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 .
Claims 1-19 are pending in the application.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/19/2024 and 12/12/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
The following claims are objected to.
Claims 1, 3 and 11-19 “and;” should be “and”.
Claim 3 4th line “generate” should be “generating”, and 6th line “predict” should be “predicting”.
Claim 4 1st line “comprises” should be “comprising”.
Claim 11 2nd line there should be a comma at the end.
Claim 12 2nd line there should be a word “for” following the word “map”.
Claim 13 7th line recites “process the extracted features”. The “extracted features” has no antecedent basis.
Claim 16 1st line a word “the” should be inserted between the word “wherein” and the word “key”.
Claim 18 page 37 top 2 lines “average the combined peripheral features with the central features and then reshaped to generate the set of segmentation filters” need to be rephrased.
Claim 19 page 37 the phrase “wherein the classification head is configured to locate centres of all salient instances supervised a ground truth centre map” is suggested to be: wherein the classification head is configured to locate centres of all salient instances and is supervised by a ground truth centre map.
Claim 19 page 38 6th line from the bottom “configured” should be “configured to”.
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.
Note: To claim a means for performing a specific computer-implemented function and then to disclose only a general purpose computer as the structure designed to perform that function amounts to pure functional claiming. Aristocrat, 521 F.3d 1328 at 1333, 86 USPQ2d at 1239. In this instance, the structure corresponding to a 35 U.S.C. 112(f) claim limitation for a computer-implemented function must include the algorithm needed to transform the general purpose computer or microprocessor disclosed in the specification. Aristocrat, 521 F.3d at 1333, 86 USPQ2d at 1239; Finisar Corp. v. DirecTV Group, Inc., 523 F.3d 1323, 1340, 86 USPQ2d 1609, 1623 (Fed. Cir. 2008); WMS Gaming, Inc. v. Int’l Game Tech., 184 F.3d 1339, 1349, 51 USPQ2d 1385, 1391 (Fed. Cir. 1999); Rain Computing, Inc. v. Samsung Electronics America Co., 989 F.3d 1002, 1007-8, 2021 USPQ2d 284 (Fed. Cir. 2021). See MPEP 2181 II B.
The following table lists the occurrences that use means and corresponding structure and associated algorithms.
Claim no.
112(f) elements
Corresponding structure (PGPub)
Associated algorithm (PGPub)
13
an image gateway to receive
Fig. 1; para. [0059] “computing device (i.e., a computer or computer apparatus)”; para. [0060] “a microprocessor or microcontroller”; para. [0062] “Central Processing Unit (CPU), Math Co-Processing Unit (Math Processor), Graphic Processing Unit (GPUs) or Tensor processing unit (TPUs)”
FIG. 4 #402; para. [0084]
13
a multi-level feature extraction module configured to identify … process
Same as above
FIG. 4 #404; FIG. 5 #502, #504, #506; para. [0093], [0100]
13/14/15/16/18
Claim 13: a key points guided dynamic convolution module configured to … identify … segment … predict …
Claim 14: the key points guided dynamic convolution module configured to … identify … generate … predict …
Claim 15: the key points guided dynamic convolution module … configured to concatenate and convolute
Claim 16: the key points guided dynamic convolution module … configured to … generate … apply … segment …
Claim 18: the key points guided dynamic convolution module is configured to: select … identify … compute … determine …
Claim 18: the key points guided dynamic convolution module further comprises a differentiated patterns fusion module that is configured to: compute … combine … average
Claim 18: the key points guided dynamic convolution module is configured to concatenate … utilize
Same as above
FIG. 3; FIG. 4 #406; FIG. 5 #530; FIG. 8; para. [0085]-[0097], [0105]-[0123]
15
the bottom module configured to generate
Same as above
FIG. 4 #408; FIG. 5 #520; para. [0078], [0088], [0096], [0104]
17
a semantic saliency module configured to … estimate … compute
Same as above
FIG. 4 #410; FIG. 5 #522; para. [0081], [0089], [0096], [0103], [0104]
17
a score adjustment module configured to update
Same as above
FIG. 4 #412; FIG. 5 #524; para. [0131]-[0132]
17
a prediction module configured to: update … generate …
Same as above
FIG. 4 #414; FIG. 5 #526; para. [0081], [0090], [0097], [0132]
19
a backbone network configured to receive … extract
the classification head is configured to locate
a key points guided dynamic convolution module configured to: predict … determine … generate …
the bottom module is configured to: receive … generate …
the semantic guided saliency module is configured to: estimate … compute …
a score adjustment module configured to update
a prediction module configured: update … generate
Same as above
FIG. 5 #502; para. [0093], [0099]-[0100]
FIG. 5 #508; para. [0100]-[0101]
FIG. 3; FIG. 4 #406; FIG. 5 #530; FIG. 8; para. [0085]-[0097], [0105]-[0123]
FIG. 4 #408; FIG. 5 #520; para. [0078], [0088], [0096], [0104]
FIG. 4 #410; FIG. 5 #522; para. [0081], [0089], [0096], [0103], [0104]
FIG. 4 #412; FIG. 5 #524; para. [0131]-[0132]
FIG. 4 #414; FIG. 5 #526; para. [0081], [0090], [0097], [0132]
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 § 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.
Claim 6 is 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.
In claim 6 “the first set of dynamic convolution filters” has no sufficient antecedent basis. It is noted that “a first set of dynamic convolution filters” is defined in claim 3. Therefore for the purpose of prior art rejection and 101 rejection presented below, claim 5, the parent claim of claim 6, is considered dependent upon claim 3.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-2 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Analysis for claim 1 is provided in the following. Claim 1 is reproduced in the following (annotation added):
1. A computer-implemented method of image processing to identify one or more objects in an image comprising:
(a) receiving one or more input images, wherein each input image includes one or more salient instances, wherein each salient instance is indicative of an object,
(b) identifying a plurality of key points associated with each salient instance within each input image,
(c) segmentation of salient instances in each image by utilizing the plurality of key points, wherein the key points comprise a centre point and peripheral points of each salient instance, and;
(d) predicting one or more objects within each image based on the segmentation of each salient instance.
Step 1: Evaluating whether the claim belongs to one of the statutory categories.
Claim 1 recites a plurality of acts. Thus, the claim is directed to a process, which is one of the statutory categories of invention (Step 1: YES).
Step 2A Prong One: Evaluating whether the claim recites a judicial exception (an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon). If no exception is recited, the claim is eligible. This concludes the eligibility analysis. If the claim recites an exception, go to Step 2A Prong Two.
Claim 1 recites an abstract idea of mental processes. In claim 1, under broadest reasonable interpretation, steps b)-d) can be practically performed in the human mind. These concepts fall into the “mental processes” group of abstract ideas, which includes observation, evaluation, judgment and opinion. See MPEP 2106.04 and the 2019 PEG. (Step 2A Prong One YES)
Step 2A Prong Two: Evaluating whether the claim recites additional elements that integrate the exception into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. If the answer to (a) is YES and (b) is NO, go to Step 2B; if the answer to (a) and (b) is YES, go to PATHWAY B, i.e., the claim is not directed to a judicial exception and the claim is eligible.
In claim 1, step a) can be regarded as additional elements. step a), i.e., “receiving one or more input images”, recites data acquisition and collection, an insignificant extra-solution activity. Note even if the specification discloses that the invention pertains to an improvement in technology, the claim must be evaluated to ensure the claim itself reflects the improvement in technology. It is also important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. Therefore, the additional elements do not integrate the abstract idea into a practical application. (Step 2A Prong Two NO).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim.
In claim 1, step a) can be regarded as additional elements. step a), i.e., “receiving one or more input images”, recites data acquisition and collection, an insignificant extra-solution activity which is also a well-understood, routine and conventional activity previously known to the industry. The additional elements, taken individually and in combination, do not result in the claim, as a whole, amounting to significantly more than the abstract idea. See MPEP 2106.05. (Step 2B: NO). Claim 1 is not eligible.
Claim 2 recites:
“performing a multi-level feature extraction on the received images to extract multiple features, and
processing the extracted features with multiple instance aware heads”.
The recited step of “performing a multi-level feature extraction on the received images to extract multiple features”, can be practically performed mentally, under broadest reasonable interpretation. For example, a user can extract multi-level feature in a displayed image by zooming in and out the image. The act “processing the extracted features” can also be practically performed mentally.
Claim 2 further recites “multiple instance aware heads”. Note “multiple instance aware heads” is recited in a high level of generality, and merely adds a generic computer component to perform the act and therefore fails to provide an improvement to the technology or technical field. Claim 2 is not eligible.
Claim 3 recites “identifying a centre point for each salient instance, generate a first set of dynamic convolution filters based on the identified centre point, and predict a plurality of peripheral points using the dynamic convolution filters”. The act of “identifying” can be practically performed mentally. The “generate” and “predict” acts, however, integrate the abstract idea into a practical application. Claim 3 is eligible.
Claim 13, an apparatus claim, recites similar functions as recited in claim 1 and claim 2. These functions, when interpreted under broadest reasonable interpretation, recite abstract ideas, and are not eligible under 35 USC 101. Claim 13, however, recites elements that are interpreted as invoking 35 USC 112(f), specifically computer-Implemented means-plus-function limitations. Therefore the corresponding algorithms of such elements are being read into the limitations. As identified above in the 112(f) section, the algorithms described in FIG. 3-5 and 8 and para. [0084]-[0097], [0100]-[0123] are incorporated into the elements of “an image gateway to receive”, “a multi-level feature extraction module configured to identify …process” and “a key points guided dynamic convolution module configured to … identify … segment … predict”. These paragraphs, especially para. [0105]-[0122], describe a specific manner of how objects in an image are segmented based on a plurality of key points using machine learning models, therefore provide improvements to the functioning of a computer, and apply or use the judicial exception in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. Claim 13 is eligible.
Claim Rejections - 35 USC § 102
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 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-3 and 5 is/are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by GAO et al. (US 20230237666 A1, hereafter GAO).
As per claim 1, GAO teaches a computer-implemented method (Abstract) of image processing to identify one or more objects in an image comprising:
receiving one or more input images (FIG. 2 #20a), wherein each input image includes one or more salient instances (FIG. 2 #20a; FIG. 7 #40a), wherein each salient instance is indicative of an object (FIG. 2 #20a showing a cat; FIG. 7 #40a showing 2 objects; para. [0055] “The source image includes one or more instances. One or more instances contained in the source image are referred to as a target object in this embodiment of this disclosure. The purpose of performing image instance segmentation on the source image is to find the target objects contained in the source image, and recognize and segment the target objects found out. Where the target object may include but is not limited to: people, animals, plants, vehicles, traffic signs, road obstacles, signs and slogans.”),
identifying a plurality of key points associated with each salient instance within each input image (FIG. 2 #20i, 20j, 20k, and 20m; FIG.3 S102 “contour sampling points”; FIG. 5 #30s),
segmentation of salient instances in each image by utilizing the plurality of key points, wherein the key points comprise a centre point and peripheral points of each salient instance (FIG. 2 and 5 showing that for each instance, there is a center point and a plurality of peripheral points; para. [0046] “The object contour of the target object can be formed by connecting the predicted endpoints of the 36 rays, and then detection and segmentation of the target object contained in the source image are unified in a same framework. Where predicting the endpoints of the 36 rays may be understood as predicting the target predicted polar radii, the center of the target object contained in the source image can be predicted based on the center confidence levels, and an object edge shape (the real object contour of the target object) corresponding to the target object can be formed by connecting the endpoints of the target predicted polar radii corresponding to the center.”; The endpoints correspond to a plurality of peripheral points.), and
predicting one or more objects within each image based on the segmentation of each salient instance (FIG. 2/5 a cat is recognized; FIG. 7 multiple objects are predicted; para. [0052], [0093]).
As per claim 2, dependent upon claim 1, GAO further teaches the steps of:
performing a multi-level feature extraction on the received images to extract multiple features (FIG. 2 #20b; para. [0048] “where the feature extractor 20b is configured to extract multi-scale features in the source image 20a”), and
processing the extracted features with multiple instance aware heads (FIG. 2 “Prediction head 1 … 5”; para. [0050] “ For example, the object feature map P1 can be predicted in a prediction head 1, and the object feature map P2 can be predicted in a prediction head 2, and so on.”)
As per claim 3, dependent upon claim 2, GAO teaches the step of identifying a plurality of key points comprises:
identifying a centre point for each salient instance, generate a first set of dynamic convolution filters based on the identified centre point (FIG. 2 #20i, 20j, 20k, 20m each having a center point; para. [0046] “In this disclosure, object contour modeling can be performed based on a polar coordinate system, and the target object is detected by predicting the distance between the center point of the target object contained in the source image and an object edge”; para. [0048] “the classification component is configured to predict the category of an object contained in the source image 20a and the center point of the object contained in the source image 20a (also may be understood as an instance center)”; para. [0050] “Where the category confidence level 20e is used for determining the classification result of the object contained in the source image 20a, and the center confidence level 20f is used for determining the center point of the object contained in the source image 20a”; FIG. 5 #30v; para. [0066] “Specifically, for any object feature map P.sub.i of the M object feature maps, the object feature map P.sub.i is convolved in the regression component of the target image segmentation model to obtain a distance prediction feature map corresponding to the object feature map P.sub.i; pixel points in the distance prediction feature map are determined as candidate centers”; para. [0067] “In the coarse regression module 30k, the image feature 30f passes through four convolutional layers (the four convolutional layers here all use the convolution kernel with the size of 3×3) to obtain an image feature 30m (the image feature 30m may be understood as the distance prediction feature map for polar radius regression, the size of which may be H×W×256). An initial predicted polar radius 30n is predicted by each 1×1×256-dimensional vector in the distance prediction feature map”); and;
predict a plurality of peripheral points using the dynamic convolution filters (para. [0066] “Specifically, for any object feature map Pi of the M object feature maps, the object feature map Pi is convolved in the regression component of the target image segmentation model to obtain a distance prediction feature map corresponding to the object feature map Pi; pixel points in the distance prediction feature map are determined as candidate centers, and initial predicted polar radii corresponding to the candidate centers are acquired based on the distance prediction feature map; and sampling feature coordinates are determined based on the initial predicted polar radii and the pixel points in the object feature map Pi, and contour sampling points matching the sampling feature coordinates are acquired from the object feature map Pi”; Here “contour sampling points” correspond to “a plurality of peripheral points”; para. [0067]-[0068]).
As per claim 5, dependent upon claim 3 (see 112b rejections above), GAO teaches the step of generating bottom features for each of the received images, wherein the bottom features are generated based on outputs from a feature pyramid network (FIG. 2 P1, … P5 are considered bottom features. P1-P5 are generated based on outputs C3-C5 from a residual component in the feature extractor 20b. C3-C5 are then input into a feature fusion component to generate P1-P5. The feature fusion component is a feature pyramid network; para. [0056] “… where the feature extractor may include a residual component and a feature fusion component. In some embodiments, the residual component is a residual network (ResNet). The number of network layers of the residual network is designed according to actual needs. In some embodiments, the feature fusion component is a feature pyramid network (FPN)”).
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.
Claim(s) 4 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over GAO et al. (US 20230237666 A1, hereafter GAO), as applied to claim 3, in view of HSIAO et al. (US 20180040257 A1, hereafter HSIAO).
As per claim 4, GAO does not teach predicting a minimum number of peripheral points per instance, wherein the minimum number of peripheral points denote the limits of the instance in four directions.
HSIAO discloses a method for detecting if the shape of a generated handwriting stroke conforms with the shape of a standard image (Abstract). As shown in FIG. 12 and described in para. [0067]-[0069], a word character comprises multiple instances, i.e., strokes. HSIAO defines an acceptance region for each of the standard strokes 21 to 24. Four acceptance regions 211, 221, 231 and 241 are defined for the standard strokes 21 to 24, respectively. Each of the acceptance regions 211 to 241 is in a rectangular shape, and has a boundary including four edges passing through an upmost point, a lowermost point, a leftmost point and a rightmost point of the respective one of the standard strokes 21 to 24. If considering the word character as a whole, the boundary of the standard image 20 also includes four edges 20A to 20D passing through an upmost point 2A, a lowermost point 2B, a leftmost point 2C and a rightmost point 2D of the standard word character 2, respectively. That is to say, HSIAO detects a minimum number of peripheral points per instance (either individual strokes or the character as a whole), and the minimum number of peripheral points denote the limits of the instance in four directions (up, down, left and right).
GAO and HSIAO are both considered to be analogous to the claimed invention because they are in the same field of detecting an acceptable region for an object. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of GAO to incorporate the teachings of HSIAO to predict a minimum number of peripheral points per instance, wherein the minimum number of peripheral points denote the limits of the instance in four directions. The motivation for doing so would have been defining an acceptance region for each of the standard stroke and detecting error based on the defined acceptance region as suggested by HSIAO (para. [0074]-[0075]).
As per claim 10, dependent upon claim 4, GAO in view of HSIAO teaches wherein the minimum number of peripheral points is four peripheral points, the four peripheral points defining the upper most point, bottom most point, left most point and right most point of an instance (HSIAO FIG. 12 showing that for each instance 4 peripheral points are defined as upper most point, bottom most point, left most point and right most point; para. [0069]).
Claim(s) 7-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over GAO et al. (US 20230237666 A1, hereafter GAO), as applied to claim 5, in view of Rao et al. (US 20220398718 A1, hereafter Rao).
As per claim 7, dependent upon claim 5, GAO teaches generating masks for each salient instance (FIG. 7), but does not teach the rest limitations.
Rao in an analogous field discloses a deep neural network for segmentation of medical images (para. [0043]; FIG. 9). Rao specifically teaches the steps of:
generating a plurality of segmentation filters (See below),
applying the segmentation filters to generate masks for each salient instance (see below), and
segmenting the one or more images based on the generated masks to identify the one or more objects within each image (Rao FIG. 6; para. [0052] “Further, the first anatomical feature 612, the second anatomical feature 614, and the third anatomical feature 616, are shown in highlight, emphasizing the accuracy of the segmentation masks of the first, second, and third anatomical features, respectively”; para. [0101] “ … a 1×1 convolution is performed on feature map 954 using P distinct convolutional filters, to produce segmentation masks 956, wherein P is the number of distinct segmentation masks to be output by deep neural network architecture”).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of GAO to incorporate the teachings of Rao to generate a plurality of segmentation filters, apply the segmentation filters to generate masks for each salient instance, and segment the one or more images based on the generated masks to identify the one or more objects. The motivation for doing so would be mapping a medical image to a positional attribute of an anatomical feature and further determining an image quality metric based on the positional attribute of the anatomical feature, as recognized by Rao (para. [0003]).
As per claim 8, dependent upon claim 7, GAO in view of Rao teaches the step of generating masks comprises convoluting the bottom features by the segmentation filters (Rao para. [0101] “ … a 1×1 convolution is performed on feature map 954 using P distinct convolutional filters, to produce segmentation masks 956, wherein P is the number of distinct segmentation masks to be output by deep neural network architecture”).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over GAO et al. (US 20230237666 A1, hereafter GAO), in view of Rao et al. (US 20220398718 A1, hereafter Rao), as applied to claim 7 above, and further in view of LIU et al. (US 20210350183 A1, hereafter LIU).
As per claim 9, dependent upon claim 7, GAO in view of Rao does not teach the recited limitations.
LIU in an analogous field discloses a method for segmentation of a point cloud (Abstract). Specifically, as shown in FIG. 5, the method includes identifying central points (#502), identifying a plurality of adjacent points corresponding to each central point (#504), fusing semantic features of the plurality of adjacent points corresponding to each central point (#506), to obtain an instance-fused semantic feature of the corresponding central point, and determining a semantic category (e.g., segmentation) to which each point belongs according to the instance-fused semantic feature of each central point in the point cloud. Note the plurality of adjacent points includes the central point itself (para. [0089]). Further determining a plurality of adjacent points includes selecting a plurality of points whose feature distances from the central point are less than a preset distance as the plurality of adjacent points corresponding to the central point (para. [0091]). Therefore the feature fusing is performed in an adaptive manner.
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of GAO and Rao to incorporate the teachings of LIU to generate the segmentation filters by adaptively fusing central features associated with central points, and peripheral features associated with peripheral points. Doing so would allow segmentation no longer rely on a feature-based similarity matrix, thereby improving segmentation efficiency (LIU para. [0009]).
Allowable Subject Matter
Claim 6 would be allowable if the 112b rejection presented above is overcome, and if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claims 11-12 and 19 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.
Claims 13-18 are allowed.
The following is Examiner’s reasons for identification of allowable subject matter.
Claim 13 recites elements that are interpreted as invoking 35 USC 112(f), specifically computer-Implemented means-plus-function limitations. Therefore the corresponding algorithms of such elements are being read into the limitation. As identified above in the 112(f) section, the algorithms described in FIG. 3-5 and 8 and para. [0084]-[0097], [0100]-[0123] are incorporated into the elements of “an image gateway to receive”, “a multi-level feature extraction module configured to identify …process” and “a key points guided dynamic convolution module configured to … identify … segment … predict”. Specifically, at least para. [0105]-[0122] of the instant specification contains allowable subject matter when taken in combination with the entirety of the claim.
The closest prior art includes GAO et al. (US 20230237666 A1, hereafter GAO), HSIAO et al. (US 20180040257 A1, hereafter HSIAO), Rao et al. (US 20220398718 A1, hereafter Rao) and LIU et al. (US 20210350183 A1, hereafter LIU). Claim 13 recites similar functions as recited in claims 1-2. These functions, when interpreted under broadest reasonable interpretation, are rejected as applied above. Prior art, either applied alone, or in combination with, fails to teach or suggest the limitations in claim 13 as interpretated under 35 USC 112(f).
Prior art searched but not cited is recorded in PTO-892.
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/XUEMEI G CHEN/Primary Examiner, Art Unit 2661
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