The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This communication is responsive to Amendment filed 06/26/2026.
Claims 1, 3-11 and 13-20 have been examined.
Response to Amendment
In the instant amendment, claims 1, 3-4, 11, 13-14 and 20 have been amended.
The 35 USC §101 rejection over claims 1, 3-11 and 13-20 is withdrawn in view of Applicant’s amendments.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 11 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over US 2020/0106856 to Megahed et al. (hereafter “Megahed”) in further view of US 2022/0351244 to Korada et al. (hereafter “Korada”), US 2005/0268146 to Jin et al. (hereafter “Jin”) and US 2023/0020899 to Zohoorian et al. (hereafter “Zohoorian”)
As per claim 1, Megahed discloses a method of automatically allocating resources to digital channels (FIG. 1; paragraphs 0010, 0019-0020, 0027, 0050-0051 and 0053: “the monitoring resources allocation system 200 is configured to: (1) receive different types of input data relating to the plurality of applications and the cloud computing environment, and (2) determine a recommendation 210 based on the different types of input data, wherein the recommendation 210 comprises one or more recommended allocations of one or more monitoring resources available for use in monitoring the plurality of applications.” [Wingdings font/0xE0] a multiple cloud applications/social media data source/news application i.e., twitter, Instagram, Facebook, CNN, NPR, AP …(digital channels as claimed)), comprising:
receiving, at a cloud server, one or more events associated with each of a plurality of users from at least one of a plurality of digital channels (FIG. 1; paragraphs 0018, 0021, 0023, 0025, and 0053: events streamed from a multiple cloud applications/social media data source/news application i.e., twitter, Instagram, Facebook, CNN, NPR, AP …associated with users/clients/tenants);
constructing, by the cloud server, a sequence of events for each of the plurality of users using a plurality of processing nodes (FIGs. 1-2; paragraphs 0023 and 0053: event streams/news data sent to users generated by the cloud applications/computing nodes and resource nodes/components provided by the cloud 50);
calculating, by the cloud server, a second value for each of the plurality of digital channels (FIGs 3-4 and 9; paragraphs 0017, 0051-0052, and 0068-0070: determining recommendation 210) based on the first value of each of the plurality of users (FIGs. 3-6; paragraph 0065-0067: “user input data 180 received by the combining system 600 comprises application weight importance data indicative of a degree to which a user prioritizes weights for the plurality of applications over weights for the plurality of metrics. If user input data 180 received by the combining system 600 includes application weight importance data, the combining system 600 is configured to place more weight on the plurality of applications (i.e., increase weights for the plurality of applications) instead of placing more weight on the plurality of metrics. For example, in one embodiment, if the user input data 180 includes a value x representing a degree to which a user prioritizes weights for the plurality of applications over weights for the plurality of metrics, weights for the plurality of applications may be substantially about x-times more than weights for the plurality of metrics”) and an attribute of each of the plurality of users (FIGs. 3-6; paragraphs 0053, 0054, 0065 and 0067: “user input data 180 comprising one or more constraints, such as user preferences, pre-defined parameters, pre-defined thresholds, etc.” [Wingdings font/0xE0] determining/calculating recommendation from average resource consumption per user of the application, and user input 180); and
automatically allocating resources to each of the plurality of digital channels based on the respective second value (FIGs. 7-9; paragraph 0051-0053 and 0068: allocating the resources to cloud applications using recommendation calculated from input 180, weights 400, 410, 500, 510, data 610 and 110).
Megahed does not explicitly disclose wherein each of the one or more events is one of: impression, click, engagement, and conversion; wherein each processing node of the plurality processing nodes is configured to construct sequences of events for a respective subset of the plurality of users that is different from the respective subset of any other processing node of the plurality of processing nodes; and generating, using a machine learning model, a first value for each of the plurality of digital channels for each of the plurality of users based on the respective sequences of events of the plurality of users.
Korada further discloses wherein each of the one or more events is one of: impression, click, engagement (paragraph 0070)
It would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to combine a teaching of Korada into Megahed’s teaching because it would provide for the purpose of responses to the actionable targeted content and other user engagement may be captured and used to derive impression data and other event level data as well as consumer data that may provide more insight into the demographics, location, financial situation, online browsing behavior, and/or preferences of each user navigating to the particular location or domain (Korada, paragraph 0017).
Jin further discloses wherein each processing node of the plurality processing nodes is configured to construct sequences of events (FIGs. 4-5 and 7; paragraphs 0025, 0034-0035 and 0042: broker nodes 404a-410b) for a respective subset of the plurality of users that is different from the respective subset of any other processing node of the plurality of processing nodes (FIGs. 4-5 and 7; paragraphs 0034-0037 and 0042: “Each subscribing client subscribes to a particular derived view. As published events enter the system from publishing clients, they are saved in their respective streams. The system is then responsible for updating each derived view according to the previously specified relational expressions and then delivering client messages to each subscriber representing the changes to the state of the respective subscribed view” [Wingdings font/0xE0] a particular client in the multiple clients receiving subscribed events from particular broker nodes).
It would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to combine a teaching of Jin into Megahed’s teaching and Korada’s teaching because it would provide for the purpose of the capability of recovering from failures that may occur when a stateful publish-subscribe service is implemented on an overlay network (Jin, paragraph 0010).
Zohoorian further discloses generating, using a machine learning model, a first value for each of the plurality of digital channels for each of the plurality of users based on the respective sequences of events of the plurality of users (FIG. 1; paragraphs 0035 and 0038: “VNA 160 of NMS 150 may apply machine learning techniques to identify the root cause of error conditions or poor wireless network performance metrics detected or predicted from the streams of event data. For example, in some aspects, VNA 160 may utilize a machine learning model 137 that has been trained using either supervised or unsupervised machine learning techniques to identify the root cause of error conditions or poor network performance based on network data.” [Wingdings font/0xE0] detecting/predicting metrics from stream events associated with a network communication between a particular user and a particular network)
It would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to combine a teaching of Zohoorian into Megahed’s teaching because it would provide for the purpose of invokes one or more corrective actions to correct the root cause of the error condition or poor wireless network performance metrics, thus automatically improving the underlying wireless network performance metrics (e.g., one or more SLE metrics) and also automatically improving the user experience (Zohoorian, paragraph 0035).
As per claim 11, it is system claim, which recite(s) the same limitations as those of claim 1. Accordingly, claim 11 is rejected for the same reasons as set forth in the rejection of claim 1.
As per claim 20, it is a medium claim, which recite(s) the same limitations as those of claim 1. Accordingly, claim 20 is rejected for the same reasons as set forth in the rejection of claim 1.
Claims 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Megahed in view of Korada, Jin, and Zohoorian, as applied to claims 1 and 11, and further in view of US 2019/0164057 to Doshi
As per claim 3, Megahed does not explicitly disclose wherein the machine learning model is a Markov chain Monte Carlo (MCMC) model.
Doshi further discloses wherein the machine learning model is a Markov chain Monte Carlo (MCMC) model (paragraph 0075).
It would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to combine a teaching of Doshi into Megahed’s teaching and Zohoorian’s teaching because it would provide for the purpose of hardware acceleration for the machine learning application can be enabled via a machine learning (Doshi, paragraph 0075).
As per claim 13, it is system claim, which recite(s) the same limitations as those of claim 3. Accordingly, claim 13 is rejected for the same reasons as set forth in the rejection of claim 3.
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Megahed in view of Korada, Jin, and Zohoorian, as applied to claims 1 and 11, and further in view of US 2022/0156519 to Ghorbani et al. (hereafter “Ghorbani”)
As per claim 4, Megahed does not explicitly disclose wherein the machine learning model is used in conjunction with a Shapley values algorithm to generate the first value.
Ghorbani further disclose wherein the machine learning model is used in conjunction with a Shapley values algorithm to generate the first value (paragraphs 0017 and 0057).
It would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to combine a teaching of Ghorbani into Megahed’s teaching, Korada’s teaching, Jin’s teaching, and Zohoorian’s teaching because it would provide for the purpose of In estimating or predicting the value or contribution of a data point to the performance of a neural model, in some cases, the Shapley value of the data points can be used to select those data points with high contribution that can result in improvements in the performance of the neural model. (Ghorbani, paragraph 0017).
As per claim 14, it is system claim, which recite(s) the same limitations as those of claim 4. Accordingly, claim 14 is rejected for the same reasons as set forth in the rejection of claim 4.
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Megahed in view of Korada, Jin, and Zohoorian, as applied to claims 1 and 11, and further in view of US 2017/0140260 to Manning et al. (hereafter “Manning”)
As per claim 5, Megahed does not explicitly disclose wherein the generating of the second value for each of the plurality of digital channels includes classifying the plurality of users using a convolutional neural network (CNN) model.
Manning further discloses wherein the generating of the second value for each of the plurality of digital channels includes classifying the plurality of users using a convolutional neural network (CNN) model (paragraph 0045).
It would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to combine a teaching of Manning into Megahed’s teaching, Korada’s teaching, Jin’s teaching and Zohoorian’s teaching because it would provide for the purpose of The convolutional neural network may use any number of convolution, max pooling, dropout, and hidden layers, and they may be applied in some implementations consecutively and in some implementations iteratively, as this may improve the overall quality of the resultant output of the convolutional neural network, for example, increasing categorization accuracy (Manning, paragraph 0020).
As per claim 15, it is system claim, which recite(s) the same limitations as those of claim 5. Accordingly, claim 15 is rejected for the same reasons as set forth in the rejection of claim 5.
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Megahed in view of Korada, Jin, Zohoorian and Manning, as applied to claims 5 and 15, and further in view of US 2020/0074449 to Novis.
As per claim 6, Megahed does not explicitly disclose wherein the classifying of the plurality of users by the CNN is based on a set of terms to which each user is bound and a respective activity pattern of the user.
Novis further discloses wherein the classifying of the plurality of users by the CNN is based on a set of terms to which each user is bound and a respective activity pattern of the user (paragraph 0081)
It would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to combine a teaching of Novis into Megahed’s teaching, Korada’s teaching, Jin’s teaching, Zohoorian’s teaching, and Manning’s teaching because it would provide for the purpose of enabling the retrieval and analysis of pertinent financial account data and the selection of an optimized financial account during the performance of the transaction (Novis, paragraph 0007).
As per claim 16, it is system claim, which recite(s) the same limitations as those of claim 6. Accordingly, claim 16 is rejected for the same reasons as set forth in the rejection of claim 6.
Claims 7-9 and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Megahed in view of Korada, Jin, Zohoorian, Manning, Novis, as applied to claims 6 and 16, and further in view of US 2019/0278870 to Novielli et al. (hereafter “Novielli”)
As per claim 7, Megahed does not explicitly disclose wherein the machine learning model is retrained periodically based on events that are received from the plurality of digital channels during each period.
Novielli further discloses wherein the machine learning model is retrained periodically based on events that are received from the plurality of digital channels during each period (paragraph 0050).
It would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to combine a teaching of Novielli into Megahed’s teaching, Korada’s teaching, Jin’s teaching, Zohoorian’s teaching, Manning’s teaching, and Novis’ teaching because it would provide for the purpose of the machine learning model can be retrained, a new model created, and so forth based on one or more trigger events (Novielli, paragraph 0017).
As per claim 8, Megahed does not explicitly disclose wherein the received events for retraining include events for non-users.
Novielli further discloses wherein the received events for retraining include events for non-users (paragraph 0050: “he trigger event can be time so that the machine learning model is updated (i.e., an existing model retrained or a new model created) on a periodic or aperidoc schedule. In another embodiment, the trigger even can be machine learning model performance so that the machine learning model is updated when the performance falls below a threshold such as when the number or rate of incorrect prefetches exceeds a threshold and/or the number or rate of correct prefetches falls below a threshold.”).
It would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to combine a teaching of Novielli into Megahed’s teaching, Korada’s teaching, Jin’s teaching, Zohoorian’s teaching, Manning’s teaching, and Novis’ teaching because it would provide for the purpose of the machine learning model can be retrained, a new model created, and so forth based on one or more trigger events (Novielli, paragraph 0017).
As per claim 9, Megahed does not explicitly disclose wherein the machine learning model runs on the cloud server.
Novielli further discloses wherein the machine learning model runs on the cloud server (FIG. 6; paragraphs 0126-0129)
It would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to combine a teaching of Novielli into Megahed’s teaching, Korada’s teaching, Jin’s teaching, Zohoorian’s teaching, Manning’s teaching, and Novis’ teaching because it would provide for the purpose of the machine learning model can be retrained, a new model created, and so forth based on one or more trigger events (Novielli, paragraph 0017).
As per claim 17, it is system claim, which recite(s) the same limitations as those of claim 7. Accordingly, claim 17 is rejected for the same reasons as set forth in the rejection of claim 7.
As per claim 18, it is system claim, which recite(s) the same limitations as those of claim 8. Accordingly, claim 18 is rejected for the same reasons as set forth in the rejection of claim 8.
As per claim 19, it is system claim, which recite(s) the same limitations as those of claim 9. Accordingly, claim 19 is rejected for the same reasons as set forth in the rejection of claim 9.
Claims 10 are rejected under 35 U.S.C. 103 as being unpatentable over Megahed in view of Korada, Jin, Zohoorian, as applied to claim 1, and further in view of US 2015/0128053 to Bragstad et al. (hereafter “Bragstad”)
As per claim 10, Megahed does not explicitly disclose wherein the resources allocated to the plurality of digital channels are displayed on a graphical user interface.
Bragstad further discloses wherein the resources allocated to the plurality of digital channels are displayed on a graphical user interface (FIG. 4-5 and 7; paragraphs 0070 and 0075).
It would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to combine a teaching of Bragstad into Megahed’s teaching, Korada’ teaching, Jin’s teaching, and Zohoorian’s teaching because it would provide for the purpose of resizing resource allocation in a computing environment, including: displaying, within a graphical user interface, a graphical element representing allocation parameters, the allocation parameters indicating a user's current allocation of one or more resources of the computing environment (Bragstad, paragraph 0006).
Response to Arguments
Applicants’ arguments have been considered but are moot in view of the new ground(s) of rejection. Applicants’ amendment necessitated the new ground(s) of rejection presented in this Office action.
Applicant's arguments filed on 06/26/2026 have been fully considered but they are not persuasive for the following reasons:
a) The Applicants argued Zohoorian does not disclose “generating, using a machine learning model, a first value for each of the plurality of digital channels for each of the plurality of users based on the respective sequences of events of the plurality of users" (Remarks, page 4). Applicants further argue “the claim requires the machine learning model to output a value for each channel for each user (i.e., a two-dimensional set of value indexed by both channel and user) …” (Remark page 4)
The Examiner respectfully disagrees for the following reason.
As an initial matter, in response to applicant's argument that the references fail to show certain features of applicant’s invention, it is noted that the features upon which applicant relies (i.e., “…the machine learning model to output a value for each channel for each user (i.e., a two-dimensional set of value indexed by both channel and user) …” – see Remarks, page 4) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
in view of the broadest reasonable interpretation, “a first value for each of the plurality of digital channels for each of the plurality of users” is considered as performance metrics generated from network data based on the device location in the network.
Zohoorian states:
[0005] In general, this disclosure describes techniques determining root causes of degradations in performance of networks based on location data of client devices in the network. A network management system (NMS) receives network data associated with a plurality of client devices in a wireless network at a site. The network data is indicative of one or more aspects of wireless network performance. The NMS can determine that the network data indicates degradation in performance metrics, and, based on the network data, can determine a root cause for the degradation. In some aspects, the NMS can use location data to facilitate determining the root cause. For example, the network data may indicate that some, but not all of the client devices associated with one or more APs have poor SLE metrics. In response to the indication of poor SLE metrics, the NMS can cluster the client devices experiencing poor SLE metrics based on location. For example, the NMS can cluster client devices based on the location of the client devices. If all of the client devices in the cluster have poor SLE metrics, the NMS can determine that there is not a fault in the AP, and can determine that some other factor is the cause of the poor SLE metrics of client devices the cluster. For example, transient noise (e.g., temporary electrical interference with a network signal) may be the cause of the poor SLE metrics for client devices in those clusters where the client devices exhibits poor SLE metrics. The techniques of the disclosure provide one or more technical advantages and practical applications. For example, the techniques enable the NMS to automatically and accurately determine root causes for network degradation that may be due to sources external to the network and/or network devices, such as transient noise. The ability of an NMS to identify such external causes as root causes provided by the techniques disclosed herein can avoid performance of remedial actions that may be unnecessary and/or fail to address the actual cause of poor SLE metrics of some of the client devices. This can be advantageous because it can avoid unnecessary resource costs and customer inconvenience associated with performing the unnecessary remedial actions. As an example, transient noise may be the actual root cause for degradation in SLE metrics. In the absence of location data of client devices, the root cause may appear to be a fault in an AP, potentially resulting in a reset of the AP. In the case where transient noise is the actual root cause, resetting the AP does not address the actual root cause, and there can be wasted resources and unnecessary downtime involved in resetting the AP. Additionally, the techniques facilitate detection of root causes not previously detectable in an automated manner. Further, the more accurate detection of root causes facilitated by the techniques disclosed herein can result in more rapid resolution of issues in a network, leading to greater user and network operator satisfaction.
[0034] For example, NMS 150 may include a virtual network assistant (VNA) 160 that analyzes network data received from one or more UEs 148 and/or one or more APs 142 in a wireless network, provides real-time insights and simplified troubleshooting for IT operations, and automatically takes corrective action or provides recommendations to proactively address wireless network issues. VNA 160 may, for example, include a network data processing platform configured to process hundreds or thousands of concurrent streams of network data from sensors and/or agents associated with APs 142 and/or nodes within network 134. For example, VNA 160 of NMS 150 may include a network performance engine that automatically determines one or more SLE metrics for each client device 148 in a wireless network 106. VNA 160 may also include an underlying analytics and network error identification engine and alerting system. VNA 160 may further provide real-time alerting and reporting to notify administrators of any predicted events, anomalies, trends, and may perform root cause analysis and automated or assisted error remediation.
[0035] In some examples, VNA 160 of NMS 150 may apply machine learning techniques to identify the root cause of error conditions or poor wireless network performance metrics detected or predicted from the streams of event data. For example, in some aspects, VNA 160 may utilize a machine learning model 137 that has been trained using either supervised or unsupervised machine learning techniques to identify the root cause of error conditions or poor network performance based on network data. VNA 160 may generate a notification indicative of the root cause and/or one or more corrective or remedial actions that may be taken to address the root cause of the error conditions or poor wireless network performance metrics. If the root cause may be automatically resolved, VNA 160 invokes one or more corrective actions to correct the root cause of the error condition or poor wireless network performance metrics, thus automatically improving the underlying wireless network performance metrics (e.g., one or more SLE metrics) and also automatically improving the user experience.
Therefore, Zohoorian further discloses generating, using a machine learning model, a first value for each of the plurality of digital channels for each of the plurality of users based on the respective sequences of events of the plurality of users (FIG. 1; paragraphs 0035 and 0038: “VNA 160 of NMS 150 may apply machine learning techniques to identify the root cause of error conditions or poor wireless network performance metrics detected or predicted from the streams of event data. For example, in some aspects, VNA 160 may utilize a machine learning model 137 that has been trained using either supervised or unsupervised machine learning techniques to identify the root cause of error conditions or poor network performance based on network data.” [Wingdings font/0xE0] detecting/predicting metrics from stream events associated with a network communication between a particular user and a particular network)
Conclusion
Applicants’ 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 extension fee 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 date of this final action.
Any inquiry concerning this communication should be directed to examiner Tuan Dao, whose telephone/fax numbers are (571) 270 3387 and (571) 270 4387, respectively. The examiner can normally be reached on every Monday-Thursday and the second Friday of the bi-week from 7:30AM to 5:00PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Pierre Vital, can be reached at telephone number (571) 272 4215.
The fax phone number for the organization where this application or proceeding is assigned is (571) 273 8300.
Any inquiry of a general nature of relating to the status of this application or proceeding should be directed to the TC 2100 Group receptionist whose telephone number is (571) 272 2100.
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/TUAN C DAO/ Primary Examiner, Art Unit 2198
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