CTNF 18/412,975 CTNF 95804 DETAILED ACTION This action is in response to the claims filed 01/15/2024 for Application number 18/412,975 which is a continuation of Application number #16/355,185. Claims 1-20 are currently pending. 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. Information Disclosure Statement The information disclosure statement (IDS) submitted on 01/15/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements is being considered by the examiner. Examiner notes that the references were not supplied because they were previously cited or submitted in prior Application number #16/355,185. 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Regarding claim 1 , Step 1 Analysis: Claim 1 is directed to a process, which falls within one of the four statutory categories. Step 2A Prong 1 Analysis: Claim 1 recites, in part, The limitations of: determining a corresponding prediction performance for the machine learning task using the corresponding machine learning model comprising the respective machine learning model architecture can be considered to be an evaluation in the human mind, selecting a respective one of the machine learning model architectures from among the plurality of machine learning model architectures that comprises a greatest corresponding prediction performance for the machine learning task can be considered to be an evaluation in the human mind These limitations as drafted, are processes that, under broadest reasonable interpretation, covers performance of the limitation in the mind or with the aid of pen and paper which falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Step 2A Prong 2 Analysis: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements – “A computer-implemented method when executed by data processing hardware causes the data processing hardware to perform operations...”. Thus, these elements in the claim are recited at a high level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP 2106.05(f). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim further recites: receiving a request to train a machine learning model to perform a machine learning task on a set of training examples obtaining a plurality of machine learning model architectures each capable of performing the machine learning task; These limitations are mere data gathering steps and thus are insignificant extra-solution activities. The claim further recites: for each respective machine learning model architecture of the plurality of machine learning model architectures: training a corresponding machine learning model comprising the respective machine learning model architecture and further training the machine learning model comprising the respective one of the machine learning model architectures using the set of training examples. These limitations are considered to be insignificant extra-solution activities. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim as a whole is directed to an abstract idea. Step 2B Analysis: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of utilizing data processing hardware to perform the steps of the claimed process amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Furthermore, the limitations of receiving a request to train a machine learning model to perform a machine learning task on a set of training examples obtaining a plurality of machine learning model architectures each capable of performing the machine learning task are well-understood, routine, and conventional, as evidenced by MPEP §2106.05(d)(II)(I), “receiving or transmitting data over a network”. Additionally, the limitations of: for each respective machine learning model architecture of the plurality of machine learning model architectures: training a corresponding machine learning model comprising the respective machine learning model architecture and further training the machine learning model comprising the respective one of the machine learning model architectures using the set of training examples are well-understood, routine, and conventional, as evidenced by Chauhan et al. (“A Review on Conventional Machine Learning vs Deep Learning”, pg. 347, §II. Conventional Machine Learning, ¶1) These limitations therefore remain insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. Even when considered in combination, these additional elements amount to mere instructions to apply the exception using generic computer components and insignificant extra-solution activity, which cannot provide an inventive concept. The claim is not patent eligible. Regarding claim 2 , the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the operations further comprise generating one or more new sets of training examples from the set of training examples. This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception. The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible. Regarding claim 3 , the rejection of claim 2 is further incorporated, and further, the claim recites: wherein generating the one or more new sets of training examples from the set of training examples comprises modifying the set of training examples. This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception. The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible. Regarding claim 4 , the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the plurality of machine learning model architectures comprises at least one of: a neural network model; a random forest model; a support vector machine model; or a linear regression model. This limitation amounts to generally linking the judicial exception to a field of use or technological environment. Please see MPEP 2106.05(h). The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible. Regarding claim 5 , the rejection of claim 4 is further incorporated, and further, the claim recites: wherein the neural network model comprises neural network layers. This limitation amounts to generally linking the judicial exception to a field of use or technological environment. Please see MPEP 2106.05(h). The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible. Regarding claim 6 , the rejection of claim 5 is further incorporated, and further, the claim recites: wherein the neural network layers comprise at least one of: fully-connected layers; convolutional layers; recurrent layers; or batch-normalization layers. This limitation amounts to generally linking the judicial exception to a field of use or technological environment. Please see MPEP 2106.05(h). The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible. Regarding claim 7 , the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the machine learning task comprises processing an image to predict a category associated with the processed image. This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception. The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible. Regarding claim 8 , the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the machine learning task comprises processing input text in a first language to predict output text in a second language. This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception. The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible. Regarding claim 9 , the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the machine learning task comprises processing a spoken utterance to predict output text that transcribes the spoken utterance. This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception. The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible. Regarding claim 10 , the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the operations further comprise determining one or more meta-data values characterizing the set of training examples. This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception. The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible. Regarding claims 11-20 , they are substantially similar to claims 1-10 respectively, and are rejected in the same manner, the same art, and reasoning applying. Claim Rejections - 35 USC § 102 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. 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. 07-15 AIA Claim s 1-6, 10-16 and 20 are rejected under 35 U.S.C. 102( a)(1 ) as being anticipated by Leite et al. ("Selecting Classification Algorithms with Active Testing" cited by Applicant in the IDS filed 01/15/2024, hereinafter "Leite") . Regarding claim 1 , Leite teaches A computer-implemented method when executed by data processing hardware causes the data processing hardware [pg. 124, §Step 5 implies use of “CPU”] to perform operations comprising: receiving a request to train a machine learning model to perform a machine learning task on a set of training examples (“Given a set of classification algorithms and some new classification dataset dnew, the aim is to identify the potentially best algorithm for this task with respect to some given performance measure M (e.g., accuracy, AUC or rank).” [pg. 120, 2. Relative Landmarks, ¶2]) ; obtaining a plurality of machine learning model architectures each capable of performing the machine learning task; (“ Given a set of classification algorithms and some new classification dataset dnew, the aim is to identify the potentially best algorithm for this task with respect to some given performance measure M (e.g., accuracy, AUC or rank).” [pg. 120, 2. Relative Landmarks, ¶2; a set of classification algorithms correspond to a plurality of machine learning model architectures.]) for each respective machine learning model architecture of the plurality of machine learning model architectures: training a corresponding machine learning model comprising the respective machine learning model architecture (“As in landmarking, we use the fact that each algorithm has its own learning bias, making certain assumptions about the data distribution. If the learning bias ‘matches’ the underlying data distribution of a particular dataset, it is likely to perform well (e.g., achieve high predictive accuracy). If it does not, it will likely under- or overfit the data, resulting in a lower performance.” [pg. 118, Motivation, ¶2; Learning bias implies training each classification algorithm/model]) ; and determining a corresponding prediction performance for the machine learning task using the corresponding machine learning model comprising the respective machine learning model architecture (“As such, we generate a global ranking of all algorithms using the performance results of all algorithms on all previous datasets, and choose the top-ranked algorithm as our initial candidate abest.” [pg. 122, Step 1, ¶1]) ; selecting a respective one of the machine learning model architectures from among the plurality of machine learning model architectures that comprises a greatest corresponding prediction performance for the machine learning task (“Having found ak, we can now run a CV test and compare it with abest. The winner (which may be either the current best algorithm or the competitor) is used as the new current best algorithm in the new round. The losing algorithm is eliminated from further consideration” [pg. 124, Step 4, ¶1]) ; and further training the machine learning model comprising the respective one of the machine learning model architectures using the set of training examples. (“In active learning, the goal is to select the most informative data point to be labeled next, so as to improve the predictive performance of a supervised learning algorithm with a minimum of (expensive) labelings. In active testing, the goal is to select the most informative CV test, so as to improve the prediction of the best algorithm on the new dataset with a minimum of (expensive) CV tests.” [pg. 124, §Discussion]) Regarding claim 2 , Leite teaches The computer-implemented method of claim 1, wherein the operations further comprise generating one or more new sets of training examples from the set of training examples. (“Moreover, we can use these same performance results to establish which (yet untested) algorithms are likely to perform well on the new dataset, i.e., those algorithms that outperformed or rivaled the currently best algorithm on similar datasets in the past.” [pg. 119, top para]) Regarding claim 3 , Leite teaches The computer-implemented method of claim 2, wherein generating the one or more new sets of training examples from the set of training examples comprises modifying the set of training examples. (“Moreover, we can use these same performance results to establish which (yet untested) algorithms are likely to perform well on the new dataset, i.e., those algorithms that outperformed or rivaled the currently best algorithm on similar datasets in the past. As such, we can intelligently select the most promising algorithms for the new dataset, run them, and then use their performance results to gain increasingly better estimates of the most similar datasets and the most promising algorithms.” [pg. 119, top para; new dataset being similar to previous datasets implies modifying the set of training examples]) Regarding claim 4 , Leite teaches The computer-implemented method of claim 1, wherein the plurality of machine learning model architectures comprises at least one of: a neural network model; a random forest model; a support vector machine model; or a linear regression model. (“It includes SMO (a support vector machine, SVM), MLP (Multilayer Perceptron), J48 (C4.5), and different types of ensembles, including RandomForest, Bagging and Boosting. Moreover, different SVM kernels were used with their own parameter ranges and all non-ensemble learners were used as base-learners for the ensemble learners mentioned above.” [pg. 125, top para; note: under BRI, the claim recites “at least one of” thus the examiner is only required to map to one of the recited elements.]) Regarding claim 5 , Leite teaches The computer-implemented method of claim 4, wherein the neural network model comprises neural network layers. (“It includes SMO (a support vector machine, SVM), MLP ( Multilayer Perceptron )” [pg. 125, top para]) Regarding claim 6 , Leite teaches The computer-implemented method of claim 5, wherein the neural network layers comprise at least one of: fully-connected layers; convolutional layers; recurrent layers; or batch-normalization layers. (“It includes SMO (a support vector machine, SVM), MLP ( Multilayer Perceptron )” [pg. 125, top para; Multilayer perceptrons comprise of fully connected layers.]) Regarding claim 10 , Leite teaches The computer-implemented method of claim 1, wherein the operations further comprise determining one or more meta-data values characterizing the set of training examples. (“The term metalearning stems from the fact that we try to learn the function that maps dataset characterizations (meta-data) to algorithm performance estimates (the target variable).” [pg. 118, top para]) Regarding claims 11-16 and 20 , they are substantially similar to claims 1-6 and 10 respectively, and are rejected in the same manner, the same art, and reasoning applying . 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. 07-23-aia AIA The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-21-aia AIA Claim s 7-9 and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Leite in view of Hughes et al. ("US 20200202171 A1", hereinafter "Hughes") . Regarding claim 7 , Leite teaches The computer-implemented method of claim 1, however fails to explicitly teach wherein the machine learning task comprises processing an image to predict a category associated with the processed image. Hughes teaches wherein the machine learning task comprises processing an image to predict a category associated with the processed image. (“In various implementations, the unannotated data to be annotated is unannotated text, images , video, or audio data. The model is a one-class classifier, binary classifier, a multi-class classifier, or language classifier . The model may perform regression; information extraction; semantic role labeling; text summarization; sentence, paragraph or document classification; table extraction; machine translation; entailment and contradiction; question answering; audio tagging; audio classification; speaker diarization; language model tuning; image tagging; object detection ; image segmentation; image similarity; pixel-by-pixel annotating; text recognition; or video tagging” [¶0202]) It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Leite’s teachings by applying the machine learning tasks within Hughes’ disclosure. One would have been motivated to make this modification in order to create powerful models to allow users with limited knowledge of data science to perform various tasks more quickly. [Hughes, ¶0140] Regarding claim 8 , Leite teaches The computer-implemented method of claim 1, however fails to explicitly teach wherein the machine learning task comprises processing input text in a first language to predict output text in a second language. Hughes teaches wherein the machine learning task comprises processing input text in a first language to predict output text in a second language. (“In various implementations, the unannotated data to be annotated is unannotated text, images, video, or audio data. The model is a one-class classifier, binary classifier, a multi-class classifier, or language classifier. The model may perform regression; information extraction; semantic role labeling; text summarization; sentence, paragraph or document classification; table extraction; machine translation ; entailment and contradiction; question answering; audio tagging; audio classification; speaker diarization; language model tuning; image tagging; object detection; image segmentation; image similarity; pixel-by-pixel annotating; text recognition; or video tagging” [¶0202; machine translation would include processing input text in a first language to predict text in a second language.]) Same motivation to combine the teachings of Leite/Hughes as claim 7. Regarding claim 9 , Leite teaches The computer-implemented method of claim 1, however fails to explicitly teach wherein the machine learning task comprises processing a spoken utterance to predict output text that transcribes the spoken utterance. Hughes teaches (“whereas a hidden markov model with spectrogram features may be selected as a baseline algorithm for automatic speech recognition.” [¶0156; speech recognition would include processing an utterance to predict output text that transcribes the spoken utterance.]) Same motivation to combine the teachings of Leite/Hughes as claim 7. Regarding claims 7-9 , they are substantially similar to claims 17-19 respectively, and are rejected in the same manner, the same art, and reasoning applying . Double Patenting 08-33 AIA The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg , 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman , 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi , 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum , 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel , 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington , 418 F.2d 528, 163 USPQ 644 (CCPA 1969). A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b). The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13. The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA/25, or PTO/AIA/26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer. 08-34 AIA Claim s 1-4, 7, 10-14, 17, and 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim s 1, 6, 7, 3, 2, 8 of U.S. Patent No. 11900222 . Although the claims at issue are not identical, they are not patentably distinct from each other because Instant Application US Pat No. 11900222 Claim 1 Claim 1 A computer-implemented method when executed by data processing hardware causes the data processing hardware to perform operations comprising A method performed by one or more data processing apparatus, the method comprising: receiving a request to train a machine learning model to perform a machine learning task on a set of training examples; receiving a request to train a machine learning model to perform a machine learning task on a set of training examples obtaining a plurality of machine learning model architectures each capable of performing the machine learning task; receiving a predetermined set of machine learning model architectures, each machine learning model architecture in the predetermined set of machine learning model architectures represented by a respective function parameterized by the complexity of the training inputs such that the respective parameterized function has one or more parameters corresponding to the complexity of the training inputs for each respective machine learning model architecture of the plurality of machine learning model architectures: training a corresponding machine learning model comprising the respective machine learning model architecture; the corresponding target output represents an output that should be generated by processing the training input using the machine learning model; and determining a corresponding prediction performance for the machine learning task using the corresponding machine learning model comprising the respective machine learning model architecture; determining, using a mapping function mapping each of the set of one or more meta-data values characterizing the attributes of the set of training examples to one or more of the machine learning model architectures of the predetermined set of machine learning model architectures based on the respective parameterized function of each of the one or more of the machine learning model architecture selecting a respective one of the machine learning model architectures from among the plurality of machine learning model architectures that comprises a greatest corresponding prediction performance for the machine learning task; selecting, using the particular machine learning model architecture, a final machine learning model architecture for performing the machine learning task; further training the machine learning model comprising the respective one of the machine learning model architectures using the set of training examples. training a machine learning model having the final machine learning model architecture on the set of training examples. All limitations of claim 1 in the instant application are anticipated by claim 1 of the ‘222 Patent. Regarding claim 2 of the instant application, it is anticipated by claim 6 (dependent claim containing all of the limitations of the respective parent claim) of the ‘222 Patent respectively. Regarding claim 3 of the instant application, it is anticipated by claim 7 (dependent claim containing all of the limitations of the respective parent claim) of the ‘222 Patent respectively. Regarding claim 4 of the instant application, it is anticipated by claim 3 (dependent claim containing all of the limitations of the respective parent claim) of the ‘222 Patent respectively. Regarding claim 7 of the instant application, it is anticipated by claim 2 (dependent claim containing all of the limitations of the respective parent claim) of the ‘222 Patent respectively. Regarding claim 10 of the instant application, it is anticipated by claim 8 (dependent claim containing all of the limitations of the respective parent claim) of the ‘222 Patent respectively. Regarding claims 11-14, 17, and 20 of the instant application, they are anticipated by claims 1, 6, 7, 3, 2, and 8 (dependent claim containing all of the limitations of the respective parent claim) of the ‘222 Patent respectively. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL H HOANG whose telephone number is (571)272-8491. The examiner can normally be reached Mon-Fri 8:30AM-4:30PM. 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, Kakali Chaki can be reached at (571) 272-3719. 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. /MICHAEL H HOANG/PRIMARY EXAMINER, Art Unit 2122 Application/Control Number: 18/412,975 Page 2 Art Unit: 2122 Application/Control Number: 18/412,975 Page 3 Art Unit: 2122 Application/Control Number: 18/412,975 Page 4 Art Unit: 2122 Application/Control Number: 18/412,975 Page 5 Art Unit: 2122 Application/Control Number: 18/412,975 Page 6 Art Unit: 2122 Application/Control Number: 18/412,975 Page 7 Art Unit: 2122 Application/Control Number: 18/412,975 Page 8 Art Unit: 2122 Application/Control Number: 18/412,975 Page 9 Art Unit: 2122 Application/Control Number: 18/412,975 Page 10 Art Unit: 2122 Application/Control Number: 18/412,975 Page 11 Art Unit: 2122 Application/Control Number: 18/412,975 Page 12 Art Unit: 2122 Application/Control Number: 18/412,975 Page 13 Art Unit: 2122 Application/Control Number: 18/412,975 Page 14 Art Unit: 2122 Application/Control Number: 18/412,975 Page 15 Art Unit: 2122 Application/Control Number: 18/412,975 Page 16 Art Unit: 2122 Application/Control Number: 18/412,975 Page 17 Art Unit: 2122 Application/Control Number: 18/412,975 Page 18 Art Unit: 2122 Application/Control Number: 18/412,975 Page 19 Art Unit: 2122 Application/Control Number: 18/412,975 Page 20 Art Unit: 2122 Application/Control Number: 18/412,975 Page 21 Art Unit: 2122 Application/Control Number: 18/412,975 Page 22 Art Unit: 2122 Application/Control Number: 18/412,975 Page 23 Art Unit: 2122
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