DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the America Invents Act (AIA ).
Request for Continued Examination (RCE)
An RCE under 37 C.F.R. § 1.114, including the fee set forth in § 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under § 1.114, and the fee set forth in § 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to § 1.114. Applicants’ submission filed on June 15, 2026 has been entered.
Response and Claim Status
The instant Office action is responsive to the response received May 12, 2026 (the Response).
In response to the Response, the previous rejections of claims 1–20 under 35 U.S.C. § 103
are WITHDRAWN.
Claims 1–20 are currently pending.
Claim Rejections – 35 U.S.C. § 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.
Sun, Polisetty, and Hu
Claims 1–3, 7, 9–11, 15, 17, and 18 are rejected under 35 U.S.C. § 103 as being obvious over Sun et al. (US 2025/0112815 A1; filed Sept. 29, 2023) in view of Polisetty et al. (US 2026/0003723 A1; filed June 27, 2024), and in further view of Hu et al. (CN 115412921 A; filed Sept. 1, 2022)1.
Response to arguments
Applicants assert
[t]he Office Action asserts that Sun's operation 210 discloses capturing network operations data relating to the operational anomaly (Office Action, pg. 5-6). . . . Sun generally relates to analyzing error codes (Sun, Abstract). Sun's operation 210 discusses identifying a subset of subscribers impacted by a failure condition and selecting that subset using criteria (e.g., recency/number of error codes) (Id, ¶ [0033]-[0034]).
Response 7. Applicants argue “[t]his is different from filtering that removes nonessential information and produces a filtered dataset corresponding to both an anomaly time window and a network function implicated by error codes. Polisetty does not alleviate this deficiency of Sun.” Id.
The Examiner is unpersuaded of error. The Examiner relies principally on Sun for teaching many of the recited elements of claim 1. Of particular note, the Examiner finds Sun teaches filtering network operations data to remove nonessential information and generate filtered network operations data corresponding to a time window associated with the operational anomaly. The Examiner further finds Sun’s generated filtered network operations data do not also correspond to at least one network function identified by the error code information, turning to Hu to show that at least one network function identified by error code information is known in the art. Thus, the Examiner proposes to include Hu’s teaching with Sun, such that the combined system predictably yields generating filtered network operations data corresponding to a time window associated with an operational anomaly and at least one network function identified by error code information. Accordingly, Applicants’ arguments regarding Sun’s and Polisetty’s alleged individual shortcomings (see Response 7) are unavailing. Here, the rejection is not based solely on Sun or Polisetty, but rather on the Sun’s and Hu’s collective teachings. See In re Keller, 642 F.2d 413, 426 (CCPA 1981); In re Merck & Co., Inc., 800 F.2d 1091, 1097 (Fed. Cir. 1986).
Next, Applicants assert
[t]he Office Action asserts that Sun teaches prompting a model (Fig. 2, item 200) and infers that Sun provides the model with error code information, network operations data, and contextual information because item 235 does not occur but for items 205/210/215 (Office Action, pg. 5). . . . Sun discusses a mechanism which employs correlation coefficients and Bayesian networks for error-code analytics to discover causation relationships among error codes. Sun further discusses using CPDs to chain up error codes to identify an upstream event as the likely root cause before generating a message (which may include natural- language explanation/troubleshooting) (Sun, ¶ [0014], ¶ [0066]-[0068]).
Response 7. Applicants argue
[t]his is different from prompting a model with filtered network operations data corresponding to a time window associated with the operational anomaly and at least one network function identified by the error code information. Moreover, since Sun does not actually disclose generating this filtered network operations data, Sun cannot teach providing that filtered data to a model as prompt. Polisetty does not alleviate this deficiency of Sun. As such, the cited combination does not teach or reasonably suggest prompt an artificial intelligence (AI) model trained to identify root causes of operational anomalies by providing the AI model with the error code information, the filtered network operations data, and the contextual information and tasking the AI model to identify a root cause of the operational anomaly as recited by amended claim 1.
Id.
The Examiner is unpersuaded of error. As discussed above, the Examiner proposes to include Hu’s teaching with Sun, such that the combined system predictably yields generating filtered network operations data corresponding to a time window associated with an operational anomaly and at least one network function identified by error code information.
Moreover, the Examiner finds Sun teaches prompting a model to identify root causes of operational anomalies by providing the model with error code information, the above-mentioned filtered network operations data, and contextual information and tasking the model to identify a root cause of the operational anomaly. The Examiner further finds Sun’s model is not an artificial intelligence (AI) model trained to identify root causes, turning to Polisetty to show that an AI model trained to identify root causes is known in the art. Thus, the Examiner proposes to include Polisetty’s teaching with Sun, such that the combined system predictably yields prompting an AI model trained to identify root causes of operational anomalies by providing the model with error code information, the above-mentioned filtered network operations data, and contextual information and tasking the model to identify a root cause of the operational anomaly. Accordingly, Applicants’ arguments regarding Sun’s and Polisetty’s alleged individual shortcomings (see Response 7) are unavailing. Here, the rejection is not based solely on Sun or Polisetty, but rather on the Sun’s and Hu’s collective teachings. See Keller, 642 F.2d at 426; Merck, 800 F.2d at 1097.
Next, Applicants assert
The Rejection
Regarding claim 1, while Sun teaches a computing apparatus (fig. 1, item 115) comprising:
one or more computer readable storage media (fig. 1, item 135);
one or more processors (fig. 1, item 130) operatively coupled with the one or more computer readable storage media; and
program instructions (fig. 1, items 140, 150–160) stored on the one or more computer readable storage media that, when executed by the one or more processors, direct the computing apparatus to at least:
detect an operational anomaly (fig. 2, item 205; “detect the failure condition based on the collected data from the network 120” at ¶ 32) in a wireless network (fig. 1, item 120) based on error code information (“determine from the collected data if the response status codes include any error codes” at ¶ 32);
capture network operations data (fig. 2, item 210; ¶¶ 33–34) relating to the operational anomaly;
filter the network operations data (¶¶ 33–34) to remove nonessential information (subscribers that are not “[s]ubscribers that experienced the failure condition more recently (e.g., within a predetermined period of time)” at ¶ 34) and generate filtered network operations data (“[s]ubscribers that experienced the failure condition more recently (e.g., within a predetermined period of time) may be selected” at ¶ 34) corresponding to a time window associated with the operational anomaly (“more recently (e.g., within a predetermined period of time) may be selected” at ¶ 34)
capture contextual information (fig. 2, item 215; ¶¶ 35–36) relating to the operational anomaly;
prompt a model (fig. 2, item 200) to identify root causes (fig. 2, item 235; “determine that the event that generated error code EC1 is most likely the root cause of the failure condition” at ¶ 66) of operational anomalies by providing the model with the error code information (fig. 2, item 235 does not occur but for item 205), the filtered network operations data (fig. 2, item 235 does not occur but for item 210), and the contextual information (fig. 2, item 235 does not occur but for item 215) and tasking the model to identify a root cause (“analyze error codes to determine the root cause of a failure condition.” at ¶ 14; fig. 2, item 235) of the operational anomaly; and
receive, from the model in response to the prompt, output comprising a root cause analysis (fig. 2, item 240; ¶ 68) of the operational anomaly,
Sun does not teach (A) the model being an artificial intelligence (AI) model trained to identify the root causes; and (B) the generated filtered network operations data also corresponding to at least one network function identified by the error code information.
(A)
Polisetty teaches an AI model trained to identify root cause (“the generative AI model 970 is trained to identify one or more root causes for a failure” at ¶ 92).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention for Sun’s model to be an AI model trained to identify Sun’s root causes as taught by Polisetty to “provide significant advantages relative to conventional techniques. For example, technical problems related to such conventional techniques are mitigated in one or more embodiments by automatically predicting a failure of a software deployment pipeline and suggesting one or more mitigation actions to mitigate one or more causes of the failure.” Polisetty ¶ 3.
(B)
Hu teaches at least one network function identified by alarm information (“the alarm information may include a network function network element reporting the processing result” at p. 8).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention for Sun’s generated filtered network operations data to also correspond to at least one network function identified by the error code information as taught by Hu to produce “better protection effect, improving the network protection capability of the 5G core network.” Hu p. 8.
Regarding claim 2, Sun teaches wherein the error code information (“determine from the collected data if the response status codes include any error codes” at ¶ 32) comprises an indication that a transaction completion metric (“any error codes” at ¶ 32 is one or more error codes) of the wireless network exceeds a respective threshold (zero error codes).
Regarding claim 3, Sun teaches wherein the transaction completion metric comprises a quantity of error codes (“any error codes” at ¶ 32 is one or more error codes) associated with a network function (¶¶ 31–32) of the wireless network within a given period of time (the time at which the collected data is received at ¶ 32).
Regarding claim 7, while Sun teaches wherein the model (fig. 2, item 200) correlates the root causes of network anomalies (“generated error code EC1 is most likely the root cause of the failure condition” at ¶ 66) to network operations data (fig. 3, item 315) based on a historical operational anomaly dataset (fig. 3, items 300, 305, 310),
Sun does not teach the model being an AI model trained for the correlation.
Kenny teaches a trained AI model (“the generative AI model 970 is trained to identify one or more root causes for a failure” at ¶ 92).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention for Sun’s model to be a trained AI model as taught by Polisetty to “provide significant advantages relative to conventional techniques. For example, technical problems related to such conventional techniques are mitigated in one or more embodiments by automatically predicting a failure of a software deployment pipeline and suggesting one or more mitigation actions to mitigate one or more causes of the failure.” Polisetty ¶ 3.
Regarding claim 9, Sun teaches a method (fig. 2, item 200) of operating a computing device (fig. 1, item 115) comprising operations according to claim 1. Thus, references/arguments equivalent to those present for claim 1 are equally applicable to claim 9.
Regarding claims 10, 11, and 15, claims 2, 3, and 7, respectively, recite substantially similar features. Thus, references/arguments equivalent to those present for claims 2, 3, and 7 are equally applicable to, respectively, claims 10, 11, and 15.
Regarding claim 17, while Sun teaches one or more computer readable storage media (fig. 1, item 135) having program instructions stored thereon that, when executed by one or more processors (fig. 1, item 130), direct a computing apparatus (fig. 1, item 115) to at least:
detect an operational anomaly (fig. 2, item 205; “detect the failure condition based on the collected data from the network 120” at ¶ 32) in a wireless network (fig. 1, item 120) based on a transaction completion metric (“determine from the collected data if the response status codes include any error codes” at ¶ 32);
filter network operations data (¶¶ 33–34) to remove nonessential information (subscribers that are not “[s]ubscribers that experienced the failure condition more recently (e.g., within a predetermined period of time)” at ¶ 34) and generate a reduced information set (fig. 2, item 210; “process 200 includes operation 210 of identifying, by the processor, a subset of subscribers that are impacted by the failure condition” at ¶ 33) corresponding to a time window associated with the operational anomaly (“more recently (e.g., within a predetermined period of time) may be selected” at ¶ 34s)
capture contextual information (fig. 2, item 215; ¶¶ 35–36) relating to the operational anomaly;
prompt a model (fig. 2, item 200) to identify root causes (fig. 2, item 235; “determine that the event that generated error code EC1 is most likely the root cause of the failure condition” at ¶ 66) of the operational anomalies by providing the model with the transaction completion metric (fig. 2, item 235 does not occur but for item 205), the reduced information set (fig. 2, item 235 does not occur but for item 210), and the contextual information (fig. 2, item 235 does not occur but for item 215) and tasking the model to identify a root cause of the operational anomaly (“analyze error codes to determine the root cause of a failure condition.” at ¶ 14; fig. 2, item 23); and
receive, from the model in response to the prompt, output comprising a root cause analysis (fig. 2, item 240; ¶ 68) of the operational anomaly,
Sun does not teach (A) the model being an artificial intelligence (AI) model trained to identify the root causes; and (B) the generated filtered network operations data also corresponding to at least one network function identified by the error code information.
(A)
Polisetty teaches an AI model trained to identify root cause (“the generative AI model 970 is trained to identify one or more root causes for a failure” at ¶ 92).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention for Sun’s model to be an AI model trained to identify Sun’s root causes as taught by Polisetty to “provide significant advantages relative to conventional techniques. For example, technical problems related to such conventional techniques are mitigated in one or more embodiments by automatically predicting a failure of a software deployment pipeline and suggesting one or more mitigation actions to mitigate one or more causes of the failure.” Polisetty ¶ 3.
(B)
Hu teaches at least one network function identified by alarm information (“the alarm information may include a network function network element reporting the processing result” at p. 8).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention for Sun’s generated filtered network operations data to also correspond to at least one network function identified by the error code information as taught by Hu to produce “better protection effect, improving the network protection capability of the 5G core network.” Hu p. 8.
Regarding claim 18, claim 3 recites substantially similar features. Thus, references/arguments equivalent to those present for claim 3 are equally applicable to claim 18.
Sun, Polisetty, Hu, and Barrett
Claims 4, 5, 12, and 13 are rejected under 35 U.S.C. § 103 as being obvious over Sun in view of Polisetty, in further view of Hu, and in further view of Barrett et al. (US 2022/0329510 A1; filed Apr. 7, 2022).
Regarding claim 4, while Sun teaches wherein the network operations data (fig. 2, item 210; ¶¶ 33–34) comprises a subset of subscribers (“process 200 includes operation 210 of identifying, by the processor, a subset of subscribers that are impacted by the failure condition” at ¶ 33) on the wireless network,
Sun does not teach the subset of subscribers including packet capture trace records of transactions.
Barrett teaches packet capture trace records of transactions (“a packet capture trace file comprising packets associated with a synthetic transaction of a service provided by a service provider” at ¶ 16).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention for Sun’s subset of subscribers to include packet capture trace records of transactions as taught by Barrett “to evaluate the performance of the digital services, or identify issues associated with providing the digital services.” Barrett ¶ 2.
Regarding claim 5, while Sun teaches wherein the network operations data (fig. 2, item 210; ¶¶ 33–34) comprises a reduced information set (“process 200 includes operation 210 of identifying, by the processor, a subset of subscribers that are impacted by the failure condition” at ¶ 33) based on filtered records (“all subscribers” at ¶ 33 are filtered),
Sun does not teach the filtered records being filtered packet capture trace records.
Barrett teaches packet capture trace records (“a packet capture trace file comprising packets associated with a synthetic transaction of a service provided by a service provider” at ¶ 16).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention for Sun’s filtered records to be filtered packet capture trace records as taught by Barrett “to evaluate the performance of the digital services, or identify issues associated with providing the digital services.” Barrett ¶ 2.
Regarding claims 12 and 13, claims 4 and 5, respectively, recite substantially similar features. Thus, references/arguments equivalent to those present for claims 4 and 5 are equally applicable to, respectively, claims 12 and 13.
Sun, Polisetty, Hu, and Schwarzmann
Claims 6 and 14 are rejected under 35 U.S.C. § 103 as being obvious over Sun in view of Polisetty, in further view of Hu, and in further view of Schwarzmann (US 2007/0166014 A1; filed Jan. 17, 2006).
Regarding claim 6, Sun does not teach wherein the program instructions further direct the computing apparatus to filter out nonessential information from the packet capture trace records resulting in the filtered packet capture trace records.
Schwarzmann teaches program instructions further direct a computing apparatus (fig. 2, item 204) to filter out nonessential information from data resulting in the filtered data (“the removal of redundant or ‘non-essential’ data from a DVD data stream” at ¶ 11).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention for the Sun/Polisetty/Barrett combination’s program instructions to further direct the computing apparatus to filter out nonessential information from the packet capture trace records resulting in the filtered packet capture trace records as taught by Schwarzmann so that “the data storage drive is provided with additional capacity.” Schwarzmann ¶ 11.
Regarding claim 14, claim 6 recites substantially similar features. Thus, references/arguments equivalent to those present for claim 6 are equally applicable to claim 14.
Sun, Polisetty, Hu, and Mattison
Claims 8, 16, and 20 are rejected under 35 U.S.C. § 103 as being obvious over Sun in view of Polisetty, in further view of Hu, and in further view of Mattison et al. (US 2024/0320642 A1; filed Mar. 21, 2023).
Regarding claim 8, Sun does not teach wherein the historical operational anomaly dataset comprises identified root causes of historical operational anomalies correlated to historical network operations data.
Mattison teaches identified root causes of historical operational anomalies correlated to historical network operations data (“historical event codes associated with corresponding historical anomalies, historical actions that resolved corresponding historical anomalies” at ¶ 33).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention for Sun’s historical operational anomaly dataset to comprise identified root causes of historical operational anomalies correlated to historical network operations data as taught by Mattison “to identify the anomalies and/or identify actions that may be performed . . . in response to identifying particular anomalies.” Mattison ¶ 33.
Regarding claims 16 and 20, claim 8 recites substantially similar features. Thus, references/arguments equivalent to those present for claim 8 are equally applicable to claims 16 and 20.
Sun, Polisetty, Hu, Barret, and Malboubi
Claim 19 is rejected under 35 U.S.C. § 103 as being obvious over Sun in view of Polisetty, in further view of Hu, in further view of Barrett, and in further view of Malboubi et al. (US 2023/0136756 A1; filed Nov. 3, 2021).
Regarding claim 19, while Sun teaches wherein the reduced information set (“process 200 includes operation 210 of identifying, by the processor, a subset of subscribers that are impacted by the failure condition” at ¶ 33) comprises data extracted from records (¶ 33),
Sun does not teach (A) the data being transaction data; (B) the records being packet capture trace records; and (C) the data being formatted in a natural language format.
(A), (B)
Barrett teaches transaction data (“generating synthetic transactions” at ¶ 16) and packet capture trace records (“generate a packet capture trace file comprising packets associated with a synthetic transaction of a service provided by a service provider” at ¶ 16).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention for Sun’s data to be transaction data and for Sun’s records to be packet capture trace records as taught by Barrett “to evaluate the performance of the digital services, or identify issues associated with providing the digital services.” Barrett ¶ 2.
(C)
Malboubi teaches data formatted in a natural language format (“the user can enter all or a portion of the query in a desired format (e.g., natural language format” at ¶ 37).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention for Sun’s data to be formatted in a natural language format as taught by Malboubi for “enhanced (e.g., improved, faster, more efficient, and/or optimized) performance and lower (e.g., reduced or minimized) latencies.” Malboubi ¶ 55.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicants’ disclosure:
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to DAVID P. ZARKA whose telephone number is (703) 756-5746. The Examiner can normally be reached Monday–Friday from 9:30AM–6PM ET.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, Vivek Srivastava, can be reached at (571) 272-7304. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/DAVID P ZARKA/PATENT EXAMINER, Art Unit 2449
1 The Examiner relies on an English machine translation of Hu using CLARIVATE ANALYTICS as attached in the instant Office action.
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