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 is: attention mechanism in claims 12 and 27, for which [0139] appears to disclose “reference label” of fig. 10B for identifying a measured or otherwise determined lateral position of one of the anatomical structures.
Because this claim limitation is being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it is 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 limitation 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 to avoid it 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 recite sufficient structure to perform the claimed function so as to avoid it 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 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 10 and 25 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 10 and 25 recites “wherein the processing unit operatively coupled to the ultrasound transducer is further configured to differentiate at least the following anatomical structures: spinous processes, lamina, articular processes, and epidural spaces”. It is unclear if the differentiation is for all of the recited anatomical structures, at least one of the recited anatomical features or a combination of the recited anatomical features. For purposes of the examination, the limitation is being interpreted to mean that at least one of the recited anatomical structures is differentiated.
Claim Rejections - 35 USC § 101
The claims were considered under subject matter eligibility and deemed eligible because the claim requires detection of the acoustic shadows by an image processing routine, which is not performable mentally or by hand. Further, the recognizing step requires electronic input. Altogether, the steps are NOT routine, conventional image analysis and requires specific machine for accomplishing the steps.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-2, 5, 8-9, 11-12, 15-17, 19, 21, 24, 26-27, and 30 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Suri, J.S., US 20120078099 A1.
Regarding claims 1 and 16, Suri an ultrasound imaging system (claim 1) and a method of identifying anatomical structures using ultrasound imaging (claim 16) (abstract discloses a statistical (a) Computer Aided Diagnostic (CAD) technique is described for symptomatic versus asymptomatic plaque automated classification of carotid ultrasound images and (b) presents a cardiovascular risk score computation, of calcium shadows in blood vessels ([0049]) comprising:
an ultrasound transducer configured to acquire ultrasound image data from an anatomical region ([0044]);
a processing unit operatively coupled to the ultrasound transducer (processor 201 of fig. 2 and [0051]), configured to:
access the ultrasound image data ([0049]);
detect an acoustic shadow region and a boundary between the acoustic shadow region and anatomical tissue from the ultrasound image data using at least one image processing routine ([0049] discloses This application particularly takes care of Atherosclerotic Wall Region computation during the shadow cone formation. We will see how the actual LI boundaries are recovered if calcium is present causing the shadow cone. As a result, the Atherosclerotic Wall Region computation has the following advantages when using shadow cones (a) Accurate Atherosclerotic Wall Region computation in real time when the calcium is present in the proximal wall (near wall) causing the shadow cone formation; (b) The system allows computing the Atherosclerotic Wall Region in both cases: (a) when calcium is present and when calcium is not present);
electronically recognize at least one anatomical feature based on the acoustic shadow region and the boundary between the acoustic shadow region and the anatomical tissue using at least one modeling routine ([0202] discloses determining blood vessel features in shadow ultrasound scans based on boundaries);
classify one or more anatomical structure as depicted or identified within, within a known proximity to, or adjacent to, the acoustic shadow region based on the at least one recognized anatomical feature and generating classification results therefrom ([0237] Note that even though, the lumen anatomic information, which acts as a reference, provides a good test for catching a series of wrongly computed ADF boundary, it might slip from sudden bumps which may be due to the changes in grayscale intensity due presence of unusual high intensity in lumen region or a calcium deposit in the near wall causing a shadow in far wall region. This sudden spike can then be easily detected ahead using the spike detection method. This improves the accuracy of the system which in turn helps in determination of the correct region of interest and thus the classification accuracy); and
generate an output signal for transmission to a display device for presenting the classification results ([0007] This application describes a (a) Computer Aided Diagnostic (CAD) technique for symptomatic versus asymptomatic plaque automated classification of carotid ultrasound images and (b) presents a cardiovascular risk score computation in longitudinal 2D Ultrasound, cross-sectional MR, CT and 3D Ultrasound and 3D IVUS. We show this for Ultrasound, CT and MR modalities and extendable to 3D Carotid Ultrasound and 3D IVUS).
Regarding claims 2 and 17, Suri further teaches wherein the at least one modeling routine is a machine learning model ([0008]).
Regarding claims 5 and 19, Suri further teaches wherein the machine learning model is trained at least partly on supervised learning with annotated ultrasound images or annotated ultrasound image data comprising at least labeled acoustic shadow regions from a diverse anatomical dataset ([0008] discloses a supervised classifier for used on the image dataset).
Regarding claims 8 and 24, Suri further teaches wherein the machine learning model is configured to output a confidence metric indicative of accuracy of the classification results, wherein the display device visually represents the confidence metric along with the classification results ([0008] Performance of the system can be evaluated by computing the accuracy, sensitivity, specificity, and Positive Predictive Value (PPV). Hence, such a tool would prove to be a valuable inclusion in the current treatment planning protocol by vascular surgeons).
Regarding claims 9 and 30, Suri further teaches a memory unit storing the machine learning model ([0324]).
Regarding claims 11 and 26, Suri further teaches wherein classifying the one or more anatomical structure uses spatial features from the ultrasound image data comprising one or more acoustic shadow region indicative of the anatomical structure ([0049]).
Regarding claims 12 and 27, Suri further teaches wherein attention mechanisms are configured to identify key regions within, within a known proximity to, or adjacent to, the acoustic shadow region contributing to anatomical classification ([0049] discloses shadow cone indicative of the presence of calcium. Also see [0227]).
Regarding claims 15 and 21, Suri further teaches wherein the machine learning model is used to classify the one or more anatomical structure ([0007]).
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.
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 3-4 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Suri in view of Drukker, et al., US 20030161513 A1.
Regarding claims 3 and 20, Suri teaches all the limitations of claims 2 and 17 respectively above.
Suri fails to teach wherein the at least one modeling routine is selected from the group consisting of convolutional neural networks, recurrent neural networks, or transformer-based models.
However, within the same field of endeavor, Drukker discloses computerized detection and diagnostic schemes for sonographic images combine the benefits of computerized machine detection with the acquisition of non-radiographic medical images of special use for the screening of high risk, young patients who do not want the effects of ionizing characteristic of mammography. More specifically, an embodiment of the computerized detection scheme involves convoluting a sonographic image with a mask of a given ROI (region of interest) size, and calculating a skewness value for each mask location, and assembling the calculated skewness values to form a skewness image. Thresholds are applied to pixels of the skewness image to determine potential shadows. (Ultrasound images show characteristic posterior acoustic behavior for different lesion types: Posterior acoustic shadowing is often observed for malignant lesions and for some benign solid masses, while posterior acoustic enhancement is often seen for cysts.) (see abstract). Drukker further teaches wherein the at least one modeling routine is selected from the group consisting of convolutional neural networks, recurrent neural networks, or transformer-based models ([0064],[0071], [0090], [0091]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Suri, wherein the at least one modeling routine is selected from the group consisting of convolutional neural networks, recurrent neural networks, or transformer-based models, as taught by Drukker, to provide more accurate diagnosis ([0039]).
Regarding claims 4 and 18, Suri teaches all the limitations of claims 2 and 17, respectively above.
Suri fails to teach wherein the machine learning model comprises a convolutional neural network (CNN) trained at least partly on labeled acoustic shadow regions derived from ultrasound images or ultrasound image data of known anatomical landmarks, anatomical structures, or anatomical features.
However, Drukker further teaches wherein the machine learning model comprises a convolutional neural network (CNN) trained at least partly on labeled acoustic shadow regions derived from ultrasound images or ultrasound image data of known anatomical landmarks, anatomical structures, or anatomical features ([0064],[0071], [0090], [0091]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Suri, wherein the at least one modeling routine is selected from the group consisting of convolutional neural networks, recurrent neural networks, or transformer-based models, as taught by Drukker, to provide more accurate diagnosis ([0039]).
Claims 6-7, 10, 13-14, 22-23, 25, and 28-29 are rejected under 35 U.S.C. 103 as being unpatentable over Suri in view of Berton, et al., “Segmentation of the Spinous Process and its Acoustic Shadow in Vertebral Ultrasound Images”, Computers in Biology and Medicine 72 (2016) 201-211” (discloses in IDS field 07/16/2026).
Regarding claim 6, Suri teaches all the limitations of claim 2 above.
Suri fails to teach wherein one or more location of the one or more anatomical structure classified by the machine learning model is used to predict a location of a spinal midline, a spinal canal, a spinous process, an articular process, a sacrum, location of an epidural space, or combinations thereof.
However, within the same field of endeavor, Berton teaches segmentation of spinous process and its acoustic shadow in vertebral ultrasound images (see abstract), wherein one or more location of the one or more anatomical structure classified by the machine learning model is used to predict a location of a spinal midline, a spinal canal, a spinous process, an articular process, a sacrum, location of an epidural space, or combinations thereof (see “Segmentation results” section on page 208 for the segmentation of spinous process, and laminae).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Suri, wherein one or more location of the one or more anatomical structure classified by the machine learning model is used to predict a location of a spinal midline, a spinal canal, a spinous process, an articular process, a sacrum, location of an epidural space, or combinations thereof, as taught by Berton, for accurate localization of spinous process (left column of page 209).
Regarding claim 7, Suri teaches all the limitations of claim 2 above.
Suri fails to teach wherein one or more location of the one or more anatomical structure classified by the machine learning model is used to predict a depth to a spinous process tip, a depth to an epidural space, or combinations thereof.
However, Berton further teaches wherein one or more location of the one or more anatomical structure classified by the machine learning model is used to predict a depth to a spinous process tip, a depth to an epidural space, or combinations thereof (fig. 10 and last paragraph under “Segmentation results” on pages 208-209).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Suri, wherein one or more location of the one or more anatomical structure classified by the machine learning model is used to predict a depth to a spinous process tip, a depth to an epidural space, or combinations thereof, as taught by Berton, for accurate localization of spinous process (left column of page 209).
Regarding claims 10 and 25, Suri teaches all the limitations of claims 1 and 16 respectively above.
Suri fails to teach wherein the processing unit operatively coupled to the ultrasound transducer is further configured to differentiate at least the following anatomical structures: spinous processes, lamina, articular processes, and epidural spaces.
However, Berton further teaches wherein the processing unit operatively coupled to the ultrasound transducer is further configured to differentiate at least the following anatomical structures: spinous processes (first paragraph under “Segmentation results” section page 208), lamina, articular processes, and epidural spaces (third paragraph under “Segmentation results” section page 208).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Suri, wherein one or more location of the one or more anatomical structure classified by the machine learning model is used to predict a depth to a spinous process tip, a depth to an epidural space, or combinations thereof, as taught by Berton, for accurate localization of spinous process (left column of page 209).
Regarding claims 13 and 28, Suri teaches all the limitations of claims 12 and 27 respectively above.
Suri fails to teach wherein the key regions comprise one or more of: a spinal midline, a spinal canal, a spinous process, an articular process, a sacrum, and a location of an epidural space.
However Berton further teaches wherein the key regions comprise one or more of: a spinal midline, a spinal canal, a spinous process, an articular process, a sacrum, and a location of an epidural space (see “Segmentation results” section on page 208 for the segmentation of spinous process, and laminae).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Suri, wherein the key regions comprise one or more of: a spinal midline, a spinal canal, a spinous process, an articular process, a sacrum, and a location of an epidural space, as taught by Berton, for accurate localization of spinous process (left column of page 209).
Regarding claims 14 and 29, Suri teaches all the limitations of claims 13 and 28 respectively above.
Suri fails to teach wherein one or more location of the key regions is used to predict a depth to a spinous process tip, a depth to an epidural space, or combinations thereof.
However Berton further teaches wherein one or more location of the key regions is used to predict a depth to a spinous process tip, a depth to an epidural space, or combinations thereof (fig. 10 and last paragraph under “Segmentation results” on pages 208-209).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Suri, wherein one or more location of the key regions is used to predict a depth to a spinous process tip, a depth to an epidural space, or combinations thereof, as taught by Berton, for accurate localization of spinous process (left column of page 209).
Regarding claim 22, Suri teaches all the limitations of claim 16 above.
Suri fails to teach wherein the one or more classified anatomical structure comprises a spinal midline, a spinal canal, a spinous process, an articular process, a sacrum, an epidural space, or combinations thereof.
However, Berton further teaches wherein the one or more classified anatomical structure comprises a spinal midline, a spinal canal, a spinous process, an articular process, a sacrum, an epidural space, or combinations thereof (see “Segmentation results” section on page 208 for the segmentation of spinous process, and laminae).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Suri, wherein the one or more classified anatomical structure comprises a spinal midline, a spinal canal, a spinous process, an articular process, a sacrum, an epidural space, or combinations thereof, as taught by Berton, for accurate localization of spinous process (left column of page 209).
Regarding claim 23, Suri teaches all the limitations of claim 17 above.
Suri fails to teach wherein one or more location of the one or more anatomical structure is used to predict a depth to a spinous process tip, a depth to an epidural space, or combinations thereof.
However, Berton further teaches wherein one or more location of the one or more anatomical structure is used to predict a depth to a spinous process tip, a depth to an epidural space, or combinations thereof (fig. 10 and last paragraph under “Segmentation results” on pages 208-209).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Suri wherein one or more location of the one or more anatomical structure is used to predict a depth to a spinous process tip, a depth to an epidural space, or combinations thereof, as taught by Berton, for accurate localization of spinous process (left column of page 209).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 10-14, 16, 22-23, and 25-29 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-16 of U.S. Patent No. US 12333783 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because the limitations recited in the claims mentioned above of the instant application are also recited in the claims mentioned above of U.S. Patent No. US 12333783 B2.
Conclusion
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/FAROUK A BRUCE/Examiner, Art Unit 3797
