DETAILED ACTION
This office action is responsive to the above identified application filed 6/21/2023. The application contains claims 1-20, all examined and rejected.
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 .
Information Disclosure Statement
The Information Disclosure Statement with references submitted 6/21/2023, has been considered and entered into the file.
Claim Rejections - 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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3,5,10,12, and 17 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 3, 10, and 17 recites the limitation "the transfer learning algorithm". There is insufficient antecedent basis for this limitation in the claim.
Claim 5, 12, and 19 recites the limitation "the transfer algorithm". There is insufficient antecedent basis for this limitation in the claim.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claim 1 is rejected under 35 USC 101 because the claimed inventions are directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
While independent claims 1, 8 and 15 are each directed to a statutory category, it recites a series of steps which appears to be directed to an abstract idea (mental process, mathematical concept).
Claims 1-20 are rejected under 35 U.S.C. § 101 because the instant application is directed to non-patentable subject matter. Specifically, the claims are directed toward at least one judicial exception without reciting additional elements that amount to significantly more than the judicial exception. The rationale for this determination is in accordance with the guidelines of USPTO, applies to all statutory categories, and is explained in detail below.
When considering subject matter eligibility under 35 U.S.C. 101, (1) it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, (2a) it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so (2b), it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself. Examples of abstract ideas include certain methods of organizing human activities; a mental processes; and mathematical concepts, (2019 PEG)
STEP 1.
Per Step 1, the claims are determined to include process, manufacture, and machine as in independent Claim 1, 8, and 15, and in the therefrom dependent claims. Therefore, the claims are directed to a statutory eligibility category.
At step 2A, prong 1, The invention is directed to identifying features within received data that could be used for outlier data detection which is akin to Mental Process (see Alice), As such, the claims include an abstract idea. When considering the limitations individually and as a whole the limitations directed to the abstract idea are:
convert to-be-classified-data (TBC-data) from a TBC-data format to a second data format; and access features of the TBC-data in the second data format”, “perform a task comprising determining, based at least in part on the features of the TBC-data in the second data format, that the TBC-data having the second data format is an outlier” (Mental process, observation, evaluation and judgment).
The claim recites additional elements as
“A computer system comprising a memory communicatively coupled to a processor system, wherein the processor system is operable to perform processor system operations” (“Using a computer as a tool to perform a mental process”, MPEP 2106.04(a)(2)(III)(C));
“a first machine learning (ML) algorithm”, “a second ML algorithm to perform a task“ (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)).
This judicial exception is not integrated into a practical application. The elements are recited at a high level of generality, i.e. a generic computing system performing generic functions including generic processing of data. Accordingly the additional elements do not integrate the abstract into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore the claims are directed to an abstract idea. (2019 Revised Patent Subject Matter Eligibility Guidance ("2019 PEG"). Thus, under Step 2A of the Mayo framework, the Examiner holds that the claims are directed to concepts identified as abstract.
STEP 2B.
Because the claims include one or more abstract ideas, the examiner now proceeds to Step 2B of the analysis, in which the examiner considers if the claims include individually or as an ordered combination limitations that are "significantly more" than the abstract idea itself. This includes analysis as to whether there is an improvement to either the "computer itself," "another technology," the "technical field," or significantly more than what is "well-understood, routine, or conventional" (WURC) in the related arts.
The instant application includes in Claim 1 additional steps to those deemed to be abstract idea(s).
When taken the steps individually, these steps are:
“A computer system comprising a memory communicatively coupled to a processor system, wherein the processor system is operable to perform processor system operations” (“Using a computer as a tool to perform a mental process”, MPEP 2106.04(a)(2)(III)(C));
“a first machine learning (ML) algorithm”, “a second ML algorithm to perform a task“ (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h) and mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f)).
In the instant case, Claim 1 is directed to above mentioned abstract idea. Technical functions such as receiving, and extracting are common and basic functions in computer technology. The individual limitations are recited at a high level and do not provide any specific technology or techniques to perform the functions claimed.
In addition, when the claims are taken as a whole, as an ordered combination, the combination of steps does not add "significantly more" by virtue of considering the steps as a whole, as an ordered combination. The instant application, therefore, still appears only to implement the abstract idea to the particular technological environments using what is well-understood, routine, and conventional in the related arts. The steps are still a combination made to the abstract idea. The additional steps only add to those abstract ideas using well understood and conventional functions, and the claims do not show improved ways of, for example, an unconventional non-routine functions for analyzing model operations or updating the model that could then be pointed to as being "significantly more" than the abstract ideas themselves.
Moreover, Examiner was not able to identify any "unconventional" steps, which, when considered in the ordered combination with the other steps, could have transformed the nature of the abstract idea previously identified. The instant application, therefore, still appears to only implement the abstract ideas to the particular technological environments using what is well-understood, routine, and conventional (WURC) in the related arts.
Further, note that the limitations, in the instant claims, are done by the generically
recited computing devices. The limitations are merely instructions to implement the abstract idea on a computing device that is recited in an abstract level and require no more than a generic computing devices to perform generic functions.
Claim 15 recites a system comprising “A computer program product comprising a computer readable program stored on a computer readable storage medium, wherein the computer readable program, when executed on a processor system, causes the processor to perform processor system operations” configured to perform the same steps of the computer system as set forth in claim 1, the added element of “A computer program product comprising a computer readable program stored on a computer readable storage medium, wherein the computer readable program, when executed on a processor system, causes the processor to perform processor system operations” do not transform the judicial exception into a practical application because they are tantamount to a mere instruction to apply the judicial exception to a generic computer. The additional elements are also not sufficient to amount to significantly more than the judicial exception because the action of implementing the method on a general purpose computer with at least one processor and at least one memory is tantamount to a mere instruction to apply the judicial exception to a computer.
Claim 15 is therefore rejected according to the same findings and rationale as provided above.
Independent claims 8 and 15 are the same analogy and rejected using similar analysis as claim 1.
CONCLUSION
It is therefore determined that the instant application not only represents an abstract idea identified as such based on criteria defined by the Courts and on USPTO examination guidelines, but also lacks the capability to bring about "Improvements to another technology or technical field" (Alice), bring about "Improvements to the functioning of the computer itself" (Alice), "Apply the judicial exception with, or by use of, a particular machine" (Bilski), "Effect a transformation or reduction of a particular article to a different state or thing" (Diehr), "Add a specific limitation other than what is well-understood, routine and conventional in the field" (Mayo), "Add unconventional steps that confine the claim to a particular useful application" (Mayo), or contain "Other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment" (Alice), transformed a traditionally subjective process performed by humans into a mathematically automated process executed on computers (McRO), or limitations directed to improvements in computer related technology, including claims directed to software (Enfish).
The dependent claims, when considered individually and as a whole, likewise do not provide "significantly more" than the abstract idea for similar reasons as the independent claim.
claims 2 disclose “The computer system of claim 1, wherein the first ML algorithm comprises a transfer learning algorithm” (description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use and merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 3 disclose “the transfer learning algorithm comprises an outlier detection pipeline” (description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use and merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 4 disclose “the TBC-data format is different from the second data format” (description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use and merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 5 disclose “the transfer algorithm has been trained based at least in part on a plurality of diverse outlier labels” (description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use and merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 6 disclose “the features of the TBC-data in the second data format are determined based at least in part on anomaly scores generated by the first ML algorithm” (description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use and merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h); “the task further comprises determining, based at least in part on a plurality of diverse outlier labels, that the TBC-data having the second data format is the outlier” (the determination step is mental process and the description of the task data, is directed to generally linking the use of a judicial exception to a particular technological environment or field of use and merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 7 disclose “first ML algorithm comprises a transfer learning algorithm and the second ML algorithm comprises a classifier” (description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use and merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h).
It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea.
The dependent claims which impose additional limitations also fail to claim patent eligible subject matter because the limitations cannot be considered statutory. The dependent claim(s) have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 1 ; where all claims are directed to the same abstract idea, "addressing each claim of the asserted patents [is] unnecessary." Content Extraction &. Transmission LLC v, Wells Fargo Bank, Natl Ass'n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims are directed towards patent eligible subject matter, they are invited to point out the specific limitations in the claim that are directed towards patent eligible subject matter. Claims for the other statutory classes are similarly analyzed.
For at least these reasons, the claimed inventions of each of dependent claims 2-14 and 16-20,are directed or indirect to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more and are rejected under 35 USC 101.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by “Meta-learning with GANs for anomaly detection, with deployment in high-speed rail inspection system” [hereinafter D1] Published 11/2/2022 disclosed in IDS submitted on 6/21/2023.
With regard to Claim 1,
D1 teach a computer system comprising a memory communicatively coupled to a processor system, wherein the processor system is operable to perform processor system operations (P. 10, “Computing resources. We utilize a workstation with a single Nvidia GeForce GTX 1080 GPU, an Intel Core i7-7700K CPU and 16GB memory for all experiments”) comprising: using a first machine learning (ML) algorithm to:
convert to-be-classified-data (TBC-data) from a TBC-data format to a second data format (P. 10, “we divide the input image X and reconstructed image Xˆ = GωG (X, Z) into 16 × 16 blocks”); and
access features of the TBC-data in the second data format (P. 10, ¶1, “compute the SSIM loss for each block pair (Xkl, Xˆ kl) “, “pick three block pairs … with the three lowest SSIM values, and compute the difference ∆X(j) = Xi (j) − Xˆ (j)”, “a binary classifier is trained with input ∆X(1), ∆X(2), ∆X(3)”); and
using a second ML algorithm to perform a task comprising determining, based at least in part on the features of the TBC-data in the second data format, that the TBC-data having the second data format is an outlier (P. 10, “In the deployment stage without labeled data, X is labeled as abnormal if the lowest block SSIM loss is less than a predetermined threshold µ0 > 0. As described in Equation (8), a binary classifier is trained with input ∆X(1), ∆X(2), ∆X(3)”, binary classifier is trained on ∆X and explicit deployment labeling rule “X is labeled as abnormal if …”).
With regard to Claim 2,
D1 teach the computer system of claim 1, wherein the first ML algorithm comprises a transfer learning algorithm (P. 9, last paragraph, “Algorithm. Our Algorithm 1 of anomaly detection for railway inspection is divided into two steps: the meta-learning step and the line-specific anomaly detection step”, P. 10, ¶1. “In step one (the meta-learning step) … find the optimal parameters ωG and ωD … We use such optimal parameters as the meta knowledge for the anomaly reconstruction in each rail line”).
With regard to Claim 3,
D1 teach the computer system of claim 1, wherein the transfer learning algorithm comprises an outlier detection pipeline (P. 9, last paragraph, “Algorithm. Our Algorithm 1 of anomaly detection for railway inspection is divided into two steps: the meta-learning step and the line-specific anomaly detection step”, P. 10, “divide the input image X and reconstructed image Xˆ = GωG (X, Z) into 16 × 16 blocks (Xkl, Xˆkl) for k, l = 1, . . . , 16, and compute the SSIM loss for each block pair (Xkl, Xˆkl). We pick three block pairs … for j = 1, 2, 3 with the three lowest SSIM values, and compute the difference ∆X(j)”).
With regard to Claim 4,
D1 teach the computer system of claim 1, wherein the TBC-data format is different from the second data format (P. 10, “we divide the input image X and reconstructed image Xˆ = GωG (X, Z) into 16 × 16 blocks”, D1 explicitly distinguish between X and X^).
With regard to Claim 5,
D1 teach the computer system of claim 1, wherein the transfer algorithm has been trained based at least in part on a plurality of diverse outlier labels (P. 9, ¶2, “training data X … includes the original images as well as these newly synthesized abnormal samples”, “abnormal sample after pasting abnormal objects (e.g. nails)”, P.8, 4, “The most common ones are nails broken from the rail, stones, various types of garbage, and dead animals”, P. 10, ¶2, “the label for X is given”, ¶4, “we train our meta-learning framework with 100 known anomaly samples; and in the second stage, with 50 known anomaly samples”).
With regard to Claim 6,
D1 teach the computer system of claim 1, wherein:
the features of the TBC-data in the second data format are determined based at least in part on anomaly scores generated by the first ML algorithm (P. 10, ¶1, “compute the SSIM loss for each block pair (Xkl, Xˆ kl), We pick three block pairs (X(j) , Xˆ (j)) for j = 1, 2, 3 with the three lowest SSIM values “, “In the deployment stage without labeled data, X is labeled as abnormal if the lowest block SSIM loss is less than a predetermined threshold µ0”); and
the task further comprises determining, based at least in part on a plurality of diverse outlier labels, that the TBC-data having the second data format is the outlier (P.8, 4, “The most common ones are nails broken from the rail, stones, various types of garbage, and dead animals”, P. 10, ¶2, “the label for X is given”, ¶4, “we train our meta-learning framework with 100 known anomaly samples; and in the second stage, with 50 known anomaly samples”).
With regard to Claim 7,
D1 teach the computer system of claim 1, wherein: the first ML algorithm comprises a transfer learning algorithm (P. 9, last paragraph, “the meta-learning step”, P. 10, ¶1. “In step one (the meta-learning step) … find the optimal parameters ωG and ωD … We use such optimal parameters as the meta knowledge for the anomaly reconstruction in each rail line”); and the second ML algorithm comprises a classifier (P. 10, “a binary classifier is trained with input ∆X(1), ∆X(2), ∆X(3)”).
With regard to Claim 8,
Claim 8 is similar in scope to claim 1; therefore it is rejected under similar rationale.
With regard to Claim 9,
Claim 8 is similar in scope to claim 2; therefore it is rejected under similar rationale.
With regard to Claim 10,
Claim 10 is similar in scope to claim 3; therefore it is rejected under similar rationale.
With regard to Claim 11,
Claim 11 is similar in scope to claim 4; therefore it is rejected under similar rationale.
With regard to Claim 12,
Claim 12 is similar in scope to claim 5; therefore it is rejected under similar rationale.
With regard to Claim 13,
Claim 13 is similar in scope to claim 6; therefore it is rejected under similar rationale.
With regard to Claim 14,
Claim 14 is similar in scope to claim 7; therefore it is rejected under similar rationale.
With regard to Claim 15,
Claim 15 is similar in scope to claim 1; therefore it is rejected under similar rationale. D1 further teach a computer program product comprising a computer readable program stored on a computer readable storage medium, wherein the computer readable program, when executed on a processor system, causes the processor to perform processor system operations (P. 10, “Computing resources. We utilize a workstation with a single Nvidia GeForce GTX 1080 GPU, an Intel Core i7-7700K CPU and 16GB memory for all experiments”).
With regard to Claim 16,
Claim 16 is similar in scope to claim 2; therefore it is rejected under similar rationale.
With regard to Claim 17,
Claim 17 is similar in scope to claim 3; therefore it is rejected under similar rationale.
With regard to Claim 18,
Claim 18 is similar in scope to claim 4; therefore it is rejected under similar rationale.
With regard to Claim 19,
Claim 19 is similar in scope to claim 5; therefore it is rejected under similar rationale.
With regard to Claim 20,
Claim 20 is similar in scope to claims 6 and 7; therefore it is rejected under similar rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure.
US Patent Application Publication No. 20220398503 filed by IMAS et al. that disclose a ML model for anomaly detection that operates on feature vector and whose parameters are influenced by other related models via similarity regulation. Th system can detect failure, fraudulent transactions, network security breaches. Therefore it teaches an ML based anomaly detection framework with cross model knowledge transfer See at least ¶¶16-18
Examiner has pointed out particular references contained in the prior arts of record in the body of this action for the convenience of the applicant. 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 from the applicant, in preparing the response, to consider fully the entire references as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior arts or disclosed by the examiner. It is noted that any citation to specific pages, columns, figures, or lines in the prior art references any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331-33, 216 USPQ 1038-39 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMED ABOU EL SEOUD whose telephone number is (303)297-4285. The examiner can normally be reached Monday-Thursday 9:00am-6:00pm MT.
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, Michelle Bechtold can be reached at (571) 431-0762. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MOHAMED ABOU EL SEOUD/Primary Examiner, Art Unit 2148