CTNF 18/044,395 CTNF 94021 DETAILED ACTION This action is in response to the claims filed on Mar. 8 th , 2023. A summary of this action: Claims 1-22 have been presented for examination. Claim 9 is objected to Claims 1-4, 7-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of a mental process without significantly more. 07-21-aia AIA Claim (s) 1-4, 6-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Potty et al., US 2021/0131254, in view of Chen, Wei, and Ke Shi. "A deep learning framework for time series classification using relative position matrix and convolutional neural network." Neurocomputing 359 (2019): 384-394 07-21-aia AIA Claim (s) 5, 9-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Potty et al., US 2021/0131254, in view of Chen, Wei, and Ke Shi. "A deep learning framework for time series classification using relative position matrix and convolutional neural network." Neurocomputing 359 (2019): 384-394 and in further view of Thinsungnoen, Tippaya, Kittisak Kerdprasop, and Nittaya Kerdprasop. "Deep autoencoder networks optimized with genetic algorithms for efficient ECG clustering." Int. J. Mach. Learn. Comput 8.2 (2018): 112-116 . This action is non-final Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. 07-30-03-h AIA Claim Interpretation Claim 2 recites the phrase “clean fluid rate”, wherein the term “clean” may be considered a subjective term creating potential ambiguity. However, an objective standard is provided in view of ¶¶ 26 and 52, and as such the Examiner interprets this as a “aqueous fluid rate” (¶ 26, in view of ¶ 52, for the objective standard) and suggests amending the claim to expressly recite this. Parallel dependent claims are interpreted in a similar manner as the representative. Claim 6 recites “approximately 120 timesteps” - Examiner interprets this usage of the relative term “approximately” in view of the objective standard in ¶ 54 and suggests amending to expressly recite the objective standard in the broadest range expressly stated in ¶ 54: “between 60 and 180”. Parallel dependent claims are interpreted in a similar manner as the representative. The term “substantially” as recited in the dependent claims is interpreted in view of ¶ 22: “In addition, as used herein, the terms "real time", "real-time", "substantially real time", "substantially real-time" may be used interchangeably and are intended to describe operations (e.g., computing operations) that are performed without any human-perceivable interruption between operations .” Claim Objections 07-29-01 AIA Claim 9 is objected to because of the following informalities: Claim 9 recites, in part: “wherein the control center comprises: an autoencoder configured to receive a plurality of inputs relating to operational parameters of the wellsite equipment, and to compress the plurality of inputs into a plurality of outputs, wherein a number of the plurality of outputs is less than a number of the plurality of inputs; and a convolutional neural network configured to analyze time series of the plurality of outputs to determine the number of effective perforation clusters.” – these limitations are phrased in such a way which raises potential 112(f)/112(b)/112(a) concerns that the autoencoder and CNN are being claimed as structure of the control center, rather than particular computer software being implemented by a processor/computer– Examiner suggests amending the claim to more expressly reflect ¶¶ 37-38, and to clarify that the control center’s processor is executing instructions to perform operations comprising: “compressing, via an autoencoder….based on the autoencoder receiving…analyzing, using the convolution neural network…” or the like (i.e. as steps to be performed by the computational capabilities of the control center) Appropriate correction is required. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-4, 7-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of a mental process without significantly more. Claims 9 and 16 are not rejected as it recites (claim 16 as representative) “use an autoencoder to compress the plurality of inputs into a plurality of outputs, wherein a number of the plurality of outputs is less than a number of the plurality of inputs;” in addition to “use a convolutional neural network to analyze the time series of the plurality of outputs to determine a number of effective perforation clusters created during the hydraulic fracturing operations; and” which is a particular combination of distinct machine learning models and as such not generic, and autoencoder provides the improvement to technology in ¶ 53 Examiner suggests a few different potential directions of amendment to address this rejection, specifically: 1) Incorporate the following subject matter of ¶ 58 into the independent claims: “For example, in certain embodiments, the control center 174 may (e.g., automatically, in certain embodiments) send control signals to the pumps 152, as well as to other well site equipment of the well site system 100, to (e.g., automatically, in certain embodiments) adjust operational parameters such as the clean fluid rate, the total amount of fluid used, the total amount of proppant used, the concentration of proppant used, the total amount of slurry, the slurry rate, and the treatment pressure, among other operational parameters” – see example 45, dependent claims 2 and 4, as this would integrate a practical application, when taken in further view of ¶ 5. 2) Incorporate the subject matter of dependent claim 5, into the independent claims as this would provide a particular non-generic arrangement of multiple distinctive machine learning models (i.e. no longer a generic use of machine learning), wherein the one in claim 5 provides the improvement in ¶ 53) 3) Incorporate the subject matter of dependent claim 6, as such as “stacking…” cannot be done mentally and is not an insignificant extra-solution activity (¶¶ 50, 54-55) as it’s not conventional in view of MPEP § 2106.05(d)(II), or the instant disclosure, nor based on the prior art of record (see § 103 rejection below, then see Enfish in the MPEP as well as example 45, claim 3, for the evidence of record does not indicate that such a feature is routine and conventional ), i.e. it is significantly more at 2B. Step 1 Claim 1 is directed towards the statutory category of a process. Step 2A – Prong 1 The claims recite an abstract idea of a mental process. See MPEP § 2106.04(a)(2). The mental process recited in claim 1 is: converting, via the control center, the plurality of inputs into a plurality of outputs relating to operational parameters of the wellsite equipment; generating, via the control center, time series of the plurality of outputs; using, via the control center, a convolutional neural network to analyze the time series of the plurality of outputs to determine a number of effective perforation clusters created during the hydraulic fracturing operations; But for the mere instructions to do it on a computer, and a generic usage of “using a convolutional neural network” (see example 47, claim, limitations (d-e), in particular recitation of “using the trained ANN” which is akin to “using a [CNN]”, and ¶ 55 merely provides a bare conclusory assertation (MPEP 2106.05(a)) of an improvement which does not amount to an improvement to technology : “It has been found that the use of CNN s, rather than recurrent neural networks (RNNs), provides superior determination of the number of effective perforation clusters 172, as described herein.”), this is a mental process. A person, e.g. an engineer, can readily observe a collection of data, e.g. in a tabular form on paper, and convert it, such as by simple equations and using physical aids (e.g. pen/paper/calculator) to output data by mental evaluation; followed by mentally generating a time series (e.g. as another table on paper) as a mental evaluation, followed by a mental evaluation of generic analyzing the data (example 47, limitation (e) in claim 2; see Electric Power Group in MPEP 2106.04(a)(2)(III)(A)). Under the broadest reasonable interpretation, these limitations are process steps that cover mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper but for the recitation of a generic computer component. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the "Mental Process" grouping of abstract ideas. A person would readily be able to perform this process either mentally or with the assistance of pen and paper. See MPEP § 2106.04(a)(2). To clarify, see the USPTO 101 training examples, available at https://www.uspto.gov/patents/laws/examination-policy/subject-matter-eligibility. In particular, with respect to the physical aids, see example # 45, analysis of claim 1 under step 2A prong 1, including: “Note that even if most humans would use a physical aid (e.g., pen and paper, a slide rule, or a calculator) to help them complete the recited calculation, the use of such physical aid does not negate the mental nature of this limitation.”; also see example # 49, analysis of claim 1, under step 2A prong 1: “Moreover, the recited mathematical calculation is simple enough that it can be practically performed in the human mind. Even if most humans would use a physical aid, like a pen and paper or a calculator, to make such calculations, the use of a physical aid would not negate the mental nature of this limitation.”. As such, the claims recite a mental process. Step 2A, prong 2 The claimed invention does not recite any additional elements that integrate the judicial exception into a practical application. Refer to MPEP §2106.04(d). The following limitations are merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f), including the “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more”: Preambles of independents “via a control center” Step of “controlling…operational parameters…” is not controlling the wellsite equipment, but rather merely controlling data/parameters (see ¶ 58, as noted above, for what is expressly not claimed ), as such this is mere instructions to “apply it” given the lack of restriction of what this is to do The following limitations are adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g): The step of “receiving…” is mere data gathering The recitation of “using…a convolutional neural network” is rejected under a similar rationale as example 47 recitation of “using a trained ANN” in claim 2 limitations (d-e). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. See MPEP § 2106.04(d). MPEP 2106.04(II)(A)(2) “…Instead, under Prong Two, a claim that recites a judicial exception is not directed to that judicial exception, if the claim as a whole integrates the recited judicial exception into a practical application of that exception. Prong Two thus distinguishes claims that are "directed to" the recited judicial exception from claims that are not "directed to" the recited judicial exception …Because a judicial exception is not eligible subject matter, Bilski, 561 U.S. at 601, 95 USPQ2d at 1005-06 (quoting Chakrabarty, 447 U.S. at 309, 206 USPQ at 197 (1980)), if there are no additional claim elements besides the judicial exception, or if the additional claim elements merely recite another judicial exception, that is insufficient to integrate the judicial exception into a practical application. See, e.g., RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) ("Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract"); Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016) (eligibility "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself."). For a claim reciting a judicial exception to be eligible, the additional elements (if any) in the claim must " transform the nature of the claim " into a patent-eligible application of the judicial exception, Alice Corp., 573 U.S. at 217, 110 USPQ2d at 1981, either at Prong Two or in Step 2B” and MPEP § 2106(I): “Mayo, 566 U.S. at 80, 84, 101 USPQ2dat 1969, 1971 (noting that the Court in Diamond v. Diehr found “the overall process patent eligible because of the way the additional steps of the process integrated the equation into the process as a whole,”” – and see MPEP § 2106.05(e). To further clarify, MPEP § 2106.04(II)(A)(1): “Alice Corp., 573 U.S. at 216, 110 USPQ2d at 1980 (citing Mayo, 566 US at 71, 101 USPQ2d at 1965). Yet, the Court has explained that ‘‘[a]t some level, all inventions embody, use, reflect, rest upon, or apply laws of nature, natural phenomena, or abstract ideas,’’ and has cautioned ‘‘to tread carefully in construing this exclusionary principle lest it swallow all of patent law” See also Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335, 118 USPQ2d 1684, 1688 (Fed. Cir. 2016) ("The ‘ directed to’ inquiry, therefore, cannot simply ask whether the claims involve a patent-ineligible concept, because essentially every routinely patent-eligible claim involving physical products and actions involves a law of nature and/or natural phenomenon").” As a point of clarity, RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) (" Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract"); Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016) (eligibility " cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself." discussed in MPEP § 2106.04(II)(A)(2) as well as MPEP § 2106.04(I): “Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151, 120 USPQ2d 1473, 1483 (Fed. Cir. 2016) ("a new abstract idea is still an abstract idea") (emphasis in original). The claimed invention does not recite any additional elements that integrate the judicial exception into a practical application. Refer to MPEP §2106.04(d). Step 2B The claimed invention does not recite any additional elements/limitations that amount to significantly more. The following limitations are merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f), including the “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more”: Preambles of independents “via a control center” Step of “controlling…operational parameters…” is not controlling the wellsite equipment, but rather merely controlling data/parameters (see ¶ 58, as noted above, for what is expressly not claimed ), as such this is mere instructions to “apply it” given the lack of restriction of what this is to do The following limitations are adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g): The step of “receiving…” is mere data gathering The recitation of “using…a convolutional neural network” is rejected under a similar rationale as example 47 recitation of “using a trained ANN” in claim 2 limitations (d-e). In addition, the above insignificant extra-solution activities are also considered as well-understood, routine, and conventional activities, as discussed in MPEP § 2106.05(d): The step of “receiving…” is mere data gathering - this is considered similar to the example WURC activity as discussed in MPEP § 2106.05(d)(II) of: “i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);” As such, the claims are directed towards a mental process without significantly more. Regarding the dependent claims Claim 2 is further limiting the mere data gathering Claim 3 is further limiting the mental process Claim 4 is rejected as mere data gathering that is WURC in view of MPEP 2106.05(d)(II) and the generic description of the sensors (e.g. ¶¶ 48, 52). The real-time is rejected under a similar rationale as the “real-time” in Electric Power Group (see MPEP § 2106.04(a)(2)(III)(C): “• A wide-area real-time performance monitoring system for monitoring and assessing dynamic stability of an electric power grid – Electric Power Group, 830 F.3d at 1351 and n.1, 119 USPQ2d at 1740 and n.1; and”) Claim 7 is mere data gathering for the “a number of perforation clusters” for use with the generic CNN, with no restriction on how this data is to be used for it. WURC in view of MPEP 2106.05(d)(II) Claim 8 is rejected under a similar rationale as the limitation in claim 1, wherein the real-time is rejected under a similar rationale as the “real-time” in Electric Power Group (see MPEP § 2106.04(a)(2)(III)(C): “• A wide-area real-time performance monitoring system for monitoring and assessing dynamic stability of an electric power grid – Electric Power Group, 830 F.3d at 1351 and n.1, 119 USPQ2d at 1740 and n.1; and”) Claim 13 is merely further limiting the mental process – a person is readily able to write out a table to represent a time series (e.g. one column as the time index) with 120 rows with pen and paper (graph paper or the like would make it simpler to do), and another column for the data in the time series Claims 20 rejected under a similar rationale Claim 21 is merely further limiting the mental process itself, but do it in a generic computer environment (note it merely requires the processor to use this information in the analysis, not the CNN) Remaining dependent claims are rejected under similar rationales as their representative claims discussed above As such, the claims are directed towards a mental process without significantly more. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-20-aia AIA 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. 07-21-aia AIA Claim (s) 1-4, 6-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Potty et al., US 2021/0131254, in view of Chen, Wei, and Ke Shi. "A deep learning framework for time series classification using relative position matrix and convolutional neural network." Neurocomputing 359 (2019): 384-394 Regarding Claim 1 Potty teaches: A computer-implemented method, comprising: receiving, via a control center, a plurality of inputs relating to operational parameters of wellsite equipment of a wellsite system during hydraulic fracturing operations performed for the wellsite system; … using, via the control center, a convolutional neural network to analyze the time series of the plurality of [inputs] to determine a number of effective perforation clusters created during the hydraulic fracturing operations; and controlling, via the control center, operational parameters of the wellsite equipment based at least in part on the determined number of effective perforation clusters. Potty, ¶ 13: “Among the many potential advantages to the methods and systems of the present disclosure, only some of which are alluded to herein, the methods and systems of the present disclosure may provide a proppant placement plan that improves proppant placement efficiency within a fracturing zone. For example, the methods and systems of the present disclosure may be used to form a proppant placement plan during a hydraulic fracturing operation….…Fracture simulators may be used to make a similar determination before the hydraulic fracturing operation begins, but fracture simulators tend to assume that all clusters treat similarly . However, data from fiber optics ( distributed acoustic sensing (DAS), distributed temperature sensing (DTS)) and microseismic sensors demonstrate that many fracturing operations may result in unequal fluid and proppant distribution into the formation entry points . Further, fluid flow into formation entry points may start evenly during the pad stage but may become progressively uneven as the proppant stage is pumped . This unevenness in slurry distribution may lead to under-stimulation of the clusters , inefficient use of fracturing material and fracturing pump horsepower, and sometimes well bashing . Therefore, the methods and systems of the present disclosure may advantageously determine a proppant placement plan that takes advantage of measured flow distribution or resistance information when the proppant stage begins ” – to clarify, ¶ 16: “The methods and systems of the present disclosure may be used to efficiently place proppant in one or more formation entry points within a fracturing layer of a subterranean formation . As used herein, the terms "formation entry points" or " perforation clusters " designate a number of groups of perforations over the length of a perforated interval .” As well as ¶ 14, and ¶ 19: “For example, a formation entry point that has been adequately stimulated with proppant may be more efficient at production of hydrocarbons than a formation entry point that has not been adequately stimulated with proppant. Thus, in certain embodiments, the methods and systems of the present disclosure may provide a treatment plan that maximizes proppant placement within one or more formation entry points…” and ¶ 41: “Another potential advantage of methods and systems of the present disclosure is the ability to provide a statistical or machine learning model. In certain embodiments, a machine-learning model may be using the input variables to predict the magnitude of downhole proppant placement through a "final misplaced proppant" metric. As used in the present disclosure, misplaced proppant may refer to the sum of the proppant mass below or above the mean proppant allocation per [i.e. for each] cluster [i.e. clusters that have misplaced proppant are ineffective, and the clusters that don’t have misplaced proppant are effective, see ¶ 51 to clarify on BRI]. In some embodiments, the input variables may be one or more of the variables described above, including completion variables, treatment design variables, downhole response variables, proppant schedule variables, and/or subterranean formation properties...For example, in some embodiments, the machine learning model may minimize or reduce the misplaced treatment fluid, slurry, or proppant [i.e. its reducing the number of ineffective clusters and increasing the number of effective clusters]” And ¶ 43: “In certain embodiments, machine learning models can be developed using statistical or machine learning techniques, such as deep learning, machine learning, Bayesian modeling, geospatial modeling, and other techniques. In some embodiments, deep learning models can further utilize neural networks, such as convolutional neural networks , recurrent neural networks, other neural networks, or a combination of neural networks….” To clarify on the controlling, ¶ 42: “In some embodiments, the machine learning model may be or include a hardware or software module that adjusts one or more variables within the control system to increase the efficiency of future treatment plans. In some embodiments, the machine learning model may adjust one or more variables within the control system to increase the efficiency of the current treatment plan. In other embodiments, the machine learning module may adjust one or more variables within the control system to increase the efficiency of a future treatment plan for a treatment operation within the same well or subterranean formation. In still other embodiments, the machine learning model may adjust one or more variables within the control system to increase the efficiency of a future treatment plan for a treatment operation within a different well or subterranean formation.” To clarify on the input variables, see ¶ 39: “Thus, in one or more embodiments, it may be necessary to recommend the optimal treatment plan in real-time based on the downhole response of the formation, which in turn can help to achieve higher proppant placement efficiency or minimize screen out risk. In some embodiments, the treatment plan may be based on one or more properties of the treatment or pad fluids and/or one or more properties of the downhole formation. For example, in some embodiments, the treatment plan may be based on one or more completion variables, treatment design variables, downhole response variables, proppant schedule variables, and/or subterranean formation proper ties. In one or more embodiments, the completion variables may be one or more of total perforations in a stage, total clusters in a stage, magnitude of tapering of the number of holes per cluster, inter and intra cluster spacing and stage length. In one or more embodiments, the treatment design variables may be one or more of total fluid volume, total proppant volume, average treatment rate, and friction reducer type and concentration. In one or more embodiments, the downhole response variables may be a measured surface or downhole pressure, microseismic event, fiber optics measurement and titlmeter. In one or more embodiments, the proppant schedule variables may be one or more of maximum desired proppant concentration, proppant mass, proppant type, mesh, and properties or desired proppant sequence. In one or more embodiments, the subterranean formation properties may be one or more of mechanical properties of the formation, pore pressure, mineralogy, natural fracture distribution, and tortuosity” Cf. 1-2 and accompanying description in ¶¶ 49-54 to clarify on the system being controlled, and for the computer see ¶ 62: “In one or more embodiments described above, the step of creating the treatment plan further includes using a computer to create the treatment plan.” – also, POSITA would have readily inferred that the use of machine learning as discussed above was computer-implemented While Potty does not explicitly teach the following features, Potty in view of Chen teaches: converting, via the control center, the plurality of inputs into a plurality of outputs relating to operational parameters of the wellsite equipment; generating, via the control center, time series of the plurality of outputs; See P otty, as cited above, including ¶¶ 41-43 (incl. ¶ 43: “In some embodiments, deep learning models can further utilize neural networks, such as convolutional neural networks”, and ¶ 39 including that this was based on “measured” inputs, e.g. “the downhole response variables may be a measured surface or downhole pressure, microseismic event, fiber optics measurement and titlmeter.” – in “real-time” In view of Chen, abstract: “Time series classification (TSC) which has attracted great attention in time series data mining task, has already applied to various fields. With the rapid development of Convolutional Neural Network (CNN), the CNN based methods on TSC have begun to emerge until recently. However , the performance of CNN based methods is slightly worse than state-of-the-art traditional methods …Therefore, we propose a novel deep learning framework using Relative Position Matrix and Convolutional Neural Network (RPMCNN) for the TSC task. We investigate a time series data representation method called Relative Position Matrix (RPM) to convert the raw time series data to 2D images which enable the use of techniques from image recognition. We also construct an improved CNN architecture to automatically learn a high-level abstract representation of low-level raw time series data. Therefore, the combination of RPM and CNN in a unified framework is expected to boost the accuracy and generalization ability of TSC…” – and see § 1 ¶ 3: “In this research, we propose a novel deep learning framework using Relative Position Ma- trix and Convolutional Neural Network (RPMCNN) for the TSC task. Specifically, a 2D representation method which is called Relative Position Matrix RPM (RPM) is proposed to convert the raw time series data to 2D images and an improved CNN model is proposed to classify these 2D images.” – to clarify, see §§ 3.1-3.3, as visually summarized in fig. 2, i.e. first a “Dimensionality reduction” [example of the converting followed by generating, note ¶ 53 in the instant disclosure to clarify on the BRI of the converting term], followed by a second conversion to “Image Representation” of the time series, followed by the use of a CNN To clarify on the dimensionality reduction compressing/converting the inputs into a smaller number of outputs (instant ¶ 53) and then generating time series, see Chen, § 3.2: “We denote a time series as T = t 1 , t 2 , . . . , t n , where t i is the value at time stamp i and the time series length is n…Then, we apply Piecewise Aggregation Approximation (PAA) [22] method to reduce the dimensionality of Z to m ….We choose an appropriate reduction factor k to generate a new smooth time series as X = x 1 , x 2 , . . . , x m by the following equation:” – i.e. “n” time series was reduced to “m” generated time series Chen is considered to be analogous art as 1) its in the same field of endeavor of time series analysis with machine learning, and 2) reasonably pertinent to the problem faced by the instant inventor of finding ways to speed up the use of the CNN (¶ 53, last sentence) as well as the problems faced when using CNNs for time-series analysis instead of RNNs (¶ 55) It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings from Potty which used a CNN for real-time measured data with the teachings from Chen on using a “using Relative Position Matrix and Convolutional Neural Network (RPMCNN) for the TSC task” (Chen, as cited above). The motivation to combine would have been that “However, the performance of CNN based methods is slightly worse than state-of-the-art traditional methods … Therefore, the combination of RPM and CNN in a unified framework is expected to boost the accuracy and generalization ability of TSC. We conduct a comprehensive evaluation with various existing methods on a large number of standard datasets and demonstrate that our approach achieves remarkable results and outperforms the current best TSC approaches by a large margin .” (Chen, abstract) – also, see Chen, table 1 as discussed in § 4.3: “A comprehensive evaluation of our proposed approach and other TSC methods are shown in Table 1 . Note that, the numbers in brackets after error rates are the optimal PAA reduction factor of our approach. Since the error rates are missing for some methods on certain datasets, we denote them as âǣN/Aâǥand rank them in the last. As shown in Table 1 , RPMCNN achieves the best performance in all metrics at first sight, while BOSS, COTE, FCN, MCNN, and RPCNN are also competitive on different metrics…” – another motivation to combine would have ben “…Convolutional Neural Network (CNN) has been applied to solve sophisticated problems in many domains. The advantage of CNN is that it reduces the number of weights to make it easier to optimize the network and reduce the risk of overfitting to get better generalization ability based on the shared-weights architecture and translation invariance characteristics. Thus, CNN achieves remarkable successes in many tasks such as image and video recognition [5,6] , recommender systems [7] and natural language processing [8] . Inspired by recent achievements of CNN based technologies in computer vision, we intend to treat the TSC task as an image recognition task.” (Chen, § 1 ¶¶ 2-3). Also, see § 1 ¶ 4: “a new simple and effective data representation method named as RPM is developed to generate the 2D images.” – i.e. the method summarized in detail in 3.1 # 1 and detailed in § 3.2 is “simple and effective” Regarding Claim 2 Potty teaches: The computer-implemented method of claim 1, wherein the plurality of inputs comprise inputs relating to a clean fluid rate, a total amount of fluid used, a total amount of proppant used, a concentration of proppant used, a total amount of slurry, a slurry rate, and a treatment pressure. – Examiner initially notes that the claim only require that the inputs comprise inputs relating to these elements Potty, ¶ 39: “In one or more embodiments, the completion variables may be one or more of total perforations in a stage, total clusters in a stage, magnitude of tapering of the number of holes per cluster, inter and intra cluster spacing and stage length. In one or more embodiments, the treatment design variables may be one or more of total fluid volume , total proppant volume, average treatment rate , and friction reducer type and concentration. In one or more embodiments, the downhole response variables may be a measured surface or downhole pressure [examples of a treatment pressure], microseismic event, fiber optics measurement and titlmeter. In one or more embodiments, the proppant schedule variables may be one or more of maximum desired proppant concentration , proppant mass, proppant type, mesh, and properties or desired proppant sequence. In one or more embodiments, the subterranean formation properties may be one or more of mechanical properties of the formation, pore pressure, mineralogy, natural fracture distribution, and tortuosity.” – to clarify on clean fluid rate [BRI in view of ¶¶ 26 and 52 is “aqueous” fluid rate], see Potty, ¶¶ 45-46: “The treatment fluids used in the methods and systems of the present disclosure may include any base fluid known in the art, including aqueous base fluids, nonaqueous base fluids, and any combinations thereof. The term "base fluid" refers to the major component of the fluid (as opposed to components dissolved and/or suspended therein), and does not indicate any particular condition or property of that fluids such as its mass, amount, pH, etc… The treatment fluids used in the systems and methods of the present disclosure also include proppants” and ¶ 49: “…In certain embodiments, the proppants and/or other components of the treatment fluid may be metered directly into a base treatment fluid to form a treatment fluid. In certain embodiments, the base fluid may be mixed with the proppants and/or other components of the treatment fluid at a well site where the operation or treatment is conducted, either by batch mixing or continuous ("on-the-fly") mixing….” In addition, to clarify on the slurry, first see ¶ 27 to clarify on BRI: “mix or otherwise combine the base fluid and the second material to form a slurry” – and note in Potty, in treatment fluid when it was pumped downhole has a “aqueous” “base fluid” mixed with “the proppants and/or other components of the treatment fluid” (see citations above), i.e. the fluid pumped downhole for the treatment is a slurry of the treatment fluid and the proppant (Potty, ¶ 13: “This unevenness in slurry distribution may lead to under-stimulation of the clusters, inefficient use of fracturing material and fracturing pump horsepower, and sometimes well bashing”, also see ¶ 22, and in ¶ 41: “misplaced treatment fluid, slurry, or Proppant”, and in ¶ 62: “In one or more embodiments described above, the method further includes introducing a proppant slurry into the treatment subterranean formation in accordance with the treatment plan.”) – as such, POSITA would have understood that “ average treatment rate” was the slurry rate which was relating rate of the clean/aqueous base fluid as well as the rate of the proppant (both would have the a related rate as the slurry rate, as the slurry rate comprised the proppant and the base fluid) during the treatment, and that the total amount of slurry would have been the “total fluid volume” in combination with the “total proppant volume” – to clarify, see ¶¶19-20, discussing methods of changing the “proppant concentration in the treatment fluid” Regarding Claim 3 Potty in view of Chen teaches: The computer-implemented method of claim 1, wherein the plurality of outputs comprise four outputs. (Potty, as was taken in view of Chen above, in particular see Chen, § 3.2 as cited above, in further view of § 4.4: “As the length of GunPoint and OliveOil dataset is 150 and 570, we set the range of the PAA reduction factor to 1–9 and 3–35, respectively” – and see eq. 2 in §3.2 as “reduction factor k” and “m=n/k”, i.e. k = 1-9 and 3-35 respectively; n = 150 and 570 respectively, and m=n/k which comprise at least four outputs. Examiner notes that comprises is an open-ended term. MPEP § 2111.03(I): “The transitional term "comprising", which is synonymous with "including," "containing," or "characterized by," is inclusive or open-ended and does not exclude additional, unrecited elements or method steps.” – as such, as Chen’s outputs comprise at least four outputs, they comprise four outputs and other unrecited outputs not excluded in this limitation Rationale to combine is the same as discussed above. Regarding Claim 4 Potty teaches: The computer-implemented method of claim 1, wherein the plurality of inputs are received from sensors associated with the wellsite equipment in substantially real-time during the hydraulic fracturing operations. (Potty, as cited above, teaches the inputs include “measured” “real-time” inputs from the wellsite – see ¶ 18 to clarify, as well as ¶ 39 as cited above Regarding Claim 6 Potty in view of Chen teaches: The computer-implemented method of claim 1, wherein generating the time series of the plurality of outputs comprises stacking the plurality of outputs with approximately 120 timesteps. – first, see ¶ 54 to clarify on “approximately”: “However, in other embodiments, other numbers of timesteps may be used, such as between 110 and 130, between 100 and 140, between 90 and 150, between 80 and 160, between 60 and 180, and so forth.” As to the stacking, see § 3.2, in particular see the “matrix M” in eq. 3 and accompanying description: “…Obviously, every two time stamps of the time series are connected by M to obtain their relative position, each row and column of M contains the information of the whole time series by taking a certain time stamp as a reference point… Each row of M shows the time series with varies reference points and each column shows the mirror of the former, which offers a reversed perspective to view the time series. Finally, min-max normalization is applied to convert M to the gray value matrix, and the final matrix F is obtained by:… With our proposed pipeline for encoding time series data as 2D images, the converted images can be obtained from the standard datasets such as UCR archive. Fig. 2 shows the raw time series data on top five different datasets from the UCR archive, and images generated by RPM, respectively.” – i.e. it stacked the time-series outputs into a matrix (eq. 3) of the time steps which is “m x m” wherein “m” = “n/k” = “time series length”/”reduction factor” And in § 4.4: “As the length of GunPoint and OliveOil dataset is 150 and 570, we set the range of the PAA reduction factor to 1–9 and 3–35, respectively.” – i.e. m = 16 2/3 to 1; and 16.285… to 190, respectively – the PAA reduction factor of 3-35 for 570 as such includes the ranges in ¶ 54, and the Examiner notes that given the PAA reduction factor is an integer (cf. 9), this includes PAA k = 4 and k=5 (cf. 9(b)), which is 142.5 to 114 which is approximately 120 timesteps (142.5 to 114 timesteps) (see MPEP § 2144.05(I) to clarify on obviousness of ranges) Rationale to combine is the same as discussed above. Regarding Claim 7 Potty teaches: The computer-implemented method of claim 1, wherein the convolutional neural network uses a number of perforation clusters created during the hydraulic fracturing operations to determine the number of effective perforation clusters. (Potty, as cited above for the determining the effective number of clusters (¶ 41) in view of ¶ 39 as cited above, in particular: “In one or more embodiments, the completion variables may be one or more of total perforations in a stage, total clusters in a stage , magnitude of tapering of the number of holes per cluster, inter and intra cluster spacing and stage length.” Regarding Claim 8 Potty teaches: The computer-implemented method of claim 1, wherein controlling the operational parameters of the wellsite equipment comprises controlling the operational parameters of the wellsite equipment in substantially real-time. (Potty, as was cited above for claim 1) 07-21-aia AIA Claim (s) 5, 9-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Potty et al., US 2021/0131254, in view of Chen, Wei, and Ke Shi. "A deep learning framework for time series classification using relative position matrix and convolutional neural network." Neurocomputing 359 (2019): 384-394 and in further view of Thinsungnoen, Tippaya, Kittisak Kerdprasop, and Nittaya Kerdprasop. "Deep autoencoder networks optimized with genetic algorithms for efficient ECG clustering." Int. J. Mach. Learn. Comput 8.2 (2018): 112-116 . Regarding Claim 5 While Potty in view of Chen does not explicitly teach the following, Potty in view of Chen and in further view of Thinsungnoen teaches: The computer-implemented method of claim 1, wherein converting the plurality of inputs into a plurality of outputs comprises using an autoencoder to compress the plurality of inputs into a smaller number of outputs. Potty, as was taken in view of Chen above, in particular note Chen § 3.3 in particular: “Then, we apply Piecewise Aggregation Approximation (PAA) [22] method to reduce the dimensionality of Z to m” in view of Thinsungnoen, § I ¶¶ 1-3: “…In order to deal with high dimensionality, researchers typically look for data representatives. It is, however, difficult to find a good time series representation [3] because of the ordered characteristic inherent in such series. Many researchers have investigated time series representations. Representation methods such as Piecewise Aggregate Approximation [4], Adaptive Piecewise Constant Approximation [5], Symbolic Aggregate Approximation [6], Discrete Fourier Transform [7], and Wavelet Transform or Discrete Wavelet Transform [8], [9], have been proposed that can yield effective time series representations. Recently, other techniques that are potentially effective in finding time series representations have been formulated. One such technique is Deep Autoencoder Networks (DANs), which apply deep learning using multiple connected network layers to transform and transmit signals between the layers [10], [11]. The aim of an autoencoder (AE) network is to model high-level data representation by automatically finding and integrating features to another level [10]-[12].” See § III, in particular subsection (b) including fig. 3 for the input time series, and then see § 4 incl. for fig. 5 and accompanying description: “The time series representatives, which are generated from optimal DANs (called TSR-DANs), are shown in Fig. 5. Examples of ECG representatives for normal cases are shown in Fig. 5(a) and abnormal cases are shown in Fig. 5(b).” note in particular in § 4: “We use the PDC algorithm in the R package for clustering the raw data, PAA representation [“Piecewise Aggregate Approximation”, same as used in Chen] , SAX representation, and TSR-DAN representation (our proposed method for time series representation)… Based on the accuracy and purity metrics, our TSR-DANs reveal the best result s… For comparing the increase in performance (Fig. 7), our TSR-DANs are the best .” – f. 6-7 to clarify on this, e.g. it had 80.6% accuracy as compared to PAA with 67% accuracy for the clustering performed using the representation Thinsungnoen is considered analogous in the same field of endeavor of machine learning as well as being reasonably pertinent to the problem faced by the instant inventors of determine what methods to use for data compression. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings from Potty, in view of Chen on a system which used PAA for dimensionality reduction (Chen, § 3.3 as cited above) with the teachings from Thinsungnoen on using an autoencoder instead of PAA for dimensionality reduction. The motivation to combine would have been that “Based on the accuracy and purity metrics, our TSR-DANs reveal the best result s… For comparing the increase in performance (Fig. 7), our TSR-DANs are the best .” (Thinsungnoen, § 4), and c.f. 6-7 to clarify on this Regarding Claim 9 Potty in view of Chen teaches: A system, comprising: a control center configured to control operational parameters of wellsite equipment of a wellsite system during hydraulic fracturing operations performed for the wellsite system, wherein the control center is configured to control the operational parameters of the wellsite equipment based at least in part on a number of effective perforation clusters created during the hydraulic fracturing operations, wherein the control center comprises: …and a convolutional neural network configured to analyze time series of the plurality of outputs to determine the number of effective perforation clusters. See Potty as was taken in view of Chen, as was cited above for claim 1 While Potty in view of Chen does not explicitly teach the following, Potty in view of Chen and in further view of Thinsungnoen teaches: Potty, as was taken in view of Chen above, in particular note Chen § 3.3 in particular: “Then, we apply Piecewise Aggregation Approximation (PAA) [22] method to reduce the dimensionality of Z to m” in view of Thinsungnoen, § I ¶¶ 1-3: “…In order to deal with high dimensionality, researchers typically look for data representatives. It is, however, difficult to find a good time series representation [3] because of the ordered characteristic inherent in such series. Many researchers have investigated time series representations. Representation methods such as Piecewise Aggregate Approximation [4], Adaptive Piecewise Constant Approximation [5], Symbolic Aggregate Approximation [6], Discrete Fourier Transform [7], and Wavelet Transform or Discrete Wavelet Transform [8], [9], have been proposed that can yield effective time series representations. Recently, other techniques that are potentially effective in finding time series representations have been formulated. One such technique is Deep Autoencoder Networks (DANs), which apply deep learning using multiple connected network layers to transform and transmit signals between the layers [10], [11]. The aim of an autoencoder (AE) network is to model high-level data representation by automatically finding and integrating features to another level [10]-[12].” See § III, in particular subsection (b) including fig. 3 for the input time series, and then see § 4 incl. for fig. 5 and accompanying description: “The time series representatives, which are generated from optimal DANs (called TSR-DANs), are shown in Fig. 5. Examples of ECG representatives for normal cases are shown in Fig. 5(a) and abnormal cases are shown in Fig. 5(b).” note in particular in § 4: “We use the PDC algorithm in the R package for clustering the raw data, PAA representation [“Piecewise Aggregate Approximation”, same as used in Chen] , SAX representation, and TSR-DAN representation (our proposed method for time series representation)… Based on the accuracy and purity metrics, our TSR-DANs reveal the best result s… For comparing the increase in performance (Fig. 7), our TSR-DANs are the best .” – f. 6-7 to clarify on this, e.g. it had 80.6% accuracy as compared to PAA with 67% accuracy for the clustering performed using the representation It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings from Potty, in view of Chen on a system which used PAA for dimensionality reduction (Chen, § 3.3 as cited above) with the teachings from Thinsungnoen on using an autoencoder instead of PAA for dimensionality reduction. The motivation to combine would have been that “Based on the accuracy and purity metrics, our TSR-DANs reveal the best result s… For comparing the increase in performance (Fig. 7), our TSR-DANs are the best .” (Thinsungnoen, § 4), and c.f. 6-7 to clarify on this Regarding Claim 10. Rejected under a similar rationale as claim 2 above. Regarding Claim 11. Rejected under a similar rationale as claim 3 above. Regarding Claim 12. Rejected under a similar rationale as claim 4 above. Regarding Claim 13. Rejected under a similar rationale as claim 6 above, e.g. note 142 time steps as discussed above includes 120 timesteps Regarding Claim 14. Rejected under a similar rationale as claim 7 above. Regarding Claim 15. Rejected under a similar rationale as claim 8 above. Regarding Claim 16. Rejected under a similar rationale as claim 9 above. Regarding Claim 17. Rejected under a similar rationale as claim 2 above. Regarding Claim 18. Rejected under a similar rationale as claim 3 above. Regarding Claim 19. Rejected under a similar rationale as claim 4 above. Regarding Claim 20. Rejected under a similar rationale as claim 6 and 13 above. Regarding Claim 21. Rejected under a similar rationale as claims 6 and 14 above. Regarding Claim 22. Rejected under a similar rationale as claim 8 above . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Camp et al., US 2021/0255361. Abstract, ¶¶ 18-51 Ciezobka et al., US 2017 /0226838. Abstract. ¶¶ 5, 45-46, 61-76, 80, 119-120 Ciezobka et al., US 2017/0284181. Abstract, cf. 14-15 and accompanying description. See ¶¶ 60-80, 84, 90-96 Gu et al., US 2018/0259668. Abstract, cf. 2 and ¶ 106 Heidari et al., US 2022/0025753. Abstract, ¶ 43 James et al., US 2016/0333684. Abstract, ¶¶ 40 and 53. Madasu et al., US 2021/0017845. Abstract, cf. 4, and ¶¶ 16, 18-21, 33- 49, 52-53 Raterman et al., US 2018/0364381. Abstract, ¶¶ 9-10, 72, 83, 110-112 Wang et al., US 2021/0131261. Abstract, cf. 2-4g and accompanying descriptions. See ¶¶ 132, 136, and 150-151 as well. Ajisafe, F., et al. "Engineered completion workflow increases reservoir contact and production in the Wolfcamp Shale, West Texas." SPE Annual Technical Conference and Exhibition?. SPE, 2014. Pgs. 5-6 Alsheikh, Mohammad Abu, et al. "Rate-distortion balanced data compression for wireless sensor networks." IEEE Sensors Journal 16.12 (2016): 5072-5083. §§ II.A, IV, VI-VII Carpenter, Chris. "Surface drilling data can help optimize fracture treatment in real time." Journal of Petroleum Technology 71.06 (2019): 74-76. Page 76 Che, Changchang, et al. "Intelligent fault diagnosis method of rolling bearing based on stacked denoising autoencoder and convolutional neural network." Industrial Lubrication and Tribology 72.7 (2020): 947-953. Abstract and §§ 3-3.2. Cipolla, C., et al. "New algorithms and integrated workflow for tight gas and shale completions." SPE Annual Technical Conference and Exhibition?. SPE, 2011. Pgs. 4-6. Essien, Aniekan, and Cinzia Giannetti. "A deep learning framework for univariate time series prediction using convolutional LSTM stacked autoencoders." 2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA). IEEE, 2019. Cf. 1 and pages 1-2 Gu, Ming, John Quirein, and Dingding Chen. "Near Real-Time Return-on-Fracturing-Investment ROFI Optimization for Shale Fracturing by Integrating Anisotropic Acoustic Interpretation, 3D Fracture Modeling, and Neural Networks." SPE Middle East Oil and Gas Show and Conference. SPE, 2017. Page 15 and cf. 1 Lehman, Lyle V., Kale Jackson, and Bruce Noblett. "Big data yields completion optimization: using drilling data to optimize completion efficiency in a low permeability formation." SPE Annual Technical Conference and Exhibition?. SPE, 2016. Pgs. 1, 10-13. Ma, Zhiwei, and Juliana Y. Leung. "Efficient tracking of solvent chamber development during warm solvent injection in heterogeneous reservoirs via machine learning." SPE Canada Heavy Oil Conference. SPE, 2020. Abstract, pgs. 7-10 Madasu, Srinath, and Keshava Prasad Rangarajan. "Deep recurrent neural network DrNN model for real-time step-down analysis." SPE Reservoir Characterisation and Simulation Conference and Exhibition. SPE, 2019. Pgs. 1-3 Miller, Camron, George Waters, and Erik Rylander. "Evaluation of production log data from horizontal wells drilled in organic shales." SPE Unconventional Resources Conference/Gas Technology Symposium. SPE, 2011. Pgs. 5-6. Mutalova, Renata, et al. "Machine learning on field data for hydraulic fracturing design optimization." (2019). EarthArXiv. Pre-print. Oct. 15th, 2019. Pgs. 1-3 Que, Zhiqiang, et al. "Real-time anomaly detection for flight testing using AutoEncoder and LSTM." 2019 international conference on field-programmable technology (ICFPT). IEEE, 2019. Cf. 1 and § II.B, then §§ III.A-C Sezer, Omer Berat, and Ahmet Murat Ozbayoglu. "Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach." Applied Soft Computing 70 (2018): 525-538. § 4 and its subsections. Shelley, Robert, et al. "Understanding multi-fractured horizontal marcellus completions." SPE Eastern Regional Meeting. SPE, 2014. Pgs. 1,4, 12 Wang, Zhiguang, and Tim Oates. "Encoding time series as images for visual inspection and classification using tiled convolutional neural networks." Workshops at the twenty-ninth AAAI conference on artificial intelligence. Vol. 1. No. 1. 2015. Pgs. 41-43 Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID A. HOPKINS whose telephone number is (571)272-0537. The examiner can normally be reached Monday to Friday, 10AM to 7 PM EST. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /David A Hopkins/Primary Examiner, Art Unit 2188 Application/Control Number: 18/044,395 Page 2 Art Unit: 2188 Application/Control Number: 18/044,395 Page 3 Art Unit: 2188 Application/Control Number: 18/044,395 Page 4 Art Unit: 2188 Application/Control Number: 18/044,395 Page 5 Art Unit: 2188 Application/Control Number: 18/044,395 Page 6 Art Unit: 2188 Application/Control Number: 18/044,395 Page 7 Art Unit: 2188 Application/Control Number: 18/044,395 Page 8 Art Unit: 2188 Application/Control Number: 18/044,395 Page 9 Art Unit: 2188 Application/Control Number: 18/044,395 Page 10 Art Unit: 2188 Application/Control Number: 18/044,395 Page 11 Art Unit: 2188 Application/Control Number: 18/044,395 Page 12 Art Unit: 2188 Application/Control Number: 18/044,395 Page 13 Art Unit: 2188 Application/Control Number: 18/044,395 Page 14 Art Unit: 2188 Application/Control Number: 18/044,395 Page 15 Art Unit: 2188 Application/Control Number: 18/044,395 Page 16 Art Unit: 2188 Application/Control Number: 18/044,395 Page 17 Art Unit: 2188 Application/Control Number: 18/044,395 Page 18 Art Unit: 2188 Application/Control Number: 18/044,395 Page 19 Art Unit: 2188 Application/Control Number: 18/044,395 Page 20 Art Unit: 2188 Application/Control Number: 18/044,395 Page 21 Art Unit: 2188 Application/Control Number: 18/044,395 Page 22 Art Unit: 2188 Application/Control Number: 18/044,395 Page 23 Art Unit: 2188 Application/Control Number: 18/044,395 Page 24 Art Unit: 2188 Application/Control Number: 18/044,395 Page 25 Art Unit: 2188 Application/Control Number: 18/044,395 Page 26 Art Unit: 2188 Application/Control Number: 18/044,395 Page 27 Art Unit: 2188 Application/Control Number: 18/044,395 Page 28 Art Unit: 2188 Application/Control Number: 18/044,395 Page 29 Art Unit: 2188 Application/Control Number: 18/044,395 Page 30 Art Unit: 2188
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