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 terms “conration” and “nonration” found first in the claims are not terms of art and as such no known meaning can be applied to them. However, a definition of these terms was found in the specification in paragraph 87. Accordingly, these terms are interpretated to mean the following: conration is defined as “all image blocks that were selected by the user as containing a confirmed aberration,” and nonration is defined as “all image blocks that were confirmed and selected by the user as not containing any aberration.” These definitions are read into the claims.
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: “preprocessing, by a denoising unit” and “generating, by an image rendering unit” in claims 1 and 12.
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.
For examination purposes, Figures 5 and 8 disclose the different units, and the descriptions of the drawings in at least specification paragraphs 60 recite, “the computer resource assets 110 to 180 can be integrated to form fewer than the number of devices seen in FIG. 5” and paragraph 113 recites “the denoising unit 190 can be combined with or integrated in the ADE stack 160. For example, in the non-limiting embodiment where the ADE stack 160 comprises computing resources that are executable by the processor 110 to perform the processes 200, 300 or 500 (shown in FIGS. 6, 7, 9A and 9B), the ADE stack 160 can include the denoising unit 190. In that case, the denoising unit 190 can be included in the ADE stack 160 as a computing resource that is executable by the processor 110 to preprocess and remove noise from a noisy UT image scan” which are further classified in paragraph 135 as the “terms "computing resource" or "computer resource," as used in this disclosure, means software, a software application, a web application, a web page, a computer application, a computer program, computer code, machine executable instructions, firmware, or a process that can be arranged to execute on a computing device or a communicating device”.
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, 5, 12, 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mendes Rodrigues et al. (hereinafter Rodrigues), U.S. Patent Application Publication 2019/0323993, in view of Xu et al. (hereinafter Xu), U.S. Patent Application Publication 2020/0359991.
Regarding Claim 1, Rodrigues discloses a computer-implemented method for analyzing a sequence of noisy or incoherent ultrasound scan images of an asset comprising a composite material having internal defects or voids and diagnosing a health condition of a section of the asset [“Ultrasonic non-destructive testing (NDT) is a noninvasive technique that is used for determining the integrity of materials, such as fiberglass used in manufacturing, or structures, and enables the detection of internal defects in the test object” ¶3], the method comprising:
receiving, by an input-output interface, an ultrasound scan image [“ultrasound scan data as a collection of images” ¶37] of the section of the asset that contains noise or incoherence resulting from signal attenuation due to the composite material in the section of the asset [“a salient structure of an object, such as its back wall where critical defects tend to be observed” ¶41; “rows of data corresponding to noise” ¶41];
preprocessing, by a denoising unit comprising a machine learning model [“ML model” ¶37], the ultrasound scan image to remove the noise or incoherence and output a denoised ultrasound image [“the patch filtering process 100 aims to determine an optimum threshold value tscan, for filtering out irrelevant scan data, i.e. for eliminating data points where the value of t exceeds the threshold tscan. Such irrelevant data correspond to ultrasound signal echoes that are not useful for the defect analysis, such as noise.” ¶43];
training the machine learning model [The images thus produced are used in operation S400 as input in a machine learning (ML) process 400 for automated defect detection. If the ML model has not yet been trained (no, operation S202a ), the ML model is first trained on known defect image data in a training process 300 at operation S300” ¶37, 51];
executing the trained machine learning model on the denoised ultrasound scan image to detect any aberrations in the section [“the image is input to the pretrained ML model 306 to identify defects” ¶52; Fig. 8; “input in a machine learning (ML) process 400 for automated defect detection” ¶37; “Defect detection may be achieved by applying one of several known image analysis methods, for example a standard machine learning (ML) model” ¶51];
evaluating, by the machine learning model, any detected aberrations, including aberration type [“using a defect detected in the object to determine at least one of: the structural integrity of material forming at least part of the object; and flaws in material forming at least part of the object” ¶13, 54; “Defects such as wrinkles in the fibreglass layers bring about distinct visual patterns in the scan” ¶56; “flaws, such as discontinuities, wrinkles, trapped objects, air pockets, etc.” ¶4];
sending an image rendering signal to cause a computer resource asset to display an ultrasound scan image based on the raw ultrasound scan image data [“a display unit such as one or more monitors 995” ¶63; “The display unit 995 may display a representation of data stored by the computing device” ¶66]; and
generating, by an image rendering unit, an image rendering signal to cause a computer resource asset to display the denoised ultrasound scan image on a display device [“a display unit such as one or more monitors 995” ¶63; “The display unit 995 may display a representation of data stored by the computing device” ¶66]
by the machine learning model [“ML model” ¶37].
However, Rodrigues fails to explicitly disclose to assign a numerical value to one or more pixels in a conration category image block, wherein the numerical value denotes a location and a severity level of an aberration;
including assigning a numerical value to one or more pixels in a conration category image block corresponding to detected aberrations, wherein the numerical value denotes a location and a severity level of each aberration;
displaying, simultaneously, a label for each aberration including an aberration type of structural defect, an aberration location and severity level corresponding to the assigned numerical value; and
generating… a degree of health of the section of the asset based on any detected aberrations;
wherein the aberration type comprises a harmful or potentially harmful aberration.
Xu discloses to assign a numerical value to one or more pixels in a conration category image block, wherein the numerical value denotes a location and a severity level of an aberration [“the graphic 1000 includes abnormality indicators 1006, 1008, 1010 superimposed over abnormality locations” ¶70; Fig. 10; “an indicator may be color-coded red if the underlying abnormality is particularly large or fast-growing, while the indicator may be color-coded blue if the underlying abnormality is relatively minor or slowly-changing” ¶70; “indicators at the correct anatomical locations” ¶70; “each indicator may correspond to the size, shape and/or type” ¶70];
including assigning a numerical value to one or more pixels in a conration category image block corresponding to detected aberrations, wherein the numerical value denotes a location and a severity level of each aberration [“the graphic 1000 includes abnormality indicators 1006, 1008, 1010 superimposed over abnormality locations” ¶70; Fig. 10; “an indicator may be color-coded red if the underlying abnormality is particularly large or fast-growing, while the indicator may be color-coded blue if the underlying abnormality is relatively minor or slowly-changing” ¶70; “indicators at the correct anatomical locations” ¶70; “each indicator may correspond to the size, shape and/or type” ¶70];
displaying, simultaneously, a label for each aberration including an aberration type of structural defect, an aberration location and severity level corresponding to the assigned numerical value [“the graphic 1000 includes abnormality indicators 1006, 1008, 1010 superimposed over abnormality locations” ¶70; Fig. 10; “an indicator may be color-coded red if the underlying abnormality is particularly large or fast-growing, while the indicator may be color-coded blue if the underlying abnormality is relatively minor or slowly-changing” ¶70; “indicators at the correct anatomical locations” ¶70; “each indicator may correspond to the size, shape and/or type” ¶70]; and
generating… a degree of health of the section of the asset based on any detected aberrations [Fig. 10; “an indicator may be color-coded red if the underlying abnormality is particularly large or fast-growing, while the indicator may be color-coded blue if the underlying abnormality is relatively minor or slowly-changing” ¶70];
wherein the aberration type comprises a harmful or potentially harmful aberration [Fig. 10; “an indicator may be color-coded red if the underlying abnormality is particularly large or fast-growing, while the indicator may be color-coded blue if the underlying abnormality is relatively minor or slowly-changing” ¶70].
It would have been obvious to one having ordinary skill in the art, having the teachings of Rodrigues and Xu before him before the effective filing date of the claimed invention, to modify the method of Rodrigues to incorporate the user interface displaying abnormality information.
Given the advantage of comprehensively evaluating abnormalities for providing increase useful information and displaying them for easy user consumption, one having ordinary skill in the art would have been motivated to make this obvious modification.
Regarding Claim 5, Rodrigues and Xu disclose the method in claim 1. Rodrigues further discloses wherein the computer-implemented process further comprises: building an ultrasound scan dataset that includes the label [“defects are recorded in the defects database” ¶52].
Claim 12 is rejected on the same grounds as claim 1.
Claim 16 is rejected on the same grounds as claim 5
Claim(s) 4, 6-7, 10-11, 15, 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rodrigues and Xu, in view of Liu et al. (hereinafter Liu), Deep learning based crack damage detection technique for thin plate structures using guided lamb wave signals.
Regarding Claim 4, Rodrigues and Xu disclose the method in claim 1.
However, Rodrigues fails to explicitly disclose wherein the aberration type comprises a benign aberration.
Liu discloses wherein the aberration type comprises a benign aberration [“Class label 1” Table 2].
It would have been obvious to one having ordinary skill in the art, having the teachings of Rodrigues, Xu, and Liu before him before the effective filing date of the claimed invention, to modify the combination to incorporate labeling of Liu.
Given the advantage of labeling data for easy interpretation and for grouping of similar data for further analysis, one having ordinary skill in the art would have been motivated to make this obvious modification.
Regarding Claim 6, Rodrigues and Xu disclose the method in claim 5.
However, Rodrigues fails to explicitly disclose wherein the computer-implemented process further comprises: splitting the ultrasound scan dataset into a training dataset and a testing dataset.
Liu discloses wherein the computer-implemented process further comprises: splitting the ultrasound scan dataset into a training dataset and a testing dataset [“trained and tested using a total of 1016 records” §4.1 ¶1].
It would have been obvious to one having ordinary skill in the art, having the teachings of Rodrigues, Xu, and Liu before him before the effective filing date of the claimed invention, to modify the combination to incorporate the training of Liu.
Given the advantage of training and testing model to ensure accuracy, one having ordinary skill in the art would have been motivated to make this obvious modification.
Regarding Claim 7, Rodrigues, Xu, and Liu disclose the method in claim 6.
However, Rodrigues fails to explicitly disclose wherein the computer-implemented process further comprises: training the machine learning model to segment an ultrasound scan image into conration category image blocks and nonration category image blocks.
Liu discloses wherein the computer-implemented process further comprises: training the machine learning model to segment an ultrasound scan image into conration category image blocks and nonration category image blocks [Class label 2-5 and Class label 1; Table 2].
It would have been obvious to one having ordinary skill in the art, having the teachings of Rodrigues, Xu, and Liu before him before the effective filing date of the claimed invention, to modify the combination to incorporate the labeling of Liu.
Given the advantage of labeling data for easy interpretation and for grouping of similar data for further analysis, one having ordinary skill in the art would have been motivated to make this obvious modification.
Regarding Claim 10, Rodrigues, Xu, and Liu disclose the method in claim 6. Rodrigues further discloses wherein the computer-implemented process further comprises: testing the machine learning model to determine performance of the model in detecting an aberration [“until it is determined in operation S303a that optimal results have been achieved” ¶51].
Regarding Claim 11, Rodrigues, Xu, and Liu disclose the method in claim 10. Rodrigues further discloses wherein the computer-implemented process further comprises:
determining completion of training of the machine learning model based on the determined performance [“until it is determined in operation S303a that optimal results have been achieved” ¶51]; and
pushing the machine learning model into production [“resulting trained ML model 306 is then ready to be deployed in operation S305 in a defect detection process 400 in a production environment” ¶51].
Claim 15 is rejected on the same grounds as claims 4.
Claim 17 is rejected on the same grounds as claims 6.
Claim 18 is rejected on the same grounds as claims 7.
Examiner’s Note
The Examiner respectfully requests of the Applicant in preparing responses, to fully consider the entirety of the reference(s) as potentially teaching all or part of the claimed invention. It is noted, REFERENCES ARE RELEVANT AS PRIOR ART FOR ALL THEY CONTAIN. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). A reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art, including non-preferred embodiments (see MPEP 2123). The Examiner has cited particular locations in the reference(s) as applied to the claim(s) above for the convenience of the Applicant. Although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claim(s), typically other passages and figures will apply as well.
Additionally, any claim amendments for any reason should include remarks indicating clear support in the originally filed specification.
Response to Arguments
Regarding the prior art rejections, Applicant's arguments have been fully considered but have been found unpersuasive. Applicant argues that Xu fails to disclose displaying, simultaneously, a label for each aberration including an aberration type of structural defect, an aberration location and severity level corresponding to the assigned numerical value. Examiner disagrees for at least the following reasons.
Applicant correctly points Xu paragraph 70 for what Examiner relies on for the rejection, however, excludes some important passages from that paragraph. The paragraph recites in total (emphasis added):
As mentioned above, a user interface disclosed herein, in cooperation with one or more processors, e.g., data processor 126, signal processor 122 and/or display processor 128, can be configured to provide instructions or information effective to guide a user through an ultrasound scan, thereby detecting lung abnormalities, such as lung consolidations. FIG. 10 shows an example of a graphic 1000 that may be generated and displayed on a user interface, such as user interface 130. The graphic 1000 comprises a chest region of the patient being scanned, showing both lungs 1002, 1004. Within the lungs, the graphic 1000 includes abnormality indicators 1006, 1008, 1010 superimposed over abnormality locations that were determined through one or more previously performed ultrasound scans. By aligning an ultrasound probe with the locations 1006, 1008, 1010 provided on the graphic 1000, a user can obtain image frames of the previously identified lung abnormalities. Upon detection of new abnormalities, the graphic can be updated to include new indicators at the correct anatomical locations. The indicators 1006, 1008, 1010 may also be labeled in terms of the severity of an abnormality. For example, an indicator may be color-coded red if the underlying abnormality is particularly large or fast-growing, while the indicator may be color-coded blue if the underlying abnormality is relatively minor or slowly-changing, or even decreasing in size, thus providing an indication of treatment effectiveness. In some examples, each indicator may correspond to the size, shape and/or type of the underlying abnormality. For example, one or more processors herein may be configured to generate, in cooperation with the user interface, larger indicators to designate larger abnormalities, and vice-versa.
As can be seen in at least the above passage from Xu, the reference discloses the simultaneously displaying of all three elements recite in the limitation: type of structural defect, location, and severity. While Applicant’s citation to the specification for a type of structural defect “can include, for example” several non-organic structural defects, the broadest reasonable interpretation of the claim includes organic structural defects such as lung tumors as found in Xu. To further support the interpretation that organic elements can have structural defects, the Xu reference is classified in CPC classification A61B 8/085 which is to clinical applications involving detecting or locating foreign bodies or organic structures for locating body or organic structures, e.g. tumours, calculi, blood vessels, nodules.”
Accordingly, the rejections are maintained.
Conclusion
THIS ACTION IS MADE FINAL. 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 H BEJCEK II whose telephone number is (571)270-3610. The examiner can normally be reached Monday - Friday: 9:00am - 5:00pm.
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/R.B./ Examiner, Art Unit 2148
/MICHELLE T BECHTOLD/ Supervisory Patent Examiner, Art Unit 2148
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