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 .
This action is responsive to the Amendment filed on 7/24/2026.
Claims 1-12 are pending in the case.
Response to Arguments
Applicant's arguments and amendments with regards to the 35 U.S.C. § 112(b) rejection of claim(s) 4, 5, 6, and 9, have been fully considered and are persuasive. The 35 U.S.C. § 112(b) rejection of claim(s) **** is respectfully withdrawn.
Applicant's arguments and amendments with regards to the 35 U.S.C. § 112(b) rejection of claim(s) 10, have been fully considered and are persuasive. The 35 U.S.C. § 112(b) rejection of claim(s) 10 is respectfully maintained withdrawn.
However, upon further consideration, a new ground(s) of rejection is made as disclosed below.
Applicant's arguments and amendments with regards to the 35 U.S.C. § 102 and 103 rejection of claim(s) 1-12 have been considered, but are not persuasive. Applicant argues that the amended claims are allowable due to the following:
Regarding claim(s) 1, applicant argues “Part of the subject matter of claim 7 (after the step of training the offline ML system, a refining of the CP selection is performed by the user) is incorporated into claim 1. None of the references-alone or in combination-teach or fairly suggest this feature. … Ahad describes a framework where an unsupervised change-point detection is used to segment unlabeled sequences, and then those segments form similar/dissimilar constraints for training a neural network classifier in a semi-supervised way. However, Ahad is completely silent on having a user verify or refine the detected change points after a supervised training step. Ahad does not teach any interactive feedback loop or user involvement in selecting or adjusting the change point candidates before further training. Instead, the entire process in Ahad is automated and uses machine-learning on initially detected points plus labeled data to train a network for classification. There is no step where a human reviews or refines the change points. … Xu does not teach or contemplate a user refining the selection of change points after a supervised training of an offline ML system. Instead, Xu is primarily focused on automated model updates in response to detected change points in time-series data (e.g., automatically updating a time-based model when a significant change is detected). While Xu may include user interfaces for viewing detected change points and adjusting detection settings, any "user approval" in Xu relates to confirming or configuring model update conditions before or during detection, not after an offline training process as a feedback loop to refine training data”.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim(s) 10 is/are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim(s) 10, each recite “wherein, in a step after the step of training, supervised, an offline machine-learning, ML, system to refine CPs from the selected CPs, the step of classifying all time points using the learnt offline model is performed”.
The original specification does not teach using the learnt offline model for the step of classifying all time points,
and therefore does not teach “wherein, in a step after the step of training, supervised, an offline machine-learning, ML, system to refine CPs from the selected CPs, the step of classifying all time points using the learnt offline model is performed”
The original specification teaches “wherein, in a step after the step of training, supervised, an offline machine-learning, ML, system to refine CPs from the selected CPs, the step of classifying all time points using …online model is performed”,
rather than using a learnt offline model for classifying all time points.
Therefore the above noted limitations of claim(s) 10 do not have support in the original specification.
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.
Claims 1, 6-8, 10, 11, are rejected under 35 U.S.C. 103 as being unpatentable over Ahad et al “Semi-supervised sequence classification through change point detection”, dated 10/6/2020 and retrieved from arXiv:2009.11829v2 , in view of Xu (US 11651271 B1).
Ahad was cited in the IDS dated 8/24/2023.
Regarding claim 1, Ahad teaches a computer-implemented method for detecting change points (CPs) in a signal of an automation system, comprising (Ahad Intro method for detecting change points in sequential data (signal) for computer vision (computer implemented):
in an offline learning phase, detecting unsupervised candidate CPs on at least one offline signal using unsupervised detection method; selecting CPs from the candidate CPs and providing the selected CPs to a supervised process (Ahad Secs 3.1, signal changepoints can be detected, unsupervised using offline model (offline model fθ is fixed once trained and is trained on sub-sequence pairs), changepoints may be selected based on constraints);
training, supervised, an offline machine-learning, ML, system to refine CPs from the selected CPs using a supervised machine learning method (Ahad Secs 3.2-3.3 selected change points are associated with subsequences in signal, and subsequences are used to train the offline model which can include labels),
wherein after the step of training, supervised, an offline ML system, a refining of the CP selection is performed (Ahad Sec 3.4 mean empirical distribution from trained offline model along with labelled and unlabeled sub-sequences (training data) can be used to train online model (f ψ)).;
creating a training data set using the offline ML system for an online ML system ... ; training, supervised, the online ML system using the created training data set (Ahad Sec 3.4 mean empirical distribution from offline model along with labelled and unlabeled sub-sequences (training data) can be used to train online model (f ψ trained on sequences based on probabilistic clustering using empirical distributions and entropy regularization, online model); and
after the offline learning phase, and detecting CPs using the trained online ML system (Ahad Secs 4.2 and 4.3 once trained- the online ML can be used to detect CPs).
Ahad does not specifically teach projecting the refined CPs on the signal , a refining of the CP selection is performed by the user
However Xu teaches creating a training data set ... for an online ML system by projecting the refined CPs on the signal; training, supervised, the online ML system using the created training data set (Xu Col 17, line 65- Col 18, line 15, Col 18, line 47- Col 19, line 7, change points may be cluster (refined) and linear combination with signal change points indicators may be made (projection of cluster to signal) to create data used by model, created data may be used to train and update model (online model), method allows multiple methods and thresholds to be used);
a refining of the CP selection is performed by the user (Xu Col 3, line 59- Col 4, line 22, Col 12, lines 46-58, user may approve changepoints).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Xu of creating a training data set ... for an online ML system by projecting the refined CPs on the signal; training, supervised, the online ML system using the created training data set, a refining of the CP selection is performed by the user, into the invention suggested by Ahad; since both inventions are directed towards training online models to detect changepoints, and incorporating the teaching of Xu into the invention suggested by Ahad would provide the added advantage of allowing multiple methods and thresholds to be used and allowing user input to be provided with respect to the model output, and the combination would perform with a reasonable expectation of success (Xu Col 17, line 65- Col 18, line 15, Col 18, line 47- Col 19, line 7, Col 3, line 59- Col 4, line 22, Col 12, lines 46-58).
Regarding claim 6, Ahad and Xu teach(es) the invention as claimed in claim 1 above.
Ahad further teaches wherein the selecting CPs from the candidate CPs comprises ... using all the CP candidates (Ahad Secs 3.1, changepoints may be selected based on constraints, which could yield all changepoints)
Regarding claim 7, Ahad and Xu teach(es) the invention as claimed in claim 1 above.
Ahad further teaches and wherein parameters of a model are tuned according to the refinement (Ahad Sec 3.4 mean empirical distribution from trained offline model along with labelled and unlabeled sub-sequences (training data) can be used to train online model (f ψ)).
Regarding claim 8, Ahad and Xu teach(es) the invention as claimed in claim 1 above.
Ahad further teaches wherein one or a combination of the following static rules is added to the step of training, supervised, an offline machine-learning, system: determining characteristics of a sequence of the reference signal in a time span before and after a CP, comparing the characteristics to the characteristics of a sequence of the reference signal in a time span before and after a further CP and selecting CPs that show similarity (Ahad Secs 3.1, 3.2 and 3.3, similarity/dissimilarity between pairs of sub-sequences before and after a changepoint can be determined using maximum mean discrepancy, which is used to cluster sub-sequences, which are used to train offline model).
Regarding claim 10, Ahad and Xu teach(es) the invention as claimed in claim 9 above.
Ahad further teaches wherein, in a step after the step of training, supervised, an offline machine-learning, ML, system to refine CPs from the selected CPs, the step of classifying all time points using the learnt offline model is performed (Ahad Sec 3.3, trained offline model can be used classify the time series points as change points or not change points).
Regarding claim 11, Ahad and Xu teach(es) the invention as claimed in claim 1 above.
Ahad further teaches wherein, in a step after the step of creating a training data set for an online ML system, the step of training an online model is performed (Ahad Sec 3.4 mean empirical distribution from offline model along with labelled and unlabeled sub-sequences (training data) can be used to train online model (f ψ)).
Claims 2, are rejected under 35 U.S.C. 103 as being unpatentable over Ahad et al in view of Xu (US 11651271 B1), and further in view of Ghouchian (US 20220053295 A1).
Regarding claim 2, Ahad and Xu teach(es) the invention as claimed in claim 1 above.
Ahad further teaches wherein the step detecting CPs using the trained online ML system includes recording live data in the automation system and detecting, by using the online ML system, CPs in the live data (Ahad Sec 4.3 live data may be used, Ahad Secs 4.2 and 4.3 once trained- the online ML can be used to detect CPs).
Ahad does not specifically teach upon detecting a CP, triggering an action
However Ghouchian teaches upon detecting a CP, triggering an action (Ghouchian [11. 66. 67] online model can trigger updating a model based on detecting a change point, system uses a combination of offline and online model to detect change points).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Ghouchian of upon detecting a CP, triggering an action, into the invention suggested by Ahad and Xu; since both inventions are directed towards using a combination of offline and online model to detect change points, and incorporating the teaching of Ghouchian into the invention suggested by Ahad and Xu would provide the added advantage of allowing model(s) to be updated without user intervention based on detecting a change point,, and the combination would perform with a reasonable expectation of success (Ghouchian [11. 66. 67]).
Claims 3-5 are rejected under 35 U.S.C. 103 as being unpatentable over Ahad et al in view of Xu (US 11651271 B1), and further in view of Chen (US 20220107878 A1).
Regarding claim 3, Ahad and Xu teach(es) the invention as claimed in claim 1 above.
Ahad further teaches wherein the offline signal is represented by samples of the signal, the samples are samples inside a ... time-window, and the candidate CPs are selected from the samples contained in the ... time-window (Ahad Sec 4.3 samples may be used from time-windows of signal)
Ahad does not specifically teach random time-window
However Chen teaches wherein the ... signal is represented by samples of the signal, the samples are samples inside a random time-window, and the candidate CPs are selected from the samples contained in the random time-window (Chen [58] samples may have random lengths (random time-window and are used to detect CPs, randomness allow for variations in metrics)
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Chen of wherein the ... signal is represented by samples of the signal, the samples are samples inside a random time-window, and the candidate CPs are selected from the samples contained in the random time-window, into the invention suggested by Ahad and Xu; since both inventions are directed towards determining CPs of a signal using time windows, and incorporating the teaching of Chen into the invention suggested by Ahad and Xu would provide the added advantage of allowing variation in metrics, and the combination would perform with a reasonable expectation of success (Chen [58]).
Regarding claim 4, Ahad, Xu and Chen teach(es) the invention as claimed in claim 3 above.
Claim 3 further teaches the samples are samples inside a random time-window.
Ahad further teaches wherein the samples … are processed by a first algorithm and a second algorithm, and both algorithms provide candidate CPs (Ahad Secs 3.1 and 3.2, samples may be analyzed by varying mean or variance).
Regarding claim 5, Ahad, Xu and Chen teach(es) the invention as claimed in claim 4 above.
Ahad further teaches wherein each algorithm is varied by parameters, and the samples of each time window are processed by each first algorithm and each varied second algorithm are varied, such that candidate CPs are obtained by each of the varied first algorithm and each varied second algorithm (Ahad Secs 3.1 and 3.2, samples may be analyzed by varying mean or variance parameters).
Claims 9, 12, are rejected under 35 U.S.C. 103 as being unpatentable over Ahad et al in view of Xu (US 11651271 B1), and further in view of Xiao (US 20210167851 A1).
Regarding claim 9 Ahad and Xu teach(es) the invention as claimed in claim 1 above.
Ahad does not specifically teach wherein the refining comprises storing the method and parameters during performing the refining steps forming a learnt offline model
However Xiao teaches wherein the refining comprises storing the method and parameters during performing the refining steps forming a learnt offline model (Xiao [110, 120] offline model can determine change points, model with parameters may be stored and reused).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Xiao of wherein the refining comprises storing the method and parameters during performing the refining steps forming a learnt offline model, into the invention suggested by Ahad and Xu; since both inventions are directed towards determining change points using models, and incorporating the teaching of Xiao into the invention suggested by Ahad and Xu would provide the added advantage of being able to reuse information by storing model and parameters, and the combination would perform with a reasonable expectation of success (Xiao [110, 120]).
Regarding claim 12, Ahad and Xu teach(es) the invention as claimed in claim 9 above.
Ahad further teaches wherein, in a step after step of detecting CPs using the trained online ML the step of transferring the learnt online model and the learnt offline model for further processes is performed (Ahad Sec 4.3 trained models can be used for different real work datasets).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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SANCHITA ROY
Primary Examiner
Art Unit 2146
/SANCHITA ROY/Primary Examiner, Art Unit 2146
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