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 processing module in claim 10.
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) 1-7, 9-16, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Tran et. al. (Integrative few-shot classification and segmentation for landslide detection. IEEE Access. 2022 Nov 9;10:120200-12) in view of Wang et. al. (Semi-supervised semantic segmentation using unreliable pseudo-labels. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition March 2022 (pp. 4248-4257).)
Regarding claim 1:
Tran discloses: A few-shot learning method (page 120205, left col.
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comprising:
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learning, when a query image is provided with a pre-learned model, the model based on the image and a segmentation index to simultaneously perform classification and segmentation of a specific region from the query image (page 120204, right col.
X_s^i is a pixel-wise segmentation label map similar to segmentation index;
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and page 120205, left col.
a pre-trained FS-CS mode; Fcs take a query image and outputs classification (y^_C) and segmentation (Y^_S) simultaneously, learned based images and segmentation labels a_s^i.);
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wherein the model includes an attentive squeeze network (ASNet) (page 120201, left col.
);
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does not perform the classification when an object with low relevance exists in the query image (page 120205, left col.
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Page 120206; right col.
Lower relevance corresponds to every class Y^(n) having max_p Y^(n)(PP < δ. In that case, CF-ASNet sets all y_C^(n) = 0 and classifies the query as background (non), i.e., it does not output any positive class classification when non sufficiently relevant object is present);
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and performs the classification and segmentation when an object with high relevance exists in the query image (page 120206; left col.
When some class n has high relevance, the model both classifies the class as present and segments its region in the query image).
Tran does not explicitly teach: obtaining an image and a segmentation prediction for the image.
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However, in a related field, wang teaches: obtaining an image and a segmentation prediction for the image (page 3, left col.
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Page 3, right col.
Wang inputs an image in to a trained segmentation model (teacher) and obtains a pixel-wise prediction p_ij for that image, when then used as a pseudo-label).
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 Tran to incorporate the teachings of Wang by including obtaining an image and a segmentation prediction for the image in order to improve training. Both Tran and Wang address learning from limited labeled data. Also, using model-generated pseudo-labels for images is a well-known technique to augment supervision in segmentation tasks.
Regarding claim 2:
Tran in view of Wang teaches the limitation of claim 1 as applied above.
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Tran further teaches: wherein an integrative few-shot learning (iFSL) method is applied to the learning of the model (page 120205, left col.
); and
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in the integrative few-shot learning, the model is learned to identify a subset appearing in the query image (page 120205, left col.
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);
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and predict a set of problem segmentation masks corresponding to the class (page 120206; left col.
).
Regarding claim 3:
Tran in view of Wang teaches the limitation of claim 1 as applied above.
Tran further teaches: wherein the model calculates a correlation tensor between a plurality of images (page 120205, section “1) HYPERCORRELATION CONSTRUCTION” formula 4. calculates the hypercorrelation tensor)
and generates a classification map by passing the correlation tensor through a strided self-attention layer (page 120206, section “2) ATTENTIVE SQUEEZE BLOCK” and FIG. 4).
Regarding claim 4:
Tran in view of Wang teaches the limitation of claim 3 as applied above.
Tran further teaches: wherein the ASNet includes an attentive squeeze layer (AS layer) (page 120206, section “2) ATTENTIVE SQUEEZE BLOCK” and FIG. 4);
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the AS layer is prepared as a high-order self-attention layer and returns a correlation expression of different levels based on the correlation tensor (page 120206, section “2) ATTENTIVE SQUEEZE BLOCK” and FIG. 4a
).
Regarding claim 5:
Tran in view of Wang teaches the limitation of claim 4 as applied above.
Tran further teaches: wherein the ASNet has, as input, hyper- correlation which is a pyramid-shaped cross-correlation tensor between the query image and the support image (page 120205, section “1) HYPERCORRELATION CONSTRUCTION” formula 4. and page 120206, section “2) ATTENTIVE SQUEEZE BLOCK” and FIG. 4).
Regarding claim 6:
Tran in view of Wang teaches the limitation of claim 2 as applied above.
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Tran further teaches: wherein in integrated few-shot learning, inference is performed using max pooling ( Page 120206; right col.
).
Regarding claim 7:
Tran in view of Wang teaches the limitation of claim 2 as applied above.
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Tran further teaches: wherein in the integrated few-shot learning, a classification loss and a segmentation loss are used, and a learner is trained using a class tag or a segmentation annotation ( page 120204, right col.
).
Regarding claim 9:
Tran in view of Wang teaches the limitation of claim 7 as applied above.
Tran further teaches: wherein the segmentation loss is an average cross-entropy between a class distribution of an individual position and an actual segmentation annotation (page 120207, equation 8).
Regarding claims 10-16 and 18: the claims limitations are similar to those of claims 1-7, and 9; therefore, rejected in the same manner as applied above. Tran discloses a system in FIG. 1.
Claim(s) 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Tran et. al. (Integrative few-shot classification and segmentation for landslide detection. IEEE Access. 2022 Nov 9;10:120200-12) in view of Wang et. al. (Semi-supervised semantic segmentation using unreliable pseudo-labels. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition March 2022 (pp. 4248-4257) and in view of He (EP 4161007).
Regarding claim 8:
Tran in view of Wang teaches the limitation of claim 7 as applied above.
Tran in view of Wang does not specifically teach: wherein the classification loss is an average binary cross-entropy between a spatially averaged pooled class score and a correct answer class label.
However, in a related field, He teaches: wherein the classification loss is an average binary cross-entropy between a spatially averaged pooled class score and a correct answer class label (¶ [0039] “The classification model includes N preset traffic types. A pre-trained classification model, such as a softmax classifier, is modified to N binary classification networks, and multiple training samples are input into each binary classification network. A cross entropy loss of each binary classification network is acquired according to the output probability of each training sample, and the average value of N cross entropy losses is acquired. Back-propagation training is performed using an optimizer according to the average value of the cross entropy losses, and final loss parameter adjustment is performed on each binary classification network to obtain a binary classification network.”).
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 Tran in view of Wang to incorporate the teachings of He by including wherein the classification loss is an average binary cross-entropy between a spatially averaged pooled class score and a correct answer class label in order to improve supervision of the classification.
Regarding claim 17: the claims limitations are similar to those of claim 8; therefore, rejected in the same manner as applied above.
Conclusion
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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/WASSIM MAHROUKA/Primary Examiner, Art Unit 2665