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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/6/25 has been entered.
Claims 1-7 have been examined.
Response to Argument
Applicant’s arguments in the Remarks, filed on 9/12/25 have been fully considered but they are not persuasive.
In the remarks, Applicant argues that:
Chanda fails to teach 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.
In response to point (1), according to Applicant’s, “support for the amendments to the
independent claim 1 can be found at least, for example, in paragraphs [0045] and [0056]-[0058 and Figs. 2 and 5 of the Specification as published”. (Remarks at 5) Examiner has thoroughly reviewed the Specification including the cited supporting paragraphs, however, there was no mentioning of “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”. As per Applicant’s argument that Chanda fails to teach 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, Examiner respectfully disagreed. According to paragraph of [0036] of Applicant’s Specification,
“[t]he embodiments also make use of random KPIs, which are the KPI's that are not classified as 'controlled' or predictable, because in some cases these KPIs are associated with sporadic events, which can be classified as anomalies. The embodiments provide a system and a method for avoiding human-based bias in data-preparation phase and to improve predictive accuracy leveraging automated processes (i.e., processes that exclude human bias). This data preparation is automated, and data of a different and varied nature is used than the controlled KPIs used in existing systems.”
In light of Applicant’s Specification, Chanda teaches 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]. Chanda further teach 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 (i.e., subject to fairness constraints across the first source and the second sources) ([24][32][33][35][36][38]). Thus, Chanda teaches generating a first set of de-biased predictive models trained on bias-normalized KPI inputs associated with the unpredictable KPI (i.e., generating a set of new models based on automated analysis of new input data to determine anomalies and automated recognition of data as new pattern data) and subject to fairness constraints across the first source and the second sources (i.e., the new models are created subject to 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).
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]);
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
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.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor,
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