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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/05/2025 has/have been considered by the examiner.
Response to Amendment
Applicant’s amendments filed on 02/09/2026 to the claims have overcome claim rejections under 35 U.S.C. 101 and prior art rejections as preciously set forth in the Non-Final Rejection Office Action mailed on 09/08/2025.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-6, 8-13, and 15-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Cruciata et al (Journal of Imaging, 8(3), 61, 2022), hereinafter Cruciata.
-Regarding claim 1, Cruciata discloses one or more processors comprising circuitry (one or more processors has to be used in order to implement the system shown in Cruciata‘s FIG. 4 and Algorithm 1) to use one or more neural networks to generate a modified bounding box by at least (Abstract; Page 5, Sec. 3; Algorithm 1; FIGS. 1-6
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): receiving an input of one or more bounding boxes corresponding to an object within a first image (FIG. 4, image
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; page 3, last paragraph) and one or more target bounding boxes corresponding to an object within a second image (FIG. 4, image
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); and predicting, using the one or more neural networks (FIG. 4, CNN), one or more parameters of the one or more bounding boxes (FIG. 4, image
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; Page 8, Sec. 4.1, 1st paragraph, “each basic transformation depends on parameters calculated from the size of the current bounding box”).
-Regarding claim 8, Cruciata discloses a system comprising (Abstract; Page 5, Sec. 3; Algorithm 1; FIGS. 1-6): one or more processors to use one or more neural networks to generate a modified bounding box by at least (FIG. 4; Algorithm 1; one or more processors has to be used in order to implement the system shown in Cruciata‘s FIG. 4 and Algorithm 1): receiving an input of one or more bounding boxes corresponding to an object within a first image (FIG. 4, image
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, bounding box
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; page 3, last paragraph) and one or more target bounding boxes corresponding to an object within a second image (FIG. 4, image
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); and predicting, using the one or more neural networks (FIG. 4, CNN), one or more parameters of the one or more bounding boxes (FIG. 4, image
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).to modify based on one or more parameters of the one or more target bounding boxes (FIG. 4, transformation
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; Page 8, Sec. 4.1, 1st paragraph, “each basic transformation depends on parameters calculated from the size of the current bounding box”).
-Regarding claim 15, Cruciata discloses a method comprising (Abstract; Page 5, Sec. 3; Algorithm 1; FIGS. 1-6): using one or more neural networks to generate a modified bounding box by at least (FIG. 4; Algorithm 1): receiving an input of one or more bounding boxes corresponding to an object within a first image (FIG. 4, image
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t
-
1
, bounding box
b
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-
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k
; page 3, last paragraph) and one or more target bounding boxes corresponding to an object within a second image (FIG. 4, image
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, bounding box
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); and predicting, using the one or more neural networks (FIG. 4, CNN), one or more parameters of the one or more bounding boxes (FIG. 4, image
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; Page 8, Sec. 4.1, 1st paragraph, “each basic transformation depends on parameters calculated from the size of the current bounding box”).
-Regarding claims 2 and 16, Cruciata discloses the processor of claim 1, and the method of claim 15. Cruciata further discloses wherein the one or more target bounding boxes comprise a modified bounding box or an unmodified bounding box (FIG. 4).
-Regarding claim 3, Cruciata discloses the processor of claim 1. Cruciata further discloses to generate a confidence score to indicate whether to use the modified bounding box as data to train one or more second neural networks (FIG. 4; Page 7, Sec. 4., 1st paragraph, “The last output branch network, namely, the subnet Confidence-Net, provides a confidence score …”; Algorithm 1; Page 9, Sec. 4.3.).
-Regarding claim 4, Cruciata discloses the processor of claim 1. Cruciata further discloses wherein the one or more circuits are to use the modified bounding box as input to train one or more second neural networks to perform object detection (FIG. 4; Algorithm 1; Page 9, Sec. 4.3.).
-Regarding claim 5, Cruciata discloses the processor of claim 1. Cruciata further discloses wherein the one or more neural networks is to be trained based, at least in part, on one or more unmodified bounding boxes and one or more pseudo-labels (FIGS. 2, 4; Algorithm 1; Page 9, Sec. 4.3.).
-Regarding claim 6, Cruciata discloses the processor of claim 1. Cruciata further discloses to adjust a size and/or position of the target bound box to match a size and/or position of the one or more bounding boxes (FIG. 4; Page 8, Sec. 4.1., 1st paragraph, “… size … shifts …”).
-Regarding claim 9, Cruciata discloses the system of claim 8. Cruciata further discloses to adjust a bounding box identified within the first image to generate the modified bounding box (FIG. 4).
-Regarding claim 10, Cruciata discloses the system of claim 8. Cruciata further discloses to remove the modified bounding box and an unmodified bounding box if the modified bounding box is assigned a score that does not exceed a predetermined threshold (FIG. 4; Algorithm 1).
-Regarding claim 11, Cruciata discloses the system of claim 8. Cruciata further discloses to keep the modified bounding box if the modified bounding box is assigned a score that meets or exceeds a predetermined threshold FIG. 4; Algorithm 1).
-Regarding claim 12, Cruciata discloses the system of claim 8. Cruciata further discloses to use the generated modified bounding box to train one or more second neural network to infer one or more object (FIG. 4; Algorithm 1; Page 9, Sec. 4.3.).
-Regarding claim 13, Cruciata discloses the system of claim 8. Cruciata further discloses to generate the modified bounding box by adjusting a size of a bounding box to meet a size of the one or more target bounding boxes (FIG. 4; Algorithm 1).
-Regarding claim 17, Cruciata discloses the method of claim 15. Cruciata further discloses comprising using the modified bounding box as a result of a confidence score satisfying a set of conditions (FIG. 4; Algorithm 1; Page 9, Sec. 4.3.).
-Regarding claim 18, Cruciata discloses the method of claim 15. Cruciata further discloses comprising using one or more unmodified bounding boxes and the one or more modified bounding box to train one or more second neural networks to perform object detection ((FIG. 4; Algorithm 1; Page 4, Sec. 2.1., last paragraph, “Our work focuses on tracking-by-detection and regression … at each iteration, the model has to decide whether to regress the bounding box (by applying discrete transformations) or re-detect the target (by classification of multiple candidate bounding boxes)”).
-Regarding claim 19, Cruciata discloses the method of claim 15. Cruciata further discloses generating the modified bounding box by adjusting a size of a bounding box to include the entirety of the object in the image (FIG. 4; Algorithm 1; Page 3, last paragraph, “… the bounding box enclosing the target on the image.”; Page 4, Sec. 2.1., last paragraph).
-Regarding claim 20, Cruciata discloses the method of claim 15. Cruciata further discloses using the one or more neural networks to adjust a bounding box associated with a label in an image to match a bounding box of the one or more target bounding boxes associated with the same label (FIGS. 2, 4; Algorithm 1; Page 9, Sec. 4.3.).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 7 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over by Cruciata et al (Journal of Imaging, 8(3), 61, 2022), hereinafter Cruciata in view of Rajaram et al (IEEE Transactions on Intelligent Vehicles, 2016), hereinafter Rajaram.
-Regarding claim 7, Cruciata discloses the processor of claim 1.
Cruciata does not disclose to cause an autonomous vehicle to use the one or more neural networks to detect one or more objects. However, a person of ordinary skills in the art would understand that Fang’s method can be applied to object detection for many practical applications including autonomous vehicles.
In the same field of endeavor, Rajaram teaches a method for improving object localization with a deep convolutional neural network (Rajaram: Abstract; FIGS. 1-7). Rajaram further teaches refining object detectors with bounding box regression for autonomous driving (Rajaram: FIGS. 1-2; Sec. III).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Cruciata with the teaching of Rajaram by using the one or more neural networks to detect one or more objects for autonomous vehicle in order to provide a real-world application and improve the accuracy of object detection.
-Regarding claim 14, Cruciata discloses the system of claim 8
Cruciata does not disclose to generate a modified bounding box to infer one or more objects in a driving environment. In the same field of endeavor, Rajaram teaches a method for improving object localization with a deep convolutional neural network (Rajaram: Abstract; FIGS. 1-7). Rajaram further teaches generating a modified bounding box to infer one or more objects in a driving environment (Rajaram: FIGS. 1-2; Sec. III).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Cruciata with the teaching of Rajaram by using the one or more neural networks to detect one or more objects for autonomous vehicle in order to provide a real-world application and improve the accuracy of object detection.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Applicant's amendment necessitated the 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.
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/XIAO LIU/Primary Examiner, Art Unit 2664
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