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, 19-20 are rejected under 35 U.S.C. 101 because:
Eligibility Step 1 (MPEP 2106. 3, subsection II): The claims, after reviewing the entire application disclosure, considered as a whole, are determined to be directed to one of the statutory category (processes, machines, manufactures, and compositions of matter): A method/apparatus/system.
Eligibility Step 2A (MPEP 2106. 4, subsection II):
Prong One: The claims recite the limitation of inputting values to a first machine-learned model to output a prediction residual associated with a second machine-learned model. This limitation as analyzed can “practically be performed in the Human Mind” with/without sketching on paper. As stated in MPEP 2106.04(a)(2), III. Mental Processes, “A claim that encompasses a Human Performing the step(s) mentally with or without a physical aid recites a mental process”; as a result, the claim recites a mental process that falls within at least one of the abstract idea groupings (MPEP 2106.04(a) Abstract Ideas: The enumerated groupings of abstract ideas: Mathematical concepts, Certain methods of organizing human activity, Mental processes). As a result, the claims recite a judicial exception.
Prong Two: The additional steps/actions/elements recited in the claims:
- generating a saliency map by using the first machine-learned model (Insignificant post solution activity (MPEP 2106.05(g))
When viewed in combination of as a whole, the recited additional steps/actions/elements do no more than add insignificant extra-solution to the judicial exception. As a result, these additional steps/actions/elements do not integrate the judicial exception into a practical application because they do not impose any meaningful limits on practicing the abstract idea. These claims are therefore directed to an abstract idea.
Eligibility Step 2B (MPEP 2106. 5: Whether a claim amounts to significantly more):
The additional step/action/element recited in the claims, generating a saliency map by using the machine-learned model, is well known in the field as evidenced by Brin et al. (US 2023/0111047), do not add an inventive concept to the claim because they do is no more than adding insignificant pre-solution and post-solution activities to the judicial exception.
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 1 and 19-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being incomplete for omitting essential structural cooperative relationships of elements, such omission amounting to a gap between the necessary structural connections. See MPEP § 2172.01.
The omitted structural cooperative relationships are the linking in between the generated saliency map and the root cause analysis, and in between the generated saliency map and the second machine-learned model.
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) 1-5, 7-14, 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Buhro et al. (US 2022/0306314) in view of Brin et al. (US 2023/0111047).
Regarding to claims 1, 19-20:
Buhro et al. discloses a method of root cause analysis, comprising:
providing, by a computing system comprising one or more computing devices, a plurality of input values to a first machine-learned model (FIG. 5, step 502: A data query from a source); and
generating, by the computing system using the first machine-learned model based on the plurality of input values, an output (FIG. 5, step 504: The determined likelihood reads on the claimed saliency map. Paragraph [0038]: Applying the plurality of signals to a first machine-learning model to output the likelihood);
wherein the first machine-learned model was trained to predict a prediction residual associated with a second machine-learned model (FIG. 5, step 506: Input the likelihood into a second machine-learned model).
Buhro et al. discloses the claimed invention as discussed above except wherein the output generated by using the first machine-learned model is a salient map.
Brin et al. discloses a machine learning model that has been trained for generating a saliency map for tokens in the training dataset to identify causes of failure (Abstract).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to modify Buhro’s first machine learning model to be able to generate the saliency map for tokens in the input dataset in order to identify causes of failure as taught by Brin et al. (Abstract).
Regarding to claims 2-3: wherein the plurality of input values comprises time series data, wherein the plurality of input values comprises measurements associated with a plurality of measurement channels (Buhro et al.: paragraph [0021]: A signal may include sensor data. It is conventional for the sensor data obtained in the time-series form).
Regarding to claims 4-5: wherein the first machine-learned model comprises at least one channel-wise layer, wherein the channel-wise layer is a convolutional layer (Buhro et al.: paragraph [0023]: A likelihood model is a machine learning model such as convolutional neural network).
Regarding to claims 7-8: wherein the plurality of measurement channels comprise measurement channels associated with an industrial process, wherein the second machine-learned model was trained to predict an outcome of the industrial process during normal operating behavior (Buhro et al.: paragraph [0021]: The measurement data is from sensors in an aircraft entity in the aircraft industry).
Regarding to claims 9-10 and 13-14: wherein the first machine-learned model was trained using a training dataset comprising prediction residuals of the second machine-learned model, wherein the prediction residuals were determined based on data associated with both normal and anomalous operating behavior of the industrial process, wherein the plurality of input values comprises one or more values associated with anomalous behavior of the industrial process, wherein the recommended maintenance action comprises a repair or replacement, wherein the recommended maintenance action comprises an inspection (Buhro et al.: paragraph [0021]: A likelihood of replacement of a hardware component may be derived for the maintenance purpose).
Regarding to claims 11-12 and 18: further comprising identifying, based on the saliency map, one or more root causes associated with anomalous operating behavior of the industrial process, and determining, by the computing system based at least in part on the saliency map, a recommended maintenance action associated with the one or more root causes, further comprising identifying, by the computing system based on the saliency map, a cause associated with a high absolute value of an output of the first machine-learned model, wherein the output corresponds to an expected prediction residual associated with the second machine-learned model (Brin et al.: Abstract: Based on the distributional properties, the saliency maps, and the classifications, identify causes of failure. In Brin et al., the saliency maps are the output of the machine learned model; as a result, in the combination with Buhro et al., the first machine-learned model generate such saliency maps that are used as the input for the second machine-learned model).
Claim(s) 6 and 15-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Buhro et al. (US 2022/0306314) in view of Brin et al. (US 2023/0111047), and further in view of Lad et al. (US 2022/0351373).
Buhro et al. discloses the claimed invention as discussed above except wherein the saliency map comprises a plurality of channel-wise saliencies indicative of a contribution of a respective measurement channel to a prediction of the first machine-learned model.
Lad et al. discloses a deep learning algorithm comprising generating saliency maps in form of a plurality of channel-wise saliencies, each associates with an input channel (FIG. 1: Saliency map Si associates with the input xi. Saliency map Sj associates with the input xj).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to modify Buhro’s first machine learning model, as modified, to be able to generate the saliency map in form of a plurality of channel-wise saliencies as taught by Lad et al. to greatly facilitate the interpretability of predictions (paragraph [0123]).
Regarding to claim 15: wherein generating a saliency map comprises: processing, by the computing system using at least one layer of the first machine-learned model, the input values to generate a machine-learned embedding, wherein the at least one layer comprises one or more weights and one or more activation functions; and processing, by the computing system based at least in part on the one or more weights, the embedding to generate a saliency map (Lad et al.: FIG. 8 and paragraphs [0020], [0074]).
Regarding to claim 16: wherein the one or more weights comprise a weight matrix, and processing based at least in part based on one or more weights comprises processing the embedding based on a transpose of the weight matrix (Lad et al.: paragraph [0149]).
Regarding to claim 17: wherein generating a saliency map further comprises aggregating, by the computing system, a plurality of processed values, wherein the processed values were determined by processing the embedding (Lad et al.: paragraph [0074]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LAM S NGUYEN whose telephone number is (571)272-2151.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, DOUGLAS RODRIGUEZ, can be reached on 571-431-0716. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LAM S NGUYEN/ Primary Examiner, Art Unit 2853
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