DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This non-final office action is in response to the application filed 30 May 2024.
Claims 1-16 are pending. Claims 1, 9, and 16 are independent claims.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d).
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 30 May 2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The examiner accepts the drawings filed 30 May 2024.
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 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Step 1:
According to Step 1 of the two Step analysis, claims 1-8 are directed toward a method (process). Claims 9-15 are directed toward a method (process). Claim 16 is directed toward a system (machine). Therefore, each of these claims falls within one of the four statutory categories.
Claim 1:
Step 2A, Prong 1:
The claim recites:
extracting data for a specific time and data segments corresponding to a period before the specific time from target time-series data (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation to extract data for a specific time and data segments corresponding to a period before the specific time from time-series data)
predicting a conditional score for the data segments through the trained score predictor and conducting an anomaly determination for the data for the specific time using the predicted conditional score (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation to predict a conditional score and determine an anomaly for the period using the conditional score)
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional element:
acquiring a score predictor trained using normal time-series data, wherein the score predictor is a deep learning model configured to output a conditional score for previous time-series data and the conditional score represents a gradient of data density
The additional element is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The claim recites the additional element:
acquiring a score predictor trained using normal time-series data, wherein the score predictor is a deep learning model configured to output a conditional score for previous time-series data and the conditional score represents a gradient of data density
The additional element is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 2:
With respect to claim 2, the claim depends upon claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites the abstract idea identified in claim 1.
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional element:
wherein the score predictor includes a convolution layer that performs a one-dimensional (1D) convolution operation
This element is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The claim recites the additional element:
wherein the score predictor includes a convolution layer that performs a one-dimensional (1D) convolution operation
This element is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 3:
With respect to claim 3, the claim depends upon claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites the abstract idea identified in claim 1.
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional element:
wherein the score predictor is configured based on a neural network with a U-Net architecture
This element is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The claim recites the additional element:
wherein the score predictor is configured based on a neural network with a U-Net architecture
This element is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 4:
With respect to claim 4, the claim depends upon claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
wherein the conducing the anomaly determination for data for the specific time comprises (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation to predict a conditional score and determine an anomaly for the period using the conditional score):
extracting noise samples from a prior distribution (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation to identify and extract noise samples from a prior distribution)
predicting a conditional score of the noise samples for the data segment by inputting the noise samples and the data segments to the trained score predictor (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation to predict a conditional score based upon noise samples and data segments)
generating synthetic data corresponding to the specific time by updating the noise samples using the predicted conditional score (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation to create synthetic data based upon the evaluated predicted conditional scores and noise samples)
determining the data for the specific time as being abnormal when a reconstruction loss between the data for the specific time and the synthetic data exceeds a threshold (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a judgement to determine that data is abnormal when the loss exceeds a threshold (evaluation))
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 5:
With respect to claim 5, the claim depends upon claim 4. The analysis of claim 4 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
a plurality of synthetic data are generate from different noise samples extracted from the prior distribution (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation to generate a plurality of synthetic data, with the aid of pencil and paper, from noise samples and prior distribution)
the determining the data for the specific time as being abnormal, comprises: aggregating reconstruction losses for the plurality of synthetic data and determining the data for the specific time as being abnormal when the aggregated reconstruction loss exceeds the threshold (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation to aggregate reconstruction losses and determine data is abnormal when the aggregate loss exceeds a threshold)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 6:
With respect to claim 6, the claim depends upon claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
wherein the conducting the anomaly determination for the data for the specific time comprises (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation to predict a conditional score and determine an anomaly for the period using the conditional score):
determining an Ordinary Differential Equation (ODE) corresponding to a Stochastic Differential Equation (SDE) used in a synthetic data generation process, wherein the SDE is an equation using the predicted conditional score as a coefficient (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation to determine an ODE corresponding to a SDE)
calculating a conditional probability of the data for the specific time for the data segments using the ODE (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation to calculate a conditional probability of the data)
determining the data for the specific time as being abnormal when the calculation conditional probability is less than a threshold (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a judgement that data is abnormal when the calculation conditional probability is less than a threshold)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 7:
With respect to claim 7, the claim depends upon claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
wherein the conducting the anomaly determination for the data for the specific time, comprises determining the data for the specific time as being normal when a magnitude of the predicted conditional score is less than or equal to a threshold (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a judgement that data is normal when the predicted conditional score is less than or equal to a threshold)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 8:
With respect to claim 8, the claim depends upon claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
wherein the conducting the anomaly determination for the data for the specific time, comprises calculating a loss for the predicted conditional score using a loss function used in training the score predictor and determining the data for the specific time as being abnormal when the calculated loss exceeds a threshold (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation to calculate a loss based on a loss function and determining data as being abnormal based upon an observation that the calculated loss exceeds a threshold)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 9:
Step 2A, Prong 1:
The claim recites:
extracting data for a specific time and data segments corresponding to a period before the specific time from target time-series data (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation to extract data for a specific time and data segments corresponding to a period before the specific time from time-series data)
generating second data segments by adjusting the first data segments through the trained score predictor (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation to generate second data segments, with the aid of pencil and paper, by adjusting the first data segments)
predicting a conditional score for the second data segments through the trained score predictor and conducting an anomaly determination for the data for the specific time using the predicted conditional score (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation to predict a conditional score and determine an anomaly for the period using the conditional score)
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional element:
acquiring a score predictor trained using normal time-series data, wherein the score predictor is a deep learning model configured to output a conditional score for previous time-series data and the conditional score represents a gradient of data density
The additional element is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The claim recites the additional element:
acquiring a score predictor trained using normal time-series data, wherein the score predictor is a deep learning model configured to output a conditional score for previous time-series data and the conditional score represents a gradient of data density
The additional element is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 10:
With respect to claim 10, the claim depends upon claim 9. The analysis of claim 9 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
wherein the generating the second data segments, comprises (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation to generate second data segments, with the aid of pencil and paper, by adjusting the first data segments):
generating noisy data segments by adding noise to the first data segment (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation to generate second data segments, with the aid of pencil and paper, by adjusting the first data segments by adding noisy data segments)
predicting a score of the noisy data segments by inputting the noisy data segments to the trained score predictor (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation to predict a conditional score based upon noise samples and data segments)
generating the second data segments by updating the noisy data segments using the predicted score (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation to create second data segments based upon the evaluated predicted conditional scores and noise samples)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 11:
With respect to claim 11, the claim depends upon claim 10. The analysis of claim 10 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
the predicted score is a general score calculated in a state where previous time-series data of the noisy data segments is not input to the trained score predictor (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation calculate a predicted score without including previous time-series data of the noisy data segments)
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional element:
the score predictor is trained using a first loss function related to the conditional score for the previous time-series data and the second loss function relates to a general score that does not condition on the previous time-series data
This element is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The claim recites the additional element:
the score predictor is trained using a first loss function related to the conditional score for the previous time-series data and the second loss function relates to a general score that does not condition on the previous time-series data
This element is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 12:
With respect to claim 12, the claim depends upon claim 9. The analysis of claim 9 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
wherein the conducing the anomaly determination for data for the specific time comprises (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation to predict a conditional score and determine an anomaly for the period using the conditional score):
extracting noise samples from a prior distribution (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation to identify and extract noise samples from a prior distribution)
predicting a conditional score of the noise samples for the second data segments by inputting the noise samples and the second data segments to the trained score predictor (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation to predict a conditional score based upon noise samples and data segments)
generating synthetic data corresponding to the specific time by updating the noise samples using the predicted conditional score (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation to create synthetic data based upon the evaluated predicted conditional scores and noise samples)
determining the data for the specific time as being abnormal when a reconstruction loss between the data for the specific time and the synthetic data exceeds a threshold (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a judgement to determine that data is abnormal when the loss exceeds a threshold (evaluation))
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 13:
With respect to claim 13, the claim depends upon claim 9. The analysis of claim 9 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
wherein the conducting the anomaly determination for the data for the specific time comprises (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation to predict a conditional score and determine an anomaly for the period using the conditional score):
determining an Ordinary Differential Equation (ODE) corresponding to a Stochastic Differential Equation (SDE) used in a synthetic data generation process, wherein the SDE is an equation using the predicted conditional score as a coefficient (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation to determine an ODE corresponding to a SDE)
calculating a conditional probability of the data for the specific time for the data segments using the ODE (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation to calculate a conditional probability of the data)
determining the data for the specific time as being abnormal when the calculation conditional probability is less than a threshold (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a judgement that data is abnormal when the calculation conditional probability is less than a threshold)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 14:
With respect to claim 14, the claim depends upon claim 9. The analysis of claim 9 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
wherein the conducting the anomaly determination for the data for the specific time, comprises determining the data for the specific time as being normal when a magnitude of the predicted conditional score is less than or equal to a threshold (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a judgement that data is normal when the predicted conditional score is less than or equal to a threshold)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 15:
With respect to claim 15, the claim depends upon claim 9. The analysis of claim 9 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
wherein the conducting the anomaly determination for the data for the specific time, comprises calculating a loss for the predicted conditional score using a loss function used in training the score predictor and determining the data for the specific time as being abnormal when the calculated loss exceeds a threshold (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation to calculate a loss based on a loss function and determining data as being abnormal based upon an observation that the calculated loss exceeds a threshold)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 16:
With respect to independent claim 16, the claim recites the limitations substantially similar to those in claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites the abstract idea identified with respect to claim 1.
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional element:
at least one process and a memory storing a computer program executed by the at least one processor, wherein the computer program includes instructions for performing operations
The additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The claim recites the additional element:
at least one process and a memory storing a computer program executed by the at least one processor, wherein the computer program includes instructions for performing operations
The additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
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.
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 1-16 are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. (WO 2023/059396, published 13 April 2023, hereafter Huang) and further in view of Savalle et al. (US 2021/0294818, published 23 September 2021, hereafter Savalle) and further in view of Kreis et al. (US 2023/0377099, filed 18 May 2023, hereafter Kreis).
As per independent claim 1, Huang discloses an anomaly detection method performed by at least one computing device, comprising:
extracting data for a specific time and data segments corresponding to a period before the specific time from target time-series data (page 4, lines 18-26: Here, input data is multivariate time-series data formed by multiple time-series data of an observed entity. This dataset may be segmented using a sliding window of length n to obtain data for a specific time and data segments corresponding to a period before the specific time)
predicting a conditional score for the data segment through the trained score predictor (page 6, lines 5-15: Here, a predicted value (conditional score) for data is generated by a forecasting-based model (trained score predictor)) and conducting an anomaly determination for the data for the specific time using the predicted conditional score (page 6, lines 20-28: Here, an anomaly is detected based upon the prediction value calculated by the forecasting-based model and the reconstruction probability obtained from the reconstruction-based model)
Huang fails to specifically disclose:
acquiring a score predictor trained using normal time-series data, wherein the score predictor is a deep learning model configured to output a conditional score for previous time-series data and the conditional score representing a gradient of data density
However, Savalle, which is analogous to the claimed invention because it is directed toward anomaly detection, discloses:
acquiring a score predictor trained using normal time-series data (paragraph 0104: Here, a model is trained for forecasting using time-series data), wherein the score predictor is a deep learning model configured to output a conditional score for previous time-series (paragraph 0111: Here, a deep-learning model is trained and used to produce precise KPI predictions and detect anomalies)
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Savalle with Huang, with a reasonable expectation of success, as it would have allowed for using a deep learning model to leverage large amounts of training data, to identify anomalies in data (Savalle: paragraph 0111).
Further, Kreis, which is analogous to the claimed invention because it is directed toward synthesizing data, discloses and the conditional score representing a gradient of data density (paragraph 0028-0029: Here, a score function is based on the gradient of the log density). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Kreis with Huang-Savalle, with a reasonable expectation of success, as it would have allowed for performing denoising diffusion to improve the synthesis quality of predicted data (Kreis: paragraph 0028).
As per dependent claim 2, Huang, Savalle, and Kreis disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Huang discloses wherein the score predictor includes a convolution layer that performs a one-dimensional (1D) convolution operation (page 4, lines 27-31: Here, a 1D convolution layer performs pre-processing of time-series data for use by the forecasting-based model and the reconstruction-based model).
As per dependent claim 3, Huang, Savalle, and Kreis disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Kreis discloses wherein the score predictor is configured based on a neural network with U-Net architecture (paragraph 0043). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Kreis with Huang-Savalle, with a reasonable expectation of success, as it would have allowed for performing denoising diffusion to improve the synthesis quality of predicted data (Kreis: paragraph 0028).
As per dependent claim 4, Huang, Savalle, and Kreis disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Huang discloses generating synthetic data corresponding to the specific time by updating the samples using the predicted conditional source and determining the data for the specific time as being abnormal when a reconstruction loss between the data for the specific time and the synthetic data exceeds a threshold (page 6, lines 5-15: Here, a predicted value (conditional score) for data is generated by a forecasting-based model (trained score predictor)) and an anomaly is detected based upon the prediction value calculated by the forecasting-based model and the reconstruction probability obtained from the reconstruction-based model).
Huang fails to specifically disclose:
extracting noise samples from a prior distribution
predicting a conditional score of the noise samples for the data segments by inputting the noise samples and the data segments to the trained score predictor
However, Kreis, which is analogous to the claimed invention because it is directed toward synthesizing data, discloses:
extracting noise samples from a prior distribution (paragraph 0030: Here, noise is added to a training sample. A trained network then generates synthetic samples and a prediction to denoise the samples)
predicting a conditional score of the noise samples for the data segments by inputting the noise samples and the data segments to the trained score predictor (paragraph 0030: Here, noise is added to a training sample. A trained network then generates synthetic samples and a prediction to denoise the samples)
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Kreis with Huang-Savalle-Kreis, with a reasonable expectation of success, as it would have allowed for performing a denoising operation to improve models (Kreis: paragraph 0030).
As per dependent claim 5, Huang, Savalle, and Kreis disclose the limitations similar to those in claim 4, and the same rejection is incorporated herein. Huang discloses the determining the data for the specific time as being abnormal, comprise: aggregating reconstruction losses for the plurality of synthetic data and determining the data for the specific time as being abnormal when the aggregated reconstructions loss exceeds the threshold page 6, lines 5-15: Here, a predicted value (conditional score) for data is generated by a forecasting-based model (trained score predictor)) and an anomaly is detected based upon the prediction value calculated by the forecasting-based model and the reconstruction probability obtained from the reconstruction-based model).
Kreis discloses wherein a plurality of synthetic data are generated from different noise samples extracted from the prior distribution (paragraph 0030: Here, noise is added to a training sample. A trained network then generates synthetic samples and a prediction to denoise the samples).
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Kreis with Huang-Savalle-Kreis, with a reasonable expectation of success, as it would have allowed for performing a denoising operation to improve models (Kreis: paragraph 0030).
As per dependent claim 6, Huang, Savalle, and Kreis disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Huang discloses determining the data for the specific time as being abnormal when the calculated condition probability is greater than a threshold (page 6, lines 5-15: Here, a predicted value (conditional score) for data is generated by a forecasting-based model (trained score predictor)) and an anomaly is detected based upon the prediction value calculated by the forecasting-based model and the reconstruction probability obtained from the reconstruction-based model).
Huang fails to specifically disclose wherein data is identified as abnormal when the value is less than a threshold. However, the examiner takes official notice that it was notoriously well-known in the art at the time of the applicant’s effective filing date to perform comparisons using thresholds and triggering operations when a value is below a threshold. It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined the well-known with Huang-Savalle-Kreis, with a reasonable expectation of success, as it would have allowed for triggering an action, such as identifying contents as abnormal, based upon the value not meeting a threshold.
Additionally, Kries discloses wherein the conducting the anomaly determination for the data for the specific time, comprises:
determining an Ordinary Differential Equation (ODE) corresponding to a Stochastic Differential Equation (SDE) used in a synthetic data generation process, wherein the SDE is an equation using the predicted conditional score as a coefficient (paragraph 0028)
calculating a conditional probability of the data for the specific time for the data segments using the ODE (paragraph 0031)
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Kreis with Huang-Savalle-Kreis, with a reasonable expectation of success, as it would have allowed for performing a denoising operation to improve models (Kreis: paragraph 0030).
As per dependent claim 7, Huang, Savalle, and Kreis disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Huang discloses wherein the conducting the anomaly determination for the data for the specific time, comprises determining the data for the specific time as being normal when the magnitude of the predicted conditional score is less than or equal to a threshold (page 4, lines 18-26: Here, if the magnitude of the score is greater than a threshold, it is determined that an anomaly is present at the timestamp. Therefore, if the magnitude of the score is less than or equal to a threshold, then no anomaly is present at the timestamp. The examiner interprets the absence of an anomaly to equate to a “normal” state).
As per dependent claim 8, Huang, Savalle, and Kreis disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Huang discloses wherein the conducting the anomaly determination for the data for the specific time, comprises: calculating a loss for the predicted conditional score using a loss function used in training the score predictor (page 12, line 28- page 13, line 4), and determining the data for the specific time as being abnormal when the calculated loss exceeds a threshold (page 12, lines 5-25).
As per independent claim 9, Huang discloses an anomaly detection method performed by at least one computing device, comprising:
extracting data for a specific time and data segments corresponding to a period before the specific time from target time-series data (page 4, lines 18-26: Here, input data is multivariate time-series data formed by multiple time-series data of an observed entity. This dataset may be segmented using a sliding window of length n to obtain data for a specific time and data segments corresponding to a period before the specific time)
generating second data segments by adjusting the first data segments through the trained score predictor (page 4, lines 18-26: Here, input data is multivariate time-series data formed by multiple time-series data of an observed entity. This dataset may be segmented using a sliding window of length n to obtain data for a specific time and data segments corresponding to a period before the specific time)
predicting a conditional score for the second data segment through the trained score predictor (page 6, lines 5-15: Here, a predicted value (conditional score) for data is generated by a forecasting-based model (trained score predictor)) and conducting an anomaly determination for the data for the specific time using the predicted conditional score (page 6, lines 20-28: Here, an anomaly is detected based upon the prediction value calculated by the forecasting-based model and the reconstruction probability obtained from the reconstruction-based model)
Huang fails to specifically disclose:
acquiring a score predictor trained using normal time-series data, wherein the score predictor is a deep learning model configured to output a conditional score for previous time-series data and the conditional score representing a gradient of data density
However, Savalle, which is analogous to the claimed invention because it is directed toward anomaly detection, discloses:
acquiring a score predictor trained using normal time-series data (paragraph 0104: Here, a model is trained for forecasting using time-series data), wherein the score predictor is a deep learning model configured to output a conditional score for previous time-series (paragraph 0111: Here, a deep-learning model is trained and used to produce precise KPI predictions and detect anomalies)
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Savalle with Huang, with a reasonable expectation of success, as it would have allowed for using a deep learning model to leverage large amounts of training data, to identify anomalies in data (Savalle: paragraph 0111).
Further, Kreis, which is analogous to the claimed invention because it is directed toward synthesizing data, discloses and the conditional score representing a gradient of data density (paragraph 0028-0029: Here, a score function is based on the gradient of the log density). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Kreis with Huang-Savalle, with a reasonable expectation of success, as it would have allowed for performing denoising diffusion to improve the synthesis quality of predicted data (Kreis: paragraph 0028).
As per dependent claim 10, Huang, Savalle, and Kreis disclose the limitations similar to those in claim 9, and the same rejection is incorporated herein. Kries discloses:
generating noisy data segments by adding noise to the first data segments paragraph 0030: Here, noise is added to a training sample. A trained network then generates synthetic samples and a prediction to denoise the samples)
predicting a score of the noisy data segments by inputting the noisy data segments to the trained score predictor paragraph 0030: Here, noise is added to a training sample. A trained network then generates synthetic samples and a prediction to denoise the samples)
generating the second data segments by updating the noisy data segments using the predicted score (paragraph 0030: Here, noise is added to a training sample. A trained network then generates synthetic samples and a prediction to denoise the samples)
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Kreis with Huang-Savalle-Kreis, with a reasonable expectation of success, as it would have allowed for performing a denoising operation to improve models (Kreis: paragraph 0030).
As per claim 11, Huang, Savalle, and Kreis disclose the limitations similar to those in claim 10, and the same rejection is incorporated herein. Huang discloses:
wherein the score predictor is trained using a first loss function related to the conditional score for the previous time-series data and a second loss function related to a general score that does not condition on the previous time-series data (page 6, lines 20-28: Here, an anomaly is detected based upon the prediction value calculated by the forecasting-based model and the reconstruction probability obtained from the reconstruction-based model)
wherein the predicted score is a general score calculated in a state where previous time-series data of the segment is not input to the trained score predictor (page 6, lines 20-28: Here, an anomaly is detected based upon the prediction value calculated by the forecasting-based model and the reconstruction probability obtained from the reconstruction-based model)
Huang fails to specifically disclose noisy data segments. However, Kreis, which is analogous to the claimed invention because it is directed toward synthesizing data, discloses noisy data segments (paragraph 0030: Here, noise is added to a training sample. A trained network then generates synthetic samples and a prediction to denoise the samples). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Kreis with Huang-Savalle-Kreis, with a reasonable expectation of success, as it would have allowed for performing a denoising operation to improve models (Kreis: paragraph 0030).
As per dependent claim 12, Huang, Savalle, and Kreis disclose the limitations similar to those in claim 9, and the same rejection is incorporated herein. Huang discloses:
predicting a conditional score on the samples for the second data segments by inputting the samples and the second data segments to the trained score predictor (page 6, lines 5-15: Here, a predicted value (conditional score) for data is generated by a forecasting-based model (trained score predictor)) and conducting an anomaly determination for the data for the specific time using the predicted conditional score (page 6, lines 20-28: Here, an anomaly is detected based upon the prediction value calculated by the forecasting-based model and the reconstruction probability obtained from the reconstruction-based model)
generating synthetic data corresponding to the specific time by updating the samples using the predicted conditional score (page 6, lines 5-15: Here, a predicted value (conditional score) for data is generated by a forecasting-based model (trained score predictor)) and conducting an anomaly determination for the data for the specific time using the predicted conditional score (page 6, lines 20-28: Here, an anomaly is detected based upon the prediction value calculated by the forecasting-based model and the reconstruction probability obtained from the reconstruction-based model)
determining the data for the specific time as being abnormal when a reconstruction loss between the data for the specific time and the synthetic data exceeds a threshold (page 6, lines 5-15: Here, a predicted value (conditional score) for data is generated by a forecasting-based model (trained score predictor)) and conducting an anomaly determination for the data for the specific time using the predicted conditional score (page 6, lines 20-28: Here, an anomaly is detected based upon the prediction value calculated by the forecasting-based model and the reconstruction probability obtained from the reconstruction-based model)
Additionally, Kreis discloses:
extracting noisy samples from a prior distribution (paragraph 0030: Here, noise is added to a training sample. A trained network then generates synthetic samples and a prediction to denoise the samples). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Kreis with Huang-Savalle-Kreis, with a reasonable expectation of success, as it would have allowed for performing a denoising operation to improve models (Kreis: paragraph 0030).
With respect to claims 13-15, the claims recite the limitation substantially similar to those in claims 6-8, respectively. Claims 13-15 are rejected under similar rationale.
With respect to independent claim 16, the claim recites the limitations substantially similar to those in claim 1. The rejection of claim 1 is incorporated herein by reference.
Huang further discloses at least one processor (Figure 9, item 910), a memory storing a computer program executed by the at least one processor (Figure 9, item 920), wherein the computer program includes instructions for performing operations (page 20, lines 13-20).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Saharia et al. (US 2023/0067841): Discloses a score matching model using a gradient of the data log-density (paragraph 0037)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KYLE R STORK whose telephone number is (571)272-4130. The examiner can normally be reached 8am - 2pm; 4pm - 6pm.
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, Omar Fernandez Rivas can be reached at 571/272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/KYLE R STORK/Primary Examiner, Art Unit 2128
Prosecution Insights