DETAILED ACTION
The United States Patent & Trademark Office appreciates the response filed for the
current application that is submitted on 01/13/2026. The United States Patent & Trademark
Office reviewed the following documents submitted and has made the following comments below.
Priority
Acknowledgment is made that this application is a CON of application no. PCT/JP2022/021329 filed on 05/25/2022, which further claim for domestic priority under 35 U.S.C.119 (e) based on the provisional applications PRO 63/193,785 filed on 05/27/2021.
Information Disclosure Statement
The IDS dated 11/17/2023 has been considered and placed in the application file.
Overview
Claims 1-7 are pending in this application and have been considered below.
Claims 1-3 and 5-7 are rejected.
Claim 4 is objected to.
Applicant Arguments:
In regards to the argument on Argument 1, Applicant/s state/s “ However, Applicant notes that the cited portion of Tong fails to teach "adding noise to a first area in an original image," as required by the above-noted features of claim 1". (See Remarks Pg 3 ¶04). Therefore the 35 U.S.C 103 rejection on the amended claims should be withdrawn.
In regards to the argument on Argument 2, Applicant/s state/s “ Additionally, although FIG. 1 of Tong generally teaches that noise is overall added to the original image, Applicant notes that Tong contains no disclosure that noise is added to a first area in an original image, as required by the above-noted features of claim 1.". (See Remarks Pg 3 ¶05). Therefore the 35 U.S.C 103 rejection on the amended claims should be withdrawn.
In regards to the argument on Argument 3, Applicant/s state/s “ Accordingly, it is respectfully submitted that the cited portion of Tong does not disclose or suggest "generating a second image by adding noise to a second area that is an area excluding the first area in the original image," as required by the above-noted features of claim 1.” (See Remarks Pg 04 ¶01). Therefore the 35 U.S.C 103 rejection on the pending claims should be withdrawn.
In regards to the argument on Argument 4, Applicant/s state/s “ Ando merely teaches about noise that "small noise may be added to the image to which the correct answer label is assigned," and as such, it is respectfully submitted that Ando fails to provide disclosure that would obviate the above-mentioned deficiencies of Tong.” (See Remarks Pg 04 ¶02). Therefore the 35 U.S.C 103 rejection on the amended claims should be withdrawn.
In regards to the argument on Argument 5, Applicant/s state/s “ although cited paragraph [0015] of Tong describes "a weighted combination of a source image and a noise image," it noted that this description merely describes the weighted addition of two images and does not teach the weighted addition of two labels, as required by the above-noted features of claim 1.” (See Remarks Pg 05 ¶03). Therefore the 35 U.S.C 103 rejection on the amended claims should be withdrawn.
In regards to the argument on Argument 6, Applicant/s state/s “ Tong necessarily fails to teach "generating a first training label for the first image by weighted addition of a first base label corresponding to a correct label of the original image and a second base label corresponding to an incorrect label of the original image at a second ratio that is a ratio between a size of the first area and a size of the second area," "generating a second training label for the second image by weighted addition of the first base label and the second base label at an inverse ratio of the second ratio," as required by the above noted features of claim 1. (See Remarks Pg 05 ¶04). Therefore the 35 U.S.C 103 rejection on the pending claims should be withdrawn.
In regards to the argument on Argument 7, Applicant/s state/s “Applicant respectfully submits that the references necessarily fails to teach "a second ratio that is a ratio between a size of the first area and a size of the second area," as required by the above-noted features of claim 1.”(See Remarks Pg 06 ¶01). Therefore the 35 U.S.C 103 rejection on the pending claims should be withdrawn.
In regards to the argument on Argument 8, Applicant/s state/s “Applicant respectfully submits that any combination of Tong and Ando fails to disclose, suggest, or otherwise render obvious the above-noted features of claim 1. Accordingly, claim 1 is patentable over any combination of Tong and Ando.” (See Remarks Pg 06 ¶03). Therefore the 35 U.S.C 103 rejection on the pending claims should be withdrawn.
In regards to the argument on Argument 9, Applicant/s state/s “Claims 2, 3, 5, and 7 are patentable over any combination of Tong and Ando based at least on their dependency from claim 1.” (See Remarks Pg 06 ¶04). Therefore the 35 U.S.C 103 rejection on the pending claims should be withdrawn.
In regards to the argument on Argument 10, Applicant/s state/s “Claim 6 recited features generally corresponding to the above-noted features of claim 1. Accordingly, Applicant respectfully submits that any combination of Tong and Ando fails to disclose, suggest, or otherwise render obvious these corresponding features of claim 6 for reasons similar to those discussed above with respect to claim 1, and as such, claim 6 is patentable over any combination of Tong and Ando.” (See Remarks Pg 06 ¶04). Therefore the 35 U.S.C 103 rejection on the pending claims should be withdrawn.
Examiners Responses:
In response to Argument 1, see remarks filed 01/13/2026, the Examiner respectfully disagrees. In response to Argument that Tong fails to teach "adding noise to a first area in an original image," the Examiner respectfully disagrees. We determine claim scope not solely on the basis of claim language, but also on giving claims their broadest reasonable construction in light of the specification as it would be interpreted by one of ordinary skill in the art. In re Am. Acad. of Sci. Tech. Ctr., 367 F.3d 1359, 1364 (Fed. Cir. 2004). See also Superguide Corp. v. DirecTV Enterprises, Inc., 358 F.3d 870, 875 (Fed. Cir. 2004) (“Though understanding the claim language may be aided by explanations contained in the written description, it is important not to import into a claim limitations that are not part of the claim.”). The Examiner interprets under broadest reasonable interpretation that the Claim states “generating a first image by adding noise to a first area in an original image”, specifically, “the first area” within the claim can be interpreted as adding noise to the source image locally as taught in ¶0077 by Tong. Furthermore, the claim does not limit what “the first area” is; thus allowing a person skilled in the art to interpret broadly that the first area in which noise is added to the image can be interpreted as adding noise to the source image locally. The examiner finds that Tong in combination with Ando does disclose adding noise to a first area in an original image. Tong does disclose generating a training image by adding noise to the original image in ¶0075-¶0078, and ¶0161. Ando discloses the original image being split to focus on different areas in Fig 3 and ¶0128. Therefore, Examiner made a proper determination of obviousness under 35 U.S.C. §103, and also provided an appropriate supporting rationale in view of the decision by the Supreme Court in KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007). The Examiner’s rational are based on the Office’s current understanding of the law, and are believed to be fully consistent with the binding precedent of the Supreme Court. Furthermore, the Examiner supported the rejection under 35 U.S.C. §103 via making the clear articulation of the reason(s) why the claimed invention would have been obvious by citing the specific areas in the prior art references. Further the Examiner, clearly stating the modification of the inventions, supported the rejection under 35 U.S.C. §103 by making the analysis explicit. Last, the Examiner did not make conclusory statements. The Court quoting In re Kahn, 441 F.3d 977, 988, 78 USPQ2d 1329, 1336 (Fed. Cir. 2006), stated that “‘[R]ejections on obviousness cannot be sustained by mere conclusory statements; instead, there must be some articulated reasoning with some rational underpinning to support the legal conclusion of obviousness.’” KSR, 550 U.S. at, 82 USPQ2d at 1396. Therefore, the Examiner has established a proper 35 U.S.C. §103 rejection with Tong in view of Ando and the rejection will be maintained, which is disclosed in detail below.
In response to Argument 2, see remarks filed 01/13/2026, the Examiner respectfully disagrees. In response to Argument that Tong contains no disclosure that noise is added to a first area in an original image the Examiner respectfully disagrees. Additionally, “though understanding the claim language may be aided by the explanations contained in the written description, it is important not to import into a claim limitations that are not a part of the claim. For example, a particular embodiment appearing in the written description may not be read into a claim when the claim language is broader than the embodiment.” Superguide Corp. v. DirecTV Enterprises, Inc., 358 F.3d 870, 875 (Fed. Cir. 2004). The Examiner interprets under broadest reasonable interpretation that the Claim states “generating a first image by adding noise to a first area in an original image”, specifically, “the first area” within the claim can be interpreted as adding noise to the source image locally as taught in ¶0077 by Tong. Furthermore, the claim does not limit what “the first area” is, even though the specification does address the size of the first image being determined it does not address where it is located in the original image; thus allowing a person skilled in the art to interpret broadly that the first area in which noise is added to the image can be interpreted as adding noise to the source image locally. The examiner finds that Tong in combination with Ando does disclose that noise is added to a first area in an original image. Tong does disclose generating a training image by adding noise to the original image in ¶0075-¶0078, and ¶0161. Ando discloses the original image being split to focus on different areas in Fig 3 and ¶0128. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Therefore, the Examiner has established a proper 35 U.S.C. §103 rejection with Tong in view of Ando and the rejection will be maintained, which is disclosed in detail below
In response to Argument 3, see remarks filed 01/13/2026, the Examiner respectfully disagrees. In response to Argument that Tong does not disclose or suggest "generating a second image by adding noise to a second area that is an area excluding the first area in the original image,", the Examiner respectfully disagrees. During prosecution, claims must be given their broadest reasonable interpretation while reading claim language in light of the specification as it would be interpreted by one of ordinary skill in the art. In re Am. Acad. of Sci. Tech. Ctr., 367 F.3d 1359, 1364 (Fed. Cir. 2004). In construing the meaning of claims terms, caution must be taken not to import limitations from the specification as “[i]t is the claims that measure the invention.” See SRI Int’l v. Matsushita Elec. Corp. of Am., 775 F.2d 1107, 1121 (Fed. Cir. 1985) (en banc) (citations omitted). The examiner interprets under broadest reasonable interpretation that IM2 taken from the original image is different from IM1 taken from the original image as shown in Fig 3 of Ando, and the content of the two images do not overlap as evidenced by the clouds being in different locations in the images of Fig 3 and the content in the images differing from each other as taught by ¶0128 in Ando, therefore it can interpreted by someone of ordinary skill in the art that IM1 is excluded from IM2, satisfying the limitation that “a second area that is an area excluding the first area in the original image”. The examiner finds that Tong in combination with Ando does disclose generating a second image by adding noise to a second area that is an area excluding the first area in the original image. Tong does disclose generating a second set of training images by adding noise to the original image in Fig 2, 206, ¶0008, ¶0014, ¶0060, and ¶0062. The examiner finds that the SSNR value taught by Tong does not negate the second set of images being produced with noise. Ando discloses the original image being split to focus on different areas in Fig 3 and ¶0128. The Examiner made a proper determination of obviousness under 35 U.S.C. §103, and also provided an appropriate supporting rationale in view of the decision by the Supreme Court in KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007). The Examiner’s rational are based on the Office’s current understanding of the law, and are believed to be fully consistent with the binding precedent of the Supreme Court. Furthermore, the Examiner supported the rejection under 35 U.S.C. §103 via making the clear articulation of the reason(s) why the claimed invention would have been obvious by citing the specific areas in the prior art references. Further the Examiner, clearly stating the modification of the inventions, supported the rejection under 35 U.S.C. §103 by making the analysis explicit. Last, the Examiner did not make conclusory statements. The Court quoting In re Kahn, 441 F.3d 977, 988, 78 USPQ2d 1329, 1336 (Fed. Cir. 2006), stated that “‘[R]ejections on obviousness cannot be sustained by mere conclusory statements; instead, there must be some articulated reasoning with some rational underpinning to support the legal conclusion of obviousness.’” KSR, 550 U.S. at, 82 USPQ2d at 1396. Therefore, the Examiner has established a proper 35 U.S.C. §103 rejection Tong in view of Ando and the rejection will be maintained, which is disclosed in detail below.
In response to Argument 4, see remarks filed 01/13/2026, the Examiner respectfully disagrees. In response to Argument that Ando fails to provide disclosure that would obviate the above-mentioned deficiencies of Tong, the Examiner respectfully disagrees. “During examination, ‘claims . . . are to be given their broadest reasonable interpretation consistent with the specification, and . . . claim language should be read in light of the specification as it would be interpreted by one of ordinary skill in the art.’” In re Am. Acad. of Sci. Tech Ctr., 367 F.3d 1359, 1364 (Fed. Cir. 2004) (citation omitted); In re Morris, 127 F.3d 1048, 1053-54 (Fed. Cir. 1997). The examiner interprets the claim language as detailed in the above arguments and finds that Tong in combination with Ando does disclose that noise is added to a first area in an original image and generating a second image by adding noise to a second area that is an area excluding the first area in the original image. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Therefore, the Examiner has established a proper 35 U.S.C. §103 rejection with Tong in view of Ando and the rejection will be maintained, which is disclosed in detail below.
In response to Argument 5, see remarks filed 01/13/2026, the Examiner respectfully disagrees. In response to Argument that Tong does not teach the weighted addition of two labels, the Examiner respectfully disagrees. No special definition of weighted addition is found in the present specification, and, absent a special definition, Examiner is obligated to take the broadest reasonable interpretation not in conflict with the specification. It is noted that the feature upon which applicant relies (i.e., “weighted addition of two labels”) has been given its broadest reasonable interpretation. MPEP 2111-2111.01. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). The examiner finds that Tong does teach a first and second training label added to the weighted images in ¶0086 as Asource and Anoise being the labels being applied to the images, and that the first and second images are weighted before they are combined in ¶0015 and ¶0086 in Tong. Under broadest reasonable interpretation the examiner interprets that one of ordinary skill in the art would find that, “weighted addition of the labels,” can be interpreted as two images with labels being weighted and combined as taught by Tong. The specification is silent as to the weighted addition of the labels and the timing of the addition; the specification does not prohibit such an interpretation; therefore, Examiner's interpretation is both reasonable and not in conflict with the specification, and the limitation is met by the prior art. Therefore, the Examiner has established a proper 35 U.S.C. §103 rejection with Tong in view of Ando and the rejection will be maintained, which is disclosed in detail below.
In response to Argument 6, see remarks filed 01/13/2026, the Examiner respectfully disagrees. In response to the Argument that Tong necessarily fails to teach "generating a first training label for the first image by weighted addition of a first base label corresponding to a correct label of the original image and a second base label corresponding to an incorrect label of the original image at a second ratio that is a ratio between a size of the first area and a size of the second area," "generating a second training label for the second image by weighted addition of the first base label and the second base label at an inverse ratio of the second ratio,", the Examiner respectfully disagrees. We determine claim scope not solely on the basis of claim language, but also on giving claims their broadest reasonable construction in light of the specification as it would be interpreted by one of ordinary skill in the art. In re Am. Acad. of Sci. Tech. Ctr., 367 F.3d 1359, 1364 (Fed. Cir. 2004). See also Superguide Corp. v. DirecTV Enterprises, Inc., 358 F.3d 870, 875 (Fed. Cir. 2004) (“Though understanding the claim language may be aided by explanations contained in the written description, it is important not to import into a claim limitations that are not part of the claim.”). Under broadest reasonable interpretation of the claims the examiner interprets that Tong does teach a first and second training label added to the weighted images in ¶0086 as Asource and Anoise being the labels being applied to the images, and that the first and second images are weighted before they are combined in ¶0015 and ¶0086 in Tong. Under broadest reasonable interpretation the examiner interprets that one of ordinary skill in the art would find that, “weighted addition of the labels,” can be interpreted as two images with labels being weighted and combined as taught by Tong. The specification is silent as to the weighted addition of the labels and the timing of the addition; the specification does not prohibit such an interpretation; therefore, Examiner's interpretation is both reasonable and not in conflict with the specification, and the limitation is met by the prior art. The Examiner interprets under broadest reasonable interpretation that the Claim states “corresponding to a correct label of the original image”, specifically, “a correct label” within the claim can be interpreted as the validation trained network correctly labeling validation images in ¶0158 by Tong. Furthermore, the claim does not limit what “a correct label” is, even though the specification does address the correct label but does not specific what the label is; thus allowing a person skilled in the art to interpret broadly that the correct label added to the image can be interpreted as the network correctly labeling validation images. The Examiner interprets under broadest reasonable interpretation that the Claim states “corresponding to an incorrect label of the original image”, specifically, “an incorrect label” within the claim can be interpreted as the validation trained network identifying incorrectly labeling validation images in ¶0158 by Tong. Furthermore, the claim does not limit what “an incorrect label” is, even though the specification does address the incorrect label it does not specific what the label is; thus allowing a person skilled in the art to interpret broadly that an incorrect label added to the image can be interpreted as the network identifying incorrectly labeled images. The Examiner interprets under broadest reasonable interpretation that the Claim states “original image at a second ratio”, specifically, “a second ratio” within the claim can be interpreted as the source image having varying noise ratios that could include 0.75, 0.5, 0.4, 0.3, 0.2 or others in ¶0007 by Tong. Furthermore, the claim does not limit what value “a second ratio” is; thus allowing a person skilled in the art to interpret broadly that an original image at a second ratio can be interpreted as source image having varying noise ratios that could include 0.75, 0.5, 0.4, 0.3, 0.2. The Examiner interprets under broadest reasonable interpretation that the Claim states “a ratio between a size of the first area and a size of the second area”, specifically, “a ratio between a size” within the claim can be interpreted as the source image having a large segmentation area size and an second area that does not include the content of the first area being small in ¶0056 and ¶0159 by Ando. Furthermore, the claim does not limit what value “a ratio between size” is; thus allowing a person skilled in the art to interpret broadly that a ratio between size can be interpreted as the first area being large and the second area being small. The Examiner interprets under broadest reasonable interpretation that the Claim states “generating a second training label”, specifically, “generating a second training label” within the claim can be interpreted as generating and applying labels for images used for categorization for training the model in ¶0032-¶0033 by Tong. Furthermore, the claim does not limit what “second training label” is; thus allowing a person skilled in the art to interpret broadly that a second training label be interpreted as labeling the image categories in a set of images for training a model. Under broadest reasonable interpretation of the claims the examiner interprets that Tong does teach a first and second training label added to the weighted images in ¶0086 as Asource and Anoise being the labels being applied to the images, and that the first and second images are weighted before they are combined in ¶0015 and ¶0086 in Tong. Under broadest reasonable interpretation the examiner interprets that one of ordinary skill in the art would find that, “weighted addition of the labels,” can be interpreted as two images with labels being weighted and combined as taught by Tong. The specification is silent as to the weighted addition of the labels and the timing of the addition; the specification does not prohibit such an interpretation; therefore, Examiner's interpretation is both reasonable and not in conflict with the specification, and the limitation is met by the prior art. The Examiner interprets under broadest reasonable interpretation that the Claim states “first base label and the second base label at an inverse ratio in of the second ratio”, specifically, “inverse ratio in of the second ratio” within the claim can be interpreted as using an inverse function to determine the pixel noise within the images in ¶0018 by Tong. Furthermore, the claim does not limit the value of what “inverse ratio in of the second ratio” is; thus allowing a person skilled in the art to interpret broadly that an inverse ratio in of the second ratio is using an inverse function to determine the pixel noise within the images. Tong discloses the original image at a second ratio in ¶0074 while Ando teaches a ratio between a size of the first area and a size of the second area in ¶0056 and ¶0159. Tong then continues to disclose generating a second training label in ¶0032-¶0033 for the second image by weighted addition in ¶0015 and ¶0086 and of the first base label and the second base label at an inverse ratio in ¶0018 of the second ratio in ¶0074. Therefore Tong in combination of Ando do teach the limitations of Claim 1 and one of ordinary skill in the art would be motivated to combine these two references to make an invention that can more robustly identify objects due to partial images being used in the training data, since there is a need to increase the amount of training data available without increasing cost. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Therefore, the Examiner has established a proper 35 U.S.C. §103 rejection with Tong in view of Ando and the rejection is maintained, which is disclosed in detail below.
In response to Argument 7, see remarks filed 01/13/2026, the Examiner respectfully disagrees. In response to Argument that the references necessarily fails to teach "a second ratio that is a ratio between a size of the first area and a size of the second area," the Examiner respectfully disagrees. The Examiner interprets under broadest reasonable interpretation that the Claim states “a second ratio that is a ratio between a size of the first area and a size of the second area specifically, “a second ratio between a size” within the claim can be interpreted as the source image having a large segmentation area size and an second area that does not include the content of the first area being small in ¶0056 and ¶0159 by Ando. Furthermore, the claim does not limit what value “a second ratio between size” is; thus allowing a person skilled in the art to interpret broadly that a second ratio between size can be interpreted as the first area being large and the second area being small or vice versa. The examiner finds that Tong in combination with Ando does disclose at a second ratio that is a ratio between a size of the first area and a size of the second area. With Tong disclosing the second ratio in the form of varying ratio in ¶0074. Ando then discloses that is a ratio between a size of the first area and a size of the second area in ¶0056 and ¶0159. The Examiner made a proper determination of obviousness under 35 U.S.C. §103, and also provided an appropriate supporting rationale in view of the decision by the Supreme Court in KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007). The Examiner’s rational are based on the Office’s current understanding of the law, and are believed to be fully consistent with the binding precedent of the Supreme Court. Furthermore, the Examiner supported the rejection under 35 U.S.C. §103 via making the clear articulation of the reason(s) why the claimed invention would have been obvious by citing the specific areas in the prior art references. Further the Examiner, clearly stating the modification of the inventions, supported the rejection under 35 U.S.C. §103 by making the analysis explicit. Last, the Examiner did not make conclusory statements. The Court quoting In re Kahn, 441 F.3d 977, 988, 78 USPQ2d 1329, 1336 (Fed. Cir. 2006), stated that “‘[R]ejections on obviousness cannot be sustained by mere conclusory statements; instead, there must be some articulated reasoning with some rational underpinning to support the legal conclusion of obviousness.’” KSR, 550 U.S. at, 82 USPQ2d at 1396. Therefore, the Examiner has established a proper 35 U.S.C. §103 rejection Tong in view of Ando, which is disclosed in detail below.
In response to Argument 8, see remarks filed 01/13/2026, the Examiner respectfully disagrees. In response to Argument that any combination of Tong and Ando fails to disclose, suggest, or otherwise render obvious the above-noted features of claim 1, the Examiner respectfully disagrees. “During examination, ‘claims . . . are to be given their broadest reasonable interpretation consistent with the specification, and . . . claim language should be read in light of the specification as it would be interpreted by one of ordinary skill in the art.’” In re Am. Acad. of Sci. Tech Ctr., 367 F.3d 1359, 1364 (Fed. Cir. 2004) (citation omitted); In re Morris, 127 F.3d 1048, 1053-54 (Fed. Cir. 1997). The examiner finds that under broadest reasonable interpretations Tong in combination with Ando does disclose the limitations of Claim 1 as detailed in the arguments above and in the rejection below. The Examiner made a proper determination of obviousness under 35 U.S.C. §103, and also provided an appropriate supporting rationale in view of the decision by the Supreme Court in KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007). The Examiner’s rational are based on the Office’s current understanding of the law, and are believed to be fully consistent with the binding precedent of the Supreme Court. Furthermore, the Examiner supported the rejection under 35 U.S.C. §103 via making the clear articulation of the reason(s) why the claimed invention would have been obvious by citing the specific areas in the prior art references. Further the Examiner, clearly stating the modification of the inventions, supported the rejection under 35 U.S.C. §103 by making the analysis explicit. Last, the Examiner did not make conclusory statements. The Court quoting In re Kahn, 441 F.3d 977, 988, 78 USPQ2d 1329, 1336 (Fed. Cir. 2006), stated that “‘[R]ejections on obviousness cannot be sustained by mere conclusory statements; instead, there must be some articulated reasoning with some rational underpinning to support the legal conclusion of obviousness.’” KSR, 550 U.S. at, 82 USPQ2d at 1396. Therefore, the Examiner has established a proper 35 U.S.C. §103 rejection Tong in view of Ando which will be maintained, which is disclosed in detail below.
In response to Argument 9, see remarks filed 01/13/2026, the Examiner respectfully disagrees. In response to Argument that Claims 2, 3, 5, and 7 are patentable over any combination of Tong and Ando based at least on their dependency from claim 1, the Examiner respectfully disagrees. The examiner finds that Tong in combination with Ando does disclose the limitations of Claim 1 as detailed in the arguments above and in the rejection below. Therefore, the Examiner has established a proper 35 U.S.C. §103 rejection Tong in view of Ando for Claim 1 and its dependent claims and the rejection will be maintained below.
In response to Argument 10, see remarks filed 01/13/2026, the Examiner respectfully disagrees. In response to Argument that Claim 6 recited features generally corresponding to the above-noted features of claim 1. Applicant respectfully submits that any combination of Tong and Ando fails to disclose, suggest, or otherwise render obvious these corresponding features of claim 6 for reasons similar to those discussed above with respect to claim 1, and as such, claim 6 is patentable over any combination of Tong and Ando, the Examiner respectfully disagrees. We determine claim scope not solely on the basis of claim language, but also on giving claims their broadest reasonable construction in light of the specification as it would be interpreted by one of ordinary skill in the art. In re Am. Acad. of Sci. Tech. Ctr., 367 F.3d 1359, 1364 (Fed. Cir. 2004). See also Superguide Corp. v. DirecTV Enterprises, Inc., 358 F.3d 870, 875 (Fed. Cir. 2004) (“Though understanding the claim language may be aided by explanations contained in the written description, it is important not to import into a claim limitations that are not part of the claim.”). The examiner finds under broadest reasonable interpretation that Tong in combination with Ando does disclose the limitations of Claim 1 and therefore the limitations of Claim 6 as detailed in the arguments above and in the rejection below. Therefore, the Examiner has established a proper 35 U.S.C. §103 rejection Tong in view of Ando for Claim 6 and its dependent claims and will maintain this rejection.
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 1-3 and 5-7, are rejected under 35 U.S.C. 103 as unpatentable over Tong et al (US Patent Pub 2020/0074234 A1 hereafter referred to as Tong) in view of Ando (US Patent Pub 2016/0026900 A1hereafter referred to as Ando).
Regarding Claim 1, Tong teaches a training method (Tong ¶0159, ¶0177, ¶0053 discloses a training method) for generating a learning model (Tong Fig 8 and Fig 9, ¶0125, ¶0128 discloses noise trained CNN models) for use in image recognition (Tong ¶0002, discloses a network robust in the recognition of objects in images), the training method comprising:
generating a first image by adding noise (Tong ¶0075-¶0078 discloses generating a noisy training image) in an original image (Tong ¶0075-¶0077 discloses adding noise to the original image);
generating a second image by adding noise (Tong ¶0106, ¶0109, ¶0008 discloses generating a second set of noisy training images) in the original image (Tong ¶0075-¶0077 discloses adding noise to the original image);
generating a combined image (Tong ¶0013, ¶0064, disclose generating a combined noise image) by weighted addition (Tong ¶0015, ¶0086 discloses that each image is weighted before they are combined) of the first image (Tong ¶0075-¶0078 discloses generating a noisy training image) and the second image (Tong ¶0106, ¶0109, ¶0008 discloses generating a second set of noisy training images) at a first ratio (Tong ¶0074 discloses combining the images with varying SSNR values including 0.5);
generating a first training label (Tong ¶0032 discloses generating and applying labels for image categories) for the first image by weighted addition (Tong ¶0015, ¶0086 discloses that each image is weighted before they are combined) of a first base label corresponding to a correct label of the original image (Tong ¶0158 discloses the trained network correctly labeling the validation images compared to the untrained network) and a second base label corresponding to an incorrect label (Tong ¶0158 discloses correctly identifying if an image was labeled incorrectly) of the original image at a second ratio (Tong ¶0074 discloses combining the images with varying SSNR values including 0.5, 0.75 etc.);
generating a second training label (Tong ¶0032 discloses generating and applying labels for image categories) for the second image by weighted addition (Tong ¶0015, ¶0086 discloses that each image is weighted before they are combined) of the first base label and the second base label at an inverse ratio (Tong ¶0018 discloses inventing the noise ratio) of the second ratio (Tong ¶0074 discloses combining the images with varying SSNR values including 0.5, 0.75 etc.);
for the combined image (Tong ¶0013, ¶0064, disclose generating a combined noise image) by weighted addition (Tong ¶0015, ¶0086 discloses that each image is weighted before they are combined) of the first training label and the second training label (Tong ¶0032 discloses generating and applying labels for image categories) at the first ratio (Tong ¶0074 discloses combining the images with varying SSNR values including 0.5); and
generating the learning model by machine learning (Tong Fig 8 and Fig 9, ¶0125, ¶0128 discloses noise trained CNN models) using the combined image (Tong ¶0013, ¶0064, disclose generating a combined noise image).
Tong does not explicitly teach to a first area, to a second area that is an area excluding the first area, that is a ratio between a size of the first area and a size of the second area, generating a combined training label, and the combined training label.
Ando is in the same field of generating sets of training pattern. Further, Ando teaches to a first area (Ando Fig 3 discloses segmenting the original area into different areas including IM1), to a second area that is an area excluding the first area (Ando Fig 3 discloses segmenting the original area into different areas including IM1 which is different from IM2), that is a ratio between a size of the first area and a size of the second area (Ando ¶0056, ¶0159 discloses the ratio of the size of the large area in comparison to the objects in the other areas of the original image), generating a combined training label (Ando ¶0135 discloses a label for the combined images used for training), and the combined training label (Ando ¶0135 discloses a label for the combined images used for training).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Tong by incorporating different areas of the original image to be processed and using ratios to determine size, as well as combined labeling of the images as taught by Ando, to make an invention that can more robustly identify objects due to partial images being used in the training data; thus, one of ordinary skilled in the art would be motivated to combine the references since an object of the present invention is to increase the amount of training data available without increasing cost (Ando, ¶0008-¶0009).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 2, Tong in view of Ando teaches the training method according to claim 1, wherein a plurality of combined images (Tong ¶0013, ¶0064, disclose generating a combined noise image) and a plurality (Tong ¶0007, ¶0023-¶0025 discloses a plurality of images used) of combined training labels (Ando ¶0135 discloses a label for the combined images used for training) are generated by generating, for each of a plurality of first areas (Ando ¶0070, ¶0073, ¶0076, ¶0103 discloses segmenting the image into a plurality of areas), the first image (Tong ¶0075-¶0078 discloses generating a noisy training image), the second image (Tong ¶0106, ¶0109, ¶0008 discloses generating a second set of noisy training images), the combined image (Tong ¶0013, ¶0064, disclose generating a combined noise image), the first training label, the second training label, and the combined training label (Ando ¶0135 discloses a label for the combined images used for training), each of the plurality of combined images being the combined image (Tong ¶0013, ¶0064, disclose generating a combined noise image), each of the plurality of combined training labels (Ando ¶0102-¶0104, discloses applying labels to a plurality of images for training data) being the combined training label (Ando ¶0135 discloses a label for the combined images used for training), each of the plurality of first areas (Ando ¶0070-¶0073 discloses a plurality of areas) being the first area (Ando Fig 3 discloses segmenting the original area into different areas including IM1) , and
the learning model (Tong Fig 8 and Fig 9, ¶0125, ¶0128 discloses noise trained CNN models) is generated by machine learning (Tong ¶0006 discloses the formulation of a CNN/DNN) using the plurality of combined images (Tong ¶0013, ¶0064, disclose generating a combined noise image) and the plurality of combined training labels (Ando ¶0135 discloses a label for the combined images used for training). See Claim 1 for rationale, its parent claim.
Regarding Claim 3, Tong in view of Ando teaches the training method according to claim 1, wherein
a plurality of combined images (Tong ¶0013, ¶0064, disclose generating a combined noise image) and a plurality of combined training labels (Ando ¶0102-¶0104, discloses applying labels to a plurality of images for training data) are generated by generating the combined image (Tong ¶0013, ¶0064, disclose generating a combined noise image) and the combined training label (Ando ¶0135 discloses a label for the combined images used for training) at each of a plurality of first ratios (Tong ¶0074 discloses combining the images with varying SSNR values including 0.5, 0.75 etc.), each of the plurality (Tong ¶0007, ¶0023-¶0025 discloses a plurality of images used) of combined images being the combined image (Tong ¶0013, ¶0064, disclose generating a combined noise image), each of the plurality of combined training labels (Ando ¶0102-¶0104, discloses applying labels to a plurality of images for training data) being the combined training label(Ando ¶0135 discloses a label for the combined images used for training), each of the plurality of first ratios (Tong ¶0074 discloses combining the images with varying SSNR values including 0.5, 0.75 etc.) being the first ratio (Tong ¶0074 discloses combining the images with varying SSNR values including 0.5), and
the learning model (Tong Fig 8 and Fig 9, ¶0125, ¶0128 discloses noise trained CNN models) is generated by machine learning (Tong ¶0006 discloses the formulation of a CNN/DNN) using the plurality of combined images (Tong ¶0013, ¶0064, disclose generating a combined noise image) and the plurality of combined training labels (Ando ¶0135 discloses a label for the combined images used for training). See Claim 1 for rationale, its parent claim.
Regarding Claim 5, Tong in view of Ando teaches the training method according to claim 1, wherein the first ratio (Tong ¶0074 discloses combining the images with varying SSNR values including 0.5) is determined in accordance with a beta distribution of B(a, a) (Tong Fig 4C discloses the beta distribution of the test SSNR and accuracy), where B denotes a beta function (Tong Fig 4C discloses the beta distribution of the test SSNR and accuracy), and a denotes a positive real number (Tong Fig 4C discloses the beta distribution of the test SSNR (0 to 1) and accuracy (0 to 100)). See Claim 1 for rationale, its parent claim.
Regarding Claim 6, Tong teaches a training device (Tong ¶0177 discloses a computing device where the training is preformed) that generates a learning model (Tong Fig 8 and Fig 9, ¶0125, ¶0128 discloses noise trained CNN models) for use in image recognition (Tong ¶0002, discloses a network robust in the recognition of objects in images), the training device comprising:
a processor (Tong ¶0179, ¶0183 discloses a processor); and
memory (Tong ¶0070, ¶179 discloses a memory) wherein
using the memory, the processor (Tong Fig 16 the dashed line discloses the processor and memory bring linked):
generating a first image by adding noise (Tong ¶0075-¶0078 discloses generating a noisy training image) in an original image (Tong ¶0075-¶0077 discloses adding noise to the original image);
generating a second image by adding noise (Tong ¶0106, ¶0109, ¶0008 discloses generating a second set of noisy training images) in the original image (Tong ¶0075-¶0077 discloses adding noise to the original image);
generating a combined image (Tong ¶0013, ¶0064, disclose generating a combined noise image) by weighted addition (Tong ¶0015, ¶0086 discloses that each image is weighted before they are combined) of the first image (Tong ¶0075-¶0078 discloses generating a noisy training image) and the second image (Tong ¶0106, ¶0109, ¶0008 discloses generating a second set of noisy training images) at a first ratio (Tong ¶0074 discloses combining the images with varying SSNR values including 0.5);
generating a first training label (Tong ¶0032 discloses generating and applying labels for image categories) for the first image by weighted addition (Tong ¶0015, ¶0086 discloses that each image is weighted before they are combined) of a first base label corresponding to a correct label of the original image (Tong ¶0158 discloses the trained network correctly labeling the validation images compared to the untrained network) and a second base label corresponding to an incorrect label (Tong ¶0158 discloses correctly identifying if an image was labeled incorrectly) of the original image at a second ratio (Tong ¶0074 discloses combining the images with varying SSNR values including 0.5, 0.75 etc.);
generating a second training label (Tong ¶0032 discloses generating and applying labels for image categories) for the second image by weighted addition (Tong ¶0015, ¶0086 discloses that each image is weighted before they are combined) of the first base label and the second base label at an inverse (Tong ¶0018 discloses inventing the noise ratio) ratio of the second ratio (Tong ¶0074 discloses combining the images with varying SSNR values including 0.5, 0.75 etc.);
for the combined image (Tong ¶0013, ¶0064, disclose generating a combined noise image) by weighted addition (Tong ¶0015, ¶0086 discloses that each image is weighted before they are combined) of the first training label and the second training label (Tong ¶0032 discloses generating and applying labels for image categories) at the first ratio (Tong ¶0074 discloses combining the images with varying SSNR values including 0.5); and
generating the learning model by machine learning (Tong Fig 8 and Fig 9, ¶0125, ¶0128 discloses noise trained CNN models) using the combined image (Tong ¶0013, ¶0064, disclose generating a combined noise image).
Tong does not explicitly teach to a first area, to a second area that is an area excluding the first area, that is a ratio between a size of the first area and a size of the second area, generating a combined training label, and the combined training label.
Ando is in the same field of generating sets of training pattern. Further, Ando teaches to a first area (Ando Fig 3 discloses segmenting the original area into different areas including IM1), to a second area that is an area excluding the first area (Ando Fig 3 discloses segmenting the original area into different areas including IM1 which is different from IM2), that is a ratio between a size of the first area and a size of the second area (Ando ¶0056, ¶0159 discloses the ratio of the size of the large area in comparison to the objects in the other areas of the original image), generating a combined training label (Ando ¶0135 discloses a label for the combined images used for training) and the combined training label (Ando ¶0135 discloses a label for the combined images used for training).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Tong by incorporating different areas of the original image to be processed and using ratios to determine size, as well as combined labeling of the images as taught by Ando, to make an invention that can more robustly identify objects due to partial images being used in the training data; thus, one of ordinary skilled in the art would be motivated to combine the references since an object of the present invention is to increase the amount of training data available without increasing cost (Ando, ¶0008-¶0009).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 7, Tong in view of Ando teaches a non-transitory computer-readable recording medium (Tong ¶0034, ¶0036 discloses a non-transitory computer readable medium) having recorded thereon a computer program (Tong ¶0182- ¶0183 discloses a computer being programed or loaded with instructions or program code) for causing a computer to execute (Tong ¶0181 discloses a computer device providing instructions to be executed) the training method according to claim 1. See Claim 1 for rationale, its parent claim.
Allowable Subject Matter
Claim 4 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form and was overcome including all of the limitations of the base claim and any intervening claims.
Conclusion
37. THIS ACTION IS MADE FINAL. 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.
39. Any inquiry concerning this communication or earlier communications from the examiner should be directed to RACHEL LYNN ROBERTS whose telephone number is (571)272-6413. The examiner can normally be reached Monday- Friday 7:30am- 5:00pm. 32. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. 33. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Oneal Mistry can be reached on 313-446-4912. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. 34. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/RACHEL L ROBERTS/Examiner, Art Unit 2674
/ONEAL R MISTRY/Supervisory Patent Examiner, Art Unit 2674