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
This action is responsive to patent application as filed on 3/26/2024
This action is made Non-Final.
Claims 1 – 9 are pending in the case. Claims 1 is the independent claim.
Drawings
The drawings filed on 3/26/2024 have been accepted by the Examiner.
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, 8 and 9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 1 recites at least “A deep learning backdoor attack method based on an ordinal network, comprising the following steps: obtaining a training sample image; generating an ordinal network based on the training sample image; generating a trigger by using the ordinal network, wherein the trigger is used for generating a malicious image”. These limitations are construed as abstract ideas for being performable in the human mind and/or on paper. A human can certainly receive an image, generate an ordinal network based on the image, and generate a trigger for generating a malicious image.
This judicial exception is not integrated into a practical application. Additional limitations directed toward mere instructions to apply the exception to generic computing components, alone or in combination, do not integrate the judicial exception into a practical application (See MPEP§2106.05(f)).
Further, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually; there is no indication that the combination of elements improves the functioning of a computer or improves any other technology including AI/ML technology, - their collective functions merely provide conventional computer implementation. None of the additional elements "offers a meaningful limitation beyond generally linking 'the use of the [method] to a particular technological environment,' that is, implementation via computers." Alice Corp., slip op. at 16 (citing Bilski v. Kappos, 561 U.S. 610, 611 (U.S. 2010)).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements identified above, being directed toward mere instructions to apply the exception to generic computing components, alone or in combination, are well-understood routine and conventional, do not provide an inventive concept, and thus, do not amount to significantly more than the judicial exception. Therefore, independent claim 1 is directed toward ineligible subject matter.
Dependent claims 8 and 9 recite additional limitations that are also construed as mere instructions to apply the judicial exception to generic computing components and are, therefore, also directed toward ineligible subject matter.
The analysis of dependent claims 2-7 has resulted in the determination that these claims recite eligible subject matter.
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.
Claim(s) 1, 8 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over “Pessa et al (“Mapping images into ordinal networks”, American Physical Society, November 2020, 14 pages hereinafter Pessa) in view of Andreina (USPAT 11,977,626 B2 filed Jun. 9, 2021).
Claim 1:
Pressa teaches a deep learning backdoor attack method based on an ordinal network, comprising the following steps: obtaining a training sample image; generating an ordinal network based on the training sample image; generating an ordinal network based on the training sample image (Sections III:C and III:D: Figure 2(a) illustrates this procedure, where the probability of randomly shuffling pixels values p controls the transition from a periodic image (p = 0) to a random one (p = 1). We map these sample images (of size 250 × 250) into their corresponding ordinal networks.... We generate an ensemble containing 100 fractional Brownian landscapes of size 256 × 256 for each value of h ∈{0.10, 0.15, 0.20, . . . , 0.90} (with the turning bands method), and map each sample into an ordinal network with embedding dimensions dx = dy = 2. Figure 3(b) presents visualizations of the ordinal networks mapped from the sample images of Fig. 3(a).)
Pressa, by itself, does not seem to completely teach generating a trigger by using the ordinal network, wherein the trigger is used for generating a malicious image.
The Examiner maintains that these features were previously well-known as taught by Andreina.
Andreina teaches generating a trigger wherein the trigger is used for generating a malicious image (Col 6 ln 59-67: each of the backdoored models are generated by: generating a respective one of the triggers as a pattern recognizable by the genuine machine learning model; adding the respective trigger to a plurality of training samples; changing a target class of the training samples having the respective trigger added to a respective one of the backdoor classes; and training another version of the genuine machine learning model using the training samples having the respective trigger added).
Pressa and Andreina are analogous art because they are from the same problem-solving area, image analysis and processing.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the ordinal network of Pressa and the trigger generation used to generate a malicious image of Andreina before him or her, to combine the teachings of Pressa and Andreina. The rationale for doing so would have been to securing a genuine machine learning model against adversarial samples, as taught by Andreina (Abstract).
Therefore, it would have been obvious to combine Pressa and Andreina to obtain the invention as specified in the instant claim(s).
Claim 8:
Pressa, by itself, does not seem to completely teach A deep learning backdoor attack device based on an ordinal network, comprising a host computer, wherein the host computer can implement the deep learning backdoor attack method based on the ordinal network according to claim 1 when executing programs.
The Examiner maintains that these features were previously well-known as taught by Andreina.
Andreina teaches A deep learning backdoor attack device based on an ordinal network, comprising a host computer, wherein the host computer can implement the deep learning backdoor attack method based on the ordinal network according to claim 1 when executing programs (Claim 13).
Pressa and Andreina are analogous art because they are from the same problem-solving area, image analysis and processing.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Pressa and Andreina before him or her, to combine the teachings of Pressa and Andreina. The rationale for doing so would have been to implement the steps in a networked computer system.
Therefore, it would have been obvious to combine Pressa and Andreina to obtain the invention as specified in the instant claim(s).
Claim 9:
Pressa, by itself, does not seem to completely teach A computer readable storage medium, wherein computer programs are stored on the computer readable storage medium, and when the computer programs are executed by a processor, the deep learning backdoor attack method based on the ordinal network according to claim 1 is implemented.
The Examiner maintains that these features were previously well-known as taught by Andreina.
Andreina teaches A computer readable storage medium, wherein computer programs are stored on the computer readable storage medium, and when the computer programs are executed by a processor, the deep learning backdoor attack method based on the ordinal network according to claim 1 is implemented (Col 8 ln 3-8).
Pressa and Andreina are analogous art because they are from the same problem-solving area, image analysis and processing.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Pressa and Andreina before him or her, to combine the teachings of Pressa and Andreina. The rationale for doing so would have been to implement the steps in various embodiments.
Therefore, it would have been obvious to combine Pressa and Andreina to obtain the invention as specified in the instant claim(s).
Allowable Subject Matter
Claims 2-7 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. Pressa nor Andreina, alone nor in combination, disclose or teach every feature of claims 2-7.
Note
The Examiner cites particular columns, line numbers and/or paragraph numbers in the references as applied to the claims below for the convenience of the Applicant(s). Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the Applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner. See MPEP 2123.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and is listed in the attached PTOL-892 form.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMED-IBRAHIM ZUBERI whose telephone number is (571)270-7761. The examiner can normally be reached on M-Th 8-6 Fri: 7-12/OFF.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Steph Hong can be reached on (571) 272-4124. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MOHAMMED H ZUBERI/ Primary Examiner, Art Unit 2178