DETAILED ACTION
Notice of 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 .
Response to Amendment
Applicant’s Amendment and remarks dated 7/27/2026 have been considered. New claims 21-22 have been added. Claims 1-22 are pending.
35 U.S.C. 112(f) Interpretation. Claims 8-14 are no longer being interpreted under 35 U.S.C. 112(f) in view of Applicant’s amendments to claim 8.
Written Description Support. The examiner acknowledges that paras. 0041-0044 provide sufficient written description support for the amendments to the independent claims and that the new claims are supported at least by paras. 0021, 0034, 0039-0041, and 0046-0048 of the instant specification.
Response to Arguments
On pages 9-10 of Applicant’s 7/27/2026 Amendment and remarks, with respect to the rejections under 35 U.S.C. 101, with respect to Step 2A, Prong 1, Applicant asserts that the “predicting a doubly robust outcome for the test data and a doubly robust outcome for the training data by combining a treatment predicted by a first predictive model, the treatment predicted based on a control of the ordered data stream, and an outcome predicted by a second predictive model, the outcome predicted based on the treatment and the control of the ordered data stream”, as amended, cannot be performed in the human mind.
The examiner respectfully disagrees. While the limitation recites to a “first predictive model” and a “second predictive model”, the limitation does not affirmatively require such models to be run. Rather, the claim merely requires the outputs of such predictive models to be combined, and using such combination to predict doubly robust outcomes for test data and training data, where such predictions can be made mentally based on the combined outputs.
On pages 10-13 of Applicant’s 7/27/2026 Amendment and remarks, with respect to the rejections under 35 U.S.C. 101, with respect to Step 2A, Prong 2, Applicant asserts that the claimed invention is an improvement to “data streaming.”
The examiner respectfully disagrees.
On pages 10-12, Applicant cites to paras. 0013-0014, 0019, 0022, 0033-0034, and 0040 to explain that the claimed invention relates to monitoring samples from a data stream, using a “doubly robust” approach to measure concept drift, separating the impact of data drift from concept drift, and then selecting certain training samples to retrain a machine learning model to maintain performance.
However, Applicant’s explanation does not adequately explain why Applicant believes the claimed invention to be an improvement to data streaming. If anything, the improvement is to the mental process of determining concept drift over time due to changes in the underlying streaming data. The streaming data itself is not improved in any way. There is no technical improvement disclosed as to how the data is streamed, or any technical improvements to improve the efficiency or reliability of data transmissions.
As described in para. 0019 of the instant specification, the data stream 102 is just a stream of data samples received over time. There is no disclosure as to how such data stream 102 is improved. The data stream is just the input data, and the improvement is to figuring out whether to retrain the machine learning models based on concept drift determined within the underlying data stream.
On pages 12-13, Applicant argues that the claim reflects the disclosed improvement. However, as explained above, the examiner respectfully disagrees that there is any improvement to “data streaming” as argued by Applicant. If anything, the improvement is to the mental process of determining concept drift and then determining whether to re-train the machine learning model in view of such detected drift. Therefore, the claims do not reflect any improvement to computer technology or other technical field.
On pages 13-14 of Applicant’s 7/27/2026 Amendment and remarks, with respect to the rejections under 35 U.S.C. 101, with respect to Step 2B, Applicant asserts that the claimed invention is an improvement to “data streaming.”
Applicant’s arguments generally allege that the claimed invention relates to “an improvement to the technology of data streaming.” As discussed above, the examiner respectfully disagrees that there is any improvement to the technology of data streaming.
Applicant asserts, without providing any evidence, that the claims are “not well-understood, routine, or conventional.” However, MPEP 2106.05(d) explains that this is a factual question, and because there are no facts either in favor nor disfavor of subject matter eligibility, that this “well-understood, routine, or conventional” activity consideration does not favor, nor disfavor, a finding of subject matter eligibility.
Applicant argues that new claims 21 and 22 provide further rationale for patent eligibility. The examiner respectfully disagrees. See detailed rejections below.
On pages 14-15 of Applicant’s 7/27/2026 Amendment and remarks, with respect to the rejections under 35 U.S.C. 103, Applicant argues that the prior art of record does not teach the “predicting a doubly robust outcome for the test data and a doubly robust outcome for the training data by combining a treatment predicted by a first predictive model, the treatment predicted based on a control of the ordered data stream, and an outcome predicted by a second predictive model, the outcome predicted based on the treatment and the control of the ordered data stream” limitation as amended.
The examiner agrees that this limitation is not anticipated nor made obvious by the prior art of record. In particular, the prior art does not specifically disclose using 2 separate prediction models, each having their own specific inputs and outputs as claimed, and then combining such outputs in order to predict a doubly robust outcome. All rejections under 35 U.S.C. 103 are hereby withdrawn.
Claim Objections
Claims 1 and 15 are objected to because of the following informalities:
In the last line of claim 1, “using the retraining vectors” should be amended to read “using the retraining vector[[s]]” to reflect the previous claim amendments specifying that there may be only a single retraining vector.
In the last line of claim 15, “using the retraining vectors” should be amended to read “using the retraining vector[[s]]” to reflect the previous claim amendments specifying that there may be only a single retraining vector.
Appropriate correction is required.
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 1-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Step 1 of the Alice/Mayo framework, Claims 1-7 and 21-22 are directed to a method (a process), Claims 8-14 are directed to a system (a machine), and Claims 15-20 are directed to one or more tangible processor-readable storage media (an article of manufacture), which each fall within one of the four statutory categories of inventions.
Regarding Claim 1
Step 2A, prong 1 (Is the claim directed to a law of nature, a natural phenomenon or an abstract idea).
Claim 1 recites the following mental processes, that in each case under the broadest reasonable interpretation, 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 (e.g., “ordered data stream”, “machine learning model”, “predictive model”).
A method of managing model drift in a machine learning model, the method comprising: (under the broadest reasonable interpretation, a human, such as an machine learning engineer, can manage model drift in machine learning models, for example, such a machine learning engineer could determine if drift is caused by either data or concept drift, and then choose to retrain the machine learning model only using training data available after a specific event occurred)
predicting a doubly robust outcome for the test data and a doubly robust outcome for the training data by combining a treatment predicted by a first predictive model, the treatment predicted based on a control of the ordered data stream, and an outcome predicted by a second predictive model, the outcome predicted based on the treatment and the control of the ordered data stream; (under the broadest reasonable interpretation, a human, such as a machine learning engineer, can take the outputs of a prediction model and the outputs of a treatment model, combine such outputs, and taking a control feature into account (e.g., age or gender as discussed in para. 0017 of the instant specification), and performing such combination for both test data and training data)
measuring concept drift between the test data and the training data with respect to the machine learning model as an expectation of differences between the doubly robust outcome for the test data and the doubly robust outcome for the training data, resulting in a measured concept drift; (under the broadest reasonable interpretation, a human, such as an machine learning engineer, can measure concept drift using pencil and paper, using the mathematical equations described in paras. 0032-0033 and 0037-0039 of the instant specification)
selecting, based on the measured concept drift satisfying a retraining condition, a retraining feature vector from a feature vector of the ordered data stream; and (under the broadest reasonable interpretation, a human, such as an machine learning engineer, can select a certain feature vector from a group of feature vectors created from the ordered stream data, when the machine learning engineer decides that a concept drive measurement exceeds a threshold for retraining)
Step 2A, prong 2 (Does the claim recite additional elements that integrate the judicial exception into a practical application?).
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements (e.g., “ordered data stream”, “machine learning model”) which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “extracting test data and training data from an ordered data stream, the test data being extracted from a detection window in the ordered data stream and the training data being extracted from a sliding reference window that precedes the detection window in the ordered data stream” limitation, such additional element of a data gathering step 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 (see MPEP 2106.05(g)).
Regarding the “retraining, based on the measured concept drift satisfying the retraining condition, the machine learning model using the retraining feature vectors” limitation, such limitation 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. In particular, the claim only recites the additional element of retraining a machine learning model. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (any generic training of a machine learning model). 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, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
Step 2B (Does the claim recite additional elements that amount to significantly more than the judicial exception?)
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements (e.g., “ordered data stream”, “machine learning model”) are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “extracting test data and training data from an ordered data stream, the test data being extracted from a detection window in the ordered data stream and the training data being extracted from a sliding reference window that precedes the detection window in the ordered data stream” limitation, as discussed above, the additional element of a data gathering step 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").
Regarding the “retraining, based on the measured concept drift satisfying the retraining condition, the machine learning model using the retraining feature vectors” limitation, such limitation 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, because the limitation merely provides 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 add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Accordingly, at Step 2B, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not amount to significantly more than the judicial exception.
Regarding Claim 2
Step 2A, Prong 1
the doubly robust outcome predicted for the test data and the doubly robust outcome for the training data predicted for the ordered data stream are based on the outcome predicted based on the treatment and the control of the ordered data stream added to an inverse propensity weighting of residuals between the observed outcome and the outcome predicted based on the treatment and the control of the ordered data stream (under the broadest reasonable interpretation, a human, such as a machine learning engineer, can predict outcomes for both test data and training data as explained with respect to claim 1, and can further perform an inverse propensity weighting of residuals on paper using the equations in para. 0037 of the instant specification)
Step 2A, Prong 2
Regarding the “wherein the training data includes an observed outcome” limitation, such limitation merely describes the types of training data that will be used to retrain a machine learning model, and therefore such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not integrate a judicial exception into a practical application.
Step 2B
Regarding the “wherein the training data includes an observed outcome” limitation, such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use as explained above, which does not amount to significantly more than the judicial exception. MPEP 2106.05(h).
Regarding Claim 3
Step 2A, Prong 1
wherein the inverse propensity weighting of the residuals is based on the treatment predicted based on the control of the ordered data stream. (under the broadest reasonable interpretation, a human, such as machine learning engineer, can perform an inverse propensity weighting of residuals on paper using the equations in para. 0037 of the instant specification)
Regarding Step 2A, Prong 2, the claim does not include any additional elements that integrate the judicial exception into a practical application and regarding Step 2B, there are no additional elements recited that amount to significantly more than the judicial exception.
Regarding Claim 4
Step 2A, Prong 1
wherein the selecting operation comprises: selecting the retraining feature vector based on a score generated by an adversarial feature classifier. (under the broadest reasonable interpretation, a human, such as an machine learning engineer, can select certain feature vectors from a group of feature vectors created from the ordered stream data, where such selection takes into consideration scores generated by an adversarial feature classifier)
Regarding Step 2A, Prong 2, the claim does not include any additional elements that integrate the judicial exception into a practical application and regarding Step 2B, there are no additional elements recited that amount to significantly more than the judicial exception.
Regarding Claim 5
Step 2A, Prong 1
selecting the retraining feature vector based on an area-under-the-curve (AUC) score generated by the adversarial feature classifier and an AUC condition hyperparameter. (under the broadest reasonable interpretation, a human, such as an machine learning engineer, can select certain feature vectors from a group of feature vectors created from the ordered stream data, where such selection takes into consideration AUC scores and hyperparameters, which are merely values for consideration)
Regarding Step 2A, Prong 2, the claim does not include any additional elements that integrate the judicial exception into a practical application and regarding Step 2B, there are no additional elements recited that amount to significantly more than the judicial exception.
Regarding Claim 6
Step 2A, Prong 1
wherein the selecting operation comprises: selecting the retraining feature vector based on a raw feature importance value and a raw feature importance condition hyperparameter. (under the broadest reasonable interpretation, a human, such as an machine learning engineer, can select certain feature vectors from a group of feature vectors created from the ordered stream data, where such selection takes into consideration a raw feature importance value and related hyperparameter, which are merely values for consideration)
Regarding Step 2A, Prong 2, the claim does not include any additional elements that integrate the judicial exception into a practical application and regarding Step 2B, there are no additional elements recited that amount to significantly more than the judicial exception.
Regarding Claim 7
Step 2A, Prong 1
wherein the selecting operation comprises: selecting the retraining feature vector based on a raw permutation feature importance value and a raw permutation feature importance condition hyperparameter. (under the broadest reasonable interpretation, a human, such as an machine learning engineer, can select certain feature vectors from a group of feature vectors created from the ordered stream data, where such selection takes into consideration a raw permutation feature importance value and related hyperparameter, which are merely values for consideration)
Regarding Step 2A, Prong 2, the claim does not include any additional elements that integrate the judicial exception into a practical application and regarding Step 2B, there are no additional elements recited that amount to significantly more than the judicial exception.
Regarding Claim 8
Step 2A, Prong 1
Claim 8 recites a computing system that corresponds to the method of claim 1, and therefore the analysis under Step 2A, Prong 1 with respect to claim 1 also applies to this claim 8. While claim 8 recites additional generic computing components (“hardware processor”, “ordered data stream”, “machine learning model”, “memory”, “predictive model”), such additional generic computing components do not change the analysis under Step 2A, Prong 1.
Step 2A, Prong 2
Claim 8 recites a computing system that corresponds to the method of claim 1, and therefore the analysis under Step 2A, Prong 2 with respect to claim 1 also applies to this claim 8. While claim 8 recites additional generic computing components (“hardware processor”, “ordered data stream”, “machine learning model”, “memory”, “predictive model”), such additional generic computing components do not change the analysis under Step 2A, Prong 2. Such limitations are recited at a high-level of generality and amount to no more than adding the words “apply it” (or an equivalent) with the judicial exception. These additional elements are recited at a high-level of generality and amount to no more than mere instructions to apply the exception using generic computer components. 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 (See MPEP 2106.05(f)).
Step 2B
Claim 8 recites a computing system that corresponds to the method of claim 1, and therefore the analysis under Step 2B with respect to claim 1 also applies to this claim 8. While claim 8 recites additional generic computing components (“hardware processor”, “ordered data stream”, “machine learning model”, “memory”, “predictive model”), such additional generic computing components do not change the analysis under Step 2B. Such limitation are 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, because the limitations merely provide 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 add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Claims 9-14 depend from claim 8 and correspond to the methods of claims 2-7, respectively, and are therefore rejected for the same reasons explained above with respect to claim 8 and claims 2-7, respectively.
Regarding Claim 15
Step 2A, Prong 1
Claim 15 recites one or more tangible processor-readable storage media that corresponds to the method of claim 1, and therefore the analysis under Step 2A, Prong 1 with respect to claim 1 also applies to this claim 15. While claim 15 recites additional generic computing components (“processors and circuits”, “ordered data stream”, “machine learning model”, “predictive model”), such additional generic computing components do not change the analysis under Step 2A, Prong 1.
Step 2A, Prong 2
Claim 15 recites one or more tangible processor-readable storage media that corresponds to the method of claim 1, and therefore the analysis under Step 2A, Prong 2 with respect to claim 1 also applies to this claim 15. While claim 15 recites additional generic computing components (“processors and circuits”, “ordered data stream”, “machine learning model”, “predictive model”), such additional generic computing components do not change the analysis under Step 2A, Prong 2. Such limitations are recited at a high-level of generality and amount to no more than adding the words “apply it” (or an equivalent) with the judicial exception. These additional elements are recited at a high-level of generality and amount to no more than mere instructions to apply the exception using generic computer components. 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 (See MPEP 2106.05(f)).
Step 2B
Claim 15 recites one or more tangible processor-readable storage media that corresponds to the method of claim 1, and therefore the analysis under Step 2B with respect to claim 1 also applies to this claim 15. While claim 15 recites additional generic computing components (“processors and circuits”, “ordered data stream”, “machine learning model”, “predictive model”), such additional generic computing components do not change the analysis under Step 2B. Such limitation are 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, because the limitations merely provide 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 add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Claims 16-20 depend from claim 15 and correspond to the methods of claims 2-5 and 7, respectively, and are therefore rejected for the same reasons explained above with respect to claim 15 and claims 2-5 and 7, respectively.
Regarding Claim 21
Step 2A, Prong 2
Regarding the “wherein the selecting operation is performed by an adversarial feature selector” limitation, such limitation 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. In particular, the claim only recites the additional element of an adversarial feature selector, which is merely a computer function. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (an adversarial feature selector, which is merely a computer function). 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)).
Regarding the “the retraining operation updates a learned model parameter of the machine learning model for subsequent inference on new data samples” limitation, such limitation 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. In particular, the claim only recites the additional element of generic machine model retraining. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (generic machine model retraining). 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)).
Step 2B
Regarding the “wherein the selecting operation is performed by an adversarial feature selector” limitation, such limitation 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, because the limitation merely provides 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 add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding the “the retraining operation updates a learned model parameter of the machine learning model for subsequent inference on new data samples” limitation, such limitation 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, because the limitation merely provides 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 add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding Claim 22
Step 2A, Prong 2
Regarding the “wherein the machine learning model is operating on a time-ordered production data stream” limitation, this limitation merely describes the types of data being processed by a machine learning model, and therefore such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not integrate a judicial exception into a practical application.
Step 2B
Regarding the “wherein the machine learning model is operating on a time-ordered production data stream” limitation, such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use as explained above, which does not amount to significantly more than the judicial exception. MPEP 2106.05(h).
Allowable Subject Matter
Claims 1-22 would be allowed if the rejections under 35 U.S.C. 101 are overcome.
The following is a statement of reasons for the indication of allowable subject matter:
Independent claims 1, 8, and 15 would be considered allowable because none of the references of record either alone or in combination fairly disclose or suggest the combination of limitations specified in the independent claims, including at least:
predicting a doubly robust outcome for the test data and a doubly robust outcome for the training data by combining a treatment predicted by a first predictive model, the treatment predicted based on a control of the ordered data stream, and an outcome predicted by a second predictive model, the outcome predicted based on the treatment and the control of the ordered data stream
The closest prior art of record discloses:
Pan, Jing, et al. "Adversarial validation approach to concept drift problem in user targeting automation systems at uber." arXiv preprint arXiv:2004.03045 (2020), hereinafter referenced as PAN teaches validation methods for detecting concept drift between training and test data. (p. 3, section 3).
Last, Mark. "Online classification of nonstationary data streams” (2002), hereinafter referenced as LAST teaches operating on sliding windows of continuous data streams. (p. 12, section 3.1, p. 13, section 3.1 and p. 15, section 3.1)
Reddi, Sashank, et al. "Doubly robust covariate shift correction." Proceedings of the AAAI conference on artificial intelligence. Vol. 29. No. 1. 2015, hereinafter referenced as REDDI discloses a doubly robust covariate shift correction (corresponding to recited “data drift”). (pp. 2950, 2953).
Chen, Zhiqiang, et al. "A multi-level weighted concept drift detection method." The Journal of Supercomputing 79.5 (Sept. 2022), hereinafter referenced as CHEN determining whether to re-train a classifier if drift exceeds a threshold. (p. 5165, section 3.2).
US 12423960 B1, hereinafter referenced as BALLES teaches utilizing first and second machine learning models, where the first model takes as input one or more outputs from the second model. (col. 14, lines 29-38).
However, the examiner has found that the distinct feature of the Applicant's claimed invention over the prior art is the explicit claiming of the aforementioned limitations in combination with all the other limitations as specified in independent claims 1, 8, and 15. Moreover, the examiner finds that one of ordinary skill would not have been motivated to combine the prior art of record specifically in the manner recited in the independent claims without the hindsight of Applicant’s disclosure. Therefore, independent claims 1, 8, and 15 would be allowed over the prior art, provided that the rejections under 35 U.S.C. 101 are overcome.
Dependent claims 1-7, 9-14, and 16-22 would be allowed because they depend from an allowable independent base claim, provided that the rejections under 35 U.S.C. 101 are overcome.
Conclusion
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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20240037457 A1 (Bhattacharjee). “Embodiments detect data drift associated with machine learning (“ML”) models. Embodiments identify a first feature stored by a feature store, where the feature store includes an offline store and an online store. Embodiments determine one or more first trained ML models that are using the first feature. For each of the first trained ML models, embodiments invoke the first trained ML model using synthetic data or validation data, generate metrics to determine an accuracy of the first trained ML model and, when the accuracy is below a threshold, generate an alert notifying of a first data drift for the first trained ML model.” (para. 0005).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL C LEE whose telephone number is (571)272-4933. The examiner can normally be reached M-F 12:00 pm - 8:00 pm ET.
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.
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/MICHAEL C. LEE/Examiner, Art Unit 2128
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