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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,088,463. Although the claims at issue are not identical, they are not patentably distinct from each other because the instant claims are fully within the scope of the claims of ‘463, and thus are deemed to be an obvious variation.
For instance, claim 1 of the instant application uses machine learning to classify the traffic into one of two categories (conditional and non-normal), then acts based on the classification, with feedback being requested to the user for the conditional category and adjusting of a parameter for the non-normal category. Meanwhile, claim 1 of ‘463 teaches these details, but then provides additional details providing the use of both a machine learning and non-machine learning process, three categories (adding a normal category), with the conditional category “to trigger a request for input” and the categories resulting in the adjustment of a setting responsive to detecting a performance condition. Thus, the details of claim 1 are included in those of claim 1 of ‘463 in a rephrased form while omitting details that were included in the claim of ‘463, and is thus fully within the scope of claim 1 of ‘463.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2021/0051060 (Parvataneni) in view of US 10,320,813 (Ahmed).
With regard to claim 1, Parvateneni discloses at least one non-transitory machine-readable medium including instructions, which when executed by processing circuitry, cause the processing circuitry to perform operations to:
receive operational data of a network environment, an application, or a user device (Parvateneni: Paragraphs [0033] and [0053]. The state of the network may be monitored, where the instant claim presents the three options of a network environment, an application, or a user device in the alternative, and thus only requires the receipt of one of the listed options to teach the instant claim, as a whole.);
use a machine learning model to classify the operational data into one of at least two categories (Parvateneni: Paragraph [0033] and Abstract), the at least two categories including:
a non-normal category indicating a non-normal condition including at least one of an error condition, a high load condition, or an unexpected user input, the non-normal category associated with a specified configuration parameter to be applied to the network environment, the application, or the user device (Parvateneni: Paragraphs [0033] and [0053]. Parvateneni can determine whether a modification is necessary or not, where when a modification is not necessary, the system is in a normal condition. When a modification is necessary, the system is in a non-normal condition, which necessitates adjustments to the scaling of the system, such as when during peak load times, which would correspond with a “high load condition,” as claimed.);
in accordance with a determination by the machine learning model that the operational data is classified in the non-normal category, adjust, by the processing circuitry, a setting of the network environment, the application, or the user device using the specified configuration parameter associated with the non-normal category (Parvateneni: Paragraphs [0033] and [0053]. The scaling parameters are set based on the analysis of the collected information indicating that the modification should occur.); and
operate the network environment, the application, or the user device using the adjusted setting according to the specified configuration parameter (Parvateneni: Figure 3).
Parvateneni fails to disclose, but Ahmed teaches that one of the at least two categories includes a conditional category generating a request for input from a user to verify whether the operational data is non-normal; and in accordance with a determination by the machine learning model that the operational data is classified in the conditional category, output the request to the user to verify whether the operational data is non-normal (Ahmed: Column 12, line 57 to Column 13, line 14. It was known to implement a confidence evaluation with machine learning functions, where Ahmed shows that it was also known to, in cases of a low confidence, generate a request for input from a user (create a ticket for human intervention).).
Accordingly, it would have been obvious to one of ordinary skill in the art at the time of filing to provide a conditional category (low confidence) to request input from a user to ensure that the proper decisions are made by the machine learning algorithm. Further, such feedback would be able to be utilized for further training the machine learning algorithm to raise confidence in future decisions. Further, ensuring that the proper decisions are made with regard to the scaling of Parvateneni ensures that resources are efficiently allocated (Parvateneni: Paragraph [0011]). As a note, being in the conditional category, as claimed, does not prevent the system from taking action, where such request could simply be proactively requesting feedback as to whether the correct determination was made with the system taking action before receiving the user input as if the operational data is non-normal.
With regard to claim 2, Parvateneni teaches that the specified configuration parameter includes a parameter related to a resource assignment on a server (Parvateneni: Paragraph [0033]).
With regard to claim 3, Parvateneni fails to teach expressly, but knowledge possessed by one of ordinary skill in the art at the time of filing teaches that the machine learning model includes a proactive model to predict the non-normal condition (More specifically, Official Notice is taken that the use of machine learning algorithms to predict future conditions (such as relating to the collected information of Parvateneni (Parvateneni: Paragraph [0037])), then act based on the future conditions, were very well known to one of ordinary skill in the art at the time of filing.). Accordingly, it would have been obvious to one of ordinary skill in the art at the time of filing to utilize a proactive model, thus predicting future information that would have been collected, then act based on the predictions to act even more rapidly to changing conditions, where Parvateneni is specifically concerned with improving the responsiveness of the adjustments (Parvateneni: Paragraph [0011]).
With regard to claim 4, Parvateneni teaches that the the machine learning model includes a reactive model to respond to the non-normal condition (Parvateneni: Paragraph [0054] and Figure 3. The system reacts based on the collected information.).
With regard to claim 5, Parvateneni fails to teach, but knowledge possessed by one of ordinary skill in the art at the time of filing teaches that the operations further cause the processing circuitry to: identify an application pattern for the application; update the machine learning model based on a relationship between calendar dates and the application pattern; and predict, using the updated machine learning model, a subsequent application pattern based on the relationship (More specific, Official Notice is taken that proactive machine learning models that identify patterns of use corresponding to dates, then applies the historic patterns to predict future patterns for different dates were well-known to one of ordinary skill in the art at the time of filing.). Accordingly, it would have been obvious one of ordinary skill in the art at the time of filing to utilize collected historic information (Parvateneni: Paragraph [0011]) to determine trends with relation to dates to predict future patterns to improve the responsiveness of the adjustments of Parvateneni (Parvateneni: Paragraph [0011]).
With regard to claim 6, Parvateneni teaches that the operations further cause the processing circuitry to: acquire network traffic transaction data from the network environment (Parvateneni: Paragraphs [0023] and [0037]). Parvateneni fails to teach, but knowledge possessed by one of ordinary skill in the art at the time of filing teaches predict, using the updated machine learning model, a traffic load based on the network traffic transaction data (More specifically, as addressed with regard to claim 3, above, Official Notice is taken that the use of machine learning algorithms to predict future conditions (such as relating to the collected information of Parvateneni (Parvateneni: Paragraph [0037])), then act based on the future conditions, were very well known to one of ordinary skill in the art at the time of filing.). Accordingly, it would have been obvious to one of ordinary skill in the art at the time of filing to utilize a proactive model, thus predicting future information (such as traffic load) that would have been collected, then act based on the predictions to act even more rapidly to changing conditions, where Parvateneni is specifically concerned with improving the responsiveness of the adjustments (Parvateneni: Paragraph [0011]).
With regard to claim 7, Parvateneni teaches wherein the operations to identify the application pattern for the application include operations to detect a user interaction with the application (Parvateneni: Paragraph [0037]).
With regard to claim 8, Parvateneni teaches wherein the operations to learn the relationship include operations to detect a peak traffic level using the updated machine learning model (Parvateneni: Paragraph [0033]. Peak loads may be identified, where claim 5, from which claim 8 depends, addresses the prediction of future conditions.).
With regard to claim 9, Parvateneni teaches wherein the operations further cause the processing circuitry to: identify a result of a non-normal performance condition within the network environment; update the machine learning model based on a rule for reacting to the non-normal performance condition; and provide the rule to the application (Parvateneni: Figure 3 and paragraphs [0034] and [0040].).
With regard to claim 10, Parvateneni teaches that the operations further cause the processing circuitry to set a configuration parameter corresponding to the non-normal category for at least one other application related to the application (Parvateneni: Paragraph [0048]. Multiple applications can be provided, where the nature of the applications is not claimed. For example, one application could be a server application and another application could be a client application, where the parameter would be for both applications, as it would modify the service realized by the combination of the two.).
With regard to claims 11-20, the instant claims are similar to claims 1-10, and are rejected for similar reasons.
Conclusion
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SCOTT B. CHRISTENSEN
Examiner
Art Unit 2444
/SCOTT B CHRISTENSEN/Primary Examiner, Art Unit 2444