CTNF 18/785,309 CTNF 73893 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. 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. Claim 19 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claim 19 recites “ A computer readable medium embodying programmed instructions which, when executed by a processor, are operable for performing a method comprising: ” storing instructions that perform various functions. In the Specification of the present application, the “ A computer readable medium ” is not excluding transmission media. Further the claim recites a “ A computer readable medium ”, and the specification fails to provide a definition for that term. It also does not provide any indication that such storage medium is non-transitory. Thus, the broadest, reasonable interpretation of “a computer readable medium ” encompasses non-statutory subject matter (transmission media) that is unpatentable under 35 U.S.C. 101. Accordingly, Claim 19 fails to recite statutory subject matter under 35 U.S.C. 101. 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. 07-23-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) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over NAG et al . US Patent Application Publication No. 2021/0256436) in view of Guo et al. (US Patent Application Publication No. 2022/0044081). Regarding claim 1, Nag discloses a method comprising: obtaining a dataset comprising labeled text samples associated with a plurality of subject matter categories, wherein a respective label associated with each of the labeled text samples comprises a risk level corresponding to a respective labeled text sample [see para. 0103; the numeric vectors generated are mapped to numeric vectors associated with the defined datasets of known litigious vocabulary and known non-litigious vocabulary (e.g., from the previously generated document term matrix. In other words, the machine learning classifier processes the numeric vectors generated from the email correspondence by at least matching and comparing the numeric vectors to known numeric vectors that were generated from a set of defined litigious vocabulary and a set of defined non-litigious vocabulary; which corresponds to represent processing occur group risk and ensemble classification] ; applying a pre-trained machine learning model to the labeled text samples to vectorize the labeled text samples into text sample vectors [see para. 0103; the machine learning classifier processes the numeric vectors generated from the email correspondence by at least matching and comparing the numeric vectors to known numeric vectors that were generated from a set of defined litigious vocabulary and a set of defined non-litigious vocabulary; which corresponds to represent processing occur group risk and ensemble classification] ; displaying an electronic document [see para. 0107; the system is configured to generate an electronic notice when the final label indicates that the correspondence is litigious. The electronic notice includes data that identifies the correspondence, the associated construction project, and an alert message regarding the potential litigation risk. The electronic notice may also include additional data such as the email sender and receiver; which corresponds to the electronic notice is then transmitted to a remote device and/or displayed on a graphical user interface to allow a user to receive the notice and have access to the correspondence in near real time so that an action may be taken to address issues in the correspondence ] ; extracting a text segment from the electronic document [see para. 0100 and figure 4; Each incoming email correspondence further passes through a number of functions for programmatically cleaning the correspondence. For example, each email may be cleaned by removal of all non-Latin alphabet characters, html tags, punctuations, numbers and stop words. The email text may be tokenized by identifying and breaking down the correspondence text into words, punctuation marks, numeric digits, other objects in the text, etc] ; applying the pre-trained machine learning model to the extracted text segment to vectorize the extracted text segment into a text segment vector [see para. 0101; the tokenized text words from the email are vectorized and feature scaled, vectorizing the text includes converting each word into numbers, which are numeric vectors. Vectorization maps words or phrases from vocabulary to a corresponding vector of real numbers, which may be used to find word predications, word similarities and/or semantics] ; mapping the text segment vector to a subject matter category of the plurality of subject matter categories [see para 0102 and figure 1; after cleaning the correspondence text and feature scaling, the ensemble machine learning classifier is initiated to identify construction terms and classify litigation risk of the correspondence. The correspondence text is passed through each of the three machine learning classifiers. Each classifier makes an individual prediction of whether the email text is litigious or non-litigious based on the learned training data] ; determining the risk level associated with the extracted text segment based on a relation between the text segment vector and representative vectors associated with the mapped subject matter category [see para. 0103, 0104; the numeric vectors generated are mapped to numeric vectors associated with the defined datasets of known litigious vocabulary and known non-litigious vocabulary. In other words, the machine learning classifier processes the numeric vectors generated from the email correspondence by at least matching and comparing the numeric vectors to known numeric vectors that were generated from a set of defined litigious vocabulary and a set of defined non-litigious vocabulary, each of the three classifiers independently evaluates the correspondence and generates a prediction of a probability risk value for the correspondence being evaluated as previously described above. If the probability risk value exceeds the defined threshold value set for the associated classifier, then that classifier labels the correspondence as litigious(e.g., a value of “1”) or non-litigious (e.g., value of “0”). In general, the output label is viewed as a “vote” since the output is either a “1” (litigious YES) or “0” (litigious NO). The multiple “votes” generated by the multiple classifiers is then combined for a majority vote determination; which corresponds to probability scores threshold and majority voting ] ; annotating the extracted text segment with an annotation based on the risk level associated with the extracted text segment [see para. 0107; the electronic notice includes data that identifies the correspondence, the associated construction project, and an alert message regarding the potential litigation risk. The electronic notice may also include additional data such as the email sender and receiver. The electronic notice may highlight or visually distinguish the text from the email correspondence related to litigious vocabulary as identified by the machine learning classifiers ; which corresponds to highlighting and dashboard displaying ] ; and displaying the annotation for the extracted text segment in the displayed electronic document [see para. 0107; The electronic notice may highlight or visually distinguish the text from the email correspondence related to litigious vocabulary as identified by the machine learning classifiers ; which corresponds to highlighting and dashboard displaying ] ; however, NAG fails to explicitly teach determining a respective representative vector for respective groups of the text sample vectors associated with a same risk level in each subject matter category. Gou discloses determining a respective representative vector for respective groups of the text sample vectors associated with a same risk level in each subject matter category [see para. 0040-0041; the plurality of supporting sentences are input into a trained intention recognition model, in which the trained intention recognition model is configured to generate a sample sentence vector corresponding to the sample sentence and category vectors corresponding to the plurality of supporting sentences based on the sample sentence and the plurality of supporting sentences, calculate matching degrees between the sample sentence vector and the category vectors, and obtain a predicted intention category of the sample sentence based on the matching degrees; which corresponds to support plurality of categories with vector space relations for classification ]. It would have been obvious to one of an ordinary skill in the art, having the teachings of NAG and Guo before the affective filing date of the claimed invention to modify, the risk detection annotation system of NAG to include per-category representative vector generation, as taught by Gou. One would have been motivated to make such a combination in order to perform programming in navigation command required for each menu button and to enable better handling of diverse subject matter domains, risk scoring and improved accuracy in multi-class scenarios. Regarding claims 2 and 11, Gou discloses wherein the determining the risk level comprises: determining a nearest representative vector to the text segment vector from the representative vectors associated with the mapped subject matter category, wherein the risk level associated with the nearest representative vector comprises the risk level associated with the extracted text segment [see para. 0047, 0048 and figures 2-3; a sample sentence with an intention category to be predicted and a plurality of supporting sentences each labeled with intention category are obtained; the sample sentence and the supporting sentences are input into a pre-trained intention recognition model, in which the pre-trained intention recognition model generates a sample sentence vector corresponding to the sample sentence and category vectors corresponding to the supporting sentences according to the sample sentence and the supporting sentences, calculates matching degrees between the sample sentence vector and the category vectors, and obtains a predicted intention category of the sample sentence according to the matching degrees; which corresponds to matching degree to assign the category risk ] . Regarding claims 3 and 12, Gou discloses wherein the determining the risk level comprises: determining the risk level associated with the extracted text segment based on an interpolation between two or more of the representative vectors associated with the mapped subject matter category [see para. 0051, 0054; in ML (Machine Learning), feature learning or representation learning is a collection of technologies for learning a feature, i.e., converting raw data into a form that can be effectively developed by machine learning. In the embodiments of the present disclosure, natural language text can be serialized in combination with the feature extraction processing using the representation learning technology, and a vector representation of the text is obtained through a deep network model, i.e., the supporting sentence vectors corresponding to the supporting sentences; which corresponds to extend risk-level interpolation is a predictable in vector space risk scoring ] . Regarding claims 4 and 13, Gou discloses further comprising: performing few-shot learning with the labeled text samples for N number of the subject matter categories, and K number of the labeled text samples from each of the N number of the subject matter categories [see para. 0044, 0047; a sample sentence with an intention category to be predicted and a plurality of supporting sentences each labeled with intention category are obtained; the sample sentence and the supporting sentences are input into a pre-trained intention recognition model, in which the pre-trained intention recognition model generates a sample sentence vector corresponding to the sample sentence and category vectors corresponding to the supporting sentences according to the sample sentence and the supporting sentences, calculates matching degrees between the sample sentence vector and the category vectors, and obtains a predicted intention category of the sample sentence according to the matching degrees] ; wherein the K number of the labeled text samples is in a range of one to five [see para. 0044, 0047 , 0205; a sample sentence with an intention category to be predicted and a plurality of supporting sentences each labeled with intention category are obtained; the sample sentence and the supporting sentences are input into a pre-trained intention recognition model, in which the pre-trained intention recognition model generates a sample sentence vector corresponding to the sample sentence and category vectors corresponding to the supporting sentences according to the sample sentence and the supporting sentences, calculates matching degrees between the sample sentence vector and the category vectors, and obtains a predicted intention category of the sample sentence according to the matching degrees. Therefore, combines the few shot learning technology, which may reduce the degree of dependence of the intention recognition model on the scale of the supporting sentences, avoid the overfitting phenomenon caused by a few of supporting sentences each labeled with the intention category, ensure the accuracy of the recognition of a dialogue intention, improve the ability of quickly recognizing a dialogue intention, and improve the reliability and efficiency in the recognition of a dialogue intention; which corresponds to a small scale of labeled data ]. Regarding claims 5 and 14, Nag discloses wherein: the annotating comprises annotating the extracted text segment when the risk level is above a risk threshold [see para. 0130; threshold values for identifying a high risk level, a medium risk level, and a low risk level may be determined by applying the frequency formula to a data set of correspondences and using the library of keywords. A litigious score is calculated for each correspondence. Scores are grouped together for emails that are considered high, medium, and low risk levels. The threshold values for each level are then selected. For example, a litigious score that is greater than or equal to X is high risk. A score between X and Y is medium risk and a score less than Y is low risk. In the following example, X=1.61 and Y=0.95. Of course, these threshold values and associated ranges will be different when using different sample data sets of correspondence to determine the thresholds; which corresponds to perform annotation, labeling, alerting and conditionally when the risk level is above threshold ] . Regarding claims 6 and 15, Nag discloses wherein: the annotating comprises highlighting the extracted text segment with a distinguishing color [see para, 0092; the system highlights the topics and keywords that potentially point to the reason why the emails or correspondences have potential litigious content as identified by the machine learning models. The warnings and issues tracking also be combined with recommendations. Recommendations, the system categorize a project as a high, medium or low risk category depending on a number of litigious emails exchanged in a project visible to a particular organization/individual with the project within a specific interval of time]. Regarding claims 7 and 16, Nag discloses wherein: the annotating comprises displaying a suggested modification regarding the extracted text segment [see para. 0092-0093; The warnings and issues tracking also be combined with recommendations. Recommendations, the system categorize a project as a high, medium or low risk category depending on a number of litigious emails exchanged in a project visible to a particular organization/individual with the project within a specific interval of time]. Regarding claims 8 and 17, Nag discloses wherein: the extracting comprises applying another machine learning model to partition the electronic document into a collection of text segments [see para. 0102, 0103; cleaning the correspondence text and feature scaling, the ensemble machine learning classifier is initiated to identify construction terms and classify litigation risk of the correspondence. The correspondence text is passed through each of the three machine learning classifiers. Each classifier makes an individual prediction of whether the email text is litigious or non-litigious based on the learned training data. The numeric vectors generated at block are mapped to numeric vectors associated with the defined datasets of known litigious vocabulary and known non-litigious vocabulary, the machine learning classifier processes the numeric vectors generated from the email correspondence by at least matching and comparing the numeric vectors to known numeric vectors that were generated from a set of defined litigious vocabulary and a set of defined non-litigious vocabulary]. Regarding claims 9 and 18, Nag discloses wherein: the electronic document comprises a legal contract [see para. 0119; representing a high severity level of litigation risk (highly litigious keywords), for example to: ‘arbitration’, ‘arbitrate’, ‘lawsuit’, ‘court case’, ‘legal notice’, ‘legal proceeding’, ‘gross’, ‘critical’, ‘legal implications’, ‘serious’, ‘harm’, etc] . Regarding claim 10, Nag discloses an apparatus comprising: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to [see para. 0163; configuration of the computer, the processor a variety of various processors including dual microprocessor and other multi-processor architectures. A memory include volatile memory and/or non-volatile memory] : obtain a dataset comprising labeled text samples associated with a plurality of subject matter categories, wherein a respective label associated with each of the labeled text samples comprises a risk level corresponding to a respective labeled text sample [see para. 0103; the numeric vectors generated are mapped to numeric vectors associated with the defined datasets of known litigious vocabulary and known non-litigious vocabulary (e.g., from the previously generated document term matrix. In other words, the machine learning classifier processes the numeric vectors generated from the email correspondence by at least matching and comparing the numeric vectors to known numeric vectors that were generated from a set of defined litigious vocabulary and a set of defined non-litigious vocabulary; which corresponds to represent processing occur group risk and ensemble classification] ; apply a pre-trained machine learning model to the labeled text samples to vectorize the labeled text samples into text sample vectors [see para. 0103; the machine learning classifier processes the numeric vectors generated from the email correspondence by at least matching and comparing the numeric vectors to known numeric vectors that were generated from a set of defined litigious vocabulary and a set of defined non-litigious vocabulary; which corresponds to represent processing occur group risk and ensemble classification] ; display an electronic document [see para. 0107; the system is configured to generate an electronic notice when the final label indicates that the correspondence is litigious. The electronic notice includes data that identifies the correspondence, the associated construction project, and an alert message regarding the potential litigation risk. The electronic notice may also include additional data such as the email sender and receiver; which corresponds to the electronic notice is then transmitted to a remote device and/or displayed on a graphical user interface to allow a user to receive the notice and have access to the correspondence in near real time so that an action may be taken to address issues in the correspondence ] ; extract a text segment from the electronic document [see para. 0100 and figure 4; Each incoming email correspondence further passes through a number of functions for programmatically cleaning the correspondence. For example, each email may be cleaned by removal of all non-Latin alphabet characters, html tags, punctuations, numbers and stop words. The email text may be tokenized by identifying and breaking down the correspondence text into words, punctuation marks, numeric digits, other objects in the text, etc;]; apply the pre-trained machine learning model to the extracted text segment to vectorize the extracted text segment into a text segment vector [see para. 0101; the tokenized text words from the email are vectorized and feature scaled. In one embodiment, vectorizing the text includes converting each word into numbers, which are numeric vectors. Vectorization maps words or phrases from vocabulary to a corresponding vector of real numbers, which may be used to find word predications, word similarities and/or semantics] ; map the text segment vector to a subject matter category of the plurality of subject matter categories [see para 0102 and figure 1; after cleaning the correspondence text and feature scaling, the ensemble machine learning classifier is initiated to identify construction terms and classify litigation risk of the correspondence. The correspondence text is passed through each of the three machine learning classifiers. Each classifier makes an individual prediction of whether the email text is litigious or non-litigious based on the learned training data] ; determine the risk level associated with the extracted text segment based on a relation between the text segment vector and representative vectors associated with the mapped subject matter category [see para. 0103, 0104; the numeric vectors generated are mapped to numeric vectors associated with the defined datasets of known litigious vocabulary and known non-litigious vocabulary. In other words, the machine learning classifier processes the numeric vectors generated from the email correspondence by at least matching and comparing the numeric vectors to known numeric vectors that were generated from a set of defined litigious vocabulary and a set of defined non-litigious vocabulary, each of the three classifiers independently evaluates the correspondence and generates a prediction of a probability risk value for the correspondence being evaluated as previously described above. If the probability risk value exceeds the defined threshold value set for the associated classifier, then that classifier labels the correspondence as litigious (e.g., a value of “1”) or non-litigious (e.g., value of “0”). In general, the output label is viewed as a “vote” since the output is either a “1” (litigious YES) or “0” (litigious NO). The multiple “votes” generated by the multiple classifiers is then combined for a majority vote determination; which corresponds to probability scores threshold and majority voting ] ; annotate the extracted text segment with an annotation based on the risk level associated with the extracted text segment [see para. 0107; the electronic notice includes data that identifies the correspondence, the associated construction project, and an alert message regarding the potential litigation risk. The electronic notice may also include additional data such as the email sender and receiver. The electronic notice may highlight or visually distinguish the text from the email correspondence related to litigious vocabulary as identified by the machine learning classifiers ; which corresponds to highlighting and dashboard displaying ] ; and display the annotation for the extracted text segment in the displayed electronic document [see para. 0107; The electronic notice may highlight or visually distinguish the text from the email correspondence related to litigious vocabulary as identified by the machine learning classifiers ; which corresponds to highlighting and dashboard displaying ] ; however, NAG fails to explicitly teach determine a respective representative vector for respective groups of the text sample vectors associated with a same risk level in each subject matter category. Gou discloses determine a respective representative vector for respective groups of the text sample vectors associated with a same risk level in each subject matter category [see para. 0040-0041; the plurality of supporting sentences are input into a trained intention recognition model, in which the trained intention recognition model is configured to generate a sample sentence vector corresponding to the sample sentence and category vectors corresponding to the plurality of supporting sentences based on the sample sentence and the plurality of supporting sentences, calculate matching degrees between the sample sentence vector and the category vectors, and obtain a predicted intention category of the sample sentence based on the matching degrees; which corresponds to support plurality of categories with vector space relations for classification ]. It would have been obvious to one of an ordinary skill in the art, having the teachings of NAG and Guo before the affective filing date of the claimed invention to modify, the risk detection annotation system of NAG to include per-category representative vector generation, as taught by Gou. One would have been motivated to make such a combination in order to perform programming in navigation command required for each menu button and to enable better handling of diverse subject matter domains, risk scoring and improved accuracy in multi-class scenarios. Regarding claim 20, Gou discloses wherein the determining the risk level comprises: determining a nearest representative vector to the text segment vector from the representative vectors associated with the mapped subject matter category, wherein the risk level associated with the nearest representative vector comprises the risk level associated with the extracted text segment [see para. 0047, 0048 and figures 2-3; a sample sentence with an intention category to be predicted and a plurality of supporting sentences each labeled with intention category are obtained; the sample sentence and the supporting sentences are input into a pre-trained intention recognition model, in which the pre-trained intention recognition model generates a sample sentence vector corresponding to the sample sentence and category vectors corresponding to the supporting sentences according to the sample sentence and the supporting sentences, calculates matching degrees between the sample sentence vector and the category vectors, and obtains a predicted intention category of the sample sentence according to the matching degrees; which corresponds to matching degree to assign the category risk ] . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure (See PTO-892) . Gandhi (US 2019/0325029) discloses systems, apparatuses, and methods for the interpretation and routing of short text messages, such as those that might be received as part of a “chat” between a customer and a customer service representative. In some embodiments, this is achieved by constructing word “vectors” based on the text in a message, with a token corresponding to each word. The word vectors are then compared to a set of mutually orthogonal unit vectors representing the “classes” or “categories” of messages that are received and are intended to be acted upon by a person or automated process. The orthogonal class unit vectors are generated by training a machine learning model using a set of previously classified text or messages. A reference to specific paragraphs, columns, pages, or figures in a cited prior art reference is not limited to preferred embodiments or any specific examples. It is well settled that a prior art reference, in its entirety, must be considered for all that it expressly teaches and fairly suggests to one having ordinary skill in the art. Stated differently, a prior art disclosure reading on a limitation of Applicant's claim cannot be ignored on the ground that other embodiments disclosed were instead cited. Therefore, the Examiner's citation to a specific portion of a single prior art reference is not intended to exclusively dictate, but rather, to demonstrate an exemplary disclosure commensurate with the specific limitations being addressed. In re Heck, 699 F.2d 1331, 1332-33,216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006,1009, 158 USPQ 275, 277 (CCPA 1968)). In re: Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005); In re Fritch, 972 F.2d 1260, 1264, 23 USPQ2d 1780, 1782 (Fed. Cir. 1992); Merck & Co. v. Biocraft Labs., Inc., 874 F.2d 804, 807, 10 USPQ2d 1843, 1846 (Fed. Cir. 1989); In re Fracalossi, 681 F.2d 792,794 n.1,215 USPQ 569, 570 n.1 (CCPA 1982); In re Lamberti, 545 F.2d 747, 750, 192 USPQ 278, 280 (CCPA 1976); In re Bozek, 416 F.2d 1385, 1390, 163 USPQ 545, 549 (CCPA 1969). Any inquiry concerning this communication or earlier communications from the examiner should be directed to CAO H NGUYEN whose telephone number is (571)272-4053. The examiner can normally be reached on Mon-Fri 9am-5pm . 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, Kieu Vu can be reached on 571-272-4057. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /CAO H NGUYEN/ Primary Examiner, Art Unit 2171 Application/Control Number: 18/785,309 Page 2 Art Unit: 2171 Application/Control Number: 18/785,309 Page 3 Art Unit: 2171 Application/Control Number: 18/785,309 Page 4 Art Unit: 2171 Application/Control Number: 18/785,309 Page 5 Art Unit: 2171 Application/Control Number: 18/785,309 Page 6 Art Unit: 2171 Application/Control Number: 18/785,309 Page 7 Art Unit: 2171 Application/Control Number: 18/785,309 Page 8 Art Unit: 2171 Application/Control Number: 18/785,309 Page 9 Art Unit: 2171 Application/Control Number: 18/785,309 Page 10 Art Unit: 2171 Application/Control Number: 18/785,309 Page 11 Art Unit: 2171 Application/Control Number: 18/785,309 Page 12 Art Unit: 2171 Application/Control Number: 18/785,309 Page 13 Art Unit: 2171 Application/Control Number: 18/785,309 Page 14 Art Unit: 2171 Application/Control Number: 18/785,309 Page 15 Art Unit: 2171 Application/Control Number: 18/785,309 Page 16 Art Unit: 2171 Application/Control Number: 18/785,309 Page 17 Art Unit: 2171 Application/Control Number: 18/785,309 Page 18 Art Unit: 2171
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