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 .
DETAILED ACTION
This action is in response to the amendments filed on 08/11/2026, in which claims 1, 4, 6-7, 10 have been amended, claim 11 has been cancelled, claims 1-10, 12-15 are presented for the examination.
Response to Arguments
Applicant's arguments filed 08/11/2026 have been fully considered but they are not persuasive.
Claim Rejections - 35 U.S.C. § 101
Applicant’s arguments regarding the mental-process grouping is persuasive. Accordingly, the mental-process rationale is withdrawn.
However, examiner would like to point out that claim limitations recite, analyzing the data using a predictive model to calculate a risk score and notifying a user when the risk exceeds a threshold.
These operations amount to collecting and evaluating information, performing a mathematical calculation, comparing the calculated result against a predetermined criterion such operations are consistent with the abstract-idea groupings of mathematical concepts. These limitations expressly involve mathematical calculations and comparisons and therefore, recite a mathematical concept within the abstract idea grouping of mathematical concepts.
Accordingly, although the mental-process rationale is withdrawn, the rejection under 35 U.S.C. 101 is maintained based on the mathematical-concepts grouping. The remaining analysis under 2A, Prong two and step 2B, as set forth in the office action is maintained.
THE OBVIOUSNESS REJECTION OF CLAIMS 1-9, 14, AND 15
Batta, Morris, and Hirani, whether taken alone or in any combination, fail to disclose or suggest the explainable boosting machine model recited in amended claim 1.
Response:
Examiner respectfully disagrees. Specification of Instant application [0076] recites, “The predictive model according to the present invention may be implemented with an Explainable Boosting Machine (EBM) model, which implements boosted tree-based techniques. Such a model is a tree-based, cyclic gradient boosting Generalized Additive Model with automatic interaction detection.”
Prior-art Hirani discloses in Col. 18, lines 1-24, The ML model 400 may be realized …. (v) decision-tree ensembles (e.g., gradient-boosted trees, random forests).
It is clear that specification defines EBM model to be a model is a tree-based, cyclic gradient boosting Generalized Additive Model and Hirani teaches ML model to be defined as gradient-boosted trees.
Therefore, examiner believes combinations of Batta, Morris, and Hirani teaches each and every limitations recited in amended claim 1.
Claim Rejections - 35 U.S.C. § 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 1-15 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
As to claim 1, 14, 15:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim 1 is to a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
The claim 1 recites steps of: analyzing the data using a predictive model to calculate a risk score and notifying a user when the risk exceeds a threshold.
These operations amount to collecting and evaluating information, performing a mathematical calculation, comparing the calculated result against a predetermined criterion such operations are consistent with the abstract-idea groupings of mathematical concepts.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
The claim does not integrate the abstract idea into practical application.
The steps are performed on a generic “computer-implemented method” without any improvement to computer functionality.
The “trained predictive model” is recited at a high level of generality and merely serves as a tool to perform the abstract analysis.
The claim does not recite any specific technical implementation, data structure or improvement to computing systems.
The additional elements therefore amount to no more than instructions to apply the abstract idea on a generic computer.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
The claim does not include additional elements that amount to significantly more than the abstract idea.
Analyzing/processing data and generating notification are well-known, understood, routine and conventional activities. The use of predictive model trained on historical data is also conventional data analysis technique. No element or combination of elements provides an inventive concept sufficient to transform the abstract idea into patent-eligible subject matter.
Accordingly, claim 1 is directed to an abstract idea and does not include significantly more. Claim 1 is therefore rejected under 35 U.S.C. § 101. Dependent claims 2-13 are also rejected for the same rationale as in claim 1,
Claim 14 and 15 recite similar limitations to those of claim 1 and are therefore rejected for the same rationale as in claim 1.
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 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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 1-9, 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Batta et al. (US 2022/0230090, referred herein after Batta) in view of Morris II, et al. (US 2018/0314947, referred herein after Morris) and Hirani et al. (US 12,602,223, referred herein after Hirani).
As per claim 1, 14, 15, Batta discloses a computer-implemented method for flagging risk when handling updates to hardware and/or software with a computing environment, the method comprising:
receiving, by at least one computer processor, a change request comprising one or more change features associated with a change in hardware and/or software within the computing environment (Fig. 4, proposed change 412, (e.g., a change proposed in a computing environment), [0059], [0039], identifying features in change data representing the defined change and identifying the features in change events data representing the change events);
receiving, by at least one computer processor, a change record associated with the change request, the change record comprising one or more fields corresponding to the one or more change features, wherein the one or more fields corresponding to the one or more change features characterize the change in hardware and/or software within the computing environment ([0068], change events (e.g., source code changes, configuration changes, environment changes, outdated security changes, and/or another change event) all these change are interpreted as change in hardware and/or software within the computing environment as claimed);
calculating, by at least one computer processor, a risk score for the change by applying a trained predictive model (Fig. 4, 410) to the one or more fields (Fig. 7, step 702, [0071]), the predictive model having been trained on a dataset comprising historical changes to the computing environment and their associated historical change records ([0071], risk assessment system to assign a change risk assessment score (e.g., change risk assessment score 414) to a defined change (e.g., proposed change 412) in a computing environment), wherein the risk score is indicative of a likelihood of change-induced computing environment failure or malfunction; and ([0071] ”model to assign a change risk assessment score (e.g., change risk assessment score 414) to a defined change (e.g., proposed change 412) in a computing environment (e.g., resource and/or service interruption, failure, delay, and/or damage) based on change events (e.g., source code changes, configuration changes, environment changes, outdated security changes, and/or another change event) that induced one or more incidents (e.g., resource and/or service interruption, failure, delay, and/or damage) in the computing environment.”)
Batta does not disclose determining by the at least one computer processor, that the calculated risk score is above a predetermined threshold, raising, by the at least one computer processor, a flag to the change request to notify a user;
However, Morris discloses determining by the at least one computer processor, that the calculated risk score is above a predetermined threshold, raising, by the at least one computer processor, a flag to the change request to notify a user ([0009], [0055], [0056], a notification to an operator when the comparison indicates that a prediction threshold of interest has been reached or exceeded by the first generated prediction);
Therefore it would have been obvious to the one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate teaching of Morris’s predictive analysis system into Batta’s risk assessment of proposed change in a computing environment because one of the ordinary skill in the art would have been motivated to provide more actionable, efficient and accurate predictive analytics;
Neither Batta nor Morris discloses wherein the predictive model comprises an explainable boosting machine model;
However, Hirani discloses the predictive model comprises an explainable boosting machine model (Col. 18, lines 1-15, specification defines in [0076] EBM model to be a model is a tree-based, cyclic gradient boosting Generalized Additive Model and Hirani teaches ML model to be defined as gradient-boosted trees);
Therefore it would have been obvious to the one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate teaching of Hirani’s dynamic patch management system into Batta’s risk assessment of proposed change in a computing environment and Morris’s predictive analysis system because one of the ordinary skill in the art would have been motivated to analyze tabular telemetry data containing numeric and categorical metrics.
As per claim 2, Batta discloses the method according to claim 1, wherein the one or more fields in the change record include at least one of:
a change category;
whether a product or disaster recovery is impacted;
a number of computing environment portions impacted by the respective change;
a number of computing environment user groups impacted by the respective change;
a number of assignees or assignee groups of the respective change;
a number of configuration items within the computing environment impacted by the respective change ([0068], change events (e.g., source code changes, configuration changes, environment changes, outdated security changes, and/or another change event));
a number of service offerings in the computing environment affected by the respective change;
a number of services in the computing environment impacted by the respective change;
a number of approval services in the computing environment impacted by the respective change;
a highest resilience level of a service affected by the respective change;
a highest criticality level of an application affected by the respective change;
a fraction of previous unsuccessful changes associated with the assignee or assignee group requesting the respective change;
a number of previous changes carried out by the assignee or assignee group requesting the respective change;
an indication of whether the change is an automated change; and
a number of recent incidents associated with changes associated with the assignee or assignee group requesting the respective change.
As per claim 3, Batta discloses the method according to claim 2, wherein a change category is one of: adding or installing; user permissions; third party related; retiring or decommissioning; resource uplift; reboot, recycle, restart, physical access, patching, and application install (e.g., resource and/or service interruption, failure, delay, and/or damage) based on change events (e.g., source code changes, configuration changes, environment changes, outdated security changes, and/or another change event)
As per claim 4, Morris discloses the method according to claim 1, further comprising determining, by the at least one computer processor, based on the calculated risk score for the change associated with the change request, a risk level of the change identified by the change request ([0009], [0036], [0055], [0056], level of risk determined).
As per claim 5, Batta discloses the method according to claim 4, wherein determining the risk level of the change identified by the change request comprises determining the risk level as one of: high risk, medium risk, low risk, and minimal risk by comparing the calculated risk score against one or more predetermined thresholds ([0053], [0055], “risk levels -“low risk,” to “high risk,” to “medium risk”).
As per claim 6, Morris discloses the method according to claim 5, wherein each risk level is associated with a corresponding predetermined lead time, and the method further comprises determining, by the at least one computer processor, a lead time for the change associated with the change request based on the determined risk level ([0019], [0020], unit of analysis is a predicted probability that a defined event will occur during or prior to the end of such time period.).
As per claim 7, Morris discloses the method according to claim 5, wherein if it is determined that the risk level of the change identified by the change request is high risk, raising a flag comprises outputting, by the at least one computer processor, a notification via a user interface ([0009], [0055], [0056], hen a defined risk category or a prediction threshold of interest is reached or exceeded by a generated probability, at least one notification can be presented to the operator.).
As per claim 8, Morris discloses the method according to claim 1, wherein the predictive model comprises an Application Programming Interface (API) layer ([0055], REST API), the API layer being configured to enable one or more applications or services to perform at least one of: sending input data to the predictive model, retrieving results from the predictive model, monitor the performance of the predictive model, and adjust one or more parameters of the predictive model ([0055], an application programming interface (API), a representational state transfer API (REST API), and/or another interface component that can enable change risk assessment system 102).
As per claim 9, Batta discloses the method according to claim 1, wherein the predictive model is configured to perform a classification of the change request, the classification being a prediction of whether or not a change-induced computing environment failure or malfunction will occur ([0058], vector representations to train a predictive model 410 (e.g., a forecast model, a classification model, an outliers model, a time series model, a clustering model, and/or another model) to assign a change risk assessment score to a defined change (e.g., a change proposed in a computing environment, [0059], historic evidence of changes contain such features and were problematic (e.g., they failed or induced incidents)), and
Batta does not discloses the calculated risk score is a prediction accuracy metric corresponding to a confidence that a classification that a change-induced computing environment failure or malfunction will occur is correct;
However, Morris teaches wherein the calculated risk score is a prediction accuracy metric corresponding to a confidence that a classification that a change-induced computing environment failure or malfunction will occur is correct ([0009], [0032], [0058], prediction thresholds or categories, such as “less likely,” or “more likely,” where such thresholds or categories can be associated with probability values generated by the prediction engine, one operational outcome of interest, such as a machine or system failure, is likely to occur).
Therefore it would have been obvious to the one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate teaching of Morris’s predictive analysis system into Batta’s risk assessment of proposed change in a computing environment because one of the ordinary skill in the art would have been motivated to provide more actionable, efficient and accurate predictive analytics.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Batta and Morris in view of Kolman et al. (US 8,875,267, referred herein after Kolman).
As per claim 10, neither Batta nor Morris disclose the method according to claim 9, further comprising calculating, by the at least one computer processor, a non-risk score for the change by applying the trained predictive model to the one or more fields, wherein the risk score is a second prediction accuracy metric corresponding to a confidence that a classification that a change-induced computing environment failure or malfunction will not occur is correct;
However, Kolman discloses calculating a non-risk score for the change by applying the trained predictive model to the one or more fields, wherein the risk score is a second prediction accuracy metric corresponding to a confidence that a classification that a change-induced computing environment failure or malfunction will not occur is correct (abstract, Col. 2, lines 51-60, non-risk based criteria is associated with low riskiness score).
Therefore it would have been obvious to the one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate teaching of Kolman’s fraud detection method into Batta’s risk assessment of proposed change in a computing environment and Morris’s predictive analysis system because one of the ordinary skill in the art would have been motivated to provide more actionable, efficient and accurate predictive analytics.
Claims 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Batta and Morris in view of Wang et al. (US 2024/0256371, referred herein after Wang).
As per claim 12, Batta discloses the method according to claim 1, wherein the historical changes to the computing environment and their associated historical change records that are used to train the predictive model have associated historical failure scores used to train the model (Fig. 4, [0026], [0028], [0039], [0058], trainer component to train a sequential model (e.g., LSTM, Bi-LSTM, and/or another model) using such generated model training data),
Neither Batta nor Morris discloses optionally wherein the historical failure scores are Boolean values indicating whether or not a respective historical change caused a change-induced computing environment failure or malfunction;
However, Wang discloses the historical failure scores are Boolean values indicating whether or not a respective historical change caused a change-induced computing environment failure or malfunction ([0017], [0022], [0060], An positive attribution score may indicate the amount that the log line contributes to the failure type), and a zero attribution score may indicate that the log line may have no contribution to the failure type);
Therefore it would have been obvious to the one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate teaching of Wang’s method for predicting failure and time-to-failure based on attribute scores of log data into Batta’s risk assessment of proposed change in a computing environment and Morris’s predictive analysis system because one of the ordinary skill in the art would have been motivated to improve the efficiency and timely detection of future failures.
As per claim 13, Wang discloses the method according to claim 12, wherein the historical failure scores are weighted based on an impact caused by the historical change ([0017], model is trained to assign scores to historical log).
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KAMINI B PATEL whose telephone number is (571)270-3902. The examiner can normally be reached on M-F 8-4:30.
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, Ashish Thomas can be reached on 571-272-0631. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KAMINI B PATEL/Primary Examiner, Art Unit 2114
