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 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:
a learning model generation part, a landmark estimation part, and a cervical artificial disc modeling part in claim 1.
a training medical image collection part, a training data generation part, and an artificial intelligence training part 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 § 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) 1-3 and 7-9 are rejected under 35 U.S.C. 103 as being unpatentable over Wimmer at al. (US 2018/0365876 A1; “Wimmer”) in view of Pacheco et al. (US 2008/0009945 A1; Pacheco).
Regarding claim 1:
Wimmer discloses: a device for modeling a cervical artificial disc based on artificial intelligence (Wimmer teaches an apparatus comprising an image processor that applies a learning-based algorithm and trained local models to medical images of the spine. Wimmer's trained models encompass intervertebral discs throughout the spine beginning at the cervical level C2/C3. See Wimmer ¶¶[0011]-[0018], [0053]-[0057], [0065]-[0076], Figs. 1-3, and claim 23), the device comprising:
a learning model generation part configured to generate a learning model through training a plurality of landmarks constituting a plurality of spaces for a cervical disc (Wimmer teaches obtaining a local model during a training phase by annotating training images, extracting sparse landmarks from the annotated images, and building the local model from the extracted landmarks. See Wimmer ¶¶[0033]-[0035], Fig. 3; claims 13, 20-23. More specifically, Wimmer teaches three-disc models “trained from sparse landmarks.” Each three-disc model includes a middle intervertebral disc and adjacent upper and lower discs. Wimmer trains such models throughout the spine and states that the “complete spinal region from C2/C3 to L5/S1 is covered.” See Wimmer ¶¶[0065]-[0069], Figs. 2-3. Wimmer further teaches that the landmarks used for model building include vertebral-body center positions, center positions of the middle, upper, and lower intervertebral discs, sampled points along surfaces of cylinders placed within the discs to approximate disc dimensions, and corresponding spinal-canal landmarks. See Wimmer ¶¶[0069]-[0076], Figs. 2-3);
a landmark estimation part configured to estimate the plurality of landmarks by applying medical images for the plurality of spaces of the cervical disc of a surgical patient to be operated to the generated learning model (Wimmer teaches applying the previously learned local model to an unseen patient MR image. The corresponding trained model is placed in the unseen scan and iteratively matched to the patient's spinal anatomy. See Wimmer ¶¶[0079]-[0086], Fig. 4. As a result of the model matching, “candidate positions for the landmarks are obtained,” including positions corresponding to the middle disc, adjacent upper and lower discs, and vertebral centers. Wimmer further iterates the model through the spinal column up to C2/C3. See Wimmer ¶¶[0087]-[0089], [0097]-[0103], Fig. 4; claims 13 and 23.);
Wimmer does not teach: a cervical artificial disc modeling part configured to model the cervical artificial disc of the surgical patient by using the estimated plurality of landmarks.
However, in the same field of endeavor, Pacheco teaches: a cervical artificial disc modeling part configured to model the cervical artificial disc of the surgical patient by using the estimated plurality of landmarks (Pacheco teaches obtaining CT or MRI images of a patient's spine and generating a dimensionally accurate three-dimensional computer representation. The computer determines intervertebral-disc-space parameters including height, width, depth, and lordosis between adjacent vertebral endplates. See Pacheco ¶¶[0039]-[0041], Figs. 1a-5.
Pacheco then creates a computer-generated prosthetic-disc volume corresponding to an actual artificial disc and determines the prosthetic-disc volume that fits within the patient's intervertebral-disc space. See Pacheco ¶¶[0042]-[0045], Figs. 6-20; claim 1. Pacheco further teaches determining disc-space geometry using anatomical reference points associated with the vertebrae and endplates. See Pacheco ¶[0043], Figs. 13-18; claim 2. Pacheco additionally identifies the pedicle-base circumference as a “consistent radiographic landmark” usable for artificial-disc placement and teaches registration using internal vertebral-body landmarks. See Pacheco ¶¶[0048]-[0054], Figs. 21-27).
Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Wimmer to incorporate the teachings of by including Pacheco's artificial-disc modeling process in order to automatically determine the ideal height, width, depth and lordosis or actual placement of an artificial intervertebral disc prosthesis. Such a modification represents application of a known automated spinal-image-analysis technique to a computerized artificial-disc modeling method that already requires the corresponding patient-specific spinal anatomical information, with each technique performing its known function and producing a predictable result
Regarding claim 2:
Wimmer further discloses: wherein the learning model generation part comprises: a training medical image collection part configured to collect training medical images for the plurality of spaces for the cervical disc (Wimmer teaches obtaining and using training medical images of the spine. Wimmer states that training images may comprise a set of images acquired from different subjects, MR scanners, and MRI protocols. See Wimmer ¶[0027]. Wimmer further teaches learning three-dimensional ETMs from an annotated set of T1- and T2-weighted MR volume datasets and, in one embodiment, using eight scans to train twenty-one three-disc models. See Wimmer ¶¶[0065]-[0066], [0074], Fig. 2. Wimmer's three-disc models cover the complete spinal region from C2/C3 through L5/S1. See Wimmer ¶[0068], Fig. 2);
a training data generation part configured to generate training data by adding the plurality of landmarks to the collected training medical images (Wimmer teaches generating training data by adding anatomical landmarks to the training medical images. Specifically, Wimmer teaches that anatomical landmarks and structures are placed in the acquired training dataset by a domain expert and used for model building. See Wimmer ¶¶[0069]-[0073]. Wimmer further teaches extracting, from the annotated ground truth, vertebral-body centers, center positions of the middle, upper, and lower discs, sampled points along disc-representative surfaces, and spinal-canal landmarks. See Wimmer ¶[0075], Figs. 2-3. Wimmer also expressly claims obtaining the local model by annotating training images and extracting landmarks from the annotated images. See Wimmer claims 13, 21-22); and
an artificial intelligence training part configured to generate the learning model by training the learning model with the generated training data (Wimmer teaches generating a learned model by training with the generated landmark-containing training data. Wimmer describes a learning-based algorithm employing trained entropy-optimized texture models. See Wimmer ¶[0055]. Wimmer states that the result of the training is a learned model. See Wimmer ¶[0063]. In the spine-specific embodiment, three-disc models are “trained from sparse landmarks,” and corresponding landmarks extracted from annotated MR data are used to build the model. See Wimmer ¶¶[0065]-[0068], [0075]-[0079], Figs. 2-3).
Regarding claim 3:
Wimmer further discloses: wherein the learning model is configured to comprise individually generating each of the plurality of landmarks or collectively generating all the plurality of landmarks at a time (Wimmer teaches a single three-disc learning model comprising a plurality of landmarks associated with a middle disc and its adjacent upper and lower discs. Specifically, Wimmer teaches that three-disc models including a middle disc, an adjacent upper disc, and an adjacent lower disc are “trained from sparse landmarks,” and that such models cover the spinal region from C2/C3 through L5/S1. See Wimmer ¶[0068], Fig. 2. Wimmer further teaches that the landmark set used to construct each three-disc model includes two vertebral-body center positions, center positions of the middle, upper, and lower discs, sampled points along the surfaces of the annotated disc-representative cylinders, and corresponding spinal-canal landmarks. Wimmer expressly states that these extracted landmarks are used together to build the three-disc model and that a shape model or mesh is automatically generated from the extracted landmark data. See Wimmer ¶¶[0075]-[0076], Figs. 2-3. Thus, Wimmer teaches the claimed collectively generating all the plurality of landmarks at a time, because a single learning model is generated from and collectively represents the plurality of constituent landmark positions rather than requiring a separate model for each landmark); and
wherein the estimating of the plurality of landmarks is configured to perform individually estimating each of the plurality of landmarks according to the learning model, or collectively estimating all the plurality of landmarks at a time (Wimmer teaches applying an instance of the learned three-disc model to an unseen patient MR image and performing iterative model matching between the learned model and the patient's spinal image. See Wimmer ¶¶[0080], [0084]-[0086], Fig. 4. Wimmer further teaches that “candidate positions for the landmarks are obtained,” identifying, for example, the middle-disc, upper-disc, lower-disc, and vertebral-center positions. See Wimmer ¶[0087], Fig. 4. Wimmer further derives disc orientation from the landmark positions of the matched model instance. See Wimmer ¶[0089]. Because the trained three-disc model is a single model constructed from the plurality of landmarks, and matching that model to the patient image provides the corresponding landmark positions from the matched model instance, Wimmer teaches collectively estimating the plurality of landmarks according to the learning model).
Allowable Subject Matter
Claims 4-6 and 10-12 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure
Buisseret (US 20130336553) teaches: Identifying one or more anatomical landmarks in the segmented image data using the model includes identifying one or more anatomical landmarks in the corrected segmented image data using the model. Receiving image data representing tissue includes receiving image data including scan data acquired using different measurement parameters, and segmenting the image data includes identifying tissues of different types using the scan data acquired using different measurement parameters.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WASSIM MAHROUKA whose telephone number is (571)272-2945. The examiner can normally be reached Monday-Thursday 8:00-5:00 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Stephen Koziol can be reached at (408) 918-7630. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/WASSIM MAHROUKA/Primary Examiner, Art Unit 2665
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