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 .
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.
Claims 21-23, 34, and 37-40 are rejected under 35 U.S.C. 103 as being unpatentable over Potter (20210065353) in view of Kass (20180246894).
Regarding claim 21, Potter teaches an apparatus comprising a memory and a processor operatively coupled to the memory (pars. 55-56),
train at least one model based on each image from a set of images of a set of collectables and at least one of a first grade classification label for a surface condition, a second grade classification label for an edge condition, or a third grade classification label for a corner condition (pars. 92, 94 and 99, grading corners),
cause the at least one model to be applied to an image of a collectable not included in the set of collectables (see par. 91, deploys the trained model on newly captured images of cards not in the training database),
and cause an output to be displayed indicating that the collectable includes a defect (see par. 95, displays a grade report highlighting the biggest flaws of the card, such as torn corners, worn edges, and surface scratches)
Kass teaches independent, attribute-specific scoring pipelines a corner algorithm, an edge algorithm, and a surface algorithm that each apply machine learning (k-nearest neighbors) to produce a score for the respective attribute of the card and the at least one model including a first model, a second model, and a third model each trained using each image and the respective first, second, or third grade classification label (pars. 87, 93 and 114).
It would have been obvious prior to the effective filing date of the invention to one of ordinary skill in the art to include in Potter separate trained models corresponding to the independently evaluated surface, edge, and corner attributes taught by Kass. The reason is to permit attribute specific learning and calibration and thereby improve the independent grading of the respective surface, edge, and corner conditions.
Regarding claim 22, see Kass pars. 65-67, images of the collectible captured under each of a plurality of different lighting conditions.
Regarding claim 23, see Kass, the support surface can be angled with respect to the imaging device, and high-angle and low-angle configurations are used, so that images are taken at different angles relative to the collectable.
Regarding claim 34, see the rejection of claim 21.
Regarding claim 37, see Potter par. 92 and Kass, abstract.
Regarding claim 38, see Kass, abstract and par. 39.
Regarding claim 39, see Potter par. 102, augmenting.
Regarding claim 40, see Potter par. 102, the data augmentation is applied across the database images.
Claims 28-29 and 33 are rejected under 35 U.S.C. 103 as being unpatentable over Potter in view of Kass, and further in view of Tabernik (“Segmentation-Based Deep-Learning Approach for Surface-Defect Detection”).
Regarding claim 28, Potter teaches a method comprising applying a machine learning model to an image of an object, the image having a plurality of unique portions that are separately evaluated (Potter isolates and separately evaluates spatial features like corners, edge regions, central regions, and surface impressions of the card image; pars. 98-99),
the ML model trained using a training dataset that includes at least one image and at least one synthetic image generated by modifying a brightness of the at least one image (Potter applies data augmentation including brightness or color tuning of the training images; par. 102),
and causing the image to be displayed (Potter par. 0095, grade report presenting the card image; Kass detailed image report showing the collectible).
Tabernik teaches the model generating a plurality of defect confidence levels, each defect confidence level associated with a unique portion of the image and indicating a likelihood that at least one defect is present within that unique portion (Section Proposed Approach, subsection Inference, and Fig. 1 teach a segmentation network that outputs the probability of a defect for each 8×8 group),
It would have been obvious prior to the effective filing date of the invention to one of ordinary skill in the art to apply the per-region defect-probability segmentation of Tabernik in the card-grading method of Potter in order to localize surface defects and quantify the likelihood that a defect is present. The reason is to improve accuracy.
Regarding claim 29, see Tabernik, the per-region defect probability is thresholded to decide the presence of a defect (Tabernik, Performance Metrics), and Kass identifies the location of noted defects on the displayed image in par. 125-129 and figure 7.
Regarding claim 33, see Potter par. 0010, the method determines card metadata like a card type, player, or character information and inputs uses a ML model.
Claim 25 is rejected under 35 U.S.C. 103 as being unpatentable over Potter in view of Kass, and further in view of Srivastava et al. (“Dropout: A Simple Way to Prevent Neural Networks from Overfitting”).
Regarding claim 25, Potter teaches the apparatus of claim 21 using a convolutional neural network (Potter par. 92) but does not expressly teach at least one dropout layer to reduce overfitting.
Srivastava teaches dropout layers (Abstract and section 6.1).
It would have been obvious prior to the effective filing date of the invention to one of ordinary skill in the art to include a dropout layer as taught by Srivastava in the neural network of Potter in order to reduce overfitting and improve generalization of the trained model.
Claim 26 is rejected under 35 U.S.C. 103 as being unpatentable over Potter in view of Kass, and further in view of Bergstra et al. (“Random Search for Hyper-Parameter Optimization”).
Regarding claim 26, Potter and Kass do not expressly teach improving hyperparameters using a random search, hyperband, or Bayesian optimization algorithm.
Bergstra teaches this (Abstract).
It would have been obvious prior to the effective filing date of the invention to one of ordinary skill in the art to improve the hyperparameters of the model of Potter and Kass using the random search of Bergstra in order to more efficiently obtain a well-performing model configuration.
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 § 2146 et seq. 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).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 21-40 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,159,393 B2. Although the conflicting claims are not identical, they are not patentably distinct from one another because each instant claim is an obvious variant of the patent claims.
Regarding claim 21, see claim 1 and 17 of the patent. Note that claim 21 of the application is broader.
Regarding claim 28, see claim 10 of the patent.
Regarding claim 34, see claim 17 of the patent.
Regarding claims 22, 23, 24, 25, 26, and 27, they correspond respectively to patent claims 2, 3, 4, 7, 8, and 9.
Regarding claims 29, 30, 31, 32, and 33, they correspond respectively to patent claims 10, 11, 12, 16, and 13.
Regarding claims 35, 36, 37, 38, 39, and 40, they correspond respectively to patent claims 17-20.
Allowable Subject Matter
Claims 24, 30-32 and 36 are objected to as being dependent upon a rejected base claim and would be allowable once the double patenting rejection is overcome.
For claim 24, the prior art does not teach a first image taken with a first background and a second image taken with a second background different than the first.
Regarding claim 36, the prior art does not teach a first image captured using a first camera setting and a second image captured using a second camera setting different than the first.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Kass et al. (20160210734) teaches computerized grading of collectible cards by evaluating corners, edges, surface, and centering using blob analysis and k-nearest-neighbors machine learning under a plurality of predefined lighting conditions.
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/HADI AKHAVANNIK/Primary Examiner, Art Unit 2676
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