DETAILED ACTION
Contents
Notice of Pre-AIA or AIA Status 2
Claim Rejections - 35 USC § 101 2
Claim Rejections - 35 USC § 103 3
Conclusion 8
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 .
This action is responsive to applicant’s claim set received on 4/2/24. Claims 1-4 are currently pending.
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 1-4 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter as follows. In particular, claims 1 and 4 are directed to an abstract idea because the claims merely receive organizing, transforming and analyzing image data to produce a trained model. The claims do not recite additional elements nor integrate into a practical application.
For claim 2, the claim is also directed to an abstract idea with no incorporation of a practical idea and no inventive concept. Claims 3 is also directed to an abstract idea, with no practical application and no inventive concept.
Thus, all claims 1-4 are considered to be non-statutory subject matter.
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 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 claimedinvention 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 1-4 is rejected under 35 U.S.C. 103 as being unpatentable over Cooper et al (US 2022/0180130 A1) in view of Honzatko et al (MVA: “Defect segmentation for multi-illumination quality control systems”).
Regarding claim 1, Cooper teaches a machine learning device comprising: one or more processors (see 0008; processors); and one or more memories coupled to the one or more processors, wherein the one or more processors are configured to cooperate with a program in the one or more memories to execute a process comprising (see 0008; one or more processors and non-transitory computer storage media storing instructions that when executed by the one or more processors, cause the processors to perform operations comprising): acquiring image combinations multiple times (see 0007, 0024; identifying a set of images captured by a set of cameras while affixed to one or more image collection systems); performing a manipulation process that includes manipulating the brightness images (see 0007, 0030-0032; generating an augmented image for a set of augmented images by modifying the image with an image manipulation function), and
generating a machine learning model configured to receive the manipulated brightness images and the training image as input (see 0007; training the set of parameters of the predictive computer model to predict the training output based on an image training set including the images and the set of augmented images), and
wherein the manipulation process comprises a noise addition process that includes adding partial noise to different positions in the brightness images in an image combination of the image combinations (see 0019, 0034-0037; images may be augmented with a “cutout” function that removes a portion of the original image. The removed portion of the image may then be replaced with other image content, such as a specified color, blur, noise, or from another image. The number, size, region, and replacement content for cutouts may be varied and may be based on the label of the image (e.g., the region of interest in the image, or a bounding box for an object). Cooper does not teach expressly each of the image combinations including brightness images captured at different imaging positions and a training image associated with one of the brightness images.
Honzatko, in the same field of endeavor, teaches each of the image combinations including brightness images (see section 3; luminance channels) captured at different imaging positions (see section 3, under different lighting conditions) and a training image associated with one of the brightness images (see section 4; ground-truth binary mask representing the defects).
It would have been obvious (before the effective filing date of the claimed invention) or (at the time the invention was made) to one of ordinary skill in the art to modify Cooper to utilize the cited limitations as suggested by Honzatko. The suggestion/motivation for doing so would have been to enhance performance increase (see abstract). Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in the manner explained above using known engineering design, interface and/or programming techniques, without changing a “fundamental” operating principle of Cooper, while the teaching of Honzatko continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Regarding claim 2, Cooper teaches a scale change process that includes changing scales of the brightness images, and wherein the one or more processors are configured to execute, in the manipulation process, the noise addition process after the scale change process (see 0004-0005, 0030-0033).
Regarding claim 3, Cooper with Honzatko teaches all elements as mentioned above in claim 1. Cooper with Honzatko does not teach expressly a scale change process that includes changing scales of the brightness images under a condition that the brightness images have a same scale factor and a same disposition.
Honzatko, in the same field of endeavor, teaches a scale change process that includes changing scales of the brightness images under a condition that the brightness images have a same scale factor and a same disposition (see section 3.1, 3.2).
It would have been obvious (before the effective filing date of the claimed invention) or (at the time the invention was made) to one of ordinary skill in the art to modify Cooper with Honzatko to utilize the cited limitations as suggested by Honzatko. The suggestion/motivation for doing so would have been to enhance performance increase (see abstract). Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in the manner explained above using known engineering design, interface and/or programming techniques, without changing a “fundamental” operating principle of Cooper with Honzatko, while the teaching of Honzatko continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Regarding claim 4, Cooper teaches a vehicle comprising a machine learning model (see 0024, 0025; vehicle… image collection system 140 may be manually operated or may be operated be a partially- or fully-automated vehicle) to be obtained by a processor configured to cooperate with a program in a memory to execute a process (see 0008; one or more processors and non-transitory computer storage media storing instructions that when executed by the one or more processors, cause the processors to perform operations comprising) comprising acquiring image combinations multiple times (see 0007, 0024; identifying a set of images captured by a set of cameras while affixed to one or more image collection systems), performing a noise addition process that includes adding partial noise to different positions in the brightness images in an image combination of the combinations (see 0019, 0034-0037; images may be augmented with a “cutout” function that removes a portion of the original image. The removed portion of the image may then be replaced with other image content, such as a specified color, blur, noise, or from another image. The number, size, region, and replacement content for cutouts may be varied and may be based on the label of the image (e.g., the region of interest in the image, or a bounding box for an object), and generating the machine learning model configured to receive the manipulated brightness images and the training image as input (see 0007; training the set of parameters of the predictive computer model to predict the training output based on an image training set including the images and the set of augmented images). Cooper does not teach expressly each of the image combinations including brightness images captured at different imaging positions and a training image associated with one of the brightness images.
Honzatko, in the same field of endeavor, teaches each of the image combinations including brightness images (see section 3; luminance channels) captured at different imaging positions (see section 3, under different lighting conditions) and a training image associated with one of the brightness images (see section 4; ground-truth binary mask representing the defects).
It would have been obvious (before the effective filing date of the claimed invention) or (at the time the invention was made) to one of ordinary skill in the art to modify Cooper to utilize the cited limitations as suggested by Honzatko. The suggestion/motivation for doing so would have been to enhance performance increase (see abstract). Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in the manner explained above using known engineering design, interface and/or programming techniques, without changing a “fundamental” operating principle of Cooper, while the teaching of Honzatko continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Conclusion
Claims 1-4 are rejected.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EDWARD PARK. The examiner’s contact information is as follows:
Telephone: (571)270-1576 | Fax: 571.270.2576 | Edward.Park@uspto.gov
For email communications, please notate MPEP 502.03, which outlines procedures pertaining to communications via the internet and authorization. A sample authorization form is cited within MPEP 502.03, section II.
The examiner can normally be reached on M-F 9-6 CST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Moyer, can be reached on (571) 272-9523. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/EDWARD PARK/
Primary Examiner, Art Unit 2666