NON-FINAL REJECTION, FIRST DETAILED ACTION
Status of Prosecution
The present application, 18/561,104 filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The application was filed in the Office on November 15, 2023 and is a section 371 national stage application of PCT/US2022/052814 filed on Dec. 14, 2022.
Claims 1-20 are pending and all are rejected. Claims 1, 12 and 20 are independent claims.
Status of Claims
Claim 11 is rejected as being indefinite.
Claims 1-10 and 12-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-2, 5-8, 12-13 and 16-20 are rejected under 35 USC. § 103 as being unpatentable over Luo et al. (“Luo”), United States Patent 10,853,696 published on Dec. 1, 2020 in view of Koo et al. (“Koo”), United States Patent 11,023,777 published on June. 1, 2021.
Claims 3-4 and 14-15 are rejected under 35 USC. § 103 as being unpatentable over Luo in view of Koo in further view of non-patent literature in view of non-patent literature Yuan et al., (“Yuan”), Stealthy Porn: Understanding Real-World Adversarial Images for Illicit Online Promotion,” published in 2023 (Applicant-cited).
Claims 9-10 are rejected under 35 USC. § 103 as being unpatentable over Luo in view of Koo in further view of non-patent literature in view of Cao et al., (“Cao”), United States Patent Application 10,824,897 published in Nov. 3, 2020.
Claim Rejection – 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claim 11 is rejected as being indefinite, due to the great deal of confusion and uncertainty as to the proper interpretation of the limitations of the claim. Specifically, Claim 11 recites, “further comprising comparing the detected target concept to a host's target concept.” What the term “host” may mean is not clear based on the plain text reading to a person having ordinary skill in the art.
No prior art rejection is made. See MPEP 2173.06(II) (“As stated in In re Steele, 305 F.2d 859, 134 USPQ 292 (CCPA 1962), a rejection under 35 U.S.C. § 103 should not be based on considerable speculation about the meaning of terms employed in a claim or assumptions that must be made as to the scope of the claims.”).
Applicant’s is invited to interview with Examiner to discuss this claim.
Claim Rejections – 35 USC § 101 – Subject Matter Eligibility
Claims 1-10 and 12-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding representative claim 1, at step 1, the claim recites an apparatus, and therefore is a manufacture, which is a statutory category of invention. See MPEP § 2106.03.
At step 2A, prong one, the claim recites a learning apparatus. The following limitations are the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C):
associating, by the one or more processors, at least one label of a first set of labels to respective training images, wherein the first set of labels includes policy labels relating to a policy for approving content for publication.
At step 2A prong 2, the claim language is analyzed to determine whether it recites additional elements that integrate the judicial exception into a practical application. See MPEP § 2106.04(d).
The limitations:
receiving as input to a machine learning model, by one or more processors, training images, wherein the training images includes at least one obfuscated training image;
training, by the one or more processors based on the training images and the associated at least one label of the first set of labels, the machine learning model, wherein: the machine learning model outputs policy predictions, and the policy predictions include an indication of a violation or approval based on the respective policy labels.
are related to receiving data and training with that data a mathematical model from the mathematical calculations which is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g).
Next, at step 2B of the analysis, the claim is considered if it recites additional elements that amount to significantly more than the judicial exception. See MPEP § 2106.05.
The additional element of a processor and training are ones the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is therefore directed to an abstract idea.
Therefore, claim 1 is ineligible.
As to dependent claims 2, 4, 6-8, the analysis of the respective parent claim is incorporated. In the step 2A, prong one analysis, the additional limitations deal with more specificity of the labels or the policy prediction types which all do not add significantly more to the abstract idea of a mathematical calculation or are mental processes. See MPEP § 2106.04(a)(2).
The claim is also ineligible.
As to dependent claim 3, the analysis of the respective parent claim is incorporated. In the step 2A, prong two analysis, the additional limitation deals with the association of labels to indicate whether or not the training images are obfuscated. This additional element does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is therefore directed to an abstract idea. The claim is also ineligible.
As to dependent claim 5, the analysis of the respective parent claim is incorporated. In the step 2A, prong two analysis, the additional limitation deals with receiving images and determining using the model, which merely is using the model. This additional element does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is therefore directed to an abstract idea. The claim is also ineligible.
As to dependent claims 9-10, the analysis of the respective parent claim is incorporated. In the step 2A, prong two analysis, the additional limitation deals with identifying violative content and detecting target concepts. This additional element does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is therefore directed to an abstract idea. The claim is also ineligible.
As to independent claims 12 and 20, they are rejected for similar reasons as claim 1. Their dependent claims are rejected similarly to their corresponding dependent claims.
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.
A.
Claims 1-2, 5-8, 12-13 and 16-20 are rejected under 35 USC. § 103 as being unpatentable over Luo et al. (“Luo”), United States Patent 10,853,696 published on Dec. 1, 2020 in view of Koo et al. (“Koo”), United States Patent 11,023,777 published on June. 1, 2021.
As to Claim 1, Luo teaches: A method, comprising:
receiving as input to a machine learning model, by one or more processors, training images (Luo: Fig. 4, col. 16, lines 11 to 32, the online system obtains a set of training images at step [410]);
associating, by the one or more processors, at least one label of a first set of labels to respective training images, wherein the first set of labels includes policy labels relating to a policy for approving content for publication(Luo: col. 16, lines 24 to 28, “Each training image of the set is labeled with a combination of a maintained policy that a training image of the set was determined to have violated and a source from which the training image was obtained by the online system”) ; and
training, by the one or more processors based on the training images and the associated at least one label of the first set of labels, the machine learning model (Luo: col. 17, lines 34 to 38, “to train a machine learning embedding model that generates an embedding describing evaluation of images against 35 one or more of the maintained policies, the online system applies the machine learning embedding model to each training image of the set”), wherein:
the machine learning model outputs policy predictions (Luo: col. 18, lines 60 to 63, “where the classification model predicts a maintained policy violated by an image based on the embedding generated for the image”), and
the policy predictions include an indication of a violation or approval based on the respective policy labels (Luo: col. 18, lines 60 to 63, the violation is based on the respective policy label per the embedding training process).
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Luo may not explicitly teach: wherein the training images includes at least one obfuscated training image.
Koo teaches in general concepts related to training an obfuscation network for performing distinct concealing processes for distinct regions of an original image (Koo: Abstract). Specifically, Koo teaches that the network is trained using obfuscated images input into discriminators (Koo: col. 2, lines 62 to 66, the n discriminators are capable of determining a respective reference images to allow for the generation of obfuscation image scores).
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the invention to have modified the Luo apparatuses and processes and devices by utilizing obfuscated images as taught by Koo. Such a person would have been motivated to do so with a reasonable expectation of success to allow for the use of computer technology to allow for the policy image violation model to use obfuscated images accordingly to expand the use cases.
As to Claim 2, Luo and Koo teach the elements of claim 1.
Luo further teaches: wherein the policy labels indicate a policy violation or a policy approval (Luo: col. 18, lines 60 to 63, “where the classification model predicts a maintained policy violated by an image based on the embedding generated for the image”).
As to Claim 5, Luo and Koo teach the elements of claim 1.
Luo further teaches: executing the machine learning model to identify violative content, the executing comprising:
receiving one or more images as input into the machine learning model(Koo: col. 2, lines 62 to 66, the n discriminators are capable of determining a respective reference images to allow for the generation of obfuscation image scores); and
determining, using the machine learning model, a policy prediction for each of the one or more images (Luo: col. 18, lines 60 to 63, the violation is based on the respective policy label per the embedding training process).
As to Claim 6, Luo and Koo teach the elements of claim 5.
Luo further teaches: wherein the policy prediction for each of the one or more images includes an indication of a violation or approval (Luo: col. 18, lines 60 to 63, “where the classification model predicts a maintained policy violated by an image based on the embedding generated for the image”).
As to Claim 7, Luo and Koo teach the elements of claim 6.
Luo further teaches: wherein when the policy prediction includes the violation indication, the method further includes rejecting, by the one or more processors, the respective image such that the respective image is not provided for output (Luo: col. 16, lines 3 to 10, a policy may prevent presentation of images including “violent content … illegal content … [or] offensive test”).
As to Claim 8, Luo and Koo teach the elements of claim 6.
Luo and Koo may not explicitly teach: wherein when the policy prediction includes the approval indication, the method further includes providing for output, by the one or more processors, the respective image.
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the application to have further implemented the Luo-Koo disclosures and teachings by outputting for presentation the content that is not violative of a policy and therefore approved. Such a person would have been motivated to do so with a reasonable expectation of success to allow for non-violative content to be eligible for presentation for regular use cases.
As to Claim 12, it is rejected for similar reasons as claim 1.
As to Claim 13, it is rejected for similar reasons as claim 2.
As to Claim 16, it is rejected for similar reasons as claim 5.
As to Claim 17, it is rejected for similar reasons as claim 6.
As to Claim 18, it is rejected for similar reasons as claim 7.
As to Claim 19, it is rejected for similar reasons as claim 8.
As to Claim 20, it is rejected for similar reasons as claim 1 and 12.
B.
Claims 3-4 and 14-15 are rejected under 35 USC. § 103 as being unpatentable over Luo et al. (“Luo”), United States Patent 10,853,696 published on Dec. 1, 2020 in view of Koo et al. (“Koo”), United States Patent 11,023,777 published on June. 1, 2021 in further view of non-patent literature in view of non-patent literature Yuan et al., (“Yuan”), Stealthy Porn: Understanding Real-World Adversarial Images for Illicit Online Promotion,” published in 2023.
As to Claim 3, Luo and Koo teach the elements of claim 1.
Luo and Koo further teaches: associating, by the one or more processors, at least one label of a second set of labels to the respective training images (Luo: col. 16, line 66 to col. 17, lines 5, the images labeled with a combination of a maintained policy violated by the image and source from which the training images were obtained; Koo, col. 5, lines 16 to 20, the images are labeled with information about the image regions).
Luo and Koo may not explicitly teach: wherein the second set of labels includes obfuscation labels.
Yuan is an academic paper that generally discusses the weaknesses of deep learning with respect o adversarial techniques involving cybercriminal and image-based detection schemes (Yuan: Abstract). Specifically, Yuan teaches that adversarial explicit content are in obfuscated images that are distributed for illicit online advertising, phishing and other insidious purposes (Yuan: p. 1). A model is proposed that is able to detect different types of obfuscation tricks or methodologies (Yuan: Sec. V.B, p. 8, the obfuscation techniques include color manipulation, rotation, noising, texturing, blurring, occlusion and transparentizing and overlay).
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the application to have modified the Luo-Koo disclosures and teachings by implementing the labels with obfuscation information labels as taught and suggested by Yuan. Such a person would have been motivated to do so with a reasonable expectation of success to better understand the different obfuscation techniques and sources of the images for the user and the model (Yuan: Sec. V.B, p. 8).
As to Claim 4, Luo, Koo and Yuan teach the elements of claim 3.
Yuan further teaches: wherein the obfuscation labels identify a type of obfuscation of a plurality of obfuscation types (Yuan: Sec. V.B, p. 8, the obfuscation techniques include color manipulation, rotation, noising, texturing, blurring, occlusion and transparentizing and overlay).
As to Claim 14, it is rejected for similar reasons as claim 3.
As to Claim 15, it is rejected for similar reasons as claim 4.
C.
Claims 9-10 are rejected under 35 USC. § 103 as being unpatentable over Luo et al. (“Luo”), United States Patent 10,853,696 published on Dec. 1, 2020 in view of Koo et al. (“Koo”), United States Patent 11,023,777 published on June. 1, 2021 in further view of non-patent literature in view of Cao et al., (“Cao”), United States Patent Application 10,824,897 published in Nov. 3, 2020.
As to Claim 9, Luo and Koo teach the elements of claim 1.
Luo further teaches: further executing the machine learning model to identify violative content (Luo: col. 18, lines 60 to 63, the violation is based on the respective policy label per the embedding training process);
Luo may not explicitly teach: the executing comprising detecting that the one or more images are one or more obfuscated images.
Cao teaches in general concepts related to an online system receiving an image associated with a set of pixel values and providing the set of pixel values to a model. The model is used to determine whether a protected brand, possibly obfuscated, is detected (Cao: Abstract). Specifically, Cao teaches that obfuscated images are detected (Cao: Fig. 3, col. 9m lines 33 to 43, the detection module [245] detects obfuscated identity of the brand).
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It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the application to have modified the Luo-Koo disclosures and teachings by implementing the obfuscation detection as taught and suggested by Cao. Such a person would have been motivated to do so with a reasonable expectation of success to determine and accordingly label the region of interest in the image (Cao: col. 9, lines 55 to 60).
As to Claim 10, Luo, Koo and Cao teach the elements of claim 9.
Cao further teaches: further comprising detecting target concept of the one or more obfuscated images (Cao: col. 7, lines 14 to 18, the machine-learning module [230] may assign regions of interest within an image to one or more classes (i.e. a target concept).
Conclusion
Prior art made of the record:
Edwards et al. (“Edwards”), United States Patent Application Publication 2019/0188830 (June 20, 2019) (discussing adversarial training network to determine privacy protection layer for image obfuscation).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES T TSAI whose telephone number is (571)270-3916. The examiner can normally be reached M-F 8-5 Eastern.
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/JAMES T TSAI/Primary Examiner, Art Unit 2147
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