CTNF 17/744,167 CTNF 100323 DETAILED ACTION 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. 12-151 AIA 26-51 12-51 Status of Claims Claims 1, 8, and 15 were amended. Claims 1, 2, 5-9, 12-16, and 19-26 are pending and examined herein. Claims 1, 2, 5-9, 12-16, and 19-26 are rejected under 35 U.S.C. 112(b). Claims 1, 2, 5-9, 12-16, and 19-26 are rejected under 35 U.S.C. 101 as being directed to an abstract idea without significantly more. Continued Examination Under 37 CFR 1.114 07-42-04 AIA A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/02/2026 has been entered. Response to Arguments 07-38-01 AIA Applicant’s arguments, see page 12 , filed 03/02/2026 , with respect to the non-statutory double patenting rejection of claims 1, 5, 8, 12, and 15 have been fully considered and are persuasive. The non-statutory double patenting rejection of claims 1, 5, 8, 12, and 15 has been withdrawn. 07-38-01 AIA Applicant’s arguments, see page 12 , filed 03/02/2026 , with respect to the 35 U.S.C. 112(a) rejection of claims 1, 2, 5-9, 12-16, and 19-26 have been fully considered and are persuasive. The 35 U.S.C. 112(a) rejection of claims 1, 2, 5-9, 12-16, and 19-26 has been withdrawn. 07-38-01 AIA Applicant’s arguments, see page 13 , filed 03/02/2026 , with respect to the 35 U.S.C. 112(b) rejection of claims 1, 2, 5-9, 12-16, and 19-26 have been fully considered and are persuasive. The 35 U.S.C. 112(b) rejection of claims 1, 2, 5-9, 12-16, and 19-26 has been withdrawn. 07-37 AIA Applicant's arguments filed 03/02/2026 regarding the 35 U.S.C. 101 rejection of claims 1-20 have been fully considered but they are not persuasive. Applicant argues that "Applicant submits that the claim limitations, when interpreted as a whole, cannot practically be performed in the human mind and are not directed to mental processes. Applicant submits that the claims do not recite an exception and are therefore, directed to patent eligible subject matter." Examiner respectfully disagrees. The claims do recite abstract ideas, including mental processes and mathematical concepts. See amended 35 U.S.C. 101 rejection below for a full analysis. Applicant further argues that "the specification describes improvements provided by an example embodiment." Applicant first cites paragraph [0030] of Applicant’s published specification, which states "Instead, various embodiments in the present disclosure are directed towards a data-based pipe leak prediction system designed to predict pipe leaks. Such a system would allow any available data on pipe leaks to speak for itself, and this system would be able to determine on its own what factors are important or not important. In some embodiments, this pipe leak prediction system may also be able to determine the relative importance of the different factors and any relationships between those factors (e.g., correlation/causation). This determination can be used to accurately predict first time leaks in pipes and gain a better understanding of the underlying cause of pipe leaks." MPEP 2106.05(a) states "However, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG , 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology." The improvement in the cited paragraph is "determine the relative importance of the different factors and any relationships between those factors". This is a mental process and, in the representative claim, is also a mental process, and does not represent an improvement to technology, rather an improvement to an abstract idea. Applicant further sites paragraph [0031] of Applicant’s published specification, which states "In some embodiments, the pipe leak prediction system may be designed to utilize a large amount of data. In some embodiments, the pipe leak prediction system may consider as much data as possible-in terms of pipes ( e.g., consider data for a large number of pipes) and factors (e.g., consider data for a large variety of different factors that may affect the pipes leaking). Accordingly, the system may consider data for thousands, or millions, of pipes and there may be data on numerous attributes or factors for each pipe. The size of the data considered by the system increases greatly as more and more pipes and factors are tracked. In some cases, the total size of the data may exceed gigabytes or terabytes of data, and it would be impossible for a human being to utilize all of the data to make mental calculations or pen-and-paper computations. Thus, embodiments in the present disclosure enable data-driven analysis of a tremendous amount of data in order to accurately predict numerous cases of pipe leaks that would escape other methods." MPEP 2106.05(a) states “To show that the involvement of a computer assists in improving the technology, the claims must recite the details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient. Thus, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology.” As analyzed in the following 35 U.S.C. 101 rejection, the additional elements do not provide more than mere instructions to perform the method on a generic component or machinery and thus does not show an improvement to technology. Applicant further sites paragraph [0032] of Applicant’s published specification, which states "In some embodiments, the pipe leak prediction system may utilize various machine learning algorithms. In particular, the pipe leak prediction system may use supervised machine learning techniques, such that existing data on known pipe leaks is used to train a predictive model. Examples of such supervised machine learning techniques include-but are not limited to-analytical learning, artificial neural network, backpropagation, boosting (meta-algorithm), bayesian statistics, case-based reasoning, decision tree learning, inductive logic programming, gaussian process regression, group method of data handling, kernel estimators, learning automata, learning classifier systems, minimum message length (decision trees, decision graphs, etc.), multilinear subspace learning, naive bayes classifiers, maximum entropy classifiers, conditional random fields, nearest neighbor algorithms, probably approximately correct learning (PAC) learning, ripple down rules, support vector machines, minimum complexity machines (MCM), random forests, ensembles of classifiers, ordinal classification, data pre-processing, and statistical relational learning." MPEP 2106.05(a) states “An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. McRO, 837 F.3d at 1314-15, 120 USPQ2d at 1102-03; DDR Holdings, 773 F.3d at 1259, 113 USPQ2d at 1107. In this respect, the improvement consideration overlaps with other considerations, specifically the particular machine consideration (see MPEP § 2106.05(b)), and the mere instructions to apply an exception consideration (see MPEP § 2106.05(f)). Thus, evaluation of those other considerations may assist examiners in making a determination of whether a claim satisfies the improvement consideration.” The claims do not cite the training of machine learning algorithms in a manner that covers a particular solution to a problem or a particular way to achieve a desired outcome, rather claims the idea of a solution. As analyzed in the following 35 U.S.C. 101 rejection, the additional elements do not provide more than mere instructions to perform the method on a generic component or machinery and thus does not show an improvement to technology. Applicant further sites paragraph [0071] of Applicant’s published specification, which states "In some embodiments, the random forest model may be used to predict whether a pipe will leak based on a combination of 11 factors, such as the factors or pipe attributes shown in FIG. 3. Excluded from these 11 factors may be input variables associated with leak frequency ( e.g., the "leak _freq" variable of FIG. 3 ). This is intentional since existing expert-based models may be based on the assumption that past knowledge of whether a pipe has leaked is the most important factor in predicting whether a pipe will leak in the future. However, this assumption means that a predicted leak is heavily dependent on whether a pipe has previously leaked or not, making it difficult to predict if a pipe will leak if it has never leaked in the past (e.g., converting the problem into a pipe forecasting problem). In contrast, by excluding inputs on leak frequency the random forest model may be configured to predict whether a pipe will leak without prior knowledge that a pipe has in fact leaked.” MPEP 2106.05(a) states "However, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG , 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology." The claim limitations that this improvement appears to be recited in are "determining patterns associated with pipes without leaks using the one or more machine learning algorithms and the first data items in the training dataset, wherein the determining the patterns associated with the pipes without leaks comprises: determining an importance level of a combination of a plurality of different types of pipe attributes for pipes without leaks: and determining the patterns for the pipes without leaks having the combination of the plurality of different types of pipe attributes with the determined importance level; and determining patterns associated with pipes with leaks using the one or more machine learning algorithms and the first data items in the training dataset, wherein the determining the patterns associated with pipes with leaks comprises: determining an importance level of a combination of a plurality of different types of pipe attributes for pipes with leaks; and determining the patterns for the pipes with leaks having the combination of the plurality of different types of pipe attributes with the determined importance level." The claim limitations that reflect this improvement are all abstract ideas and do not show an improvement to technology, rather to the abstract idea of determining patterns in pipe leaks. Applicant further argues "Applicant submits that at least the following limitations are not conventional, well understood, or routine: "wherein generating the predictive model comprises: selecting one or more machine learning algorithms for generating the predictive model; and training the predictive model using one or more machine learning algorithms by: determining patterns associated with pipes without leaks using the one or more machine learning algorithms and the first data items in the training dataset, wherein the determining the patterns associated with the pipes without leaks comprises: determining an importance level of a combination of a plurality of different types of pipe attributes for pipes without leaks; and determining the patterns for the pipes without leaks having the combination of the plurality of different types of pipe attributes with the determined importance level; and determining patterns associated with pipes with leaks using the one or more machine learning algorithms and the first data items in the training dataset, wherein the determining the patterns associated with pipes with leaks comprises: determining an importance level of a combination of a plurality of different types of pipe attributes for pipes with leaks; and determining the patterns for the pipes with leaks having the combination of the plurality of different types of pipe attributes with the determined importance level; receiving, a validation dataset including second data items and second known leaks associated with second respective pipes of a second plurality of pipes, wherein the second data items include characteristics of the second respective pipes of the second plurality of pipes; validating the predictive model by at least comparing a set of leak predictions of the second respective pipes of the second plurality of pipes with the second known leaks of the pipes associated with the second respective second plurality of pipes to determine an accuracy of the leak predictions of the pipes of the second plurality of pipes; accessing a pipeline dataset including third data items associated with a third plurality of pipes; and applying the predictive model to the pipeline dataset to determine leak predictions of third respective pipes of the third plurality of pipes."" As fully analyzed in the 35 U.S.C. 101 rejection below, the limitations "wherein generating the predictive model comprises: selecting one or more machine learning algorithms for generating the predictive model; and training the predictive model using one or more machine learning algorithms by,” “receiving, a validation dataset including second data items and second known leaks associated with second respective pipes of a second plurality of pipes, wherein the second data items include characteristics of the second respective pipes of the second plurality of pipes,” and "accessing a pipeline dataset including third data items associated with a third plurality of pipes; and applying the predictive model to the pipeline dataset" amount to “mere instructions to apply an exception” and do not recite significantly more than the abstract idea. The remaining limitations are abstract ideas and therefore do not provide more than the judicial exception . Claim Rejections - 35 USC § 112 07-30-02 AIA The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. 07-34-01 Claims 1, 2, 5-9, 12-16, and 19-26 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claims 1, 8, and 15 recite the limitation "the combination of the plurality of different pipe types of pipe attributes with the determined importance level" in the fifth to last paragraph of the claim. It is unclear whether this limitation refers to “the combination of the plurality of different pipe types of pipe attributes with the determined importance level” determined in the step "determining an importance level of a combination of a plurality of different types of pipe attributes for pipes without leaks; and" or “the combination of the plurality of different pipe types of pipe attributes with the determined importance level” determined in the step "determining an importance level of a combination of a plurality of different types of pipe attributes for pipes with leaks." Therefore, the claims are rendered indefinite. 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, 2, 5-9, 12-16, and 19-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. MPEP § 2109(III) sets out steps for evaluating whether a claim is drawn to patent-eligible subject matter. The analysis of claims 1, 2, 5-9, 12-16, and 19-26, in accordance with these steps, follows. Step 1 Analysis: Step 1 is to determine whether the claim is directed to a statutory category (process, machine, manufacture, or composition of matter. Claims 1-2, 5-7, and 21-22. are directed to a process, claims 8-9, 12-14, and 23-24 are directed to a machine, and claims 15-16, 19-20, and 25-26 are directed to a manufacture. All claims are directed to statutory categories and analysis proceeds to the next step. Step 2A Prong One, Step 2A Prong Two, and Step 2B Analysis: Step 2A Prong One asks if the claim recites a judicial exception (abstract idea, law of nature, or natural phenomenon). If the claim recites a judicial exception, analysis proceeds to Step 2A Prong Two, which asks if the claim recites additional elements that integrate the abstract idea into a practical application. If the claim does not integrate the judicial exception, analysis proceeds to Step 2B, which asks if the claim amounts to significantly more than the judicial exception. If the claim does not amount to significantly more than the judicial exception, the claim is not eligible subject matter under 35 U.S.C. 101. None of the claims represent an improvement to technology. Regarding claim 1, the following claim elements are abstract ideas: determine a leak prediction of a pipe (Determining a prediction is a step that can be practically performed by the human mind. This is the mental process of evaluation.) selecting one or more machine learning algorithms for generating the predictive model; and (Selecting an algorithm can be practically performed in the human mind. This is a mental process of evaluation.) determining patterns associated with pipes without leaks using the one or more machine learning algorithms and the first data items in the training dataset, wherein the determining the patterns associated with the pipes without leaks comprises; (One could, using a machine learning algorithm, determine patterns practically in the human mind. This is a mental process. Additionally, the use of an algorithm is performing mathematical calculations, which is a mathematical concept.) determining an importance level of a combination of a plurality of different types of pipe attributes for pipes without leaks; and (Determining an importance level for a combination of attributes can be practically performed in the human mind. This is a mental process.) determining the patterns for the pipes without leaks having the combination of the plurality of different pipe types of pipe attributes with the determined importance level; and (Determining patterns with a combination of attributes can be practically performed in the human mind. This is a mental process.) determining patterns associated with pipes with leaks using the one or more machine learning algorithms and the first data items in the training dataset, wherein the determining the patterns associated with the pipes with leaks comprises: (One could, using a machine learning algorithm, determine patterns practically in the human mind. This is a mental process. Additionally, the use of an algorithm is performing mathematical calculations, which is a mathematical concept.) determining an importance level of a combination of a plurality of different types of pipe attributes for pipes with leaks; and (Determining an importance level for a combination of attributes can be practically performed in the human mind. This is a mental process.) determining the patterns for the pipes with leaks having the combination of the plurality of different pipe types of pipe attributes with the determined importance level; and (Determining patterns with a combination of attributes can be practically performed in the human mind. This is a mental process.) validating predictive model by at least comparing a set of leak predictions of the second respective pipes of the second plurality of pipes with the second known leaks associated with the second respective pipes of the second plurality of pipes to determine an accuracy of the leak predictions of the pipes of the second plurality of pipes; (Given the data, one could practically compare the data and determine an accuracy based on the data in the human mind with the aid of pen and paper. This is the mental process of evaluation.) The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception: A computer-implemented method for predicting pipe leaks, the method comprising: (This recites a generic computer, recited at a high level. This is mere instructions to apply an exception. See MPEP § 2106.05(f).) receiving a training dataset including first data items and first known leaks associated with first respective pipes of a first plurality of pipes, wherein the first data items include characteristics of the respective pipes of the first plurality of pipes, wherein the characteristics of the respective pipes include only lengths of the first plurality of pipes, soil resistivity at the first plurality of pipes, pressure rating of the first plurality of pipes, and elevation of the first plurality of pipes. (Receiving data is an existing process on computers. This is mere instruction to apply an exception. See MPEP § 2106.05(f)(2).) applying a supervised machine learning technique to generate a predictive model configured to … (Supervised machine learning is an existing process on a computer, recited at a high level. This is mere instructions to apply an exception. See MPEP § 2106.05(f).) … by training the predictive model based on the first data items associated with the first respective pipes of the first plurality of pipes; (Training a machine learning model is an existing process, recited at a high level. This is mere instructions to apply an exception. See MPEP § 2106.05(f).) wherein generating the predictive model comprises: (This recites generic machine learning. This amounts to mere instructions to apply an exception.) training the predictive model using one or more machine learning algorithms by: (This recites generic machine learning. This amounts to mere instructions to apply an exception.) receiving, a validation dataset including second data items and known leaks associated with second respective pipes of a second plurality of pipes, wherein the second data items include characteristics of the second respective pipes of the second plurality of pipes; (Receiving data is an existing process on computers. This is mere instruction to apply an exception. See MPEP § 2106.05(f)(2).) accessing a pipeline dataset including third data items associated with a third plurality of pipes; and (Receiving, which includes accessing data, is an existing process on computers. This is mere instruction to apply an exception. See MPEP § 2106.05(f)(2).) applying the predictive model to the pipeline dataset to determine leak predictions of third respective pipes of the third plurality of pipes; (Applying a machine learning model is an existing process on a computer, recited at a high level. This is mere instructions to apply an exception. See MPEP § 2106.05(f).) Regarding claim 2, the rejection of claim 1 is interpreted herein. The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception: wherein the predictive model includes a combination of a random forest model, a logistic regression, and a naïve Bayes model. (This merely limits the use of the abstract idea to a particular technological environment. This is a field of use limitation. See MPEP § 2106.05(h). Additionally, the combination of generic, well-known models is the well-known, generic concept of ensemble learning in machine learning, which amounts to mere instructions to apply an exception.) Regarding claim 5, the rejection of claim 1 is incorporated herein. Further, the following claim element is an abstract idea: ordering the pipes of the third plurality of pipes based on the determined leak predictions of the pipes of the third plurality of pipes. (Ordering pipes can practically be performed by the human mind with the aid of pen and paper, i.e. filling out a form.) Claim 5 does not recite any additional elements. Regarding claim 6, the rejection of claim 1 is interpreted herein. The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception: presenting the determined leak predictions of the third respective pipes of the third plurality of pipes using a display device. (Presenting data is insignificant application, an insignificant extra-solution activity. See MPEP § 2106.05(g)(3).) Regarding claim 7, the rejection of claim 1 is interpreted herein. The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception: wherein the training dataset and the validation dataset are generated by splitting a larger dataset, including fourth data items associated with a fourth plurality of pipes, into the training dataset and the validation dataset. (This is the insignificant extra-solution activity of sorting data. See MPEP § 2106.05(d)(II).) Regarding claim 8, the following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception: A computing system comprising: (This element recites a generic computer at a high level. This is mere instructions to apply an exception. See MPEP § 2106.05(f).) one or more data stores storing: (This element recites generic computer components at a high level. This is mere instructions to apply an exception. See MPEP § 2106.05(f).) a training dataset including first data items and known leaks associated with first respective pipes of a first plurality of pipes, wherein the first data items include characteristics of the first respective pipes; (This is a field of use limitation, as it merely limits the abstract idea to being performed in the field of pipe leaks. See MPEP § 2106.05(h).) a validation dataset including second data items and known leaks associated with second respective pipes of a second plurality of pipes, wherein the second data items include characteristics of the second respective pipes of the second plurality of pipes; (This is a field of use limitation, as it merely limits the abstract idea to being performed in the field of pipe leaks. See MPEP § 2106.05(h).) a computer processor; and (This element recites a generic computer component at a high level. This is mere instructions to apply an exception. See MPEP § 2106.05(f).) a computer readable storage medium storing program instructions configured for execution by the computer processor in order to cause the computer processor to: (This element recites a generic computer component performing a generic computer function at a high level. This is mere instructions to apply an exception. See MPEP § 2106.05(f).) The remainder of claim 8 recites substantially similar subject matter to claim 1 and is rejected with the same rationale, mutatis mutandis . Claims 9 and 12-14 recite substantially similar subject matter to claims 2 and 5-7 respectively and are rejected with the same rationale, mutatis mutandis . Regarding claim 15, the following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception: A non-transitory computer-readable medium storing a set of instructions configured for execution by a computer processor in order to cause the computer processor to: (This element recites a generic computer component performing a generic computer function at a high level. This is mere instructions to apply an exception. See MPEP § 2106.05(f).) The remainder of claim 15 recites substantially similar subject matter to claim 1 and is rejected with the same rationale, mutatis mutandis . Claims 16, 19, and 20 recite substantially similar subject matter to claims 9, 12, and 14 respectively and are rejected with the same rationale, mutatis mutandis . Regarding claim 21, the rejection of claim 1 is incorporated herein. Further, the following are abstract ideas: integrating the third data items and the fourth data items; (One could practically perform the integration of data in the human mind. This is a mental process.) converting the third data items and the fourth data items to a standard format; and (One could practically convert data into a standard format in the human mind. This is a mental process.) adding the third data items and the fourth data items to the pipeline dataset. (One could conceptually add data to a dataset practically in the human mind. This is a mental process.) The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception: receiving fourth data items from environmental sensors that are associated with the third plurality of pipes, wherein the fourth data items are received via an application programming interface; (Receiving data via an API is a known process in computing. This amounts to mere instructions to apply an exception.) Regarding claim 22, the rejection of claim 1 is incorporated herein. The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception: wherein the predictive model comprises a combination of at least one linear model and at least one nonlinear model. (This merely limits the use of the abstract idea to a particular technological environment. This is a field of use limitation. See MPEP § 2106.05(h). Additionally, the combination of generic, well-known models is the well-known, generic concept of ensemble learning in machine learning, which amounts to mere instructions to apply an exception.) Claims 23 and 24 recite substantially similar subject matter to claims 21 and 22 respectively and are rejected with the same rationale, mutatis mutandis . Claims 25 and 26 recite substantially similar subject matter to claims 21 and 22 respectively and are rejected with the same rationale, mutatis mutandis . Terminal Disclaimer 14-23 AIA The terminal disclaimer filed on 03/02/2026 disclaiming the terminal portion of any patent granted on this application which would extend beyond the expiration date of patent number 11373105 has been reviewed and is accepted. The terminal disclaimer has been recorded. Conclusion 07-40 AIA Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to JESSICA THUY PHAM whose telephone number is (571)272-2605. The examiner can normally be reached Monday - Thursday, 7:30 A.M. - 5:30 P.M.. 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) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Li Zhen can be reached at (571) 272-3768. 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. /J.T.P./Examiner, Art Unit 2121 /Li B. Zhen/Supervisory Patent Examiner, Art Unit 2121 Application/Control Number: 17/744,167 Page 2 Art Unit: 2121 Application/Control Number: 17/744,167 Page 3 Art Unit: 2121 Application/Control Number: 17/744,167 Page 4 Art Unit: 2121 Application/Control Number: 17/744,167 Page 5 Art Unit: 2121 Application/Control Number: 17/744,167 Page 6 Art Unit: 2121 Application/Control Number: 17/744,167 Page 7 Art Unit: 2121 Application/Control Number: 17/744,167 Page 8 Art Unit: 2121 Application/Control Number: 17/744,167 Page 9 Art Unit: 2121 Application/Control Number: 17/744,167 Page 10 Art Unit: 2121 Application/Control Number: 17/744,167 Page 11 Art Unit: 2121 Application/Control Number: 17/744,167 Page 12 Art Unit: 2121 Application/Control Number: 17/744,167 Page 13 Art Unit: 2121 Application/Control Number: 17/744,167 Page 14 Art Unit: 2121 Application/Control Number: 17/744,167 Page 15 Art Unit: 2121 Application/Control Number: 17/744,167 Page 17 Art Unit: 2121
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