DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The following action is in response to the amendment and remarks of 03/30/2026.
By the amendment, claims 1 and 15 are amended.
Claims 1-20 are pending and have been considered below.
Response to Amendment/Arguments
The 35 USC 112(b) rejection of claims 1-20 have been withdrawn in light of the claims amendment and corresponding remarks.
The 35 USC 101 rejection of claims 1-20 have been withdrawn in light of the claims amendment and corresponding remarks.
The 35 USC 102 rejection of claims 1-20 have been updated to reflect the claims amendment.
Regarding the 35 USC 102 rejection of claim 1 by Gama, Applicant argues that Gama fails to teach or suggest the distribution model is configured to provide an output as input to a functional model of the machine learning fabric, the functional model for performing a machine learning task, nor does Gama provide any detailed disclosure regarding techniques to solve concept drift beyond techniques for re-training or further training a model using new data. The Examiner respectfully disagrees.
The requirement of claim 1 in regards to the functional model is only that the distribution model, having one or more monitoring components, provides an adapted output and the functional model is provided the output as input and the functional model performs a machine learning task. The claim provides no additional steps or components for the functional model to perform the machine learning task, how the input is received, where the logical bounds of the functional model and distribution model differ beyond the distribution model having broadly recited monitoring components. Further, the claim does not provide any detailed disclosure regarding techniques to solve concept drift beyond the broadly claimed distribution model, which adapts its output based on a metric representing a corresponding between a first received data point and a plurality of further data points that were used to train its monitoring components. Accordingly, a prior art that teaches the details of machine learning system comprising a distribution model portion adapting an output and providing the adapted output to a functional model portion to perform a task would anticipate the invention as claimed.
Gama teaches this. Gama teaches adaptive learning algorithms for mitigating the effect of concept drift (Gama 44:3: “Adaptive learning algorithms can be seen as advanced incremental learning algorithms that are able to adapt to evolution of the data-generating process over time.”). Gama teaches updating an output of a learning component that was previously trained on other data based on newly received data (Gama 44:20: “The online learning mode updates the current model with the most recent example. They are error driven, updating the current model depending on whether it misclassifies the current example.”). Gama teaches that several machine learning task model examples requiring adaptive learning systems (Gama 44:7-10: “Next we discuss motivating application cases within each category to illustrate the demand of adaptive learning systems that can handle concept drift.”). The example task models comprise functional components (Grama 44:9: “For each user, an adaptive learning system is built, consisting of a simple ensemble with separate models for short-term and long-term interests of users.”, 44:10: “The winning team developed an ensemble approach including multiple models for handling these various kinds of changes.”, popular windowing and instance weighing approaches”). Gama discloses that the adaptive part of the system can instruct the task part of the system to use different weighted combinations of functional components (Gama 44:22: “The final prediction is typically a weighted average of the individual predictions, where the weight reflects the performance of the individual models on the most recent data. The weights change over time.”); this anticipates the adaptive distribution model outputting an instruction to the functional model for achieving the machine learning task. Accordingly, Gama does clearly teach the invention as claimed. The argument is not persuasive.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-9 and 11-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Gama, João, et al. "A survey on concept drift adaptation." ACM computing surveys (CSUR) 46.4 (2014): 1-37 [“GAMA”].
Regarding claim 1, GAMA discloses a method for adapting a distribution model of a machine learning fabric, the distribution model for mitigating the effect of concept drift (page 44:3 Section 2. – adaptive learning algorithms to adapt to concept drift), the distribution model configured to provide an output as input to a functional model of the machine learning fabric, the functional model for performing a machine learning task (page 44:7-10 Section 2.5 – several examples of the machine learning task models that will use output of adaptive learning algorithms), the method comprising:
providing a first data point as input to one or more distribution monitoring components of the distribution model, wherein the one or more distribution monitoring components have been trained on a plurality of further data points (44:20 Section 3.3.1 – incremental algorithms process input examples and to determine whether or not to update a trained model, online learning mode processes most recent example);
determining, by at least one of the one or more distribution monitoring components, a metric representing a correspondence between the first data point and the plurality of further data points (44:20 Section 3.3.1 – incremental algorithms, online learning mode, process input examples and to determine whether or not to update a trained model based on an error driven misclassification threshold); and
based on the metric, adapting the output of the distribution model (44:20 Section 3.3.1 – incremental algorithms process input examples and to determine whether or not to update a trained model based on an error driven misclassification threshold).
Regarding claim 2, GAMA discloses the method according to claim 1, wherein the adapting the output of the distribution model comprises, if the metric determined by the at least one distribution monitoring component exceeds a drift threshold, generating a training distribution monitoring component associated with the data point (44:20 Section 3.3.1 – incremental algorithms/online learning mode error driven misclassification determination).
Regarding claim 3, GAMA discloses the method according to claim 2, further comprising training the training distribution monitoring component on subsequent data points for which the metric determined by the at least one distribution monitoring component exceeds the drift threshold (44:20 Section 3.3.1 – incremental algorithms/online learning mode error driven misclassification determination for the most recent data example).
Regarding claim 4, GAMA discloses the method according to claim 3, further comprising adding the training distribution monitoring component to the one or more distribution monitoring components of the machine learning fabric after completion of the training (44:20 Section 3.3.1, page 44:22 Section 3.3.3 – ensemble learners including new learners being added as the models are updated online using new data).
Regarding claim 5, GAMA discloses the method according to claim 1, wherein the adapting the output of the distribution model comprises outputting a weighted combination of two or more distribution monitoring components (page 44:22 Section 3.3.3 – ensemble learning of plural models makes a combined prediction using weighted combination).
Regarding claim 6, GAMA discloses the method according to claim 5, wherein the weighted combination comprises a weighted average inversely proportional to the metric of the two or more distribution monitoring components (page 44:23 Section 3.3.3 – multiplying misclassified model weights by inverse multiplicative constant).
Regarding claim 7, GAMA discloses the method according to claim 1, wherein the output of the distribution model takes into account the distribution model output of one or more previous data points of the plurality of further data points (44:20 Section 3.3.1 – incremental algorithms, online learning mode, process previous input examples and to determine whether or not to update a trained model based on an error driven misclassification threshold).
Regarding claim 8, GAMA discloses the method according to claim 1, wherein at least one of the one or more distribution monitoring components comprises a machine learning algorithm that outputs a metric that reflects how well a data point matches a known data distribution associated with that at least one distribution monitoring component (44:20 Section 3.3.1 – incremental algorithms, online learning mode, process recent input examples and to determine whether or not to update a trained model based on an error driven misclassification threshold).
Regarding claim 9, GAMA discloses the method according to claim 1, wherein the metric comprises a measure of a correlation between the first data point and a reconstruction of the first data point generated by the one or more distribution monitoring components (44:20 Section 3.3.1 – incremental algorithms, online learning mode, process recent input examples and to determine whether or not to update a trained model based on an error driven misclassification threshold).
Regarding claim 11, GAMA discloses the method according to claim 1, wherein the functional model comprises one or more functional components, configured to undertake the machine learning task (page 44:7-10 Section 2.5).
Regarding claim 12, GAMA discloses the method according to claim 11, wherein the one or more functional components are linked to the one or more distribution monitoring components, and wherein the output of the distribution model comprises an instruction of one or more functional components to be used when undertaking the machine learning task (page 44:7-10 Section 2.5: applications requiring adaptation).
Regarding claim 13, GAMA discloses the method according to claim 11, wherein the output of the distribution model instructs the functional model to use a weighted combination of two or more functional components (page 44:22 Section 3.3.3).
Regarding claim 14, GAMA discloses the method according to claim 4, further comprising generating a new functional component of the machine learning fabric, based on the added distribution monitoring component (page 44:7-10 Section 2.5).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over GAMA in view of Feng et al., US 2019/0072482 A1 [“FENG”].
Regarding claim 10, GAMA discloses the method according to claim 1, wherein the one or more distribution monitoring components comprise a group comprising: SVM models (page 44:24 Section 3.4 leave-one-out error of SVM), and wherein the metric comprises a reconstruction error (page 44:20 Section 3.3.1).
GAMA fails to disclose wherein the models group further comprises: an autoencoder, a variational autoencoder and/or an isolation forest.
FENG discloses methods for modeling mass metrology, an analogous art to the claimed invention. In particular, FENG discloses that examples of machine learning models for use in modeling can include selection from a group including: SVM models, forest models, and/or autoencoders (¶13, ¶46). Therefore it would have been obvious to one having ordinary skills in the art and the teachings of GAMA and FENG before them before the effective filing of the claimed invention to combine the modeling model selection group including SVM, forest models and/or autoencoders of FENG with the modeling group including SVM of GAMA. One would have been motivated to make this combination in order to provide a known variety of models for use in modeling, as suggested by FENG (¶13, ¶46).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zhu, Hongxiao, Philip J. Brown, and Jeffrey S. Morris. "Robust, Adaptive Functional Regression in Functional Mixed Model Framework." Journal of the American Statistical Association 106.495 (2011): 1167-1179.
Song, Xueguan, et al. "An advanced and robust ensemble surrogate model: extended adaptive hybrid functions." Journal of Mechanical Design 140.4 (2018): 041402.
THIS ACTION IS MADE FINAL. 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 ANDREW L TANK whose telephone number is (571)270-1692. The examiner can normally be reached Monday-Thursday 9a-6p.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Ell can be reached at 571-270-3264. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDREW L TANK/Primary Examiner, Art Unit 2141
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