed/pre-morbid glenoid surface from which rays are shot toward the eroded glenoid surface.)
The limitations “the first and second anatomical structures being different bones; determining a first shape model based on the first image data and a joint statistical shape model (SSM), the first shape model including a first estimated shape of the first anatomical structure and a first estimated shape of the second anatomical structure and the joint SSM is a shape model that is a combination of the first and second anatomical structures; determining a second shape model based on the first shape model, the first image data, and the second image data, the second shape model including a second estimated shape of the first anatomical structure and a second estimated shape of the first anatomical structure” are not explicitly taught by Plessers (Plessers, e.g. section Anatomic parameters, paragraph 2, figure 3, indicates that a shape model is used to fit a shaft axis to the humerus in order to recover the humeral head center point as a landmark for evaluating the defects of the glenoid as in section Defect quantification, but does not explicitly suggest using a statistical shape model to model the humerus, or more specifically as claimed, a joint statistical shape model comprising a representation of the first anatomical structure (humerus in the above mapping) and second anatomical structure (scapula).) However, this limitation is taught by Alemneh (Alemneh, e.g. abstract, sections 1-6, describes an articulated statistical shape model for modeling kinematics of the shoulder joint, where, analogous to Plessers, Alemneh, e.g. sections 3, 3.1, 4.2.1, teaches generating FFDMs, i.e. statistical shape models, for the scapula and humerus in order to register a shoulder joint combined statistical shape model to the scapula and humerus of an input CT image. Alemneh describes details of the shoulder joint ASSM in section 4, including, e.g. section 4.3, performing registration using a first rigid alignment determined by applying an ICP algorithm, followed by using a posterior model to perform non-rigid registration, and determining a relative movement of the humerus to the fixed position scapula in order to determine the kinematic parameters of the shoulder joint in the CT image. Finally, Alemneh, e.g. section 4.3.2, indicates that the shoulder joint ASSM can be fit with minimal amounts of error, and, e.g. section 2.6, applying registration for multiple objects while considering the spatial inter-relationship is desirable for analyzing an anatomical relationship, as well as, e.g. section 1.1, the shoulder joint ASSM can be used for a variety of practical applications including prosthesis design, patient specific modeling, and surgery planning.)
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Plessers’ glenoid bone defect modeling system to use Alemneh’s shoulder joint ASSM modeling technique in order to fit an articulated SSM for both the scapula and humerus. As noted above, Alemneh indicates that the joint ASSM achieves an accurate fit and that it is beneficial to model the spatial inter-relationship in analyzing the anatomical relationship between the bones of the joint, analogous to Plessers' use of the non-statistical shape model for the humerus, thereby motivating one of ordinary skill in the art to modify Plessers' system. In Plessers’ modified system, as taught by Alemneh, e.g. sections 4.2, 4.3, the shoulder joint ASSM would comprise, in addition to the scapula SSM, a humerus SSM, and the transformation between the fixed scapula SSM and movable humerus SSM, i.e. the claimed joint statistical shape model. Further, in Plessers’ modified system, the iterative process of section SSM-based reconstruction would be used to register the shoulder joint ASSM, i.e. analogous to Alemneh, section 4..3, Plessers' modified first step performs rigid ICP registration for the scapula and humerus SSM, followed by performing a non-rigid shape coefficient update using posterior shape modeling, and repeating the four steps until the shape coefficients converge, i.e. the output of the ICP registration step based on the CT image data and the shoulder joint ASSM corresponds to the claimed first estimated shape model based on the first image data and joint SSM, including first and second estimate shapes for the first and second anatomical structures, and the result of the fourth step corresponds to the claimed determining a second shape model based on the first shape model and the first and second image data, i.e. the updated shape coefficients change the shape of the first/registered estimated shape model to generate the second shape model, wherein when the shoulder joint ASSM is used, said second shape model includes second estimated shapes of the first and second anatomical structures.
Regarding claim 4, the limitation “determining the second shape model further comprises morphing the first shape model to the first image data and the second image data” is taught by Plessers in view of Alemneh (As discussed in the claim 1 rejection above, in Plessers’ modified system, the iterative process of section SSM-based reconstruction would be used to register the shoulder joint ASSM, and the result of the fourth step corresponds to the claimed determining a second shape model based on the first shape model and the first and second image data, i.e. the updated shape coefficients change the shape of the first/registered estimated shape model to generate the second shape model, wherein when the shoulder joint ASSM is used, said second shape model includes second estimated shapes of the first and second anatomical structures. As indicated by Plessers, section SSM-based reconstruction, the SSM shape coefficients are updated to more closely match the shape of the target bone, i.e. while the ICP step corresponds to a rigid registration step, the updated shape coefficients correspond to a non-rigid registration step, i.e. the claimed morphing the shape model to the image data. Further, the non-rigid alignment step is performed for both the humerus SSM and the scapula SSM of the combined shoulder joint ASSM, i.e. the humerus SSM is morphed to the first image data and the scapula SSM is morphed to the second image data, corresponding to the first and second image/shape model/anatomical structures, respectively.)
Regarding claim 5, the limitation “wherein the second image data includes a pathological portion and a non-pathological portion” is taught by Plessers (Plessers, e.g. sections Data, SSM-based reconstruction, Anatomic parameters, Defect quantification, indicates that the defect CT scans include pathological and non-pathological portions, i.e. Plessers indicates that the fitting of the scapula SSM can be based on points on the base surface only, where the defective/pathological portions are on the glenoid.)
Regarding claim 6, the limitations “wherein is non-pathological and includes a pathological portion” are taught by Plessers (As discussed in the claim 5 rejection above, the second image data of the scapula includes a pathological portion, whereas the humerus is modeled as non-pathological in Plessers’ system. Further, while not explicitly stated, in many instances the humerus would be non-pathological, i.e. the purpose of the system is modeling scapula pathological defects so only the second anatomical structure is presumed to include pathological portions.)
Regarding claim 8, the limitation “wherein the second anatomical structure comprises at least one of a humerus or a scapula, and wherein the anatomical information includes a visual representation of at least one of a pre-morbid shape of the humerus or scapula” is taught by Plessers (As discussed in the claim 1 rejection above, Plessers, e.g. sections Anatomic parameters, Defect quantification, Visual representation, Evaluation, Results, teaches that after fitting the SSM and reconstructing the original, i.e. pre-morbid, shape of the scapula, anatomical information is generated quantifying the defects, including presenting a visual representation of the pre-morbid shape of the scapula, e.g. as shown in figure 4c, the red dots represent the reconstructed/pre-morbid glenoid surface from which rays are shot toward the eroded glenoid surface.)
Regarding claim 9, the limitation “further comprising, based on the anatomical information, generating a surgical plan to correct a pathological state of at least one of a humerus or a scapula” is taught by Plessers in view of Alemneh (Plessers, e.g. abstract, keywords, indicates that the system is intended for use in preoperative planning, i.e. that the defect information corresponding to the claimed anatomical information is used in preparing a surgical plan to correct the defect in a patient’s scapula. Similarly, Alemneh, e.g. section 1.1, indicates that one application of the shoulder joint ASSM is for generating surgical plans.)
Regarding claims 11 and 21, the limitations are similar to those treated in the above rejection(s) and are met by the references as discussed in claim 1 above, except for the non-transitory memory storing shape models/instructions used by the processor to perform the method of claim 1, which are implicitly taught by Plessers and Alemneh, i.e. although Plessers and Alemneh do not describe the computing systems relied on in detail, one of ordinary skill in the art would recognize that the computer modeling techniques describes by Plessers and Alemneh are directed to computer implemented systems, where one of ordinary skill in the art would further have found it implicit that Plessers relied on a conventional programmable computer, i.e. a computer having a programmable processor executing a modeling program stored in a non-transitory memory, e.g. a hard drive.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement Plessers’ glenoid bone defect modeling system, using Alemneh’s shoulder joint ASSM modeling technique, using a computer having a programmable processor executing a modeling program stored in a non-transitory memory because one of ordinary skill in the art would have found it implicit that Plessers' modeling system was implemented using a conventional programmable computer, i.e. a computer having a programmable processor executing a modeling program stored in a non-transitory memory, e.g. a hard drive.
Regarding claim 14, the limitations are similar to those treated in the above rejection(s) and are met by the references as discussed in claim 4 above.
Regarding claim 15, the limitations are similar to those treated in the above rejection(s) and are met by the references as discussed in claim 5 above.
Regarding claim 16, the limitations are similar to those treated in the above rejection(s) and are met by the references as discussed in claim 6 above.
Regarding claim 18, the limitations are similar to those treated in the above rejection(s) and are met by the references as discussed in claim 8 above.
Regarding claim 19, the limitations are similar to those treated in the above rejection(s) and are met by the references as discussed in claim 9 above.
Claims 2, 12 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over “Automated quantification of glenoid bone defects using 3-dimensional measurements” by Katrien Plessers, et al. (hereinafter Plessers) in view of “Articulated Statistical Shape Modeling of the Shoulder Joint” by Tewodros Alemneh (hereinafter Alemneh) as applied to claims 1, 11, and 21 above, and further in view of “3D-2D projective registration of free-form curves and surfaces” by Jacques Feldmar, et al. (hereinafter Feldmar).
Regarding claim 2, the limitations “determining a first aligned shape generated from the first image data and the second image data; determining a plurality of first shape models based on the first image data and the joint SSM, each of the plurality of first shape models including respective first estimated shapes of the first anatomical structure and respective first estimated shapes of the second anatomical structure, wherein the plurality of first shape models includes the first shape model; … wherein determining the first shape model comprises selecting the first shape model from the plurality of first shape models based on the first shape model having a cost value [based on the first aligned shape] that satisfies a function for the cost value” are taught by Plessers in view of Alemneh (As discussed in the claim 1 rejection above, in Plessers’ modified system, the iterative process of section SSM-based reconstruction would be used to register the shoulder joint ASSM, i.e. analogous to Alemneh, section 4.3, Plessers' first step performs rigid ICP registration for the scapula and humerus SSM, where the output of the ICP registration step based on the CT image data and the shoulder joint ASSM corresponds to the claimed first estimated shape model based on the image data. Plessers, e.g. section Data, paragraph 2, indicates that the target bones segmented from the first and second image data, i.e. the humerus and scapula, respectively, are used to generate 3D surface models, i.e. the claimed first aligned shape generated from the first and second image data. Further, the iterative closest point algorithm used by Plessers and Alemneh involves repeatedly generating modified first shape models having iteratively closer rigid alignment of the SSM to the target shape until a convergence function is satisfied, e.g. Alemneh, section 2.5.3, paragraph 2, i.e. in Plessers’ modified system performing the rigid ICP registration for the humerus SSM using the surface model generated from the humerus CT image data and the scapula SSM using the surface model generated from the scapula CT image data, each iteration produces a modified joint SSM based on the CT image data and joint SSM comprising an estimated humerus SSM and an estimate scapula SSM, wherein the modified joint SSM satisfying the convergence function is selected as the output, ending the algorithm. That is, as claimed, a plurality of first shape models including the first shape model are determined based on the image data and the joint SSM, wherein each of the first shape models includes a first estimated shape for the first anatomical structure, i.e. the humerus SSM, and a second estimated shape for the second anatomical structure, i.e. the scapula SSM, and the output first shape model is selected in response to the first shape model satisfying a function of a cost value measured relative to the first aligned shape.)
The limitations “determining respective distance and orientation values for each of the plurality of first shape models, wherein for a respective first shape model of the plurality of shape models, the distance and orientation difference values for the respective first shape model are indicative of a difference in distance and orientation between the respective first shape model and the first aligned shape … the cost value being based on the respective distance and orientation difference values for the first shape model” are partially taught by Plessers and Alemneh (As discussed above, in Plessers’ modified system performs the rigid ICP registration for the humerus SSM using the surface model generated from the humerus CT image data and the scapula SSM using the surface model generated from the scapula CT image data, repeatedly generating modified joint SSMs until a modified joint SSM satisfying the convergence function is generated, which is selected as the output, ending the algorithm. While one of ordinary skill in the art, being generally familiar with ICP algorithms, would know it is conventional to use a position distance metric for the corresponding features for evaluating the convergence criterion noted by Alemneh, section 2.5.3, i.e. the claimed determining distance difference values for each of the plurality of first shape models, where the convergence function is based on the cost value based on the distance values for a respective first shape model, Plessers and Alemneh do not discuss the distance difference metric, per se, or the claimed orientation difference values.) However, this limitation is taught by Feldmar (Feldmar, e.g. sections 1, 2-2.5, describes 3D-3D rigid surface registration using the conventional ICP algorithm, e.g. section 2.2, which relies on a cost function measuring the difference in position between corresponding points of the source and target models, using three convergence criteria, i.e. a fixed distance threshold, a successive iteration distance difference threshold, or a number of iterations. Feldmar, e.g. sections 2.3-2.4, teaches an improved ICP algorithm which measures the summed Euclidian distance between position and normal coordinates of corresponding points, i.e. the claimed distance and orientation difference values determined between the respective first shape model and the first aligned shape used to determine a cost value, where a function based on the cost value is used to select the output first shape model. Finally, Feldmar, e.g. table 1, indicates that using normal distances in addition to position distances allows the modified ICP algorithm to determine an accurate alignment with a greater success rate than the conventional ICP algorithm.)
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Plessers’ glenoid bone defect modeling system, using Alemneh’s shoulder joint ASSM modeling technique, using Feldmar’s modified ICP algorithm to perform Plessers’ ICP step because Feldmar indicates that the modified ICP algorithm can achieve higher accuracy than the conventional ICP algorithm. In the modified system, Plessers’ ICP step would be performed using Feldmar’s distance metric measuring position and normal distances between corresponding points of each respective iteration of the modified joint SSM and the surface model of the humerus and scapula, where the output modified joint SSM would be selected based on the modified distance metric satisfying either the fixed distance threshold or the successive iteration distance difference threshold noted by Feldmar, section 2.2, i.e. as claimed, distance and orientation difference values are determined between the respective first shape model and the first aligned shape used to determine a cost value, where a function based on the cost value is used to select the output first shape model.
Regarding claims 12 and 22 the limitations are similar to those treated in the above rejection(s) and are met by the references as discussed in claim 2 above.
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over “Automated quantification of glenoid bone defects using 3-dimensional measurements” by Katrien Plessers, et al. (hereinafter Plessers) in view of “Articulated Statistical Shape Modeling of the Shoulder Joint” by Tewodros Alemneh (hereinafter Alemneh) as applied to claims 1 and 11 above, and further in view of “Prediction of the pre-morbid 3D anatomy of the proximal humerus based on statistical shape modeling” by S. Poltaretskyi, et al. (hereinafter Poltaretskyi).
Regarding claim 10, the limitation “further comprising, based on the anatomical information, generating a representation of the first anatomical structure and the second anatomical structure to be displayed via a mixed-reality headset” is not explicitly taught by Plessers in view of Alemneh (As discussed in the claim 1 rejection above, Plessers, e.g. sections Anatomic parameters, Defect quantification, Visual representation, Evaluation, Results, teaches that after fitting the SSM and reconstructing the original, i.e. pre-morbid, shape of the scapula, anatomical information is generated quantifying the defects, including presenting a visual representation of the pre-morbid shape of the scapula, e.g. as shown in figure 4c, the red dots represent the reconstructed/pre-morbid glenoid surface from which rays are shot toward the eroded glenoid surface. While one of ordinary skill in the art would have generally recognized that a mixed-reality headset is a type of display technology which could be used to display Plessers' visual representations, neither Plessers or Alemneh discusses mixed or augmented reality displays, per se, and therefore in the interest of compact prosecution, Poltaretskyi is cited for teaching this feature.) However, this limitation is taught by Poltaretskyi (Poltaretskyi, e.g. pages 927-933, describes a system using statistical shape modeling for determining the pre-morbid anatomy of a humerus, analogous to Plessers’ determining the pre-morbid anatomy of a scapula. Poltaretskyi, page 932, suggests that in addition to using the pre-morbid anatomy for surgical planning and prosthesis design, the pre-morbid shape can be used intra-operatively by using an augmented reality display system, i.e. the claimed mixed-reality headset.)
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Plessers’ glenoid bone defect modeling system, using Alemneh’s shoulder joint ASSM modeling technique, to use Poltaretskyi’s augmented reality display system for displaying the visualization of the pre-morbid scapula anatomy, e.g. for intra-operative use as taught by Poltaretskyi.
Regarding claim 20, the limitations are similar to those treated in the above rejection(s) and are met by the references as discussed in claim 10 above.
Response to Arguments
Applicant’s arguments, see pages 8-9, filed 2/24/26, with respect to 35 U.S.C. 112(a) rejections of claims 6 and 16 and 112(d) rejections of claims 3 and 13 have been fully considered and are persuasive. The 35 U.S.C. 112(a) rejections of claims 6 and 16 and 112(d) rejections of claims 3 and 13 have been withdrawn.
Applicant's arguments filed 2/24/26 have been fully considered but they are not persuasive.
Applicant asserts that “if one of ordinary skill in the art were to take the glenoid modeling of Plessers and update those modeling techniques using the shoulder joint ASSM of Alemneh, the result would be information related to the kinematics of the shoulder joint, as contemplated by Alemneh”. Applicants assertion is not contradictory to the finding of the rejection, i.e. simply because the modified system additionally determines shoulder joint kinematics due to using Alemneh’s ASSM as noted by Applicant, does not mean that Plessers’ modified system would not continue to operate for its original intended purpose of reconstructing the shape of the pre-morbid glenoid by fitting Alemneh’s shoulder joint ASSM in place of Plessers’ scapula SSM and non-statistical humerus model as in the modified system of the rejection. Furthermore, as noted in the rejection, Alemneh not only indicates the benefits of the shoulder joint ASSM modeling technique, per se, in sections 4.3.2 and 2.6, but also that the technique is applicable to other practical applications such as prosthesis design, patient specific modeling, and surgery planning, i.e. contrary to Applicant’s assertion, Alemneh does not suggest that the only purpose of the shoulder joint ASSM modeling technique is for determining information related to the kinematics of the shoulder joint. Therefore, this assertion cannot be considered persuasive.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERT BADER whose telephone number is (571)270-3335. The examiner can normally be reached 11-7 m-f.
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, Tammy Goddard can be reached at 571-272-7773. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ROBERT BADER/Primary Examiner, Art Unit 2611
Prosecution Insights