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 .
Claims 1-7 have been examined.
Response to Argument
Applicant’s arguments in the Remarks, filed on 5/20/26 have been fully considered but they are not persuasive.
In the remarks, Applicant argues that:
The 35 USC 112(a) rejection should be withdrawn.
Chanda fails to teach wherein the bias-normalized KPI inputs are generated by excluding human-based bias in a data-preparation phase.
In response to point (1), according to Applicant,
“the claimed generation of a ‘first set of de-biased predictive models’ subject to fairness constraints across the first source and the second sources is supported by the disclosure of multi- source KPI processing and the objective of avoiding human-based bias in the data-preparation phase [See Specification, Para. 0004-0006, 0023 and 0036], which would have been understood by a skilled artisan as a de-biasing and fairness-oriented modeling framework rather than a new and unexplained concept.”
(Remarks at 6) Examiner respectfully disagree. A person of ordinary skilled in the art would not recognize from Applicant’s disclosure of multi-source KPI processing and avoiding human-based biased in data preparation phase as the claimed “generating a first set of de-biased predictive models trained on bias-normalized KPI inputs associated with the unpredictable KPI and subject to fairness constraints across the first source and the second sources”. One skilled in the art would not understand how the disclosure of multi-source KPI processing as support for “…subject to fairness constraints across the first source and the second sources.
In response to point (2), Chanda teaches detection framework use predictive modeling to determine/classify the metric is a new pattern [24]. Chanda further teach automated analysis/processes of new input and automated recognition of data as new pattern data ([57][30], i.e., unpredictable/bias-normalized KPI input data). In other words, Chanda teaches generating/classifying KPI as new pattern using automated processes (e.g., wherein the bias-normalized KPI inputs are generated by excluding human-based bias in a data-preparation phase)
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.
Claims 1-7 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 applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Examiner has considered Applicant’s response filed on 12/6/25 including the cited paragraphs [0045] and [0056]-[0058] and Figs. 2 and 5 of the Specification as published supporting the claim amendments. However, these referred sections and the instant specification fail to disclose the amended claim limitations.
As per claims 1, 6 and 7, Applicant’s specification does not mention “de-bias predictive models”, “bias-normalized KPI inputs”, “fairness constraints”, let alone, “generating a first set of de-biased predictive models trained on bias-normalized KPI inputs associated with the unpredictable KPI and subject to fairness constraints across the first source and the second sources”. Because the specification does not disclose this limitation, the specification does not satisfy the written description requirement.
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 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 1-7 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Chanda et al, WO 2018/160177 (hereinafter Chanda).
As per claim 1, Chanda teaches the invention as claimed for de-biasing data for an incident management system, the method comprising:
receiving a key performance indicator (KPI) input from at least a first source and a second source ([22][23][32], e.g., receiving metrics inputs from different sources);
classifying the key performance indicator input as a predictable KPI or an unpredictable KPI ([24], e.g., determining/classifying the metrics is a new pattern);
generating a first set of de-biased predictive models trained on bias-normalized KPI inputs associated with the unpredictable KPI and subject to fairness constraints across the first source and the second sources (i.e., generating a set of new models trained on automated analysis of new input data to determine anomalies (i.e., automated processes) and automated recognition of data as new pattern data (i.e., unpredictable /bias-normalized KPI input data [57][30], and that the new models are created by the same analyzing process of determining whether new IP data received from different sources (i.e., across the first source and ethe second sources) is an anomaly using predictive modelling [24][32][33][35][36][38]), wherein the bias-normalized KPI inputs are generated by excluding human-based bias in a data-preparation phase ([24][57][30], e.g., generating/classifying KPI as new pattern using automated processes);
executing the first set of models to generate predicted events for the incident management system ([26][28][30][94], e.g., executing the models to generate predicted values; generating predictions using the new special models); and
outputting a set of patterns for the predicted events ([26][28][30][32][94], e.g., generating predictive values; capturing a new pattern; the new special model for generating the predications is used to capture subsequent new pattern).
As per claim 2, Chanda teaches the invention as claimed in claim 1 above. Chanda further teach comprising:
predicting subsequent KPI values for the first source where the KPI input of the first source is classified as predictable ([24][26][29][38], e.g., use predictive modeling to predict values based on metrics matching patterns of the models);
comparing predicted subsequent KPI values for the first source with the KPI input of the first source to identify anomalies ([26][38], e.g., comparing predicted values with the new data values); and
generating anomaly events for the incident management system in response to identifying the anomalies ([27][38], e.g., generating alerts in response to identifying anomalies).
As per claim 3, Chanda teaches the invention as claimed in claim 1 above. Chanda further teach comprising:
predicting subsequent KPI values for the first source where the KPI input of the first source is classified as predictable ([24][26][29][38], e.g., use predictive modeling to predict values based on metrics matching patterns of the models);
determining whether the predicted subsequent KPI values for the first source exceed a predefined limit ([27][38]); and
generating divergence events for the incident management system in response to determining that the predicted subsequent KPI values for the first source exceed the predefined limit ([24][27][38], e.g., generating alert/anomalous events/non-anomalous events that deviate from a standard in response to the score based on the predicted values exceeding a threshold).
As per claim 4, Chanda teaches the invention as claimed in claim 1 above. Chanda further teach generating a second set of models to predict events based on divergence events and anomaly events ([24]-[27], e.g., generating available models to predict values based on anomalous and/or non-anomalous data values and models based on metrics/data value that are likely to be anomalous).
As per claim 5, Chanda teaches the invention as claimed in claim 4 above. Chanda further teach merging the first set of models and the second set of models to predict events for the incident management system ([28], e.g., adding/merging the first set of special models and set of known/available pattern models to predict events).
As per claim 6 and 7, they are rejected for the same reason as set forth in claim 1 above. See figures 1 and 2, [47] for a machine-readable storage medium having stored therein a de-biasing component; and a set of processors coupled to the machine-readable storage medium, at least one processor from the set of processors to execute the de-biasing component, the de-biasing component to execute the method of claim 1.
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 extension fee 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 Philip Lee whose telephone number is (571)272-3967. The examiner can normally be reached on 6a-3p M-F.
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Glenton Burgess can be reached on 571-272-3949. The fax phone number for the organization where this
application or proceeding is assigned is 571-273-8300.
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/PHILIP C LEE/Primary Examiner, Art Unit 2454
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