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 .
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application filed in China on 11/26/2021. It is noted, however, that applicant has not filed a certified copy of the CN 20211416769.8 application as required by 37 CFR 1.55. Please note the failure status report in the application’s file wrapper issued 07/14/2023.
Status of the Claims
Claims 1-5, 8-13, and 15-19 have been amended. Claims 6-7, 14, and 20 have been canceled. Claims 1-5, 8-13, and 15-19 are currently pending and have been considered by the Examiner.
Claim Objections
Claims 1, 5, 8-10, 15, and 19 are objected to because of the following informalities: In claim 1, line 4, Examiner suggests changing “is changed” to “has changed” or “has been changed”.
In claim 5, lines 6-7, the limitation “ranking the from the uncertainties from high to low” is grammatically incorrect. This limitation should recite “ranking the uncertainties from high to low”.
In claim 8, line 3, “microservice” should recite “a microservice”.
Claim 9 recites the same minor informality as claim 1. In claim 9, Examiner suggests deleting “and” from the end of line 9.
In claim 10, line 2, “comprises is” should recite “comprises”.
Claim 13 recites the same minor informality as claim 5. Claim 15 recites the same minor informality as claim 1. Claim 19 recites the same minor informality as claim 5. Appropriate correction is required.
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 9-13 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 9 recites the limitation "the plurality of target scenarios" in line 10. There is insufficient antecedent basis for this limitation in the claim. Examiner treats this limitation as “a plurality of target scenarios”.
Claims 10-13 are rejected for failing to cure the deficiencies of claim 9.
Claim 10 recites the limitation "the at least one indicator data" in line 10. There is insufficient antecedent basis for this limitation in the claim. Claim 9 recites “acquire two or more indicator data” in line 10. It is unclear if “the at least one indicator data” in claim 10, line 2 should recite “the two or more indicator data” which would have sufficient antecedent basis. Examiner treats this limitation in claim 10 as “the two or more indicator data”.
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 1-5, 8-13, and 15-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1
Step 2A Prong 1: Determining,
Selecting reference indicator data from the at least one indicator data based on the respective uncertainty of the detection result, wherein an uncertainty of the detection result corresponding to the reference indicator data is higher than the respective uncertainty of the detection result corresponding to non-reference indicator data among the at least one indicator data is a judgement mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Updating the target indicator detection model based on the reference indicator data and the labels is a mathematical calculation. Specification paragraphs [0059]-[0060] disclose minimizing an optimization function of a deep Gaussian model. The optimization process is equivalent to a Dropout deep neural network with a cross-entropy loss function and L2 regularization.
Identify one or more abnormalities of the cloud server, the one or more identified abnormalities causes intervention and debugging to be performed
Step 2A Prong 2: The method being executed by at least one processor amounts to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
Obtaining an indication that a configuration of a cloud server that a target indicator detection model is deployed to monitor is changed amounts to mere data-gathering, an insignificant pre-solution activity under MPEP 2106.05(g).
Based on obtaining the indication: acquiring at least one indicator data that corresponds to the changed configuration amounts to mere data-gathering, an insignificant pre-solution activity under MPEP 2106.05(g).
Applying the target indicator detection model amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Acquiring labels corresponding to the reference indicator data amounts to an insignificant post-solution activity under MPEP 2106.05(g).
Causing the updated target indicator detection model to be deployed amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
The cloud server amounts to a generic computer component for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
The additional elements as disclosed above, alone or in combination, do not integrate the abstract ideas into a practical application as they are mere generic computer functions as disclosed in combination with insignificant extra solution activities that are implemented to perform the abstract ideas disclosed above. The claim is directed to an abstract idea.
Step 2B: The method being executed by at least one processor amounts to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
Obtaining an indication that a configuration of a cloud server that a target indicator detection model is deployed to monitor is changed is analogous to receiving data over a network, which courts have recognized as well-understood, routine, conventional activity under MPEP 2106.05(d)(II).
Based on obtaining the indication: acquiring at least one indicator data that corresponds to the changed configuration is analogous to receiving data over a network, which courts have recognized as well-understood, routine, conventional activity under MPEP 2106.05(d)(II).
Applying the target indicator detection model amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Acquiring labels corresponding to the reference indicator data is analogous to retrieving data from memory, which courts have recognized as well-understood, routine, conventional activity under MPEP 2106.05(d)(II).
Causing the updated target indicator detection model to be deployed amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
The cloud server amounts to a generic computer component for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
The additional elements as disclosed above, in combination with the abstract ideas, are not sufficient to amount to significantly more than the abstract ideas as they are generic computer functions as disclosed in combination with well-understood, routine and conventional activities that are implemented to perform the abstract ideas disclosed above. The claim is not patent eligible.
Claim 2 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated. Generate a plurality of detection results corresponding to each of the at least one indicator data
Determine the uncertainties corresponding to the plurality of detection results is a judgement mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Step 2A Prong 2: The target indicator detection model comprises a random dropout neural network model that randomly drops inside neuron connections based on a preset dropout rate amounts to an insignificant extra-solution activity under MPEP 2106.05(g).
Multi-time forward propagation amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Step 2B: The target indicator detection model comprises a random dropout neural network model that randomly drops inside neuron connections based on a preset dropout rate amounts to well-understood, routine, conventional activity under MPEP 2106.05(d)(I). Gal et al. (“Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning”, cited in the PTO-892 issued 04/14/2026) provides Berkheimer evidence. Page 2, lines 2-3 discloses “Dropout is used in many models in deep learning as a way to avoid over-fitting”, and page 3, col. 1, lines 6-13 and lines 1-6 below equation 1 discloses the details of dropout as claimed . A preset dropout rate is pi.
Multi-time forward propagation amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 3 incorporates the rejection of claim 2.
Step 2A Prong 1: The abstract ideas of claim 2 are incorporated. The determining of the uncertainties comprises: determining at least one of a distribution variance or a standard deviation of the plurality of detection results, wherein the at least one of the distribution variance or the standard deviation is used to select the reference indicator data is a mathematical calculation. Specification paragraph [0063] and formula (7) discloses calculating a prediction variance.
Select the reference indicator data is a judgement mental process based on a mathematical calculation which can reasonably be performed in the human mind with the aid of pencil and paper.
Step 2A Prong 2 and Step 2B: The claim does not recite any additional elements which, alone or in combination, would integrate the abstract ideas into a practical application or which, in combination with the abstract ideas, would be sufficient to amount to significantly more than the abstract ideas. The claim is not patent eligible.
Claim 4 incorporates the rejection of claim 3.
Step 2A Prong 1: The abstract ideas of claim 3 are incorporated. Determining the detection result based on computing a mean of the plurality of detection results is a judgement mental process based on a mathematical calculation which can reasonably be performed in the human mind with the aid of pencil and paper. Specification paragraph [0063] and formula (5) discloses calculating a predicted mean.
Step 2A Prong 2 and Step 2B: The claim does not recite any additional elements which, alone or in combination, would integrate the abstract ideas into a practical application or which, in combination with the abstract ideas, would be sufficient to amount to significantly more than the abstract ideas. The claim is not patent eligible.
Claim 5 incorporates the rejection of claim 2.
Step 2A Prong 1: The abstract ideas of claim 2 are incorporated. The selecting the reference indicator data comprises at least one of: based on a first uncertainty of one of the plurality of detection results corresponding to a first indicator data exceeding a preset threshold, selecting the first indicator data as the reference indicator data is a judgement mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Ranking the uncertainties from high to low is a judgement mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Selecting a preset quantity of two or more indicator data ranking higher than a ranking threshold as the reference indicator data is a judgement mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Step 2A Prong 2 and Step 2B: The claim does not recite any additional elements which, alone or in combination, would integrate the abstract ideas into a practical application or which, in combination with the abstract ideas, would be sufficient to amount to significantly more than the abstract ideas. The claim is not patent eligible.
Claim 8 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated. Identify abnormalities by monitoring at least one of microservice, a physical entity, a logical entity, a network topology, or a log data
Step 2A Prong 2 and Step 2B: The updated target indicator detection model [being] trained amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
The cloud server amounts to a generic computer component for applying the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 9
Step 2A Prong 1: Determine,
Select reference indicator data from the at least one indicator data based on the respective uncertainty of the detection result, wherein an uncertainty of the detection result corresponding to the reference indicator data is higher than the respective uncertainty of the detection result corresponding to non-reference indicator data among the at least one indicator data is a judgement mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Update the target indicator data model based on the labels is a mathematical calculation. Specification paragraphs [0059]-[0060] disclose minimizing an optimization function of a deep Gaussian model. The optimization process is equivalent to a Dropout deep neural network with a cross-entropy loss function and L2 regularization.
Identify one or more abnormalities of the cloud server, the one or more identified abnormalities causes intervention and debugging to be performed
Step 2A Prong 2: An apparatus, comprising: at least one first memory configured to store a first program code, and at least one first processor, wherein the first program code is configured to cause the at least one first processor to execute operations amount to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
Obtain an indication that a configuration of a cloud server that a target indicator detection model is deployed to monitor is changed amounts to mere data-gathering, an insignificant pre-solution activity under MPEP 2106.05(g).
Acquire two or more indicator data for the plurality of target scenarios amounts to mere data-gathering, an insignificant pre-solution activity under MPEP 2106.05(g).
Applying the target indicator detection model amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Acquire labels corresponding to the reference indicator data amounts to an insignificant post-solution activity under MPEP 2106.05(g).
Cause the updated target indicator detection model to be deployed amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
The cloud server amounts to a generic computer component for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
The additional elements as disclosed above, alone or in combination, do not integrate the abstract ideas into a practical application as they are mere generic computer functions as disclosed in combination with insignificant extra solution activities that are implemented to perform the abstract ideas disclosed above. The claim is directed to an abstract idea.
Step 2B: An apparatus, comprising: at least one first memory configured to store a first program code, and at least one first processor, wherein the first program code is configured to cause the at least one first processor to execute operations amount to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
Obtain an indication that a configuration of a cloud server that a target indicator detection model is deployed to monitor is changed is analogous to receiving data over a network, which courts have recognized as well-understood, routine, conventional activity under MPEP 2106.05(d)(II).
Acquire two or more indicator data for the plurality of target scenarios is analogous to receiving data over a network, which courts have recognized as well-understood, routine, conventional activity under MPEP 2106.05(d)(II).
Applying the target indicator detection model amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Acquire labels corresponding to the reference indicator data is analogous to retrieving data from memory, which courts have recognized as well-understood, routine, conventional activity under MPEP 2106.05(d)(II).
Cause the updated target indicator detection model to be deployed amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
The cloud server amounts to a generic computer component for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
The additional elements as disclosed above, in combination with the abstract ideas, are not sufficient to amount to significantly more than the abstract ideas as they are generic computer functions as disclosed in combination with well-understood, routine and conventional activities that are implemented to perform the abstract ideas disclosed above. The claim is not patent eligible.
Claim 10 recites an apparatus which implements similar features as the method of claim 2 and is therefore rejected for at least the same reasons.
Claim 11 recites an apparatus which implements the same features as the method of claim 3 and is therefore rejected for at least the same reasons.
In Step 2A Prong 2 and Step 2B, the limitation of the first program code configured to cause the at least one first processor amounts to mere instructions for applying the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 12 recites an apparatus which implements the same features as the method of claim 4 and is therefore rejected for at least the same reasons.
In Step 2A Prong 2 and Step 2B, the limitation of the first program code configured to cause the at least one first processor amounts to mere instructions for applying the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 13 recites an apparatus which implements the same features as the method of claim 5 and is therefore rejected for at least the same reasons.
In Step 2A Prong 2 and Step 2B, the limitation of the first program code configured to cause the at least one first processor amounts to mere instructions for applying the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 15 recites a product which implements the same features as the method of claim 1 and is therefore rejected for at least the same reasons.
In Step 2A Prong 2 and Step 2B, the limitations of a non-transitory computer-readable medium storing instructions that cause at least one processor to execute operations amount to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claims 16-19 each recites an product which implements the same features as the method of claims 2-5, respectively, and are therefore rejected for at least the same reasons.
Claim Rejections - 35 USC § 103
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 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 8-9, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Susaiyah et al. (US 20250238720 A1, cited in PTO-892 issued 04/14/2026) in view of Smith et al. (US 20200151259 A1).
Regarding claim 1, Susaiyah teaches: A method, the method being executed by at least one processor, the method comprising: ([0018], lines 1-4)
acquiring at least one indicator data samples to be input to the first model. Each data sample is an indicator data point (an indicator datum) which indicates a state of a medical condition.)
determining, by the target indicator detection model, respective uncertainty of a detection result corresponding to respective indicator data among the at least one indicator data, wherein the respective uncertainty indicates a respective degree of reliability of the detection result; ([0076]-[0080] discloses classifying two instances (unlabeled input data) and determining uncertainty based on entropy. Case II is more uncertain (less reliable) than case I because the class probabilities for case II are more similar than for case I. The limitation “a detection result” is a classification having the highest posterior probability for each instance.)
selecting reference indicator data from the at least one indicator data based on the respective uncertainty of the detection result, wherein an uncertainty of the detection result corresponding to the reference indicator data is higher than the respective uncertainty of the detection result corresponding to non-reference indicator data among the at least one indicator data; ([0077]-[0080] discloses case II has a higher entropy (uncertainty) than case I, and the corresponding instance for case II should be sent for annotation. “Reference indicator data” is the instance for case II.)
acquiring labels corresponding to the reference indicator data; ([0076], lines 5-10)
updating the target indicator detection model based on the reference indicator data and the labels; and ([0076], lines 5-10)
causing the updated target indicator detection model to be deployed to identify one or more abnormalities ([0038]-[0039] and [0122], lines 15-16 discloses the updated first model is deployed in place of the original first model after retraining. The model detects abnormalities in heart rhythm.)
However, Susaiyah does not explicitly teach: obtaining an indication that a configuration of a cloud server that a target indicator detection model is deployed to monitor is changed;
based on obtaining the indication: acquiring at least one indicator data that corresponds to the changed configuration;
identify one or more abnormalities of the cloud server, the one or more identified abnormalities causes intervention and debugging to be performed on the cloud server.
But Smith teaches: obtaining an indication that a configuration of a cloud server that a target indicator detection model is deployed to monitor is changed; ([0095]-[0096], [0112], lines 7-11 discloses monitoring whether or not a service is available in a cloud computing environment. A state of the computing system would change if a service becomes unavailable. A “target indicator detection model” is the trained machine learning model.)
based on obtaining the indication: acquiring at least one indicator data that corresponds to the changed configuration; ([0097], lines 1-9, where indicator data includes a proportion of error codes to success codes in a log file.)
identify one or more abnormalities of the cloud server, the one or more identified abnormalities causes intervention and debugging to be performed on the cloud server. ([0098] to [0100], line 2 and [0102]-[0103])
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have incorporated Smith’s smart assistant into Susaiyah to acquire indicator data corresponding to a change in a cloud server, and intervene and debug the cloud server. A motivation for the combination is to apply Susaiyah’s uncertainty-based retraining to Smith’s field of detecting abnormalities in a computing environment. (Smith, [0102]-[0103])
Regarding claim 8, the combination of Susaiyah and Smith teaches: The method according to claim 1,
Susaiyah teaches: wherein the updated target indicator detection model is trained to identify abnormalities by monitoring at least one of microservice, a physical entity, a logical entity, a network topology, or a log data of the cloud server. ([0037]-[0038] discloses the first model may detect arrhythmia events by monitoring a heart using electrocardiogram (ECG) data. A heart is a physical entity.)
Regarding claim 9, Susaiyah teaches: An apparatus, comprising: at least one first memory configured to store a first program code; and ([0018], lines 1-4)
at least one first processor, wherein the first program code is configured to cause the at least one first processor to: ([0018], lines 1-4)
acquire two or more indicator data for the plurality of target scenarios; ([0032], lines 1-5, [0035], and [0044] disclose acquiring a pool of unlabeled data samples to be input to the first model. Each data sample is an indicator data point (an indicator datum) which indicates a state of a medical condition.)
determine, by the target indicator detection model, respective uncertainty of a detection result corresponding to respective indicator data among the at least one indicator data, wherein the respective uncertainty indicates a respective degree of reliability of the detection result; ([0076]-[0080] discloses classifying two instances (unlabeled input data) and determining uncertainty based on entropy. Case II is more uncertain (less reliable) than case I because the class probabilities for case II are more similar than for case I. The limitation “a detection result” is a classification having the highest posterior probability for each instance.)
select reference indicator data from the at least one indicator data based on the respective uncertainty of the detection result, wherein an uncertainty of the detection result corresponding to the reference indicator data is higher than the respective uncertainty of the detection result corresponding to non-reference indicator data among the at least one indicator data; ([0077]-[0080] discloses case II has a higher entropy (uncertainty) than case I, and the corresponding instance for case II should be sent for annotation. “Reference indicator data” is the instance for case II.)
acquire labels corresponding to the reference indicator data; ([0076], lines 5-10)
update the target indicator data model based on the labels; and ([0076], lines 5-10)
cause the updated target indicator detection model to be deployed to identify one or more abnormalities ([0038]-[0039] and [0122], lines 15-16 discloses the updated first model is deployed in place of the original first model after retraining. The model detects abnormalities in heart rhythm.)
However, Susaiyah does not explicitly teach: obtain an indication that a configuration of a cloud server that a target indicator detection model is deployed to monitor is changed;
cause the updated target indicator detection model to be deployed to identify one or more abnormalities of the cloud server, the one or more identified abnormalities causes intervention and debugging to be performed on the cloud server.
But Smith teaches: obtain an indication that a configuration of a cloud server that a target indicator detection model is deployed to monitor is changed; ([0095]-[0096], [0112], lines 7-11 discloses monitoring whether or not a service is available in a cloud computing environment. A state of the computing system would change if a service becomes unavailable. A “target indicator detection model” is the trained machine learning model.)
identify one or more abnormalities of the cloud server, the one or more identified abnormalities causes intervention and debugging to be performed on the cloud server. ([0098] to [0100], line 2 and [0102]-[0103])
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have incorporated Smith’s smart assistant into Susaiyah to acquire indicator data corresponding to a change in a cloud server, and intervene and debug the cloud server. A motivation for the combination is to apply Susaiyah’s uncertainty-based retraining to Smith’s field of detecting abnormalities in a computing environment. (Smith, [0102]-[0103])
Claim 15 recites a product which implements the same features as the method of claim 1 and is therefore rejected for at least the same reasons.
Susaiyah teaches: A non-transitory computer-readable medium storing instructions that cause at least one processor to: ([0018], lines 1-4)
Claims 2-5, 10-13, and 16-19 are rejected under 35 U.S.C. 103 as being unpatentable over Susaiyah et al. (US 20250238720 A1, cited in PTO-892 issued 04/14/2026) in view of Smith et al. (US 20200151259 A1) and Gal et al. (“Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning”, cited in PTO-892 issued 04/14/2026).
Regarding claim 2, the combination of Susaiyah and Smith teaches: The method according to claim 1,
Susaiyah teaches: wherein the target indicator detection model comprises a
wherein the
determines the uncertainties corresponding to the plurality of detection results. (In [0078]-[0080], the entropy for each instance is calculated based on the posterior probability measures for the two classes A and B.)
However, Susaiyah and Smith do not explicitly teach: a random dropout neural network model that randomly drops inside neuron connections based on a preset dropout rate; and
wherein the random dropout neural network model: generates a plurality of detection results corresponding to each of the at least one indicator data based on multi-time forward propagation;
But Gal teaches: a random dropout neural network model that randomly drops inside neuron connections based on a preset dropout rate; (Page 3, col. 1, lines 13-20 and lines 1-6 below equation 1. A preset dropout rate is pi)
wherein the random dropout neural network model: generates a plurality of detection results corresponding to each of the at least one indicator data based on multi-time forward propagation; and (Page 3, col. 1, lines 17-18 and page 4, col. 1, start of § 4 to line 4 below equation 6 discloses obtaining T stochastic forward passes to obtain observed outputs y (“a plurality of detection results”) corresponding to inputs x (“indicator data”). The limitation “multi-time forward propagation” includes performing T stochastic forward passes of input data points x. Since the neural network classifies handwritten digits (see page 6, § 5.2, lines 12-18) the input images are indicator data and output classifications are detection results.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have substituted Susaiyah’s neural network model with Gal’s dropout neural network model, and to have computed uncertainty based on Gal’s calculations. A motivation for the combination is that Gal’s calculations better reflects classification uncertainty far from the training data when compared to softmax outputs. (Gal, page 1, col. 2, lines 5-17)
Regarding claim 3, the combination of Susaiyah, Smith, and Gal teaches: The method according to claim 2, wherein the determining of the uncertainties comprises:
Susaiyah teaches: select the reference indicator data. ([0077]-[0080] discloses case II has a higher entropy (uncertainty) than case I, and the corresponding instance for case II should be sent for annotation. “Reference indicator data” is the instance for case II.)
However, Susaiyah and Smith do not explicitly teach: determining at least one of a distribution variance or a standard deviation of the plurality of detection results, wherein the at least one of the distribution variance or the standard deviation is used to select the reference indicator data.
But Gal teaches: determining at least one of a distribution variance or a standard deviation of the plurality of detection results, (Page 4, col. 1, from the line “We estimate the second raw moment” to equation 7 in col. 2 discloses calculating a detection result variance.)
wherein the at least one of the distribution variance or the standard deviation is used to [determine the uncertainties]
In the combination of references, the determined uncertainties are used to select reference indicator data, and therefore the detection result variance would be used to select the reference indicator data in the combination. A motivation for the combination is the same as the motivation given for claim 2.
Regarding claim 4, the combination of Susaiyah, Smith, and Gal teaches: The method according to claim 3, further comprising:
However, Susaiyah and Smith do not explicitly teach: determining the detection result based on computing a mean of the plurality of detection results.
But Gal teaches: determining the detection result based on computing a mean of the plurality of detection results. (Page 4, col. 1, equation 6 discloses calculating a mean based on observed outputs y, which corresponds to “the plurality of detection results”.)
A motivation for the combination is the same as the motivation given for claim 2.
Regarding claim 5, the combination of Susaiyah, Smith, and Gal teaches: The method according to claim 2,
Susaiyah teaches: wherein the selecting the reference indicator data comprises at least one of: based on a first uncertainty of one of the plurality of detection results corresponding to a first indicator data exceeding a preset threshold, selecting the first indicator data as the reference indicator data; or ([0076] discloses comparing confidence of a model output to a threshold confidence level, and the confidence may be described using entropy of the posterior probability. An entropy that falls below the threshold confidence level corresponds to “exceeding a preset threshold” when the objective is to find low-confidence results.)
ranking the from the uncertainties from high to low, and selecting a preset quantity of two or more indicator data ranking higher than a ranking threshold as the reference indicator data.
Claims 10-13 each recites an apparatus which implements the same features as the method of claims 2-5, respectively, and are therefore rejected for at least the same reasons.
Claims 16-19 each recites an product which implements the same features as the method of claims 2-5, respectively, and are therefore rejected for at least the same reasons.
Response to Arguments
The following is the Examiner’s responses to the Applicant’s arguments filed 07/14/2026.
Applicant’s First Arguments Under 35 U.S.C. 101 (Pages 16-18): Applicant argues claim 1 is patent eligible. Applicant argues the human mind cannot practically perform the steps recited by claim 1 in lines 3-4 and 17-22. Applicant argues the claim as a whole integrates the alleged exception into a practical application, as evidenced by the Specification.
Examiner’s Response: Applicant’s arguments have been fully considered but they are not persuasive. In the 101 analysis for claim 1, in Step 2A Prong 1, the limitation “updating the target indicator detection model based on the reference indicator data and the labels” is a mathematical calculation. Specification paragraphs [0059]-[0060] disclose minimizing an optimization function of a deep Gaussian model. The optimization process is equivalent to a Dropout deep neural network with a cross-entropy loss function and L2 regularization.
The limitations “identify one or more abnormalities of the cloud server, the one or more identified abnormalities causes intervention and debugging to be performed” are observation and judgement mental processes which can reasonably be performed in the human mind with the aid of pencil and paper and/or a computer. The final sentence of specification paragraph [0041] discloses, “The main purpose of monitoring whether the indicator data in the above target business scenario is abnormal or not is to judge whether faults exist in the business scenario or not in time so that related operation and maintenance personnel can perform timely intervention and debugging.” Personnel performing intervention and debugging indicates these steps can be performed by a person.
MPEP 2106.05(a) states, “It is important to note, the judicial exception alone cannot provide the improvement.” MPEP 2106.05(a), II. states, “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology.”
Page 17 of the remarks argues, “For example, a human mind cannot practically determine, by the neural network model, updating the target indicator detection model…” Assuming the neural network model refers to a target indicator detection model, claim 1 does not recite any step “determine, by the neural network model, updating the target indicator detection model.” Rather, claim 1, lines 17-19 recites “updating the target indicator detection model…” without the feature “determine, by the neural network model”. As explained above, updating the target indicator detection model is a mathematical calculation.
In Step 2A Prong 2, obtaining an indication that a configuration of a cloud server that a target indicator detection model is deployed to monitor is changed amounts to mere data-gathering, an insignificant pre-solution activity under MPEP 2106.05(g). Causing the updated target indicator detection model to be deployed amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f). The cloud server amounts to a generic computer component for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
The additional elements as disclosed above, alone or in combination, do not integrate the abstract ideas into a practical application as they are mere generic computer functions as disclosed in combination with insignificant extra solution activities that are implemented to perform the abstract ideas disclosed above. The claim is directed to an abstract idea. Examiner respectfully disagrees with the argument that these additional elements improves the functioning of a computer or improves another technology or technical field.
Applicant’s Second Arguments Under 35 U.S.C. 101 (Pages 19-20): Similar to Ex Parte Desjardins, the claimed invention provides an improvement in the functioning of a computer, or an improvement to other technology or a technical field. For example, the Specification describes the technical problem, which is lack of training data to perform supervised learning especially when a neural network model needs to adapt into a new configuration of a cloud server. [Note: “adapt into” is treated as “adapt to”]
As a result, neural network models can be dynamically updated to monitor various aspects of the cloud server (e.g., network topology) with higher performance with low cost (e.g., less ground truth for supervised training) to comply with mode or any other configuration changes of the cloud server. Therefore, Applicant respectfully submits that claim 1 as a whole is directed to an improvement in the functioning of a computer or an improvement to other technology or a technical field.
Examiner’s Response: Applicant's arguments have been fully considered but they are not persuasive. Examiner respectfully disagrees that pending claim 1 is similar to Ex Parte Desjardins. The claims in Desjardins solve a technical problem of catastrophic forgetting in machine learning. The limitations in pending claim 1 as a whole are NOT an analogous factual setting to the claims at issue in Desjardins.
As explained in the Examiner’s response to the Applicant’s first arguments, the limitation “updating the target indicator detection model based on the reference indicator data and the labels” is a mathematical calculation. Specification paragraphs [0059]-[0060] disclose minimizing an optimization function of a deep Gaussian model. The optimization process is equivalent to a Dropout deep neural network with a cross-entropy loss function and L2 regularization. Monitoring log data of a cloud server for abnormalities (recited by claims 1 and 8) is a judgment and observation mental process which can reasonably be performed in the human mind with the aid of pencil and paper. Dynamically updating a neural network model to monitor at least log data cannot provide a technical improvement because these are abstract ideas (see MPEP 2106.05(a) and (a) subsection II).
With respect to the claim 1 features of acquiring reference indicator data and labels for updating a neural network model, these limitations amount to insignificant extra-solution activity in Step 2A Prong 2. Acquiring supervised learning data for updating a neural network model does not integrate the abstract ideas into a practical application, as they are insignificant extra solution activities that are implemented to perform the abstract ideas.
Applicant’s Arguments Under 35 U.S.C. 103 (Pages 21-23): Susaiyah does not teach the limitations of claim 1, lines 3-4 and 17-22. That is, Susaiyah does not only describe that the neural network models are to monitor abnormalities of a cloud server but also being updated based on obtaining an indication that a configuration of a cloud server that a target indicator detection model is deployed to monitor is changed. Therefore, Applicant respectfully submits Susaiyah does not disclose each and every element of claim 1.
Examiner’s Response: Applicant’s arguments have been fully considered. Susaiyah at [0038]-[0039], [0076], lines 5-10, and [0122], lines 15-16 discloses updating a model and deploying the model to detect abnormalities in heart rhythm.
Applicant’s arguments with respect to the remaining limitations in claim 1, lines 3-4 and 17-22 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/A.H.J./Examiner, Art Unit 2127
/JEREMY L STANLEY/Examiner, Art Unit 2127
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