DETAILED ACTION
1. This communication is in response to the amendments filed on March 2, 2026 for Application No. 18/296,058 in which Claims 1-20 are presented for examination.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
3. The amendments filed on March 2, 2026 have been considered. Claims 1, 8, and 15 have been amended. Thus, Claims 1-20 are pending and presented for examination.
4. Applicant’s arguments filed March 2, 2026 with respect to the 35 U.S.C. 112(b) rejection of Claims 1-7 have been fully considered and are persuasive. Thus the 35 U.S.C. 112(b) rejection of Claims 1-7 has been withdrawn. Note: However, a new 35 U.S.C. 112(b) rejection is presented below, as necessitated by amendment.
5. Applicant's arguments filed March 2, 2026 with respect to the 35 U.S.C. 101 rejection have been fully considered but they are not persuasive.
Applicant’s Arguments on Pgs. 8-9 of Arguments/Remarks state:
“The undersigned representative respectfully traverses the 35 U.S.C. 101 rejection. As amended, the independent claims recite a specific, technology-focused utilization analysis of network devices that uses a trained machine learning model to determine scores indicative of a respective value of each candidate variable on the performance of the network devices using a model, rank those variables based on the scores, and output an analysis that is rendered in a configured user interface. These limitations are directed to improving the technical field of network performance monitoring and diagnostics by automatically identifying and ranking the drivers of degraded performance based on device utilization and related performance information, which enables faster and more accurate troubleshooting and optimization than conventional metric dashboards or manual analysis.
Claim 1, as presented herein and under Step 2A, Prong One of the 2019 Updated Guidelines on Subject Matter Eligibility (hereinafter, "Updated Guidelines"), cannot reasonably be characterized as merely performing mental steps. Performance of a computer network and computer elements operating therein cannot be measured mentally by a human being. Furthermore, claimed processing is performed using a trained machine learning model includes computing scores and ranking variables across a plurality of network devices based on those scores. Such operations, performed over network-device telemetry at scale, are not practically performed in the human mind.
Furthermore, this is a computer-centric process rooted in computer technology and hence is also patentable based on Federal Circuit's decision in DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1257-59 (Fed. Cir. 2014).
Even assuming, arguendo, that some portion of the claims could be viewed as reciting an abstract idea (which the undersigned representative does not admit), under Step 2A, Prong Two of the Updated Guidelines, the claims integrate any such idea into a practical application because it improves the functioning of a computer network. The claims require application of the trained machine learning model to network-device performance and utilization information to produce a concrete output analysis including scores affecting performance of network devices and ranking of performance-indicative variables, and to generate a user interface displaying a visual representation of that model output. This provides a practical, real-world network-management application that improves the functioning of a computer system operating in a network environment by providing actionable, automatically-derived diagnostics regarding which utilization and configuration variables are driving performance issues.
For at least the reasons described above, the undersigned representative submits that claim 1 is subject matter eligible under 35 U.S.C. § 101. Claims 8 and 15 recite features that are somewhat similar to those recited in claim 1. Therefore, for the same reasons as described above with reference to claim 1, claims 8 and 15 as well as claims 2-7, 9-14, and 16-20 that depend from one of claims 1, 8, and 15, are also subject matter eligible under 35 U.S.C. § 101.
For the foregoing reasons, the undersigned representative respectfully requests reconsideration and withdrawal of the rejection of claims 1-20 under 35 U.S.C. § 101.”
Examiner respectfully disagrees. Although Applicant states that the claims recite a technological improvement, Examiner asserts that this technological improvement is not reflected by the currently drafted claim language. At Step 2A Prong 1, the claim recites the limitations “a method of utilization analysis of network devices […]”, “[…] identify one or more variables indicative of performance of the network devices”, “[…] wherein identifying the one or more variables includes determining, for each of a plurality of candidate variables and using a value model, a respective score indicative of a respective value of each candidate variable on the performance of the network devices […]”, “[…] ranking the plurality of candidate variables based on the respective scores […]”, “[…] identify the one or more variables, and provide as output an analysis of the performance of one or more of the network devices, the analysis including the influence scores and ranking”, and “generating […] a visual representation of the output of the trained machine learning model” all of which may be feasibly performed by mental process and/or mathematical process, other than the recitation of generic computer components. A user may feasibly perform utilization of analysis of network devices as outlined below. For example, the user may identify one or more variables indicative of performance of the network devices by observing/analyzing the set of received information associated with performance of the network devices operating in a network and accordingly using judgement/evaluation to identify one or more variables (such as device configuration and/or device utilization) indicative of performance, based on said analysis of the received information. Further, determining, for each of a plurality of candidate variables and using a value model, a respective score indicative of a respective value of each candidate variable on the performance of the network devices may be practically performed by mathematical process, utilizing an algorithm such as SHapley Additive exPlanations to generate a list of variables and according feature attribution scores (See specification Par. [0063-0064]). Furthermore, ranking the plurality of candidate values may be performed manually by a user observing/analyzing the candidate variables and their respective scores and accordingly using judgement/evaluation to rank (with the aid of pen and paper) the variables based on the analysis of said scores. Moreover, the user may identify the one or more variables, and provide as output an analysis of the performance of one or more of the network devices, the analysis including the influence scores and ranking, as a user is capable of observing/analyzing data. Similarly, the user may generate a visual representation of the output of the trained machine learning model by observing/analyzing the output of the model and accordingly using judgement/evaluation to generate a visual representation of the output with the aid of pen and paper.
At Step 2A Prong 2 and Step 2B, the claim recites “receiving a set of information associated with performance of the network devices operating in a network”, “processing using a trained machine learning model the set of information […] the machine learning model being trained to receive as input the set of information […]”, and “[…] a user interface to display on a user device […]” all of which are not are sufficient to amount to significantly more than the judicial exception. The limitation “receiving a set of information […]” amounts to insignificant extra-solution activity, more specifically, merely “Receiving or transmitting data over a network” which is a well-understood, routine, conventional function when it is claimed in a merely generic manner. Further, the limitation “processing using a trained machine learning model the set of information […] the machine learning model being trained to receive as input the set of information […]” amounts to merely adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). The “machine learning model” is merely “trained to receive as input the set of information” without significantly more – this does not provide an inventive concept. There are no further details provided regarding the machine learning model and its corresponding configuration/training/architecture – hence, the model may be interpreted as a black-box or off-the-shelf neural network which is merely applied to a generic “set of information”. Again, this does not provide an inventive concept. The limitation “[…] a user interface to display on a user device […]” amounts to merely “Presenting offers and gathering statistics” which is a well-understood, routine, conventional function when it is claimed in a merely generic manner. Thus, the claim does not include additional elements which are sufficient to amount to significantly more than the judicial exception.
Applicant further states that the claimed processing is “performed using a trained machine learning model includes computing scores and ranking variables across a plurality of network devices based on those scores. Such operations performed over network-device telemetry at scale, are not practically performed in the human mind” – however, there is no recitation of performing these operations over network-device telemetry at scale. Instead, the claims simply recite the use of a generically trained “machine learning model” to perform the processing on a received set of information – as stated above, simply applying a generic machine learning model to perform specific operations without significantly more does not provide an inventive concept. Further, the claims do not include technical detail/language which may preclude such a mental process/mathematical process interpretation of the aforementioned limitations. Given that the network analysis may still be performed by a combination of mental and mathematical process and the claim utilizes generic computer components without significantly more, the claims still recite an abstract idea and do not reflect any technological improvement, as currently drafted.
Thus, the 35 U.S.C. 101 rejection is maintained.
6. Applicant’s arguments with respect to the 35 U.S.C. 103 rejection have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Examiner has introduced the Thakkar reference (US PG-PUB 20230421430) to teach the newly added limitations – see updated 35 U.S.C. 103 rejection below.
Claim Rejections - 35 USC § 112
7. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
8. Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Independent Claims 1, 8, and 15 recite the limitation “[…] the influence scores […]", without prior mention of an “influence score” – instead, the claim recites “[…] determining […] a respective score indicative of a respective value […]” but this does not equate to “the influence scores”. There is insufficient antecedent basis for this limitation in the claim. This rejection applies to Independent Claim 1, 8, 15, and their respective dependent claims 2-7, 9-14, and 16-20.
Claim Rejections - 35 USC § 101
9. 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.
10. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1:
Step 1: Claim 1 is a method type claim. Therefore, Claims 1-7 are directed to either a process, machine, manufacture, or composition of matter.
2A Prong 1: If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mathematical calculation but for the recitation of generic computer components, then it falls within the “Mathematical Concepts” grouping of abstract ideas.
a method of utilization analysis of network devices […] (mental process – methods for utilization analysis of network devices may be performed manually by a user observing/analyzing the network devices and their respective performance and accordingly using judgement/evaluation to provide an analysis of the performance of one or more of the network devices)
[…] identify one or more variables indicative of performance of the network devices (mental process – other than reciting “processing using a trained machine learning model”, identifying one or more variables indicative of performance of the network devices may be performed manually by a user observing/analyzing the received set of information associated with performance of the network devices and accordingly using judgement/evaluation to identify one or more variables (such as device configurations and/or device modifications – see Claim 7) which indicate performance of said devices)
[…] wherein identifying the one or more variables includes determining, for each of a plurality of candidate variables and using a value model, a respective score indicative of a respective value of each candidate variable on the performance of the network devices […] (mathematical process – determining, for each of a plurality of candidate variables and using a value model, a respective score indicative of a respective value of each candidate variable on the performance of the network devices may be performed by mathematical process utilizing a value model such as Shapley (SHAP – see Applicant’s specification Par. [0063] for support) to generate relevant scores for each of a plurality of candidate variables)
[…] ranking the plurality of candidate variables based on the respective scores […] (mental process – ranking the plurality of candidate variables based on the respective scores may be performed manually by a user observing/analyzing the plurality of candidate variables and their respective scores and accordingly using judgement/evaluation to rank (with the aid of pen and paper) the plurality of candidate variables based on the respective scores (i.e., ranking the variables ascending/descending/etc. based on a respective score))
[…] identify the one or more variables, and provide as output an analysis of the performance of one or more of the network devices, the analysis including the influence scores and ranking (mental process – other than reciting “machine learning model”, identifying one or more variables and providing as output an analysis of the performance of one or more of the network devices may be performed manually by a user observing/analyzing the set of performance information and accordingly using judgement/evaluation to identify variables indicative of performance and provide an analysis of the performance of one or more of the network devices)
generating […] a visual representation of the output of the trained machine learning model (mental process – generating a visual representation of the output of the trained machine learning model may be performed manually by a user observing/analyzing the output, which is an analysis of the performance of one or more of the network devices, and accordingly using judgement/evaluation to generate a visual representation of said output, with the aid of pen and paper)
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
receiving a set of information associated with performance of the network devices operating in a network (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g))
processing using a trained machine learning model the set of information […] the machine learning model being trained to receive as input the set of information […] (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training and applying a machine learning model with previously determined data, without significantly more)
[…] a user interface to display on a user device […] (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g))
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
receiving a set of information associated with performance of the network devices operating in a network (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
processing using a trained machine learning model the set of information […] the machine learning model being trained to receive as input the set of information […] (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training and applying a machine learning model with previously determined data, without significantly more. This cannot provide an inventive concept)
[…] a user interface to display on a user device […] (MPEP 2106.05(d)(II) indicates that merely “Presenting offers and gathering statistics” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
For the reasons above, Claim 1 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 2-7. The additional limitations of the dependent claims are addressed below.
Regarding Claim 2:
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 2 depends on.
Step 2A Prong 2 & Step 2B:
wherein the set of information include CPU load features, packet loss features, traffic volume features, configuration features of the network devices, one or more of current device utilization, traffic levels, and feature usage of the network devices (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying the set of information does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 3:
Step 2A Prong 1:
See the rejection of Claim 2 above, which Claim 3 depends on.
Step 2A Prong 2 & Step 2B:
wherein the analysis is a root cause analysis of how the one or more variables are affecting the performance of the one or more of the network devices (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying the type of analysis does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 4:
Step 2A Prong 1: See the rejection of Claim 2 above, which Claim 4 depends on.
identifying the one or more variables affecting the performance of the network devices (mental process – identifying one or more variables may be performed manually by a user observing/analyzing the set of information associated with performance of the network devices and accordingly using judgement/evaluation to identify one or more variables affecting performance of the network devices)
Step 2A Prong 2 & Step 2B:
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 5:
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 5 depends on.
Step 2A Prong 2 & Step 2B:
wherein the analysis includes at least one corrective action to be taken to address the performance of the one or more of the network devices (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the analysis includes at least one corrective action does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 6:
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 6 depends on.
Step 2A Prong 2 & Step 2B:
wherein the analysis is a pre-change analysis of how a change in at least one of the one or more variables affects a future performance of the one or more of the network devices (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying the type of analysis does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 7:
Step 2A Prong 1:
See the rejection of Claim 6 above, which Claim 7 depends on.
Step 2A Prong 2 & Step 2B:
wherein the one or more variables include one or more device configurations and modifications in a number of the network devices operating in the network (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying the types of variables does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Independent Claim 8 recites substantially the same limitations as Claim 1, in the form of a network device, including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
For the reasons above, Claim 8 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 9-14. The additional limitations of the dependent claims are addressed below.
Claim 9 recites substantially the same limitations as Claim 2, in the form of a network device, including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
Claim 10 recites substantially the same limitations as Claim 3, in the form of a network device, including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
Claim 11 recites substantially the same limitations as Claim 4, in the form of a network device, including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
Claim 12 recites substantially the same limitations as Claim 5, in the form of a network device, including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
Claim 13 recites substantially the same limitations as Claim 6, in the form of a network device, including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
Claim 14 recites substantially the same limitations as Claim 7, in the form of a network device, including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
Independent Claim 15 recites substantially the same limitations as Claim 1, in the form of one or more non-transitory computer readable media, including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
For the reasons above, Claim 15 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 16-20. The additional limitations of the dependent claims are addressed below.
Claim 16 recites substantially the same limitations as Claim 2, in the form of one or more non-transitory computer readable media, including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
Claim 17 recites substantially the same limitations as Claim 3, in the form of one or more non-transitory computer readable media, including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
Claim 18 recites substantially the same limitations as Claim 4, in the form of one or more non-transitory computer readable media, including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
Claim 19 recites substantially the same limitations as Claim 5, in the form of one or more non-transitory computer readable media, including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
Claim 20 recites substantially the same limitations as Claim 6, in the form of one or more non-transitory computer readable media, including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
Claim Rejections - 35 USC § 103
11. 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.
12. Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Toy et al. (hereinafter Toy) (US PG-PUB 20210351989), in view of Thakkar et al. (hereinafter Thakkar) (US PG-PUB 20230421430), further in view of Roy et al. (hereinafter Roy) (US Patent 10,547,521).
Regarding Claim 1, Toy teaches a method of utilization analysis of network devices (Toy, Abstract, “A method, a system, and a non-transitory storage medium are described in which a AI-based self-management network service is provided. […] The AI-based self-routing management service may calculate predicted utilization values of network resources of a network path, and calculate a predicted path weight based on the predicted utilization values.”, thus, a method of utilization analysis of network devices is disclosed), the method comprising:
receiving a set of information associated with performance of the network devices operating in a network (Toy, Par. [0051], “According to an exemplary embodiment, orchestrator 120 may obtain resource utilization data and performance data for network devices 115, virtual network devices 117, and communication links 125 in network 110.”, therefore, a set of information (resource utilization data and performance data) associated with performance of the network devices operating in a network is received);
processing using a trained machine learning model (Toy, Par. [0029], “According to an exemplary embodiment, orchestrator 120 may include machine learning models, for example, to generate the AI-based self-routing information, the AI-based self-capacity information, and/or the AI-based self-fault and recovery information.”, therefore, the orchestrator may include an already configured/trained machine learning model used to process data) the set of information to identify one or more variables indicative of performance of the network devices (Toy, Par. [0051], “For example, for network devices 115 and virtual network devices 117, the data may include a network device identifier that identifies network device 115 or virtual network device 117, timestamp information that indicates a time or period of time at which the resource or performance parameter value is measured or relates to, and utilization values relating to different types of network resources, such as hardware utilization (e.g., processor, memory, storage, communication interface (e.g., buffer, port, or the like, etc.).” & Par. [0052], “According to an exemplary embodiment, the data may also include performance metric data. For example, the performance metric values may relate to delay, packet loss, and other metrics as described herein. The performance metric values may also relate to a network path.”, therefore, the set of information is processed by the orchestrator and may be used to analyze/identify one or more variables indicative of performance of the network devices (performance parameter values including delay, packet loss, etc.)), wherein identifying the one or more variables includes determining, for each of a plurality of candidate variables and using a value model, a respective score indicative of a respective value of each candidate variable on the performance of the network devices and ranking the plurality of candidate variables based on the respective scores (See introduction of Thakkar reference below for teaching of determining a respective score of each candidate variable and ranking the plurality of candidate variables based on the respective scores), the machine learning model being trained to receive as input the set of information, identify the one or more variables (Toy, Par. [0051], “According to an exemplary embodiment, orchestrator 120 may obtain resource utilization data and performance data for network devices 115, virtual network devices 117, and communication links 125 in network 110. This data may be stored and updated (e.g., periodically, according to a schedule, on-demand, trigger event, etc.). For example, for network devices 115 and virtual network devices 117, the data may include a network device identifier that identifies network device 115 or virtual network device 117, timestamp information that indicates a time or period of time at which the resource or performance parameter value is measured or relates to, and utilization values relating to different types of network resources, such as hardware utilization (e.g., processor, memory, storage, communication interface (e.g., buffer, port, or the like, etc.).”, therefore, the Orchestrator (machine learning model) is already configured/trained to receive as input the set of device utilization information (resource utilization data and performance data) and processes this data to identify variables (parameter performance values including delay, packet loss, etc.)), and provide as output an analysis of the performance of one or more of the network devices (Toy, Par. [0053], “Orchestrator device 120 may select the calculated future utilization values associated with network devices (virtual and/or non-virtual) and communication links 125 of relevance to a network path, and may calculate a future path utilization value based on expression (1). Similarly, orchestrator device 120 may also calculate delay or packet lost based on expressions (2) and (3), as well as other types of future metric values based on performance metric values of a network path and predictive techniques.”, therefore, the Orchestrator (machine learning model) may provide an analysis of the performance of one or more of the network devices, including calculated future utilization values and calculated future performance metric values (including delay and/or packet loss)), the analysis including the influence scores and ranking (See introduction of Thakkar reference below for teaching of the analysis including scores and ranking); and
generating a user interface to display on a user device a visual representation of the output of the trained machine learning model (See introduction of Roy reference below for teaching of generating a user interface to display a visual representation of the performance analysis output).
While Toy discloses performance metrics which may include a quality of experience (QoE) score and/or a mean opinion score (MOS) per Toy Par. [0038], Toy does not explicitly disclose:
wherein identifying the one or more variables includes determining, for each of a plurality of candidate variables and using a value model, a respective score indicative of a respective value of each candidate variable on the performance of the network devices and ranking the plurality of candidate variables based on the respective scores
the analysis including the influence scores and ranking
However, Thakkar teaches:
wherein identifying the one or more variables includes determining, for each of a plurality of candidate variables and using a value model, a respective score indicative of a respective value of each candidate variable on the performance of the network devices (Thakkar, Par. [0126], “The information collected in steps 616, 618, and 620 is then used with the gradient-boosting supervised model created in step 606 to perform a root cause model inference in step 622. The resulting model inferences are then used in step 624 to identify telemetry changes and events of interest. In certain embodiments, Shapley Additive Explanations (SHAP) approaches may be used in step 624 to identify the telemetry changes and events of interest. Skilled practitioners of the art will be familiar with SHAP values, which are often used when a complex model, such as the gradient-boosting model created in step 606, receives feature inputs and produces predictions as output.”, therefore, identifying one or more variables may include determining, for each of a plurality of candidate variables (telemetry changes and events of interest which may impact performance per Thakkar Par. [0135-0137]) and using a value model (SHAP), a respective score indicative of a respective value of each candidate variable on the performance of network devices (See Thakkar Par. [0042]) and ranking the plurality of candidate variables based on the respective scores (Thakkar, Par. [0127], “In certain embodiments, the resulting SHAP values are then used in step 626 to rank the telemetry changes and events of interest”, thus, the candidate variables may be ranked based on the respective scores/SHAP values)
the analysis including the influence scores and ranking (Thakkar, Par. [0127], “In particular, various SHAP approaches are often used to provide an understanding of what decisions the model is making and to assist in identifying the contribution of each feature of the prediction. In certain embodiments, the resulting SHAP values are then used in step 626 to rank the telemetry changes and events of interest", thus, the analysis (see Figures 4 & 5) may comprise scores and ranking)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of utilization analysis of network devices, as disclosed by Toy to include wherein identifying the one or more variables includes determining, for each of a plurality of candidate variables and using a value model, a respective score indicative of a respective value of each candidate variable on the performance of the network devices and ranking the plurality of candidate variables based on the respective scores and the analysis including the influence scores and ranking, as disclosed by Thakkar. One of ordinary skill in the art would have been motivated to make this modification to enable the use of a value model, such as SHAP, which may be utilized to rank the candidate variables and provide a more accurate indication of root causality with respect to utilization analysis of network devices (Thakkar, Par. [0127], “In certain embodiments, the resulting SHAP values are then used in step 626 to rank the telemetry changes and events of interest. In certain embodiments, the ranking provides an indication of the root causality of a particular data center issue. For example, the highest ranked telemetry changes and events of interest are more likely to provide an indication of root causality, while those that are ranked lower are less likely to.”)
While Toy teaches the use of end devices, including devices operated by an end user (See Toy Par. [0030]), Toy in view of Thakkar does not explicitly disclose generating a user interface to display on a user device a visual representation of the output of the trained machine learning model.
However, Roy teaches generating a user interface to display on a user device a visual representation of the output of the trained machine learning model (Roy, Col. 11 lines 54-63, “Dashboard 203 may include a graphical view that provides a quick, visual overview of resource utilization by instance using histograms. The bins of such histograms may represent the number of instances that used a given percentage of a resource, such CPU utilization. By presenting data using histograms, dashboard 203 presents information in a way that allows administrator 128, if dashboard 203 is presented at user interface device 129, to quickly identify patterns that indicate under-provisioned or over-provisioned instances.”, thus, a user interface is generated to display on a user device (such as an administrator viewing the dashboard on a user interface device) and includes a visual representation of the performance analysis/resource utilization output).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of utilization analysis of network devices, as disclosed by Toy in view of Thakkar to include generating a user interface to display on a user device a visual representation of the output of the trained machine learning model, as disclosed by Roy. One of ordinary skill in the art would have been motivated to make this modification to enable a graphical view which provides a quick, visual overview of resource utilization, such that users may quickly identify patterns in resource utilization of network devices operating in a network (Roy, Col. 11 lines 54-67, “Dashboard 203 may include a graphical view that provides a quick, visual overview of resource utilization by instance using histograms. The bins of such histograms may represent the number of instances that used a given percentage of a resource, such CPU utilization. By presenting data using histograms, dashboard 203 presents information in a way that allows administrator 128, if dashboard 203 is presented at user interface device 129, to quickly identify patterns that indicate under-provisioned or over-provisioned instances. In some examples, dashboard 203 may highlight resource utilization by instances on a particular project or host, or total resource utilization across all hosts or projects, so that administrator 128 may understand the resource utilization in context of the entire infrastructure.”).
Regarding Claim 2, Toy in view of Thakkar in view of Roy teaches the method of claim 1, wherein the set of information include CPU load features (Toy, Par. [0051], “Depending on the network device, the hardware utilization values may relate to virtual hardware (e.g., virtual central processing unit (vCPU)) or non-virtual hardware (e.g., CPU). The utilization values may be expressed as a percentage of total (e.g., 50% of vCPU utilization) or in other suitable unit of measurement.”, therefore, the hardware utilization values (set of information) includes CPU load/utilization features), packet loss features, traffic volume features (Toy, Par. [0052], “According to an exemplary embodiment, the data may also include performance metric data. For example, the performance metric values may relate to delay, packet loss, and other metrics as described herein.”, thus, the performance metric data (set of information) includes packet loss features and traffic volume features, such as delay), configuration features of the network devices, one or more of current device utilization, traffic levels, and feature usage of the network devices (Toy, Par. [0051], “According to an exemplary embodiment, orchestrator 120 may obtain resource utilization data and performance data for network devices 115, virtual network devices 117, and communication links 125 in network 110. This data may be stored and updated (e.g., periodically, according to a schedule, on-demand, trigger event, etc.). For example, for network devices 115 and virtual network devices 117, the data may include a network device identifier that identifies network device 115 or virtual network device 117, timestamp information that indicates a time or period of time at which the resource or performance parameter value is measured or relates to, and utilization values relating to different types of network resources, such as hardware utilization (e.g., processor, memory, storage, communication interface (e.g., buffer, port, or the like, etc.).”, therefore, configuration features of the network devices (network device identifiers, performance metrics, etc.), device utilization (hardware utilization of processor, memory, storage, etc.), traffic levels (related to delay and packet loss above) and feature usage (communication links, timestamp information, etc.) of the network devices are disclosed as the set of information received).
Regarding Claim 3, Toy in view of Thakkar in view of Roy teaches the method of claim 2, wherein the analysis is a root cause analysis of how the one or more variables are affecting the performance of the one or more of the network devices (Toy, Par. [0055], “According to an exemplary embodiment, the AI-based self-fault and recovery management service may identify a probable cause of a failure and a possible recovery action. For example, for network devices 115, the AI-based self-fault and recovery management service may identify a probable cause of failure for each network device or node type (or a component type in each network device or node).”, therefore, the analysis may include identifying a probable cause of a failure, or how one or more variables affect the performance of the one or more network devices (for example, in the case of a failure)).
Regarding Claim 4, Toy in view of Thakkar in view of Roy teaches the method of claim 2, wherein the method further comprises: identifying the one or more variables affecting the performance of the network devices (Toy, Par. [0051], “For example, for network devices 115 and virtual network devices 117, the data may include a network device identifier that identifies network device 115 or virtual network device 117, timestamp information that indicates a time or period of time at which the resource or performance parameter value is measured or relates to, and utilization values relating to different types of network resources, such as hardware utilization (e.g., processor, memory, storage, communication interface (e.g., buffer, port, or the like, etc.).” & Par. [0052], “According to an exemplary embodiment, the data may also include performance metric data. For example, the performance metric values may relate to delay, packet loss, and other metrics as described herein. The performance metric values may also relate to a network path.”, therefore, one or more variables (performance metric values such as delay, packet loss, etc.) affecting performance of network devices are identified)
Regarding Claim 5, Toy in view of Thakkar in view of Roy teaches the method of claim 1, wherein the analysis includes at least one corrective action to be taken to address the performance of the one or more of the network devices (Toy, Par. [0055], “According to an exemplary embodiment, the AI-based self-fault and recovery management service may identify a probable cause of a failure and a possible recovery action. For example, for network devices 115, the AI-based self-fault and recovery management service may identify a probable cause of failure for each network device or node type (or a component type in each network device or node).”, therefore, the analysis may include at least one corrective action (recovery action) to be taken to address the performance of the one or more network devices (See Toy Figure 9 for an exemplary process of initiating the recovery action)).
Regarding Claim 6, Toy in view of Thakkar in view of Roy teaches the method of claim 1, wherein the analysis is a pre-change analysis of how a change in at least one of the one or more variables affects a future performance of the one or more of the network devices (Toy, Par. [0054], “FIG. 4 is a diagram illustrating an exemplary process of the AI-based self-capacity management service. As illustrated, based on resource and performance data received, orchestrator device 120 may generate predicted values 405, such as future path weights or future performance metric values. Orchestrator device 120 may transmit (e.g., download) the predicted values 410 to network devices 115 and/or virtual network devices 117 of relevance to the network path to which the predicted values pertain. Network devices 115 and virtual network devices 117 may store the predicted values 415 and make routing decisions based on the predicted values 420.”, therefore, the analysis may include a pre-change analysis of how change in at least one of the one or more variables/performance metrics affects a future performance of the one or more of the network devices).
Regarding Claim 7, Toy in view of Thakkar in view of Roy teaches the method of claim 6, wherein the one or more variables include one or more device configurations and modifications in a number of the network devices operating in the network (Toy, Par. [0054], “Orchestrator device 120 may transmit (e.g., download) the predicted values 410 to network devices 115 and/or virtual network devices 117 of relevance to the network path to which the predicted values pertain. Network devices 115 and virtual network devices 117 may store the predicted values 415 and make routing decisions based on the predicted values 420. In this way, routing of traffic may be performed with minimal delay, without added delays by way of communication between network devices 115 and virtual network devices 117 with orchestrator device 120 to provide real-time updates of path weights and performance metrics relating to network paths.”, therefore, the one or more variables may include device configurations/modifications (device routing decisions, network paths, performance metrics, etc.) in a number of the network devices operating in the network).
Regarding Claim 8, Toy in view of Thakkar in view of Roy teaches a network device (Toy, Figure 6, label 600 which depicts a network device (See Toy Par. [0076] which describes Figure 6 in further detail) comprising: one or more memories having computer-readable instructions stored therein (Toy, Figure 6, label 615 which depicts a memory/storage that stores software instructions (label 620)); and one or more processors configured to execute the computer-readable instructions (Toy, Figure 6, label 610 which depicts a processor configured to execute instructions) to: […]
The rest of the claim language in Claim 8 recites substantially the same limitations as Claim 1, in the form of a network device, therefore it is rejected under the same rationale.
The reasons of obviousness have been noted in the rejection of Claim 1 above and applicable herein.
Claim 9 recites substantially the same limitations as Claim 2 in the form of a network device, therefore it is rejected under the same rationale.
Claim 10 recites substantially the same limitations as Claim 3 in the form of a network device, therefore it is rejected under the same rationale.
Claim 11 recites substantially the same limitations as Claim 4 in the form of a network device, therefore it is rejected under the same rationale.
Claim 12 recites substantially the same limitations as Claim 5 in the form of a network device, therefore it is rejected under the same rationale.
Claim 13 recites substantially the same limitations as Claim 6 in the form of a network device, therefore it is rejected under the same rationale.
Claim 14 recites substantially the same limitations as Claim 7 in the form of a network device, therefore it is rejected under the same rationale.
Regarding Claim 15, Toy in view of Thakkar in view of Thakkar in view of Roy teaches one or more non-transitory computer readable media comprising computer-readable instructions, which when executed by one or more processors of a network appliance (Toy, Par. [0119], “Additionally, embodiments described herein may be implemented as a non-transitory computer-readable storage medium that stores data and/or information, such as instructions, program code, a data structure, a program module, an application, a script, or other known or conventional form suitable for use in a computing environment. The program code, instructions, application, etc., is readable and executable by a processor (e.g., processor 610) of a device.”, therefore, a non-transitory computer readable media comprising computer readable instructions to be executed by one or more processors is disclosed), cause the network appliance to: […]
The rest of the claim language in Claim 15 recites substantially the same limitations as Claim 1, in the form of a non-transitory computer readable media, therefore it is rejected under the same rationale.
The reasons of obviousness have been noted in the rejection of Claim 1 above and applicable herein.
Claim 16 recites substantially the same limitations as Claim 2 in the form of a network device, therefore it is rejected under the same rationale.
Claim 17 recites substantially the same limitations as Claim 3 in the form of a network device, therefore it is rejected under the same rationale.
Claim 18 recites substantially the same limitations as Claim 4 in the form of a network device, therefore it is rejected under the same rationale.
Claim 19 recites substantially the same limitations as Claim 5 in the form of a network device, therefore it is rejected under the same rationale.
Claim 20 recites substantially the same limitations as Claim 6 in the form of a network device, therefore it is rejected under the same rationale.
Conclusion
13. 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.
14. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Devika S Maharaj whose telephone number is (571)272-0829. The examiner can normally be reached Monday - Thursday 8:30am - 5:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexey Shmatov can be reached at (571)270-3428. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DEVIKA S MAHARAJ/Examiner, Art Unit 2123
/ALEXEY SHMATOV/Supervisory Patent Examiner, Art Unit 2123
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