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 .
Response to Arguments
Applicant’s arguments and amendments in the Amendment filed March 2, 2026 (herein “Amendment”), with respect to the objection of claim 18 has been fully considered and is persuasive. The objection to claim 18 has been withdrawn.
Applicant's arguments and amendments filed in the amendment regarding the rejection of claims 1–20 under 35 U.S.C. 101 as being directed to an abstract idea without a practical application or significantly more have been fully considered but they are not persuasive. First, Applicants set forth on pages 7–8 of the amendment that the performing one or more operations to train a machine learning model limitations merely involve but do not recite a mathematical concept. However, the claim limitation at issue only recites training a machine learning model, which is not just involving a mathematical concept, it is a mathematical concept/calculation. Moreover, in response to Applicant’s arguments on pages 8–10 regarding recent USPTO memos1, and the ARP decision on Ex Parte Desjardins2, the Examiner notes that the facts of the present application are not analogous to those in Ex Parte Desjardins, but rather are analogous to those of Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437, 2025 WL 1142021 (Fed. Cir. Apr. 18, 2025). The Recentive court in the decision on page 16 found a claim also reciting training a machine learning model to be claiming an abstract idea, and not simply involving an abstract idea:
Recentive claims that the inventive concept in its patents is “using machine learning to dynamically generate optimized maps and schedules based on real-time data and update them based on changing conditions.” Appellant’s Br. 44. As the district court correctly recognized, see Recentive, 692 F. Supp. 3d at 456, this is no more than claiming the abstract idea itself.
In response to Applicant’s arguments on pages 8–9 regarding the claims not being directed towards abstract ideas, and in particular that the human mind would not practically input augmentation parameters, the Examiner notes that the limitations of claim 2, now amended into claim 1, and including the limitations of claim 1, set forth that some of the limitations are directed towards mental process abstract ideas, and that others, including the training of the machine learning model, are mathematical calculation abstract ideas, and that claims can be directed towards an abstract idea while reciting different types of abstract ideas in the same claim. As for Applicant’s arguments regarding the claim requiring a computing device, nonetheless, as noted in the Non-Final action issued December 16, 2025 on page 3, the recited computer elements are merely generic computer elements and in combination with the abstract ideas recited, amount to no more than a recitation of the words “apply it,” and thus are not sufficient to recite a practical application or significantly more.
Finally, on pages 10–12, Applicant argues that because the claims are directed towards an improvement to technology, the amended claims necessarily integrate any purported abstract idea into a practical application and, accordingly are not abstract. However, MPEP 2106.04(d)(1) requires not simply that the claims are broadly directed towards the improvement to technology as disclosed in the specification, but that “the claim includes the components or steps of the invention that provide the improvement described in the specification,” that is, the complete set of components or steps to realize the disclosed improvement to technology. The Examiner finds that the present claims are recited broadly and do not include the components or steps necessary to provide the improvement described in the specification, and as such, remain subject matter ineligible.
Therefore, in view of the above, while all of Applicant’s arguments and amendments regarding the rejection of claims 1–20 under 35 U.S.C. 101 as being directed to an abstract idea without a practical application or significantly more have been fully considered, they are not persuasive, and the rejection is substantively maintained, with updates to the rejection made below to reflect the present amendments to the claims.
Applicant's arguments and amendments filed in the Amendment regarding the rejection of claims 1, 11 and 20, now incorporating the limitations of claim 2, under 35 U.S.C. 102(a)(2) have been fully considered but they are not persuasive. Specifically, Applicant argues that cited reference Li does not teach or suggest “wherein performing the one or more operations to train the machine learning model comprises inputting the one or more augmentation parameters into the machine learning model as conditioning information,” in that Li fails to teach generation of its third mask using Li’s augmentation parameter as an input to Li’s mask augmentation model (MAM) during operations to train the mask augmentation model. However, in such arguments, Applicant has failed to respond to the rejection rationale providing that not the mask augmentation model alone is being mapped to the claimed “machine learning model” but that the combination of the models in the processing chain, including the mask augmentation model, the generative adversarial model and the machine learning model, together being broadly a “machine learning model” and that in this way, the augmentation parameter input into the mask augmentation model is conditioning information as downstream the training is influenced by the output of the mask augmentation model by way of the input augmentation parameter.
Therefore, in view of the above, while all of Applicant’s arguments and amendments regarding the rejection of claims 1, 11 and 20, and various claims depending therefrom under 35 U.S.C. 102 have been fully considered, they are not persuasive, and the rejection is substantively maintained, with updates to the rejection made below to reflect the present amendments to the claims.
Applicants request to hold the double patenting rejection in abeyance is denied. The outstanding obviousness double patenting rejection is herein updated to match the newly recited claim limitations. See MPEP §804(I)(B)(1).
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, 3–11, and 13–20 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, 11 and 20, 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:
receiving training data that includes one or more samples; generating one or more augmented samples based on the one or more samples and one or more augmentation parameters (abstract idea as a mental process per MPEP §2106.04(a)(2)(III) as a human mind is capable of reading/seeing (receiving) data samples and at least with pen and paper, altering/augmenting a sample).
performing one or more operations to train the machine learning model based on the one or more augmented samples and the one or more augmentation parameters to yield a trained machine learning model (abstract idea as mathematical concepts per MPEP §2106.04(a)(2)(I), as training a machine learning model is well-known to be a mathematical process),
wherein the one or more operations include inputting the one or more augmentation parameters into the machine learning model as conditioning information (mental process per MPEP §2106.04(a)(2)(I), as the human mind can input a parameter regarding augmentation to data, and understand it to be conditioning information).
Claims 1, 11 and 20 further recite additional elements: claim 20 is directed towards a system comprising one or more memories, and one or more processors, claim 11 is directed towards a non-transitory computer-readable media and claim 1 recites “computer-implemented.” While a non-transitory computer-readable media of claim 11, the computer-implemented limitation of claim 1, and the processor(s) and memor(ies) of claim 20 are additional elements, they are not sufficient to recite a practical application of the abstract ideas recited in claims 1, 11 and 20 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 1, 11 and 20 do not add significantly more (also known as an “inventive concept”) to the exception. Rather, the additional elements disclosed above perform well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d).
Therefore, independent claims 1, 11 and 20 are directed towards an abstract idea without a practical application or significantly more.
Regarding claims 3 and 13, the limitations are merely directed towards further abstract ideas, specifically mental concepts per MPEP §2106.04(a)(2)(I), as the human mind can have input information regarding data augmentation, and additional samples.
Regarding claims 4–6, 14 and 15, the limitations are merely directed towards data aspects of the previously recited abstract ideas in the independent claim, but do not add significantly more or constitute a practical application.
Regarding claims 7 and 16, the limitations are merely directed towards further abstract ideas, specifically mental concepts per MPEP §2106.04(a)(2)(I), as the human mind can add corruption to samples, at least using pen and paper.
Regarding 8 and 17, the limitations are merely directed towards characteristics of the previously recited abstract ideas in the independent claim, but do not add significantly more or constitute a practical application.
Regarding claims 9 and 19, the limitations are merely directed towards further abstract ideas, specifically mental concepts per MPEP §2106.04(a)(2)(I), as the human mind can reduce corruption to samples, at least using pen and paper. The additional element of “using the trained machine learning model” does not integrate the abstract idea into a practical application because of the high level of generality recited by the limitation. Nor does the claimed machine learning model recite significantly more.
Regarding claim 10, the limitations are merely directed towards further abstract ideas, specifically mental concepts per MPEP §2106.04(a)(2)(I), as the human mind can determine parameters.
Regarding claim 18, the limitations are merely directed towards characteristics of the previously recited abstract ideas in the independent claim, but do not add significantly more or constitute a practical application.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 11, and 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 2, 17, and 19 of U.S. Patent No. 12,321,825 (herein “‘825 Patent”). Although the claims at issue are not identical, they are not patentably distinct from each other because the limitations recited by the ‘825 patent are a species to the genus recited by the claims of the present application, and therefore anticipate the limitations, as given in the correspondence tables below.
Regarding claims 1, 11 and 20 of the present application, these claims respectively correspond to claims 2, 17 and 19 of the ‘825 patent as follows, with Examiner explanations for claim interpretation set forth in square brackets [] :
present application limitation with claim 1 as exemplary, and distinctions between the claims noted in curly brackets
‘825 patent limitation with claim 2 (including all of the limitations from which it depends – claim 1 also) as exemplary, and distinctions between the claims noted in curly brackets
{A computer-implemented method for training a machine learning model, the method comprising: - claim 1} { One or more non-transitory computer-readable media storing instructions that, when executed by at least one processor, cause the at least one processor to perform the steps of: - claim 11} { A system, comprising: one or more memories storing instructions; and one or more processors that are coupled to the one or more memories and, when executing the instructions, are configured to: - claim 20)
{A computer-implemented method for training a neural network model comprising a generator and a discriminator, comprising: - claim 1} { A non-transitory computer-readable media storing computer instructions that, when executed by one or more processors, cause the one or more processors to train a neural network model comprising a generator and a discriminator by performing the steps of: - claim 18} { A system, comprising: a memory that stores training data including example output data and ground truth outputs; and a processor that is coupled to the memory and implements a neural network model comprising a generator and a discriminator, wherein the neural network model is trained by: - claim 16}
receiving training data that includes one or more samples
receiving training data including example output data and ground truth outputs
generating one or more augmented samples based on the one or more samples and one or more augmentation parameters
applying at least one augmentation to the generated data to produce augmented generated data, wherein an augmentation operator is invertible and specifies the at least one augmentation
performing one or more operations to train the machine learning model based on the one or more augmented samples and the one or more augmentation parameters to yield a trained machine learning model.
processing only the augmented generated data by the discriminator [a type of machine learning model] to produce values; and adjusting the parameters to reduce differences between the values and the ground truth outputs [known to be a training operation for a machine learning model].
wherein the one or more operations include inputting the one or more augmentation parameters into the machine learning model as conditioning information.
wherein the example output data is associated with a first distribution and the augmentation operator transforms the first distribution into an augmented distribution that matches a second distribution associated with the augmented generated data. [because the augmentation operator affects the first distribution, it is conditioning information].
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)(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.
Claims 1, 4–6, 8, 10–11, 14–15, 17–18 and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Li et al., US Patent Application Publication No. US 2023/0196718 A1 (herein “Li”).
Regarding claims 1, 11 and 20, with claim 1 as exemplary, substantive differences between the claims noted in curly brackets {}, and deficiencies of Li noted in square brackets [], Li teaches { A computer-implemented method for training a machine learning model, the method comprising - claim 1 / One or more non-transitory computer-readable media storing instructions that, when executed by at least one processor, cause the at least one processor to perform the steps of - claim 11 / A system, comprising: one or more memories storing instructions; and one or more processors that are coupled to the one or more memories and, when executing the instructions, are configured to - claim 20} (Li Abstract, ¶ 20, an image augmentation device including a memory and processor configured for performing operations, the memory being a hard disk, therefore non-transitory computer readable media):
receiving training data that includes one or more samples (Li ¶¶ 33 and 43, first object contour is extracted from (receiving) a first image mask, where the first object contour is used to generate a sample image for performing machine learning, and therefore the first object contour is training data);
generating one or more augmented samples based on the one or more samples and one or more augmentation parameters (Li ¶35, the first object contour is superimposed to a superimposed region in a second image mask according to the augmentation parameter to generate a third image mask (augmented samples)); and
performing one or more operations to train the machine learning model based on the one or more augmented samples and the one or more augmentation parameters to yield a trained machine learning model (Li ¶¶ 43 and 31, a sample image is generated (performing operations) for performing machine learning (to yield a trained machine learning model), the sample image generated according to (based on) the second image contour in the third image mask (augmented sample), where ¶35 teaches the third image mask is generated from the augmentation parameter, thus the generated sample image/performing machine learning being based on the augmentation parameter),
wherein the one or more operations include inputting the one or more augmentation parameters into the machine learning model as conditioning information (Li ¶¶ 35–36 and 40, fig. 1, considering the combination of the mask augmentation model, the generative adversarial model and the machine learning model as all one larger machine learning model, the augmentation parameter used by the mask augmentation model to generate the third image mask “conditions” the input first object contour by for example, scaling the first object contour).
Regarding claims 4 and 14, Li teaches wherein each augmentation parameter included in the one or more augmentation parameters indicates at least one of a geometric transformation, a color change, a filtering, a masking, a cropping, a compression, a quantization, a pixelation, a decimation, a composition, a cutout, a cutmix, or a mixup (Li ¶¶ 35 and 40, the augmentation parameter as a scaling parameter, contour moving distance, and a contour rotation angle and a range for superimposition, all of which are geometric transformations).
Regarding claim 5, Li teaches wherein each augmentation parameter included in the one or more augmentation parameters indicates at least one of an isotropic scaling, an anisotropic scaling, a rotation, an integer translation, a fractional translation, or a flip along an axis (Li ¶¶ 35 and 40, the augmentation parameter as a scaling parameter, contour moving distance, and a contour rotation angle (a rotation) and a range for superimposition, all of which are geometric transformations).
Regarding claims 6 and 15, Li teaches wherein the one or more operations to train the machine learning model are further based on the one or more samples (Li fig. 1, ¶ 43, operations that generate the sample image IMG used for performing machine learning, are according to the first object contour and the second object contour (the one or more samples)).
Regarding claims 8 and 17, Li teaches wherein the trained machine learning model comprises a generative model (Li ¶ 27, fig. 1, the machine learning model executed/generated by the processor including a generative adversarial network (GAN)).
Regarding claim 10, Li teaches further comprising determining the one or more augmentation parameters (Li ¶ 42, the augmentation parameter can be adjusted (determining) according to the relationship between various object types and contours of various objects).
Regarding claim 18, Li teaches wherein the trained machine learning model comprises at least one of a generative adversarial network, a U-Net architecture, a transformer architecture, a vision transformer architecture, a recurrent interface network architecture, or a convolutional architecture (Li ¶ 27, fig. 1, the machine learning model executed/generated by the processor including a generative adversarial network (GAN)).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Li, as set forth above in the independent claims, and further in view of Luo et al., US Patent Application Publication No. US 2023/0273914 A1 (herein “Luo”).
Regarding claims 3 and 13, with claim 3 as exemplary, Li teaches comprising performing one or more operations on another sample using the trained machine learning model by inputting, into the trained machine learning model, the another sample (Li fig. 1, ¶¶33–36, mask augmentation model performing mask augmentation upon inputted image masks MSK(1)-MSK(N), thus other first image masks from the set of MSK(1)-MSK(N) being another sample). Li does not explicitly teach, where Luo teaches and an indication of no augmentation (Luo ¶¶ 356–357 and fig. 15, data augmentation performed upon a sample according to an intensity value from a Gaussian distribution, including zero values, thus zero intensity being an indication of no augmentation).
Therefore, taking the teachings of Li and Luo together as a whole, it would have been obvious to a person having ordinary skill in the art (herein “PHOSITA”) before the effective filing date of the claimed invention to have modified the augmentation of Li to include augmentation levels according to a Gaussian distribution including no augmentation as disclosed in Luo at least because doing so would improve sample diversity and thus provide training data that will avoid low precision and a low generalization capability in a model obtained through training with the training data. See Luo ¶¶ 4 and 8.
Claims 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Li, as set forth above in the independent claims, and further in view of van Walsum et al., US Patent Application Publication No. US 2020/0222018 A1 (herein “Walsum”).
Regarding claims 7 and 16, with claim 7 as exemplary, while Li teaches performing operations to the one or more augmented samples (Li fig. 1, ¶ 43, the third image mask is input to the GAN model and operated on to produce an sample image IMG), Li does not explicitly teach the operations “to add corruption.” Walsum teaches to add corruption (Walsum ¶202, in addition to the geometric transformations, the data augmented can have added Gaussian noise to the pixel values).
Therefore, taking the teachings of Li and Walsum together as a whole, it would have been obvious to a PHOSITA before the effective filing date of the claimed invention to have modified the augmentation of Li to include adding Gaussian noise as corruption as disclosed in Walsum at least because doing so would make the trained model robust to noise. See Walsum ¶ 202.
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Li, as set forth above in the independent claims, and further in view of Bhattacharya et al., US Patent No. US 12,444,051 B2 (herein “Bhattacharya”).
Regarding claims 9 and 19, with claim 9 as exemplary, Li does not explicitly teach, but Bhattacharya teaches performing one or more operations to reduce corruption in a content item using the trained machine learning model (Bhattacharya fig. 19, col. 18, l. 62–65, and col. 19, l. 7–10, image is input to the trained neural network, and a denoised image is produced as one or more operations of the trained neural network).
Therefore, taking the teachings of Li and Bhattacharya together as a whole, it would have been obvious to a PHOSITA before the effective filing date of the claimed invention to have modified the machine learning system of Li to include operations that reduce noise/corruption as disclosed in Bhattacharya at least because doing so would provide an improved tool for reducing noise in an image, and thus improve the imaging capability of an existing imaging system. See Bhattacharya col. 6, ll. 46–48 and col. 1, ll. 55–59.
Conclusion
Applicant's amendment necessitated any new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 MICHELLE M KOETH whose telephone number is (571)272-5908. The examiner can normally be reached Monday-Thursday, 09:00-17:00, Friday 09:00-13:00, EDT/EST.
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MICHELLE M. KOETH
Primary Examiner
Art Unit 2671
/MICHELLE M KOETH/Primary Examiner, Art Unit 2671
1 The Examiner treats Applicant’s reference to “101 Memo” to intend a reference of Memorandum from Charles Kim, Deputy Commissioner for Patent Examination Policy, USPTO, to Patent Examining Corps (Aug. 4, 2025), https://www.uspto.gov/sites/default/files/documents/memo-101-20250804.pdf.
2 Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision).
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