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 .
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 as the claims are directed to a judicial exception (abstract idea) without significantly more.
Step 2A, Prong One
These claims describe a method for anomaly detection including the steps of
-receiving a training dataset for anomaly detection;
-training an unsupervised machine learning model using at least a portion of the training dataset, to generate a trained unsupervised machine learning model;
-training a supervised machine learning model using an output from the unsupervised machine learning model and an anomaly detection feedback associated with the output from the unsupervised machine learning model, to generate a trained supervised machine learning model; and
-providing both the trained unsupervised machine learning model and the trained supervised machine learning model for combined use in machine learning anomaly detection inference.
These steps may be practically performed in the human mind with the aid of pen and paper and using observation, evaluation, judgment and/or opinion. For example, the human mind can receive and evaluate various data sets and user feedback to develop learning models, and then use judgment to predict anomalies based on said learning models.
The claimed method steps thus define a process that, under its broadest reasonable interpretation, can be performed mentally, i.e., the mental steps of “predicting” anomalies. That is, nothing prevents the method steps from being implemented in the mind. Further, if a claim grouping defines a process, that under its broadest reasonable interpretation, covers performance of the process in the mind, such as the process as defined in claims 2-11, then it falls within the “Mental Processes” grouping of abstract ideas. See MPEP 2106.04, II.B. Accordingly, the limitations in claims 1-11 are considered together as a single abstract idea for further analysis.
Step 2A: Prong Two:
Here, the claimed process steps, in particular the final step of “providing both the trained unsupervised machine learning model and the trained supervised machine learning model for combined use in machine learning anomaly detection inference” define quite a broad claim that merely involves the use of well-established learning models to make an anomaly detection inference. The claims do not provide any details on the training dataset for “anomaly detection”, do not discuss the validation dataset and also do not provide details on the actual anomaly detection results. Thus, the claims have a very-high level of generality and applicability across many fields of endeavor and generally apply an abstract idea without placing any limits on the application—and, moreover, fail to meaningfully integrate a judicial exception into a practical application or amount to significantly more. See MPEP 2106.059(d) and 2106.05.
Further, claims 12-20 do not recite additional elements that integrate the exception into a practical application and merely describe generic computer elements, such as memory, processor and related instructions to perform the abstract idea described above. See MPEP 2106.04(d)(1). Thus, even when viewed in combination, the additional limitations do not integrate the recited judicial exception into a practical application.
Step 2B:
As explained above, the recitation of processors, memory elements and computer program product to perform the anomaly detection steps discussed above amounts to no more than mere instructions to apply the exception using generic computer components. Thus, the process as defined by claims 1-11 or the system and computer program product as defined in claims 12-20 that recite the same steps do not integrate the claimed mental process steps into a practical application, or provide additional elements that amount to significantly more than the judicial exception or contribute an “inventive concept”. See MPEP 2106.05.II.
For these reasons, claims 1-20 are rejected under 35 U.S.C. 101 and are not patent eligible.
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.
Claim 11 is 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 pre-AIA the applicant regards as the invention.
Regarding claim 11, the language “determining and providing an indication of a feedback score corresponding to an improvement attributed to the anomaly detection feedback in predicting an anomaly compared to a previous version of the combined trained unsupervised machine learning model and trained supervised machine learning model” is non-sensical and thus indefinite. In particular, the language “an indication of a feedback score corresponding to an improvement” is unclear and does not appear to have a definite boundary (e.g., how is corresponding or improvement defined?).
Examiner requests clarification and recommends amending the claims with language that clearly sets forth the claimed invention. In the interim, and in the interests of compact prosecution, the claims have been interpreted as set forth below.
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.
Claims 1, 2, 8, 9, 12, 13 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Brouwers (US 2025/0044271) in view of Glas et al. (“Glas”)(US 2024/0275809).
Brouwers teaches a system and related method comprising:
(re: certain elements of claim 12) one or more processors; and a memory coupled to the one or more processors, wherein the memory is configured to provide the one or more processors with instructions (fig. 42 and para. 314-320 teaching control system including processor 993 and memory 994 configured to carry computer-executable instructions described below) which when executed cause the one or more processors to:
receive a training dataset for anomaly detection (para. 60, 76, 85, 90 teaching unsupervised learning step using unlabeled datasets for training and subsequent anomaly detection);
train an unsupervised machine learning model using at least a portion of the training dataset, to generate a trained unsupervised machine learning model (Id.);
train a supervised machine learning model using an output from the unsupervised machine learning model and an anomaly detection feedback associated with the output from the unsupervised machine learning model, to generate a trained supervised machine learning model (para. 14, 77, 155, 290 teaching training a supervised model using output of trained unsupervised ML model); and
provide both the trained unsupervised machine learning model and the trained supervised machine learning model for combined use in machine learning anomaly detection inference (para. 78-79 teaching making anomaly inference, i.e., prediction of “normal” or “anomalous” using the hybrid model);
(re: claim 18) wherein the memory is further configured to provide the one or more processors with instructions which when executed cause the one or more processors to detect an anomaly by performing a machine learning anomaly detection inference process,
wherein a prediction output of the trained unsupervised machine learning model is provided as an input to the trained supervised machine learning model (para. 76-78, 88-89 teaching use of prediction output from unsupervised machine learning model as input allows for detection of anomalous behavior);
(re: claims 19) wherein the unsupervised machine learning model is trained to reconstruct an input provided to the unsupervised machine learning model, and the unsupervised machine learning model provides the reconstructed input as an input to the trained supervised machine learning model (para. 14, 89, 208, 217-218 teaching reconstructing process data and using the output of the reconstruction error as input for the supervised machine learning model).
(re: claims 1, 2, 8, 9 and 20) The claimed method steps and related computer program product are taught in the normal operation of the combined system described below.
Brouwers as set forth above teaches all that is claimed except for expressly teaching
(re: certain elements of claims 1, 12) train a supervised machine learning model using anomaly detection feedback associated with the output from the unsupervised machine learning model;
(re: claims 2, 13) wherein the memory is further configured to provide the one or more processors with instructions which when executed cause the one or more processors to determine and provide an indication of a maturity of the supervised machine learning model.
Glas, however, teaches that it is well-known in the artificial intelligence arts to regularly update the machine learning model with user feedback and new training data to maintain accuracy of the model as well as the related maturity score (para. 76- 79).
It would thus be obvious to one with ordinary skill in the art to modify the base reference with these prior art teachings—with a reasonable expectation of success—to arrive at the claimed invention. The rationale for this obviousness determination can be found in the prior art itself as cited above. Further, the prior art discussed and cited demonstrates the level of sophistication of one with ordinary skill in the art and that these modifications are predictable variations that would be within this skill level. Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the invention of Brouwers for the reasons set forth above.
Claims 3-7, 10 and 14-17 are rejected under 35 U.S.C. 103 as being unpatentable over Brouwers and Glas (“Brouwers et al.”) as applied to the claims above, and further in view of Tora (US 2022/0277219), Delisle et al. (“Delisle”)(US 2023/02979888) and legal precedent.
Brouwers et al. as set forth above teach all that is claimed except for expressly teaching
(re: certain elements of claim 3) wherein determining the indication of the maturity of the supervised machine learning model includes performing a loss calculation;
(re: claims 4, 14) wherein the indication of the maturity of the supervised machine learning model is associated with a maturity score, and the maturity score is based on loss calculation results for three or more epochs associated with the supervised machine learning model;
(re: claims 5, 15) wherein determining the indication of the maturity includes converting a logarithmic value to a linear value;
(re: claims 6, 16) wherein the anomaly detection feedback includes one or more features identified by a user as contributing to a predicted anomaly;
(re: claims 7, 17) wherein the supervised machine learning model is trained at a first training rate that is greater than a second training rate at which the unsupervised machine learning model is trained;
(re: claim 10) wherein the supervised machine learning model is further trained to predict a severity score of a predicted anomaly.
Delisle, however, teaches that it is well known in the machine learning arts to calculate maturity scores using various methods—including a logarithmic loss function—and that the number of epochs in calculations can be varied (para. 68-69).
Tora further teaches that it is well known in the machine learning arts to adjust the learning rate and number of epochs in loss calculations and that the user can configure the various features and variables of the ML algorithm to test the received data (para. 30-32 teaching that learning rate and the number of epochs are well-known hyperparameters when configuring models and that a combination of supervised as well as non-supervised human-tagged or classified data can be used; para. 33 teaching user interface that allows user to select various features and configuration parameters—including what type of ML model).
Indeed, the claimed features relating to type of ML model, learning rates, number of epochs, severity and maturity scores can be regarded as common design parameters/operating variables controlled by the design incentives and/or economic considerations involved in this type of subject matter. This is especially applicable in the machine learning arts as demonstrated above. Moreover, legal precedent teaches that variations in these type of common design parameters/operating variables are obvious and are the mere optimization of result-effective variables that would be known to one with ordinary skill in the art. See MPEP 2144.05 I.II (teaching ample motivation to optimize or modify result-effective variables based on “design need(s)” or “market demand”).
It would thus be obvious to one with ordinary skill in the art to modify the combination of references with these prior art teachings—with a reasonable expectation of success—to arrive at the claimed invention as these modifications are already well-known and commonly implemented in the machine learning arts. The rationale for this obviousness determination can be found in the prior art itself as cited above and in legal precedent as described above.
Further, the prior art discussed and cited demonstrates the level of sophistication of one with ordinary skill in the art and that these modifications are predictable variations that would be within this skill level. Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the invention of Brouwers et al. for the reasons set forth above.
Conclusion
Any references not explicitly discussed above but made of record are regarded as helpful in establishing the state of the prior art and are thus considered relevant to the prosecution of the instant application.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSEPH C RODRIGUEZ whose telephone number is 571-272-3692 (M-F, 9 am – 6 pm, PST). The Supervisory Examiner is MICHAEL MCCULLOUGH, 571-272-7805.
Alternatively, to contact the examiner, send an E-mail communication to Joseph.Rodriguez@uspto.gov. Such E-mail communication should be in accordance with provisions of the MPEP (see e.g., 502.03 & 713.04; see also Patent Internet Usage Policy Article 5). E-mail communication must begin with a statement authorizing the E-mail communication and acknowledging that such communication is not secure and may be made of record. Please note that any communications with regards to the merits of an application will be made of record. A suggested format for such authorization is as follows: "Recognizing that Internet communications are not secure, I hereby authorize the USPTO to communicate with me concerning any subject matter of this application by electronic mail. I understand that a copy of these communications will be made of record in the application file”.
Information regarding the status of an application may also be obtained from the Patent Center: https://patentcenter.uspto.gov/
/JOSEPH C RODRIGUEZ/Primary Examiner, Art Unit 3655
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January 29, 2026