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 .
DETAILED ACTION
INFORMATION DISCLOSURE STATEMENT
The information disclosure statement (IDS) submitted on 6/11/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 §§ 706.02(l)(1) - 706.02(l)(3) 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).
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Claims 1-19 are rejected on the ground of nonstatutory double patenting as being unpatentable over U.S. Patent 12,039,772
As to claims 1, 8 & 14, instant application discloses a non-transitory storage medium storing a program that causes a computer to: generate, from a first image generated by capture using a first lens, a plurality of sample images being each associated with a partial region of the first image; estimate a content for each of the sample images by using an estimation model, the estimation model being generated by training a second image generated by capture using a second lens differing in characteristic from the first lens and a label indicating a content of the second image; estimate, based on the estimated content for each of the sample images, a relative positional relationship of a plurality of the sample images in the first image for learning; and correct a value of a parameter of the estimation model in response to the relative positional relationship being incorrect.(U.S. Patent 12,039,772 - Claim 8)
As to claims 2-7, 9-13 & 15-19, these claims are rejected due to their dependence on claims 1, 8 & 14 and are rejected for the same reasons.
CLAIM REJECTIONS - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-6 & 8-19 are rejected under 35 U.S.C. 103 as being unpatentable over Yu et al. (U.S. Publication 2017/0039457) in view of Chen et al. (U.S. Publication 2021/0012110) & Zellers, (“Neural Motifs: Scene Graph Parsing with Global Context”, CVPR – 2018).
As to claims 1, 8 & 14 Yu discloses a non-transitory storage medium storing a program that causes a computer to ([0011] discloses non-transitory, tangible computer readable storage medium on which computer readable instructions of a program are stored. Each window position may be considered): generate, from a first image generated by capture using a first lens, a plurality of sample images being each associated with a partial region of the first image;
([0002] discloses receiving using the one or more computing devices, a first image. [0039] discloses each window position may be considered a crop of the image. [0040] discloses a bounding box may be a rectangle on an image identifying a portion of the image therein. Image 420 may be panoramic images up to 360 degrees. [0048] discloses the images 420 may be panoramic images or image having fields of view greater than 180 degrees up to 360 degrees.)
estimate a content for each of the sample images by using an estimation model
([0048] discloses after being trained, the deep neural network 310 may be evaluate one or more images 420. Also see wherein discloses using the deep neural network 310 may evaluate one or more images to identify features of the images. [0049] discloses the deep neural network 310 may generate a second plurality of bounding boxes identifying possible business storefront locations. See wherein each bounding box 440 may be associated with a confidence score 450 representing a likelihood that each bounding box contains an image of a business storefront.),
the estimation model being generated by training a second image generated by capture using a second lens differing in characteristic from the first lens and a label indicating a content of the second image. ([0002] discloses training using one or more computing devices, a deep neural network using a set of training images and data identifying one or more business storefront locations in the training images. [0038] discloses training images associated with storefront information. [0038] discloses storefront information including ranges of pixels.);
Yu is silent to the second lens differing in characteristics from the first lens; estimate, based on the estimated content for each of the sample images, a relative positional relationship of a plurality of the sample images in the first image for learning; and correct a value of a parameter of the estimation model in response to the relative positional relationship being incorrect.
However, Chen discloses the second lens differing in characteristics from the first lens. ([0004] discloses normal images generated from a camera with a normal lens and fisheye images generated from a camera with a fisheye lens. [0024] discloses the object detection process is adaptively adjusted in response to the lens configuration information.])
It would have been obvious to one of ordinary skill in the art at the time of effective filing to modify Yu’s disclosure to include the above limitations in order to perform object detection upon the captured image according to the bounding box distribution [0006].
Yu in view of Chen is silent to estimate, based on the estimated content for each of the sample images, a relative positional relationship of a plurality of the sample images in the first image for learning; and correct a value of a parameter of the estimation model in response to the relative positional relationship being incorrect.
However, Zellers discloses estimate, based on the estimated content for each of the sample images, a relative positional relationship of a plurality of the sample images in the first image for learning; (Section 4.1 discloses for each image I, the detector predicts a set of region proposals B = {b1,…bn}. Section 4.2 discloses we use an LSTM to decode a category label for each contextualized representation. Abstract discloses object labels are highly predictive of relation labels. Given object detections, predict the most frequent relation between object pairs with the given labels. Section 3.1 discloses the predominant relations are geometric and possessive.) Correcting a value of a parameter of the estimation model being incorrect. (Section 5.2 discloses we optimize using SGD with momentum.)
It would have been obvious to one of ordinary skill in the art at the time of effective filing to modify Yu in view of Chen’s disclosure to include the above limitations in order to predict the most frequent relation between object pairs with the given labels.
As to claims 2, 9 & 15, Yu in view of Chen & Zellers discloses everything as disclosed in claims 1, 8 & 14 but is silent to wherein the estimated content is presented as a label.
However, Zellers discloses wherein the estimated content is presented as a label.
(Section 2, Formal Definition, Page 5832, Left Column discloses a corresponding set O = {o1,….0n} of objects, assigning a class label. Abstract discloses object labels are highly predictive of relation labels but not vice versa. )
It would have been obvious to one of ordinary skill in the art at the time of effective filing to modify Yu in view of Chen & Zellers’s disclosure to include the above limitations in order to
As to claims 3, 10 & 16, Yu in view of Chen & Zellers discloses everything as disclosed in claims 1, 8 & 14 but is silent to wherein 0 the processor is further configured to execute the one or more instructions to correct a value of a parameter of the estimation model, based on a stochastic gradient descent method.
However, Zellers discloses wherein 0 the processor is further configured to execute the one or more instructions to correct a value of a parameter of the estimation model, based on a stochastic gradient descent method. (5.2 Training, Page 6 Left Column discloses our loss is the sum of the cross entropy for predicates and cross entropy for objects. We optimize using SGD with momentum.)
It would have been obvious to one of ordinary skill in the art at the time of effective filing to modify Yu in view of Chen & Zellers’s disclosure to include the above limitations in order to predict object labels and to predict edge labels.
As to claims 4, 11 & 17, Yu in view of Chen & Zellers discloses everything as disclosed in claims 1, 8 & 14 but is silent to wherein the processor is further configured to execute the one or more instructions to iteratively execute the generating a plurality of sample images; the estimating a content for each of the sample images, the estimating the relative positional relationship of a plurality of the sample images, and the correcting the value of the parameter of the estimation model, until the estimation result of the relative positional relationship satisfies an end condition.
However, Zellers discloses wherein the processor is further configured to execute the one or more instructions to iteratively execute the generating a plurality of sample images; the estimating a content for each of the sample images, the estimating the relative positional relationship of a plurality of the sample images, and the correcting the value of the parameter of the estimation model, until the estimation result of the relative positional relationship satisfies an end condition. (5.2 Training, Page 6 left column discloses We optimize using SGD with momentum. See finetune the model until detection convergence.)
It would have been obvious to one of ordinary skill in the art at the time of effective filing to modify Yu in view of Chen & Zellers’s disclosure to include the above limitations in order to finetune the model until detection convergence.
As to claims 5, 12 & 18, Yu in view of Chen & Zellers discloses everything as disclosed in claims 1, 8 & 14 but is silent to wherein the first lens is a fish-eye lens, and the second lens is a lens differing from a fish-eye lens.
However, Chen discloses wherein the first lens is a fish-eye lens, and the second lens is a lens differing from a fish-eye lens. ([0004] discloses normal images generated from a camera with a normal lens. Fisheye images generated from a camera with a fisheye lens.)
It would have been obvious to one of ordinary skill in the art at the time of effective filing to modify Yu in view of Chen & Zellers’s disclosure to include the above limitations in order to perform object detection upon the captured image according to the bounding box distribution.
As to claims 6, 13 & 19, Yu in view of Chen & Zellers discloses everything as disclosed in claims 5, 12 & 18. In addition, Yu discloses each window position may be considered a crop of the image ([0039]).
Yu in view of Chen & Zellers is silent to wherein the processor is further configured to execute the one or more instructions to extract, as the sample image, a partial region in a panoramic image for learning resulting from plane development of the first image for learning generated by capture using a fish-eye lens. ([0003 discloses fisheye lens. Converted into an equi-rectangular (ERP) image. [0030] discloses one bounding box is selected for the image height in the ERP domain. )
It would have been obvious to one of ordinary skill in the art at the time of effective filing to modify Yu in view of Chen & Zellers’s disclosure to include the above limitations in order to perform object detection upon the captured image according to the bounding box distribution.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Yu et al. (U.S. Publication 2017/0039457) in view of Chen et al. (U.S. Publication 2021/0012110) & Zellers, (“Neural Motifs: Scene Graph Parsing with Global Context”, CVPR – 2018) as applied in claim 6 above further in view of WIERZYNSKI (U.S. Publication 2017/0024641)
As to claim 7, Yu in view of Chen & Zellers discloses everything as disclosed in claim 6. In addition, Chen discloses fisheye images generated from a camera with a fisheye lens ([0004]). Converted into an equi-rectangular projection (ERP) image ([0003]). Zellers discloses assigning a class label (Section 2, Formal definition, Page 2, Left Column.)
However, WIERZYNSKI discloses the first network has been previously trained on first labels for first data ([0010]). Training a second network on the second labels and the second data ([0014]). Transfer the learning of a first neural network to a second neural network. ([0034]).
It would have been obvious to one of ordinary skill in the art at the time of effective filing to modify Yu in view of Chen & Zellers’s disclosure to include the above limitations in order to transfer the learning of a first neural network to a second neural network.
CONCLUSION
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Stephen P. Coleman
Primary Examiner
Art Unit 2675
/STEPHEN P COLEMAN/Primary Examiner, Art Unit 2675
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