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 .
1. This action is responsive to communications: Amendment, filed on 03/20/2026
This action is made FINAL.
2. Claims 1-20 are pending in the case. Claims 1, 13 and 17 are independent claims. Claims 1-2, 11, 13, 16-17 and 19 have been amended.
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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-2, 5 and 7-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tomomi Hishinuma et al., US 2020/0151450 A1, and further in view of Adam Turkelson et al., US 2021/0004589 A1.
Independent claim 1, Hishinuma discloses a method, comprising:
creating a training set for training a machine learning algorithm of a machine vision system that detects objects in an environment, the training set including multiple images of an object (i.e. capture and store images for inspection – Para 58; Fig. 4); and
applying one or more training set augmentations to each of a plurality of images included in the multiple images of the object to generate additional images that include the object for inclusion in the training set, wherein the one or more training set augmentations include an object motion augmentation, a camera motion augmentation, an object clumping augmentation, an object size reduction augmentation, a first diversified background augmentation (i.e. insert an AR tag to display a modified background of an image of an object, e.g. component/instrument– Fig. 11), or a second diversified background augmentation with one or more synthetic background images.
Hishinuma fails to disclose generate additional images that include the object for inclusion in the training set to synthetically increase a size of the training set, which Turkelson discloses (i.e. Generally, the more images that are submitted for a training data set including images depicting a given object, the more accurate the object recognition model may become at identifying images that include the object ….3) add the image, the feature vector, or both the image and the feature vector, to a training data set with a label identifying the drill to be used in a subsequent training operation by which a computer vision object recognition model is updated or otherwise formed – Para 154; augment a frame determined to include an object to be added to a training data set – Para 252).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention at the time the invention was made to combine Turkelson’s method of generating additional image for inclusion in the training set to synthetically increase a size of the training set with the method Hishinuma because each is directed to augmenting an image depicting an object, which enables a system including a training data set to execute an image capture task that generates images of an object from a particular perspective, to provide the benefit of obtaining an image that is absent from the training data set (Turkelson, Para 251).
Claim 2, Hishinuma discloses the method of claim 1.
Turkelson discloses wherein the applying the object motion augmentation or the camera motion augmentation to generate an additional image that includes the object comprises blurring image pixels of an image that comprises the object (i.e. if the image-capture task relates to obtaining images depicting an object from multiple perspectives (e.g., to complete a training data set missing images of the object from multiple perspectives), then the K salient frames may each correspond to the object depicted in one of the perspectives – Para 255; the Blur score may indicate an amount of “blurring” captured within a given frame – Para 257; images having a blur score above a threshold may be a salient frame – Para 259), which Hishinuma fails to disclose.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention at the time the invention was made to include Turkelson’s method wherein the applying the object motion augmentation or the camera motion augmentation to generate an additional image that includes the object comprises blurring image pixels of an image that comprises the object with the method Hishinuma because using video analysis that includes capturing frames to complete a training data set enables the use of blur to further identify salient images that provide the benefit of identifying image frames including objects depicted in a desired perspective (Turkelson, Para 25).
Claim 5, Hishinuma discloses the method of claim 1, wherein the applying the first diversified background augmentation to generate an additional image that includes the object comprises inserting image pixels in an image that corresponds to the object into the additional image that includes background clutter (i.e. visual presentation includes inserted AR tags that correspond to the recognized object, e.g. instrument – Fig. 11 “49”).
Claim 7, Hishinuma discloses the method of claim 1, wherein the creating the training set includes:
capturing the multiple images of the object using at least one of a variety of different cameras, different camera angles, different distances of the different cameras from the object, different light conditions, and different image backgrounds (i.e. capture instrument/component using different cameras – Para 147);
labeling the object as captured in the multiple images with corresponding labels by at least segmenting the object in each of the multiple images from a corresponding background based on an inputted polygon with a perimeter that corresponds to one or more boundaries of the object and associate the object that is segmented with a corresponding label (i.e. classify recognized images captured via camera – Para 262 -using visual recognition of a bounded region of an instrument/component – Fig. 8, 10);
annotating each of the multiple images with additional annotating information about the object (i.e. insert AR tags that correspond to the recognized object, e.g. instrument – Fig. 11 “49”);
compiling the multiple images of the object, the corresponding labels, and the additional annotation information into the training set for training the machine learning algorithm of the machine vision system (i.e. management database maintains image data, AR support information and image classification information – Fig. 4 – for use in AI analysis – Para 248, 255).
Claim 8, Hishinuma discloses the method of claim 7, wherein the corresponding label of the object in an image of the multiple images is a label from a structured knowledge representation, the structured knowledge representation includes labels that are members of multiple object classes (i.e. management database maintains image data, AR support information and image classification information – Fig. 4 – for use in AI analysis – Para 248, 255).
Claim 9, Hishinuma discloses the method of claim 1, wherein the machine vision system is used by an augmented reality procedural guidance system to guide an operator in completing one or more steps for one or more objects using an augmented reality environment (i.e. the system provides information for completing maintenance work – Para 180 – and display support information, e.g. AR tags – Para 29; Fig. 11).
Claim 10, Hishinuma discloses the method of claim 1, wherein the machine learning algorithm includes a neural network (i.e. the system includes a neural network – Para 256).
Claim 11, Hishinuma discloses the method of claim 1, further comprising:
training the machine vision system to recognize optically distinguishable markers (i.e. using VSLAM to recognize markers – Para 40, 55); and
associating the optically distinguishable markers with particular objects in a knowledge base or a structured knowledge representation (i.e. markers are associated objects and stored in a management database – Para 3; Fig. 4); and
recognizing, at least via the machine vision system, an additional object in the environment as a particular object based at least on an optically distinguishable marker that is affixed to the additional object and an association of the particular object with the optically distinguishable marker in the knowledge base or the structure knowledge representation (i.e. automatically recognize the objects, e.g. instrument/components, in the environment using the marker data - Para 32, 47 – identified using machine vision – Para 40, 248, 255).
Claim 12, Hishinuma discloses the method of claim 17, wherein the optically distinguishable markers are generated by a generative cooperating network (GCN) (i.e. a generative adversarial network, e.g. GCN – Para 263).
Independent claim 13, the rationale as applied in the rejection of claim 7 applies herein.
Hishinuma fails to disclose wherein an interior of the inputted polygon defines a ground truth mask distinguishing pixels of the object from background pixels, which Turkelson discloses (i.e. the object recognition model may be a deep learning model, such as, … a Masked R-CNN – Para 174; Some embodiments may select image-capture tasks; the task may be associated with a QR code – Para 224).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention at the time the invention was made to combine Turkelson’s method of labeling the object, wherein an interior of the inputted polygon defines a ground truth mask distinguishing pixels of the object from background pixels with the method of Hishinuma because each discloses enabling object recognition, which may be obtained by a masked R-CNN using a deep learning model to classify images of object to provide the advantage identifying the location of a objects (Turkelson, Para 174).
Independent claim 17, the rationale as applied in the rejection of claim 11 applies herein.
Hishinuma fails to disclose wherein the optically distinguishable markers are qualitatively distinguishable patterns that do not encode data, which Turkelson discloses (i.e. After providing images 314B and 324B to computer-vision object recognition model…the visual features may include color descriptors, shape descriptors – Para 200; Some embodiments may select image-capture tasks; the task may be associated with a QR code – Para 224).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention at the time the invention was made to substitute Turkelson’s method wherein the optically distinguishable markers are qualitatively distinguishable patterns that do not encode data with the method of Hishinuma because computer vision recognition may include recognition of shape descriptors or QR codes, where descriptors enable storing object identifiers that provide the benefit of classifying and locating objects (Turkelson, Para 200).
Claim 18, Hishinuma discloses the method of claim 17, wherein the optically distinguishable markers are generated by a generative cooperating network (GCN) (i.e. a generative adversarial network, e.g. GCN – Para 263).
Claims 14-16, 19 and 20, the corresponding rationale as applied in the rejection of claims 1, 9, and 11 applies herein.
Claims 3-4, and 6 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHANTE HARRISON whose telephone number is (571)272-7659. The examiner can normally be reached Monday - Friday 8:00 am to 5:00 pm EST.
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/CHANTE E HARRISON/Primary Examiner, Art Unit 2615
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