DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 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.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without a practical application or significantly more.
Regarding claims 1, 10 and 19, these claims recite the following limitations which are found to be abstract ideas not reciting a practical application or significantly more, with claim 1 being exemplary:
determining, for each domain of the source domain and the target domain, an entropy loss related to the domain; (abstract idea as a mental process since a human mind can make evaluations and judgements of observations)
determining an adversarial loss for a domain discriminator configured to predict whether a given input belongs to the source domain or the target domain; and (abstract idea as a mental process since a human mind can make evaluations and judgements of observations)
executing domain adaptation training of the network using the entropy loss for the source domain, the entropy loss for the target domain, and the adversarial loss. (abstract idea as a mental process since a human mind can make evaluations and judgements of observations)
This judicial exception is not integrated into a practical application for the following reasons. Claims 10 and 19 further recite additional elements: claim 10 contains a system comprising a processor a memory performing the method; claim 19 is directed towards a computer program product comprising a non-transitory computer readable storage medium
While the a system of claim 10, a non-transitory computer readable storage medium of claims 19 are additional elements, they are not sufficient to recite a practical application of the abstract ideas recited in claims 10 and 19 as they amount to mere generic computer elements and thus amount to no more than a recitation of the words "apply it" (or an equivalent) or are no more than mere instructions to implement an abstract idea or other exception on a computer. see MPEP §2106.05(f).
Further, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because when considered separately and in combination, the above recited additional elements from claims 10 and 19 do not add significantly more (also known as an “inventive concept”) to the exception. Rather, the claimed “non-transitory computer-readable storage medium” and “processor” perform well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d).
The dependent claims are also directed to an abstract idea such that the elements can be done mentally.
The claims do not recite additional elements that integrate the judicial exception into a practical application because these additional elements in the claim do no more than automate the mental process that a person may perform.
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 of this title, 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, 9-10 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Xu et al. US2021/0390686 hereinafter referred to as Xu in view of Zheng et al. “Entropy Guided Adversarial Domain Adaptation for Aerial Image Semantic Segmentation”, 2022 hereinafter referred to as Zheng.
As per Claim 1, Xu teaches a method of training a network via a source domain of labeled image samples and a target domain of unlabeled image samples, (Xu, Figure 3, Paragraph [0022], “3-4) Use the unlabeled CT small patches in the target domain, and use the labeled CT small patches in the source domain again to calculate the summation of adversarial loss function”)
the method comprising, by a computer system:
determining an adversarial loss for a domain discriminator configured to predict whether a given input belongs to the source domain or the target domain; and (Xu, Paragraph [0019], Figure 3, “Use an adversarial learning mechanism to construct a discriminator, perform domain adaptation by optimizing an adversarial loss function, and reduce the domain deviation of the encoder features of source and target domains”)
executing domain adaptation training of the network using the adversarial loss. (Xu, Paragraph [0019], “3-2) Use an adversarial learning mechanism to construct a discriminator, perform domain adaptation by optimizing an adversarial loss function, and reduce the domain deviation of the encoder features of source and target domains”)
Xu does not explicitly teach determining, for each domain of the source domain and the target domain, an entropy loss related to the domain;
Zheng teaches determining, for each domain of the source domain and the target domain, an entropy loss related to the domain; (Zheng, page 2, Column 1, “, we propose an entropy guided adversarial (EGA) learning method… As shown in Fig. 1(a), the model trained on the labeled source domain tends to produce over-confident prediction with low entropy. And the target domain predictions based on a source-only trained model usually represent low-entropy on the source-like (domain-invariant) regions (the category regions of “Build.”) and high-entropy on the target-like (domain-variant) regions (the regions of “Imp. surf.” and “Tree”) due to the domain shift, as shown in Fig. 1(b) and (c). Furthermore, the information entropy map has a strong relationship with the error map, which is apparent in the labeled source domain. This relationship has also been studied in previous work [51], [52]. In particular, we have studied the entropy distributions of correct and incorrect predictions in the source and target domains in Fig. 1(c).”)
Thus it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the teachings of Zheng into Xu because by considering individual entropy of the domains instead of just the cross-entropy as calculated by Xu in Paragraph [0015] will provide additional parameters for computing cross-entropy and/or utilizing the parameters for improving the neural network.
Therefore it would have been obvious to one of ordinary skill to combine the two references to obtain the invention in Claim 1.
As per Claim 9, Xu in view of Zhang teaches the method of claim 1, wherein the domain discriminator is trained to produce fault predictions. (Zheng, page 2, Column 1, “Furthermore, the information entropy map has a strong relationship with the error map, which is apparent in the labeled source domain. This relationship has also been studied in previous work [51], [52]. In particular, we have studied the entropy distributions of correct and incorrect predictions in the source and target domains in Fig. 1(c).”)
The rationale applied to the rejection of claim 1 has been incorporated herein.
As per Claim 10, Claim 10 claims a system performing the method as claimed in Claim 1. Therefore the rejection and rationale are analogous to that made in Claim 1.
As per Claim 18, Claim 18 claims the same limitation as Claim 9 and is dependent on a similarly rejected independent claim. Therefore the rejection and rationale are analogous to that made in Claim 9.
As per Claim 19, Claim 19 claims a computer-program product comprising a non-transitory computer-usable medium having computer-readable program code embodied therein, the computer-readable program code adapted to be executed to implement a method as claimed in Claim 1. Therefore the rejection and rationale are analogous to that made in Claim 1.
Claims 2-4, 11-13 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Xu et al. US2021/0390686 hereinafter referred to as Xu in view of Zheng et al. “Entropy Guided Adversarial Domain Adaptation for Aerial Image Semantic Segmentation”, 2022 hereinafter referred to as Zheng as applied to Claims 1, 10, and 19 respectively and further in view of Yao et al. US2019/0073553 hereinafter referred to as Yao.
As per Claim 2, Xu in view of Zheng teaches the method of claim 1, wherein determining the entropy loss comprises:
Xu in view of Zheng teaches does not explicitly teach extracting a feature map related to one or more image samples of the domain; and estimating an offset map and a classification map based at least in part on the feature map.
Yao teaches extracting a feature map related to one or more image samples of the domain; and estimating an offset map and a classification map based at least in part on the feature map. (Yao, Paragraph [0049], [0097])
Thus it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the teachings of Yao into Xu in view of Zheng because by utilizing additional components such as feature maps, offset map, classification map will improve the accuracy of the neural network.
Therefore it would have been obvious to one of ordinary skill to combine the three references to obtain the invention in Claim 2.
As per Claim 3, Xu in view of Zheng and Yao teaches the method of claim 2, wherein the adversarial loss is based at least in part on the offset map and the classification map for the target domain. (Yao, Paragraph [0049], [0097] and Xu, Paragraph [0019])
The rationale applied to the rejection of claim 2 has been incorporated herein.
As per Claim 4, Xu in view of Zheng and Yao teaches the method of claim 2, wherein the entropy loss is determined by at least a portion of the classification map. (Yao, Paragraph [0049], [0097])
The rationale applied to the rejection of claim 2 has been incorporated herein.
As per Claim 11, Claim 11 claims the same limitation as Claim 2 and is dependent on a similarly rejected independent claim. Therefore the rejection and rationale are analogous to that made in Claim 2.
As per Claim 12, Claim 12 claims the same limitation as Claim 3 and is dependent on a similarly rejected independent claim. Therefore the rejection and rationale are analogous to that made in Claim 3.
As per Claim 13, Claim 13 claims the same limitation as Claim 4 and is dependent on a similarly rejected independent claim. Therefore the rejection and rationale are analogous to that made in Claim 4.
As per Claim 20, Claim 20 claims the same limitation as Claim 2 and is dependent on a similarly rejected independent claim. Therefore the rejection and rationale are analogous to that made in Claim 2.
Claims 5-6 and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Xu et al. US2021/0390686 hereinafter referred to as Xu in view of Zheng et al. “Entropy Guided Adversarial Domain Adaptation for Aerial Image Semantic Segmentation”, 2022 hereinafter referred to as Zheng and Yao et al. US2019/0073553 hereinafter referred to as Yao as applied to Claims 2 and 11 respectively and further in view of Zhou et al. US2023/0418250 hereinafter referred to as Zhou.
As per Claim 5, Xu in view of Zheng and Yao teaches the method of claim 2, wherein the estimating comprises, for each domain of source domain and the target domain,
Xu in view of Zheng and Yao does not explicitly teach predicting a point coordinate and a background-foreground classification, the predicted background-foreground classification comprising a predicted score of the point coordinate belonging to an object.
Zhou teaches predicting a point coordinate and a background-foreground classification, the predicted background-foreground classification comprising a predicted score of the point coordinate belonging to an object. (Zhou, Paragraph [0076])
Thus it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the teachings of Zhou into Xu in view of Zheng and Yao because by utilizing additional components such as background-foreground classification will improve the accuracy of the neural network.
Therefore it would have been obvious to one of ordinary skill to combine the four references to obtain the invention in Claim 5.
As per Claim 6, Xu in view of Zheng, Yao and Zhou teaches the method of claim 5, wherein, for each of the source domain and the target domain, the entropy loss is based at least in part on the predicted background-foreground classification. (Zhou, Paragraph [0076])
The rationale applied to the rejection of claim 5 has been incorporated herein.
As per Claim 14, Claim 14 claims the same limitation as Claim 5 and is dependent on a similarly rejected independent claim. Therefore the rejection and rationale are analogous to that made in Claim 5.
As per Claim 15, Claim 15 claims the same limitation as Claim 6 and is dependent on a similarly rejected independent claim. Therefore the rejection and rationale are analogous to that made in Claim 6.
Allowable Subject Matter
Claims 7-8 and 16-17 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
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/MING Y HON/Primary Examiner, Art Unit 2666