DETAILED ACTION
This action is in response to the claims filed 06/15/2026 for Application number 18/057,883. Claims 1, 10 and 17 have been amended. Thus, claims 1-20 are currently pending.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1,
Step 1 Analysis: Claim 1 is directed to a process, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claim 1 recites, in part, The limitations of:
generating, [by the processing device using the predictive model with the updated estimated parameters], an uncertainty interval for a future observed value can be considered to be an evaluation in the human mind
determining, [by the processing device], an observed value corresponding to the future observed value is outside of the uncertainty interval can be considered to be an evaluation in the human mind
generating, [by the processing device], an indication that the observed value is an anomaly can be considered to be an evaluation in the human mind
These limitations as drafted, are processes that, under broadest reasonable interpretation, covers performance of the limitation in the mind or with the aid of pen and paper which falls within the “Mental Processes” grouping of abstract ideas.
Additionally, the claim recites:
computing, [by a processing device], estimated parameters by a predictive model can be considered to be a mathematical calculation
computing, [by the processing device], updated estimated parameters of a [predictive model] for the time-series data by performing a rank one update on previously estimated parameters of [the predictive model] without retaining prior observations can be considered to be a mathematical calculation
This limitation as drafted, is a process that, under broadest reasonable interpretation, covers mathematical calculations thus falls under the “Mathematical Concepts” grouping of abstract ideas.
Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional element - “a processing device” and “updating, by the processing device, the predictive model using the updated estimated parameters”. Thus, this element in the claim is 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. Please see MPEP §2106.05(f). Additionally, the claim recites the additional element – “a predictive model”. This element is merely generally linked to the judicial exception. Please see MPEP §2106.05(h). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claim further recites: receiving, by the processing device via a network, time-series data in a real-time stream, the time-series data describing continuously observed values separated by a period of time and
receiving, by the processing device, the future observed value subsequent to the generating the uncertainty interval for the future observed value
communicating, by the processing device, the indication to a display device from which a response to the anomaly is initiable
These limitations are mere data gathering steps and thus are insignificant extra-solution activities. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim as a whole is directed to an abstract idea.
Step 2B Analysis: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of utilizing a processing device to perform the steps of the claimed process amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Additionally, utilizing a predictive model to perform the steps of the claimed process amount to no more than generally linking the additional element to the judicial exception. Furthermore, the limitations of receiving, by the processing device via a network, time-series data in a real-time stream, the time-series data describing continuously observed values separated by a period of time and
receiving, by the processing device, the future observed value subsequent to the generating the uncertainty interval for the future observed value
communicating, by the processing device, the indication to a display device from which a response to the anomaly is initiable
are well-understood, routine, and conventional, as evidenced by MPEP §2106.05(d)(II)(I), “receiving or transmitting data over a network”. These limitations therefore remain insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. Even when considered in combination, these additional elements amount to mere instructions to apply the exception using generic computer components, generally linking the additional element to the judicial exception and insignificant extra-solution activity, which cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 2, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the computing the estimated parameters by the predictive model includes a non-periodic first component and a periodic second component. This limitation amounts to more specifics of the judicial exception identified in the rejection of claim 1 above.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 3, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the time-series data is non-stationary. This limitation amounts to more specifics of the judicial exception identified in the rejection of claim 1 above.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 4, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the predictive model is based on an approximate Gaussian process. This claim recites additional mathematical concepts in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 5, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the predictive model is implemented using Bayesian linear regression. This claim recites additional mathematical concepts in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 6, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the uncertainty interval is generated using a maximum a posteriori estimate. This claim recites additional mathematical concepts in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 7, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the updated estimated parameters are computed using exponentially weighted updates that decay based on the period of time. This claim recites additional mathematical concepts in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 8, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the exponentially weighted updates have a decay rate based on a fraction of the period of time. This claim recites additional mathematical concepts in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 9, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the exponentially weighted updates are used to vary regression coefficients over time. This claim recites additional mathematical concepts in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Claim 10 recites features similar to claim 1 and is rejected for at least the same reasons therein. Claim 10 additionally recites a memory component. This is an additional element that amounts to mere instructions to apply the exception using a generic computer component. Please see MPEP 2106.05(f).
Regarding Claim 11-16, they recite features similar to claims 2-7 respectively and are rejected for at least the same reasons therein.
Regarding claim 17,
Step 1 Analysis: Claim 17 is directed to a machine, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claim 17 recites, in part, The limitations of:
generating, [using the predictive model with the updated estimated parameters], an uncertainty interval for a future observed value can be considered to be an evaluation in the human mind
comparing an observed value corresponding to the future observed value with a predicted value can be considered to be an evaluation in the human mind
generating an indication that the observed value is an anomaly based on comparing the observed value with the predicted value can be considered to be an evaluation in the human mind
These limitations as drafted, are processes that, under broadest reasonable interpretation, covers performance of the limitation in the mind or with the aid of pen and paper which falls within the “Mental Processes” grouping of abstract ideas.
Additionally, the claim recites:
computing estimated parameters by a predictive model based on training data can be considered to be a mathematical calculation
computing updated estimated parameters of the predictive model for time- series data received in a real-time stream via a network as describing continuously observed values of the time-series separated by a period of time by performing a rank one update on previously estimated parameters of the predictive model without retaining prior observations can be considered to be a mathematical calculation
This limitation as drafted, is a process that, under broadest reasonable interpretation, covers mathematical calculations thus falls under the “Mathematical Concepts” grouping of abstract ideas.
Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional element - “a non-transitory computer-readable storage medium”, “processing device” and “updating the predictive model using the updated estimated parameters”. Thus, this element in the claim is 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. Please see MPEP §2106.05(f). Additionally, the claim recites the additional element – “a predictive model”. This element is merely generally linked to the judicial exception. Please see MPEP §2106.05(h). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claim further recites: for time-series data received in a real-time stream via a network and receiving, by the processing device, the future observed value subsequent to the generating the uncertainty interval for the future observed value.
communicating the indication to a display device from which a response to the anomaly is initiable
These limitations are mere data gathering steps and thus is are insignificant extra-solution activities. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim as a whole is directed to an abstract idea.
Step 2B Analysis: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of utilizing a processing device, a non-transitory computer-readable storage medium, and updating the predictive model using the updated estimated parameters to perform the steps of the claimed process amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Additionally, utilizing a predictive model to perform the steps of the claimed process amount to no more than generally linking the additional element to the judicial exception. Furthermore, the limitations of for time-series data received in a real-time stream via a network and receiving, by the processing device, the future observed value subsequent to the generating the uncertainty interval for the future observed value and communicating the indication to a display device from which a response to the anomaly is initiable
are well-understood, routine, and conventional, as evidenced by MPEP §2106.05(d)(II)(I), “receiving or transmitting data over a network”. These limitations therefore remain insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. Even when considered in combination, these additional elements amount to mere instructions to apply the exception using generic computer components, generally linking the additional element to the judicial exception and insignificant extra-solution activity, which cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 18-20, they recite features similar to claims 4, 3 and 7 respectively and are rejected for at least the same reasons therein.
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.
Claims 1-3, 10-12, 17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Moghtaderi et al. ("US 20160189041 A1", hereinafter "Moghtaderi") in view of Zhang ("Online dynamic mode decomposition for time-varying systems", hereinafter "Zhang") and further in view of Wu et al. ("Developing an Unsupervised Real-Time Anomaly Detection Scheme for Time Series With Multi-Seasonality", hereinafter "Wu") and further in view of Andrabi et al. ("US 20230007023 A1", hereinafter "Andrabi").
Regarding claim 1, Moghtaderi teaches A method comprising:
receiving, by a processing device via a network (¶0077), time-series data [in a real-time stream], the time-series data describing continuously observed values separated by a period of time (“In various embodiments, the method 300 may process one or a multitude of time series such that it processes one of such time series at a time for one time point. The method 300 can then be repeated for every time point within a time duration of interest, in order, from the farthest time point to the latest.” [¶0042);
generating, by the processing device using the predictive model (See ¶0063 and ¶0065 for predictive models) with the updated estimated parameters, an uncertainty interval for a future observed value (“At operation 406, the training time series T is used to generate the parameters for each of a plurality of prediction methods… At operation 408, q prediction error time series of length L (for time points from (P-L) to (P-1) where P is smaller than L) corresponding to the q prediction methods applied in the context on time series S in the past, are extracted. These prediction error time series are the result of the operation 400 for historical time points, i.e., time points older than P… If the distances computed exceed a predetermined threshold, the time index P is marked as containing an anomaly.” [¶0050-¶0054]);
determining, by the processing device, an observed value corresponding to the future observed value is outside of the uncertainty interval (“The actual (measured) data (“observed” value) at time point t is compared with the predictions (“future observed value”) from operation 306 and a statistical test is applied to the differences between the predicted and actual data.; and
generating, by the processing device, an indication that the observed value is an anomaly. (“The results of this test are used to flag the data at time point t as anomalous or not.” [¶0045])
However, Moghtaderi fails to explicitly teach computing, by a processor device, estimated parameters by a predictive model
computing, by the processing device, updated estimated parameters of a predictive model for the time-series data by performing a rank one update on previously estimated parameters of the predictive model without retaining prior observations;
updating, by the processing device, the predictive model using the updated estimated parameters;
Zhang teaches computing, by a processor device, estimated parameters by a predictive model (“As mentioned previously, the online DMD algorithm described above is ideally suited to cases for which the system is varying in time, so that we want to revise our estimate of the DMD matrix Ak in real time.” [pg. 6, 2.3, ¶1]])
computing, by the processing device, updated estimated parameters of a predictive model for the time-series data by performing a rank one update on previously estimated parameters of the predictive model without retaining prior observations (“The algorithm does not require storage of past data, and computes the exact DMD matrix using rank-1 updates.” [Abstract]);
updating, by the processing device, the predictive model using the updated estimated parameters; (“There is an intuitive interpretation for the update formula (12). The quantity (yk+1−Akxk+1) can be considered as the prediction error from the current model Ak, and the DMD matrix is updated by adding a term proportional to this error.” [pg. 5, para under eq (12)]])
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Moghtaderi’s teachings in order to compute estimated parameters, implement a rank one update, and update the model with the estimated parameters as taught by Zhang. One would have been motivated to make this modification to provide an efficient method for computing DMD in real time and updating the approximation of a system’s dynamics as new data becomes available. [Abstract, Zhang]
However Moghtaderi/Zhang fails to explicitly teach receiving, by the processing device via a network, time-series data in a real-time stream and
receiving, by the processing device, the future observed value subsequent to the generating the uncertainty interval for the future observed value
Wu teaches receiving, by the processing device via a network, time-series data in a real-time stream (“The aim of this project is to develop the automated method to spot the potential anomalies and quantify them at real time as the process invocations are being logged in the server” [pg. 4155, left col ¶3]) and
receiving, by the processing device, the future observed value subsequent to the generating the uncertainty interval for the future observed value (“Based on the above ensemble, we propose a new metric, termed Local Trend Inconsistency (LTI), for measuring the deviation of an actual sequence from the predictions in real-time, and assigns an anomaly score to each of the newly arrived data points (which we also call frames) in order to quantify the probability that a frame is anomalous.” [pg. 4148, left col, ¶2])
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Moghtaderi’s/Zhang’s teachings by receiving real-time data and generating an uncertainty interval in real-time as taught by Wu. One would have been motivated to make this modification as this method would significantly mitigate the impact of anomalous samples while enabling the algorithm to work efficiently without maintaining or caching too many historical data frames. [Wu, pg. 4148, left col, ¶2])
However Moghtaderi/Zhang/Wu fails to explicitly teach communicating, by the processing device, the indication to a display device from which a response to the anomaly is initiable.
Andrabi teaches communicating, by the processing device, the indication to a display device from which a response to the anomaly is initiable. (“Based on the anomaly indicator, the anomalous-event-detection system can display (e.g., on an administrator device) an electronic communication that indicates the digital action as anomalous, perform or provide options to perform a remedial action (corresponds to “initiable”) in response to the anomalous action, and/or utilize data received from the administrator device in response to the electronic communication to modify anomaly-detection model.” [¶0023])
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Moghtaderi’s/Zhang’s/Wu’s teachings in order to communicate an indication of anomaly to a display and provide a solution or remedial action in response to the anomaly as taught by Andrabi. One would have been motivated ot make this modification in order to neutralize or contain anomalous actions. [Abstract, Andrabi]
Regarding claim 2, Moghtaderi/Zhang/Wu/Andrabi teaches The method as described in claim 1, where Wu further teaches wherein the computing the estimated parameters by the predictive model includes a non-periodic first component and a periodic second component (“where gc(t) is the trend term that models non-periodic changes, sc(t) represents the seasonal term that quantifies the seasonal effects” [pg. 4152, right col, ¶2])
Same motivation to combine the teachings of Moghtaderi/Zhang/Wu/Andrabi as claim 1.
Regarding claim 3, Moghtaderi/Zhang/Wu/Andrabi teaches The method as described in claim 1, Moghtaderi teaches wherein the time-series data is non-stationary. (“The anomaly-detection system is designed to seamlessly handle non-stationarity by integrating data from multiple local time regimes.” [¶0016])
Claim 10 recites features similar to claim 1 and is rejected for at least the same reasons therein. Claim 10 additionally requires A system comprising: a memory component; and a processing device coupled to the memory component, the processing device to perform operations (Moghtaderi, “For example, one hardware module may perform an operation and store the output of that operation in a memory device to which it is communicatively coupled. A further hardware module may then, at a later time, access the memory device to retrieve and process the stored output.” [¶0069])
Regarding claims 11 and 12, they are substantially similar to claims 3 and 2 respectively, and are rejected in the same manner, the same art, and reasoning applying.
Claim 17 recites features similar to claim 1 and is rejected for at least the same reasons therein. Claim 17 additionally requires A non-transitory computer-readable storage medium storing executable instructions, which when executed by a processing device (Moghtaderi, ¶0081)
Regarding claim 19, it is substantially similar to claim 3 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Claims 4, 15 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Moghtaderi in view of Zhang, Wu and Andrabi and further in view of Xu et al. ("Adaptive Streaming Anomaly Analysis", hereinafter "Xu").
Regarding claim 4, Moghtaderi/Zhang/Wu/Andrabi teaches The method as described in claim 1, however fails to explicitly teach wherein the predictive model is based on an approximate Gaussian process.
Xu teaches wherein the predictive model is based on an approximate Gaussian process. (“Then the functions (i.e. the time series) are drawn from a Gaussian process GP(µ,(ν − 2)ζ) with the kernel ζ. µ denotes mean function, and is often set as zero without loss of generality” [pg. 2, top para])
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Moghtaderi’s/Zhang’s/Wu’s/Andrabi’s teachings in order to use a Gaussian process as taught by Xu. One would have been motivated to make this modification in order to be flexible to capture the complex patterns in time series. [pg. 2, top para, Xu]
Regarding claim 15, it is substantially similar to claim 4 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Regarding claim 18, it is substantially similar to claim 4 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Claims 5 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Moghtaderi in view of Zhang, Wu, and Andrabi and further in view of Liu et al. ("Online Conditional Outlier Detection in Nonstationary Time Series", hereinafter "Liu").
Regarding claim 5, Moghtaderi/Zhang/Wu/Andrabi teaches The method as described in claim 1, however fails to explicitly teach wherein the predictive model is implemented using Bayesian linear regression.
Liu teaches wherein the predictive model is implemented using Bayesian linear regression. (“To implement the second layer, we use Bayesian linear regression, so we can add uncertainty to the model to accommodate the scarcity of examples for different sources of variability.” [pg. 86, bottom right col – pg. 87, top left col])
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Moghtaderi’s/Zhang’s/Wu’s/Andrabi’s teachings to use Bayesian linear regression as taught by Liu. One would have been motivated to make this modification in order to add uncertainty to the model to accommodate scarcity of examples.” [pg. 86, bottom right col – pg. 87, top left col, Liu]
Regarding claim 13, it is substantially similar to claim 5 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Claims 6 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Moghtaderi in view of Zhang, Wu, and Andrabi and further in view of Hill et al. ("Real-time Bayesian Anomaly Detection for Environmental Sensor Data", hereinafter "Hill").
Regarding claim 6, Moghtaderi/Zhang/Wu/Andrabi teaches The method as described in claim 1, however fails to explicitly teach wherein the uncertainty interval is generated using a maximum a posteriori estimate.
Hill teaches wherein the uncertainty interval is generated using a maximum a posteriori estimate. (“The maximum a posteriori estimate, (e.g. the most likely value given the posterior distribution) of the hidden state variable indicating the measurement status can then be used to classify the sensor measurements as normal or anomalous” [pg. 4, bottom para])
It would have been obvious to one of ordinary skill in the art before the effective filing date ot modify Moghtaderi’s/Zhang’s/Wu’s/Andrabi’s teachings to use a maximum a posteriori estimate as taught by Hill. One would have been motivated to make this modification in order to determine if a measured state is normal or anomalous. [pg. 4, bottom para, Hill]
Regarding claim 14, it is substantially similar to claim respectively, and is rejected in the same manner, the same art, and reasoning applying.
Claims 7, 8, 16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Moghtaderi in view of Zhang, Wu, and Andrabi and further in view of Rebjock et al. ("Online false discovery rate control for anomaly detection in time series", hereinafter "Rebjock").
Regarding claim 7, Moghtaderi/Zhang/Wu/Andrabi teaches The method as described in claim 1, however fails to explicitly teach wherein the updated estimated parameters are computed using exponentially weighted updates that decay based on the period of time.
Rebjock teaches wherein the updated estimated parameters are computed using exponentially weighted updates that decay based on the period of time. (“In words, more weight is given to recent rejections, and the past shrinks exponentially. This is arguably the most intuitive notion of FDR for very long, possibly infinite streams of data.” [pg. 6, top para])
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Moghtaderi’s/Zhang’s/Wu’s/Andrabi’s teachings to use exponentially weighted updates that decay as taught by Rebjock. One would have been motivated to make this modification in order to give more weight to new observations than past observations. [pg. 6, top para, Rebjock]
Regarding claim 8, Moghtaderi/ZhangWu/Andrabi/Rebjock teaches The method as described in claim 7, where Rebjock teaches wherein the exponentially weighted updates have a decay rate based on a fraction of the period of time. (“
PNG
media_image1.png
94
572
media_image1.png
Greyscale
” [pg. 6, top equation, decay rate is based on T])
Same motivation to combine the teachings of Moghtaderi/Zhang /Wu/Andrabi/Rebjock as claim 7.
Regarding claims 16 and 20, they are substantially similar to claim 7 respectively, and are rejected in the same manner, the same art, and reasoning applying.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Moghtaderi in view of Zhang, Wu, Andrabi and Rebjock and further in view of Liu.
Regarding claim 9, Moghtaderi/Zhang/Wu/Andrabi/Rebjock teaches The method as described in claim 7, however fails to explicitly teach wherein the exponentially weighted updates are used to vary regression coefficients over time.
Liu teaches wherein the exponentially weighted updates are used to vary regression coefficients over time. (“The second-layer model takes the output of the first-layer model, z, and a time series of context variables, x, as input, and outputs a sequence of final outlier scores, v. We adopt a Bayesian approach to model zt given xt. Specifically, we assume the following linear model… That is, given w, β, and xt, zt follows a normal distribution… the posterior distribution for (w, β) is again normal-Gamma with updated parameters” [pg. 88, § Second Layer model, bottom left col – top right col; β, xt corresponds to regression coefficients])
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Moghtaderi’s/Zhang’s/Wu’s/Andrabi’s/Rebjock’s teachings, specifically to use exponentially weighted updates of Rebjock to vary regression coefficients over updates as taught by Liu. One would have been motivated to make this modification in order to add uncertainty to the model to accommodate scarcity of examples.” [pg. 86, bottom right col – pg. 87, top left col, Liu]
Response to Arguments
Applicant's arguments filed 06/15/2026 have been fully considered but they are not persuasive.
Regarding the 35 U.S.C. §101 Rejection:
Applicant appears to assert the newly amended limitations of “computing …updated parameters for the time-series data by performing a rank one update on the estimated parameters of the predictive model without retaining prior observations” is not a mental process. Examiner respectfully disagrees. The examiner did not analyze this limitation to be a mental process rather the limitation was analyzed to be a mathematical calculation which recites an abstract idea. Furthermore, applicant argues the amended claim further recites “communicating, by the processing device, the indication to a display device from which a response to the anomaly is initiable” which cannot be a mental process. While the examiner agrees this step cannot be practically performed in the human mind as a mental process, the examiner is analyzing this limitation be insignificant extra-solution activity under Step 2A Prong Two and further to be well-understood, routine, and conventional as evidenced by MPEP §2106.05(d)(II)(I), “receiving or transmitting data over a network” under Step 2B. Therefore, applicant’s arguments are not persuasive.
Applicant further argues that the claims are integrated into a practical application because the claims address a technical problem rooted in computer technology. Examiner respectfully disagrees. The claims fail to reflect any improvement over existing computer technology or operation of a neural network rather the claims are directed towards an improvement in the anomaly detection method. Improvements to an abstract idea are still considered to be an abstract idea. Specifically, applicant argues the limitation “without retaining prior observations” is an improvement to how the computer system manages memory during streaming data processing. Examiner respectfully disagrees. The claims do not go into any details of how the streaming data is being managed or stored rather the claims broadly recite “without retaining prior observations” without any further details which is merely a part of the abstract idea identified above. There are no positively recited steps of memory storage or streaming data processing in relation to any memory, storage, or computer technology. Furthermore, as noted above, the limitation of “communicating… the indication…” is merely an insignificant extra-solution activity and does not integrate the judicial exception into a practical application. Therefore, applicant’s arguments are not persuasive.
Applicant further argues that the ordered combination of receiving streaming time series data, performing rank-one updates without retaining prior observations, generating uncertainty intervals and communicating anomaly indications… is not well-understood, routine or conventional. Examiner respectfully disagrees. All of these steps as currently recited either amount to an abstract, insignificant extra-solution activity or mere instructions to apply the judicial exception using a generic computer component as noted in the updated 101 rejection above. As noted in the last office action, the consideration for well-understood, routine, and conventional is only required when the analysis considers an additional element to be insignificant extra-solution activity. The examiner has provided an updated 101 rejection in regards to the newly amended limitations of the independent claims. Therefore, applicant’s arguments are not persuasive.
Regarding the 35 U.S.C. §103 Rejections:
Applicant’s arguments regarding the newly amended limitations of claim 1 have been considered but are moot because these newly amended limitations are now taught by the newly presented prior arts of Zhang and Andrabi. Please see the updated 103 rejection above.
Applicant’s arguments with respect to the rejections of the dependent claims have been fully considered but they are not persuasive as they rely upon the allowability of the independent claims
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL H HOANG whose telephone number is (571)272-8491. The examiner can normally be reached Mon-Fri 8:30AM-4:30PM.
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, Kakali Chaki can be reached at (571) 272-3719. 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.
/MICHAEL H HOANG/PRIMARY EXAMINER, Art Unit 2122
Prosecution Insights