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 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.
Claim(s) 1-4, 6-7, 11-15 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter as follows. Regarding claim(s) 1, 14, 15, the claims are directed to an abstract idea, namely mathematical operations and information processing. The claims are not integrated into a practical application and the claims lack an inventive concept. Furthermore, claim(s) 2-4, 6-7, 11-13 are also directed to an abstract idea, specifically, mathematical operations and image processing.
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.
Claim 6 recites the limitation "The learning apparatus according to claim 1, wherein the processor is further configured to execute the instructions to automatically determine at least one of the ma and the ya." in line(s) 1-3. There is insufficient antecedent basis for this limitation in the claim.
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.
Claims 1, 3-4, 6-7, 11-12, 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 20210166119 A1), hereinafter Wang, in view of Cao et al.: "Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss", arxiv.org, submitted on 18 Jun 2019, [retrieved on 6-23-2026]. Retrieved from the internet <https://arxiv.org/abs/1906.07413>, hereinafter Cao.
Regarding claim 1, Wang teaches A learning apparatus that learns a classifier model that performs multi-class classification of single-label or multi-labels for images, the learning apparatus comprising: (Para. 9 see "To achieve the object of the present disclosure, according to an aspect of the present disclosure, there is provided an information processing apparatus, comprising: a determining unit configured to respectively determine a discrimination margin of each class of a plurality of classes of a training sample set containing the plurality of classes relative to other classes and a training unit configured to use, based on the determined discrimination margin, the training sample set for training a classifying model." Para. 64 see "Taking use of an MS-Celeb-1M face image database as a training sample set to train a CNN classifying model"). a memory configured to store instructions; and a processor configured to execute the instructions to: (Para. 86 see "In FIG. 6 , a central processing unit (CPU) 601 performs various processing according to programs stored in a read-only memory (ROM) 602 or programs loaded from a storage part 608 to a random access memory (RAM) 603 ."). perform learning of the classifier model using a feature amount extracted from an image for learning as an input; (Para. 31 see "Further, ƒ(x i ) represents an extracted feature vector of the training sample x i ." Para. 74 see "Referring back to FIG. 1 , according to an embodiment of the present disclosure, the training unit 102 may use a training sample set for training a classifying model based on the determined discrimination margin m."). and give a margin to a loss function used for learning, (Para. 75 see "According to an embodiment of the present disclosure, after the determining unit 102 determines the discrimination margin m of each class, the training unit 102 may substitute the discrimination margin m into the loss function of the above equation (3) to thereby train the classifying model."). wherein the processor is further configured to execute the instructions to fix a total amount of margin to be given for the single-label or the multi-label, (Para. 49 see "According to an embodiment of the present disclosure, the determining unit 101 may determine an upper limit of the discrimination margin m according to a number of the plurality of classes and a dimension of a feature vector of the training sample." Para. 60 see "As shown in FIGS. 3A and 3B , according to an embodiment of the present disclosure, for a class having a larger number of training samples, the discrimination margin of the class is determined to be smaller, and wherein, for a class having a smaller number of training samples, the discrimination margin of the class is determined to be larger.").
Wang does not teach and give a class margin obtained by asymmetrically distributing the total amount of the margin to each of a plurality of classes of the single-label or the multi-labels.
However, Cao teaches and give a class margin obtained by asymmetrically distributing the total amount of the margin to each of a plurality of classes of the single-label or the multi-labels. (Pg. 2, Para. 4 see "Inspired by the theory, we design a label-distribution-aware loss function that encourages the model to have the optimal trade-off between per-class margins. The proposed loss extends the existing soft margin loss [53] by encouraging the minority classes to have larger margins. As a label-dependent regularization technique, our modified loss function is orthogonal to the re-weighting and re-sampling approach. In fact, we also design a deferred re-balancing optimization procedure that allows us to combine the re-weighting strategy with our loss (or other losses) in a more efficient way.").
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wang to incorporate the teachings of Cao to give a class margin obtained by asymmetrically distributing the total amount of the margin to each of a plurality of classes. Doing so would predictably improve the generalization and classification accuracy on imbalanced single-label or multi-label image datasets.
Regarding claim 3, Wang in view of Cao teaches The learning apparatus according to claim 1.
In addition, Wang teaches wherein the learning processor is further configured to execute the instructions to perform the learning of the classifier model by angular metric learning. (Para. 46 see "According to an embodiment of the present disclosure, when training is performed by using a training sample set of which training samples are distributed unevenly, in order to make a classification result more accurate, a discrimination margin m may be introduced so that ∥W 1 ∥ ∥ƒ(x 1 )∥ cos θ 1 >∥W 1 ∥ ∥ƒ(x 1 )∥ cos(θ 1 +m)>∥W 2 ∥ ∥ƒ(x 1 )∥ cos θ 2 . The discrimination margin m is reflected as the angular margin shown in FIG. 2B in the vector space, where 0≤θ+m≤π.").
Regarding claim 4, Wang in view of Cao teaches The learning apparatus according to claim 1.
In addition, Wang teaches wherein the processor is further configured to execute the instructions to give the class margin based on a proportion of samples of the class. (Para. 62 see "Specifically, the discrimination margin of a class having a larger number of training samples is smaller, while the discrimination margin of a class having a smaller number of training samples is larger, and values should be specifically taken in an interval [0, m upper ] based on the number of samples.").
Regarding claim 6, Wang in view of Cao teaches The learning apparatus according to claim 1.
In addition, Wang teaches wherein the processor is further configured to execute the instructions to automatically determine at least one of the m a and the ya. (Para. 56 see "Upon completion of the optimization process, L C may be determined as the upper limit m upper of the angular margin m.").
Regarding claim 7, Wang in view of Cao teaches The learning apparatus according to claim 1.
In addition, Wang teaches wherein the loss function is a loss function of Softmax type. (Para. 106 see "The information processing apparatus according to Solution 1, wherein the classifying model uses a Softmax function as a loss function.").
Regarding claim 11, Wang in view of Cao teaches The learning apparatus according to claim 1.
In addition, Wang teaches wherein the processor is further configured to extract the feature amount by convolutional neural network. (Para. 121 see "the classifying model is used for face recognition, and is realized by a convolutional neural network model.").
Regarding claim 12, Wang in view of Cao teaches The learning apparatus according to claim 1.
In addition, Wang teaches wherein the image is a face image. (Para. 64 see "Taking use of an MS-Celeb-1M face image database as a training sample set to train a CNN classifying model" Para. 76 see "the embodiments of the present disclosure are described in the context of applying the softmax function as a loss function to a convolutional neural network (CNN) classifying model for face recognition" Para. 121 see "wherein the classifying model is used for face recognition, and is realized by a convolutional neural network model.").
Claim 14 is rejected under the same analysis as claim 1 above.
Claim 15 is rejected under the same analysis as claim 1 above.
Claims 2, 13 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 20210166119 A1), hereinafter Wang, in view of Cao et al.: "Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss", arxiv.org, submitted on 18 Jun 2019, [retrieved on 6-23-2026]. Retrieved from the internet <https://arxiv.org/abs/1906.07413>, hereinafter Cao, and Singh et al. (US 20210124993 A1), hereinafter Singh.
Regarding claim 2, Wang in view of Cao teaches The learning apparatus according to claim 1.
Wang does not teach wherein the processor is further configured to execute the instructions to perform multi-class classification for each of the multi-labels.
However, Singh teaches wherein the processor is further configured to execute the instructions to perform multi-class classification for each of the multi-labels. (Para. 20 see "The digital image classification system can utilize an N-way K-shot classification framework, where the digital image classification system samples N classes from a set of novel classes (classes not seen during initial training) with K examples for each class." Para. 74 see "classify digital images into one or more additional classes not present in the plurality of base classes of the set of labeled digital images.").
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wang and Cao to incorporate the teachings of Singh to perform multi-class classification for each of the multi-labels. Doing so would predictably enable effective handling of multi-label images while maintaining margin-based improvements for imbalanced classes.
Regarding claim 13, Wang in view of Cao teaches The learning apparatus according to claim 1.
In addition, Wang teaches An estimating apparatus comprising: a memory configured to store instructions; and a processor configured to execute the instructions to: acquire an image; (Para. 64 see "Taking use of an MS-Celeb-1M face image database as a training sample set to train a CNN classifying model" Para. 86 see "In FIG. 6 , a central processing unit (CPU) 601 performs various processing according to programs stored in a read-only memory (ROM) 602 or programs loaded from a storage part 608 to a random access memory (RAM) 603 . In the RAM 603 , data needed when the CPU 601 performs various processes and the like is also stored, as needed. The CPU 601 , the ROM 602 and the RAM 603 are connected to each other via a bus 604 . An input/ output interface 605 is also connected to the bus 604 ." Para. 87 see "The following components are connected to the input/output interface 605 : an input part 606 (including keyboard, mouse and the like), an output part 607 (including display such as cathode ray tube (CRT), liquid crystal display (LCD) and the like, and loudspeaker and the like), a storage part 608 (including hard disc and the like), and a communication part 609 (including network interface card such as LAN card, modem and the like).").
Wang does not teach perform multi-class classification for the image by the classifier model learned by the learning apparatus according to claim 1.
However, Singh teaches perform multi-class classification for the image by the classifier model learned by the learning apparatus according to claim 1. (Para. 20 see "The digital image classification system can utilize an N-way K-shot classification framework, where the digital image classification system samples N classes from a set of novel classes (classes not seen during initial training) with K examples for each class." Para. 74 see "classify digital images into one or more additional classes not present in the plurality of base classes of the set of labeled digital images.").
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wang and Cao to incorporate the teachings of Singh to perform multi-class classification for each of the multi-labels. Doing so would predictably enable effective handling of multi-label images while maintaining margin-based improvements for imbalanced classes.
Allowable Subject Matter
Claim(s) 5, 8-10 is/are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Li et al. (US 20200342214 A1) discloses a face recognition method performed at a computer server using a classification neural network.
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/ALEXANDER JOSEPH VAUGHN/Examiner, Art Unit 2675
/EDWARD PARK/Primary Examiner, Art Unit 2675
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