DETAILED ACTION The instant application having Application No. 18/525,310 filed on 11/30/2023 is presented for examination by the examiner. Claim 1- 20 is/are pending in the application. Claims 1, 11 and 20 is/are independent claims. Notice of Pre-AIA or AIA Status The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Examiner Notes Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. Drawings The applicant’s drawings submitted are acceptable for examination purposes. Claim Rejections - 35 USC § 101 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-2 0 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claims 1- 20 , the claims are within at least one of the four categories of patent eligible subject matter as it is directing to a method/ system/medium claims under Step 1 . However, claim 1- 20 are/is rejected under 35 USC 101 because the claims are/is directed to an abstract idea without being integrated into a practical application nor being significantly more. Per claims 1 , 11 and 20 , the limitations “generating, using a machine learning model …”, “calculating, and by the cloud server …” , as drafted, recite functions that, under its broadest reasonable interpretation, covers functions that could reasonably be performed in the mind, including with the aid of pen and paper, but for the recitation of generic computer components. That is, the limitation “generating, using a machine learning model …”, “calculating, and by the cloud server …” as drafted, are functions that, under its broadest reasonable interpretation, recite the abstract idea of a mental process. The limitations encompass a human mind carrying out the functions through observation, evaluation, judgment and /or opinion, or even with the aid of pen and paper. Thus, these limitations recite and fall within the “Mental Processes” grouping of abstract ideas under Prong 1 Step 2A. Under Prong 2 Step 2A , this judicial exception is not integrated into a practical application. The claim recites the following additional elements “processor”, “cloud server”, “memory”, “receiving, at a cloud server”, “ constructing, by the cloud server … ” , and “automatically allocating resources …” The “processor”, “cloud server”, and “memory” are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component, or merely a generic computer or generic computer components to perform the judicial exception. The addition element “receiving, at a cloud server” amount to a data gathering step and a mere generic transmission and presentation of collected and analyzed data which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)). The additional elements “constructing, by the cloud server …”, and “automatically allocating resources …” fail to meaningfully limit the claim because it does not require any particular application of the recited “ constructing ” and “allocating” and are at best the equivalent of merely adding the words “apply it” to the judicial exception. Accordingly, the additional elements do not integrate the recited judicial exception into a practical application, and the claim is therefore directed to the judicial exception. See MPEP 2106.05(f). Under Step 2B , The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are “processor”, “cloud server”, “memory”, “receiving, at a cloud server”, “constructing, by the cloud server …”, and “automatically allocating resources …” the mere use of generic computer to implement the abstract idea, as discussed above, which does not amount to significantly more, thus, not an inventive concept, and the courts have identified gathering data, storing data, and outputting the result is well-understood, routine and conventional activity ( Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018) ), thus, cannot amount to an inventive concept. . Accordingly, the claim does not appear to be patent eligible under 35 USC 101. See MPEP 2106.05(d). Regarding claims 2 and 12, under prong 2, the “ wherein each of the one or more events is one of: impression, click, engagement, and conversion. ” limitations are additional elements that recite insignificant extra solution activity which do not amount to a practical application, nor amount to significantly more under step 2B as explained above. Regarding claims 3 and 1 3 , under prong 2, the “ wherein the machine learning is a Markov chain Monte Carlo (MCMC) model ” limitations are additional elements that recite insignificant extra solution activity which do not amount to a practical application, nor amount to significantly more under step 2B as explained above. Regarding claims 4 and 14, under prong 2, the “ wherein the machine learning model is used in conjunction with a Shapley values algorithm to generate the first value ” limitations are additional elements that recite insignificant extra solution activity which do not amount to a practical application, nor amount to significantly more under step 2B as explained above. Regarding claim 5 and 15 , the limitation “ 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. ” is an additional metal process under prong 1. Regarding claim 6 and 16, the limitation “ 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 ” is an additional metal process under prong 1. Regarding claims 7 and 17, under prong 2, the “ wherein the machine learning model is retrained periodically based on events that are received from the plurality of digital channels during each period ” limitations are additional elements that recite insignificant extra solution activity which do not amount to a practical application, nor amount to significantly more under step 2B as explained above. Regarding claims 8 and 18, under prong 2, the “ wherein the received events for retraining include events for non-users ” limitations are additional elements that recite insignificant extra solution activity which do not amount to a practical application, nor amount to significantly more under step 2B as explained above. Regarding claims 9 and 19, under prong 2, the “ wherein the machine learning model runs on the cloud server ” limitations are additional elements that recite insignificant extra solution activity which do not amount to a practical application, nor amount to significantly more under step 2B as explained above. Regarding claim 10, under prong 2, the “ wherein the resources allocated to the plurality of digital channels are displayed on a graphical user interface ” limitations are additional elements that recite insignificant extra solution activity which do not amount to a practical application, nor amount to significantly more under step 2B as explained above. 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 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. ” 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) , wherein each of the plurality processing nodes is configured to construct sequences of events for a subset of the plurality of users ( FIGs. 1-2; paragraphs 0023 and 0053: event streams/news data sent to a specific users using twitter or CNN 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. ” 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 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 . 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. ” 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 00 35 ). As per claim 11 , it is system claim, which recite(s) the same limitations as those of claim 1. Accordingly, claim 1 1 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 2 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Megahed in view of Zohoorian , as applied to claim s 1 and 11 , and further in view of US 2012/0259762 to Tarighat et al. (hereafter “ Tarighat ”) As per claim 2 , Megahed does not explicitly disclose wherein each of the one or more events is one of: impression, click, engagement, and conversion . Tarighat further discloses wherein each of the one or more events is one of: impression, click (paragraph 0065) , engagement, and conversion . 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 Tarighat into Megahed ’s teaching and Zohoorian’s teaching because it would provide for the purpose of an event algorithm is a type of algorithm that can be constructed using drag and droppable widgets as shown herein that causes a second event to occur in response to a first event such as a data stream input or other trigger ( Tarighat , paragraph 00 06 ). As per claim 1 2 , it is system claim, which recite(s) the same limitations as those of claim 2 . Accordingly, claim 1 2 is rejected for the same reasons as set forth in the rejection of claim 2 . Claims 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Megahed in view of Zohoorian , as applied to claim s 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 is a Markov chain Monte Carlo (MCMC) model. Doshi further discloses wherein the machine learning 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 h ardware acceleration for the machine learning application can be enabled via a machine learning ( Doshi , paragraph 00 75 ). As per claim 1 3 , it is system claim, which recite(s) the same limitations as those of claim 3 . Accordingly, claim 1 3 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 Zohoorian and Tarighat , as applied to claim s 2 and 12 , 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 , Zohoorian’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. ( Ghor ba ni , paragraph 00 17 ). As per claim 1 4 , it is system claim, which recite(s) the same limitations as those of claim 4 . Accordingly, claim 1 4 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 Zohoorian , as applied to claim s 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 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 00 20 ). As per claim 1 5 , it is system claim, which recite(s) the same limitations as those of claim 5 . Accordingly, claim 1 5 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 Zohoorian and Manning, as applied to claim s 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 , 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 00 07 ). As per claim 1 6 , it is system claim, which recite(s) the same limitations as those of claim 6 . Accordingly, claim 1 6 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 Zohoorian , Manning, Novis , as applied to claim s 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 , 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 00 1 7 ). 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 , 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 00 17 ). 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 , 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 00 17 ). As per claim 1 7 , it is system claim, which recite(s) the same limitations as those of claim 7 . Accordingly, claim 1 7 is rejected for the same reasons as set forth in the rejection of claim 7 . As per claim 1 8 , it is system claim, which recite(s) the same limitations as those of claim 8 . Accordingly, claim 1 8 is rejected for the same reasons as set forth in the rejection of claim 8 . As per claim 1 9 , it is system claim, which recite(s) the same limitations as those of claim 9 . Accordingly, claim 1 9 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 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 , 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 00 06 ). Conclusion The following prior art made of record and not relied upon is cited to establish the level of skill in the applicant’s art and those arts considered reasonably pertinent to applicant’s disclosure. See MPEP 707.05(c) . Prior arts: US 2024/0275598 to Sathyanarayana Distribution platform 103 corresponds to a CDN, cloud hosting platform, or other platform with distributed resources that may be allocated to stream service provider 101 and/or other stream service providers for the distribution of their media streams and media content. The distribution resources include servers, distribution nodes, and/or devices or machines that are distributed to different points-of-presence (“POP”) or locations, and that may be used to securely stream content to different client devices 105. The distribution platform resources are accessible from a second domain, network, and/or address. US 2023/0334558 to Lamba displaying, by a customer journey user interface of a workflow orchestration platform, a workflow canvas including a representation of a customer journey corresponding to a digital content channel, wherein the customer journey comprises an ordered sequence of event definitions US 2022/0171988 to Ma recording a plurality of event streams, each event stream corresponding to a human user interacting with a computing device to perform one or more tasks; concatenating the event streams; segmenting some or all of the concatenated event streams to generate one or more individual traces performed by the user interacting with the computing device US 2021/0176187 to Gladney The collection engine 106 instantiated by the central cloud computing account 100 may collect streamed event data. The collection engine 106 may store the collected streamed event data for a specified amount of time (e.g., 24 hours). The collection engine 106 may receive identifiable records of data in a stream. The collection engine 106 may be executed in the cloud computing system and may be designated to be executed by to the central cloud computing account 100. US 2021/0012260 to Zhang Resource customization manager 218 controls the process of customizing resource allocation for a given user of a cloud service based on machine learning. Resource customization manager 218 includes machine learning component 219. Machine learning component 219 may be, for example, an artificial intelligence algorithm. Resource customization manager 218 utilizes machine learning component 219 to classify data and make predictions without using explicit instructions, relying on patterns and inference. US 2020/0097961 to Luo Resource manager 218 broadcasts or publishes smart contract 238 to other resource providers included in list of other resource providers 242 when one or more of resource sale terms 240 are triggered or satisfied, such as, for example, at a particular time of day and/or day of the week. List of other resource providers 242 represents a list of registered other resource provider data processing systems that may purchase the use of one or more of the unused resources in list of unused resources 224 according to resource sale terms 240 of smart contract 238. Each of the other resource provider data processing systems may be located in, for example, different cloud computing environments. US 2019/0392531 to DeLuca the life event collection module 142 obtains the sequence of life events for a user. The life event collection module 142 may receive the identity of the user from the user computing device 120, via the communication module 132. The life event collection module 142 may obtain the users sequence of life events through an opt-in fashion where the user has given permission. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Tuan Dao whose telephone number is (571) 270 3387 . The examiner can normally be reached on Monday to Friday from 09am to 05pm . The examiner can also be reached on alternate Fridays . 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. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form . /TUAN C DAO/ Primary Examiner, Art Unit 2198