CTNF 18/372,394 CTNF 89242 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 Objections 07-29-01 AIA Claim s 1, 6-9, 13-17, 19 objected to because of the following informalities: In Claim 1, second to last line, “resource advancement assessment dashboard presentation” was probably meant to be: the resource advancement assessment dashboard presentation. The same objection is made for Claims 9 and 17. In Claim 6, line 4, “a resource advancement requestor” was probably meant to be: the resource advancement requestor. The same objection is made for Claim 14. In Claims 7 and 8, line 1, the dependency claim numbers are missing (probably meant to be dependent on Claim 4, see reference to “the data analysis engine”). In Claims 13-16 the right “)” after the word “model” should probably be deleted. In Claim 19, line 1, “Claim 17” should be Claim 18 to negate potential 35 USC 112 issues pertaining to “the at least one on-demand resource advancement query” recites in lines 3-4 . Appropriate correction is required. 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 2-3, 10-20 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. In Claim 2, line 3, “the resource advancement assessment dashboard” lacks antecedent basis. The same rejection is made for Claims 10 and 18. Dependent claims are subsequently rejected. In Claim 17, last line, “the resource advancement request” lacks antecedent basis (the Examiner suggest reciting this in the preamble of this independent claim, see the other two independent claims). Dependent claims are subsequently rejected. 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. Step 1 : All claims are directed towards either a method, a system or a product and thus satisfies Step 1 as falling into one of the statutory categories. Step 2A, Prong One : Independent Claim 1 recites (the same analysis applies to similar independent Claims 9 and 16): identify, based on the execution of the predetermined resource advancement queries, one or more data omissions or anomalies that prevent resource advancement processing , this limitation, under its broadest reasonable interpretation, covers concepts that can be performed in the human mind and therefore would fall under the “Mental Processes” groupings of abstract ideas. That is a person (such as a loan processing officer, equivalent to the “autonomous virtual assist engine” recited) can determine omission or anomalies (for example on a loan request application) that can prevent processing of the resource advancement (that is the loan) using observation and evaluation. in response to identifying the one or more data omissions or anomalies, generate, for each data omission or anomaly, one or more recommended actions for addressing the data omission or anomaly , this limitation, under its broadest reasonable interpretation, also covers concepts that can be performed in the human mind and therefore would fall under the “Mental Processes” groupings of abstract ideas. That is a person (such as the loan processing officer) can recommend what to do (for example to the borrower) to correct the omission or anomaly. compile a resource advancement assessment dashboard presentation that (i) is specific to the resource advancement requestor, (ii) summarizes the first resource advancement requestor data, (iii) identifies the one or more data omissions or anomalies and (iv) includes the one or more recommended actions for addressing the data omission or anomaly , this limitation, under its broadest reasonable interpretation, also covers concepts that can be performed in the human mind and therefore would fall under the “Mental Processes” groupings of abstract ideas. That is a person (such as the loan processing officer) can compile a resource advancement assessment (such as a loan approval/disapproval report) for the specific requestor (borrower) that summarizes the borrower’s information, identifies one or more data omissions or anomalies that was not provided (for the requested loan), and recommend what to do to address these omissions or anomalies. Step 2A, Prong Two : Claim 1 recites the additional elements of (the same analysis applies to similar independent Claims 9 and 16): execute predetermined resource advancement queries for a resource advancement requestor by accessing a database storing resource advancement requestor data for a plurality of resource advancement requestors and receiving first resource advancement requestor data responsive to the predetermined resource advancement queries , this limitation is considered as adding insignificant extra-solution activity (receiving data) to the judicial exception - see MPEP 2106.05(g). transform the first resource advancement requestor data into a format compatible for dashboard presentation , this limitation is also considered as adding insignificant extra-solution activity (manipulating/transforming data) to the judicial exception - see MPEP 2106.05(g). and present the resource advancement assessment dashboard presentation to a user that interacts with resource advancement assessment dashboard presentation for purposes of decisioning the resource advancement request . This limitation is also considered as adding insignificant extra-solution activity (providing data) to the judicial exception - see MPEP 2106.05(g). The further additional elements of “computing processor devices” or “computing devices” as recited in these independent claims 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. 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 claims are therefore directed to an abstract idea. Step 2B : The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are considered as appending well-understood, routine, conventional activities (receiving, providing and manipulating data) previously known to the industry, specified at a high level of generality, to the judicial exception - see MPEP 2106.05(d). The further additional elements of “computing processor devices” or “computing devices” as recited in these independent claims amounts 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. The claims are therefore not patent eligible. The limitations of dependent Claims 2, 10, 18 are similar to their independent claims and the above analysis applies in that they are considered as appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception - see MPEP 2106.05(d). Dependent Claims 3, 5-8, 11, 13-16, 19 are considered as using a machine learning model as a tool to perform the abstract idea - see MPEP 2106.05(f). Dependent Claims 4, 12, 20 are also considered as, under their broadest reasonable interpretation, to covers concepts that can be performed in the human mind and therefore would fall under the “Mental Processes” groupings of abstract ideas. That is a person (such as the loan processing officer) can perform data assessment and verification using observation and evaluation. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-20-aia AIA The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. 07-21-aia AIA Claim s 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Freed , US 2024/0378666 A1, in view of Rydzewski , US 2021/0271362 A1 . Regarding Claim 1 , Freed teaches: A system for processing a resource advancement request (paragraph 28: “a borrower can provide the platform with the requisite documents and information necessary to originate a loan”. The originating of the loan, or resource advancement, representing the necessary steps/processing), the system comprising: a first computing platform including a first memory and one or more first computing processor devices in communication with the first memory (paragraph 7: “a system for loan origination data validation and predictive analysis is disclosed, comprising: a computing device comprising a memory and a processor”); and an autonomous virtual assist engine stored in the first memory, executable by at least one of the one or more first computing processor devices, and configured to (paragraph 6: “The platform can function as a system of record and central, secure repository for a borrower's documentation and information required to apply for a loan. In some embodiments, the platform utilizes a trained generative AI model to assist platform users and to provide predictive analysis responsive to user submitted queries”. The generative AI model representative of the autonomous virtual assist engine): execute predetermined resource advancement queries for a resource advancement requestor by accessing a database storing resource advancement requestor data for a plurality of resource advancement requestors and receiving first resource advancement requestor data responsive to the predetermined resource advancement queries (paragraph 28: “In a particular use case, either a lender or a borrower can provide the platform with the requisite documents and information necessary to originate a loan, wherein the platform provides, among other functions, automated data validation, compliance, and normalization of the provided information before the data is securely stored in a one or more databases and associated with the borrower. At this point, the platform has a repository of validated and compliant data which can be provided (with borrower consent) to one or more loan origination systems (LOS) associated with a mortgage company such as a bank or other type of lender using one or more bespoke APIs provided by the platform”. And, paragraph 52: “database(s) 200 may comprise a plurality of information including, but not limited to, a plurality of borrower profiles”), identify, based on the execution of the predetermined resource advancement queries, one or more data omissions or anomalies that prevent resource advancement processing (paragraph 41: “data acquisition engine 300 can perform validation by scanning the document to identify certain data fields, determining if the data fields contain valid data, if the data is not valid generating an alert signal which can be communicated back to the borrower”. The validation of the data, including the data fields, will look for missing data and anomalies), in response to identifying the one or more data omissions or anomalies, generate, for each data omission or anomaly, one or more recommended actions for addressing the data omission or anomaly (paragraph 39: “During the validation process data may be flagged that cannot be validated or may not be compliant with existing rules, and the AI may ask the user for more information or give suggestions to the user on how to address the flagged data in order for the data to be validated and/or verified compliant”), transform the first resource advancement requestor data into a format compatible for dashboard presentation (paragraph 42: “data may be transformed into a format that is easily transmittable and ready to efficiently integrate with enterprise systems and software”. The enterprise systems including the interface/dashboard), (ii) summarizes the first resource advancement requestor data (paragraph 41: “platform may communicate with lenders via the website/web app UI and/or standard messaging with a checklist, report, and summary statement”), (iii) identifies the one or more data omissions or anomalies (paragraph 41: “data acquisition engine 300 can perform validation by scanning the document to identify certain data fields, determining if the data fields contain valid data, if the data is not valid generating an alert signal which can be communicated back to the borrower”. The validation of the data, including the data fields, will look for missing data and anomalies) and (iv) includes the one or more recommended actions for addressing the data omission or anomaly (paragraph 39: “During the validation process data may be flagged that cannot be validated or may not be compliant with existing rules, and the AI may ask the user for more information or give suggestions to the user on how to address the flagged data in order for the data to be validated and/or verified compliant”), and present the resource advancement assessment dashboard presentation to a user that interacts with resource advancement assessment dashboard presentation for purposes of decisioning the resource advancement request (paragraph 39: “The platform can obtain data from users and/or directly from third-party services, and in some embodiments uses a generational artificial intelligence (AI) system configured to drive digital questions and technical interaction with the platform user based on the obtained information and provide insight and analysis, according to some embodiments. The AI may ask questions based on the obtained data, wherein the questions require documents to be gathered and uploaded or downloaded from third-party services”. And, paragraph 50: “The platform 100 may comprise a user interface (UI) 130 which can provide a front-end user experience and interface for providing information and interacting with platform services. The UI 130 can provide a means for receiving user input (e.g., identification data, financial data, etc.) and displaying system output (e.g., system request for information, etc.). The output may be responsive to a user query or action, or based on an action or internal process of one or more platform services and/or components”). Freed may not have explicitly taught a dashboard for a specific user/requestor, however, Rydzewski shows: compile a resource advancement assessment dashboard presentation that (i) is specific to the resource advancement requestor (paragraph 4: “receiving a user request to access one or more resources. One or more remote data sources can be queried for information associated with a user of the user request as a first background task. The information can be obtained from the one or more remote data sources. A user interface schema can be caused to generate based at least on the obtained information”. The user interface schema representative of a specific dashboard presentation for the user. And paragraph 22: “by generating a user interface schema based on the obtained information, each user can for example have different but relevant contextual information from different remote data sources presented to him or her in one user interface at runtime”). (Emphasis added). It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to use the teachings of Rydzewski with that of Freed for compiling a dashboard presentation that is specific to the requestor. The ordinary artisan would have been motivated to modify Freed in the manner set forth above for the purposes of giving each user different but relevant contextual information from different remote data sources presented to him or her in one user interface at runtime [Rydzewski: paragraph 22]. Regarding Claim 2 , Freed further teaches: The system of Claim 1, wherein the autonomous virtual assist engine is further configured to: receive, via the resource advancement assessment dashboard, at least one on-demand resource advancement query from the user (paragraph 69: “platform 100 may be directed to automated, personalized generated mortgage loan estimates based at least on user input data and leveraging a generative AI model. In such a use case, a borrower can upload the documents and information he or she has available, and query the generative AI model for a mortgage estimate from one or more potential lenders”), execute the at least one on-demand resource advancement query by accessing the database storing resource advancement request data and receiving second resource advancement request data responsive to the on-demand resource advancement queries (paragraph 28: “In a particular use case, either a lender or a borrower can provide the platform with the requisite documents and information necessary to originate a loan, wherein the platform provides, among other functions, automated data validation, compliance, and normalization of the provided information before the data is securely stored in a one or more databases and associated with the borrower. At this point, the platform has a repository of validated and compliant data which can be provided (with borrower consent) to one or more loan origination systems (LOS) associated with a mortgage company such as a bank or other type of lender using one or more bespoke APIs provided by the platform”), transform the second resource advancement requestor data into the format compatible for dashboard presentation (paragraph 42: “data may be transformed into a format that is easily transmittable and ready to efficiently integrate with enterprise systems and software”. The enterprise systems including the interface/dashboard), update the resource advancement assessment dashboard presentation to include the second resource advancement requestor data (paragraph 65: “correct the data via the website/web app UI, and then updating the LOS or intended recipient platform as necessary”), and present the updated resource advancement assessment dashboard presentation to the user (paragraph 39: “The platform can obtain data from users and/or directly from third-party services, and in some embodiments uses a generational artificial intelligence (AI) system configured to drive digital questions and technical interaction with the platform user based on the obtained information and provide insight and analysis, according to some embodiments. The AI may ask questions based on the obtained data, wherein the questions require documents to be gathered and uploaded or downloaded from third-party services”. And, paragraph 50: “The platform 100 may comprise a user interface (UI) 130 which can provide a front-end user experience and interface for providing information and interacting with platform services. The UI 130 can provide a means for receiving user input (e.g., identification data, financial data, etc.) and displaying system output (e.g., system request for information, etc.). The output may be responsive to a user query or action, or based on an action or internal process of one or more platform services and/or components”). Regarding Claim 3 , Freed further teaches: The system of Claim 2, wherein the autonomous virtual assist engine includes a predetermined query-determining machine learning (ML) model configured to receive the at least one on-demand resource advancement query and determine whether the predetermined resource advancement queries require modification based on the at least one on-demand resource advancement query (paragraph 39: “During the validation process data may be flagged that cannot be validated or may not be compliant with existing rules, and the AI may ask the user for more information or give suggestions to the user on how to address the flagged data in order for the data to be validated and/or verified compliant”. The AI representing the predetermined query-determining machine learning (ML) model). Regarding Claim 4 , Freed further teaches: The system of Claim 1, further comprising: a second computing platform including a second memory and one or more second computing processor devices in communication with the second memory; and a data analysis engine stored in the second memory, executable by at least one of the one or more second computing processor devices, and configured to perform data verifications and data assessments on the resource advancement request data prior to storing the resource advancement request data in the database (paragraph 28: “either a lender or a borrower can provide the platform with the requisite documents and information necessary to originate a loan, wherein the platform provides, among other functions, automated data validation, compliance, and normalization of the provided information before the data is securely stored in a one or more databases and associated with the borrower”). Regarding Claim 5 , Freed further teaches: The system of Claim 4, wherein the data analysis engine includes an incoming resource verification machine learning (ML) model configured to receive data related to incoming resources associated with a resource advancement requestor and based on the data, verify at least source and volume of incoming resources (paragraph 39: “The platform can obtain data from users and/or directly from third-party services, and in some embodiments uses a generational artificial intelligence (AI) system configured to drive digital questions and technical interaction with the platform user based on the obtained information and provide insight and analysis”; And, paragraph 41: “Once the borrower has accessed platform 100 via UI 130 they may upload any of the required documents (e.g., pay stubs, W-2s, etc.) and information (e.g., contact information, credit report, etc.). Data uploaded to platform 100 by the borrower may be sent to a data acquisition engine 300 which can be configured to validate the borrower's data, verify the uploaded data is in compliance with various regulations and rules, and transform the data as necessary. In some implementations, data acquisition engine 300 may leverage one or more machine learning algorithms and/or models to facilitate one or more data validation processes”. The pay stubs, W-2, etc., providing source and volume of incoming resources). Regarding Claim 6 , Freed further teaches: The system of Claim 4, wherein the data analysis engine includes a real resource value assessment machine learning (ML) model configured to receive data related to one or more real resources held by a resource advancement requestor and based on the data, assess a value of real resources held by a resource advancement requestor (paragraph 39: “according to one embodiment, the generative AI may be used to generate potential mortgage offers based on input data and the underlying model. In such an embodiment, a borrower or lender may be able to upload to platform 100 whatever documentation and information they may currently possess and which is associated with information necessary to apply for a loan and the generative AI can generate an individually tailored mortgage loan estimate (e.g., including loan terms such as length, interest rate, amortization schedule, and/or the like) for the borrower using on the data provided”. And, paragraph 48: “Data acquisition engine 300 may receive borrower information and documents associated with applying for a loan. Some of the information and documents that may be obtained by platform 100 can include, but is not limited to, personal information (e.g., name, social security number, date of birth, address, phone number, email address, health information, etc.), employment and income information (e.g., current and previous employers, length of employment, and income documentation such as pay stubs, W-2s, and tax returns), assets and liabilities (e.g., bank statements, investment account statements, and information about any outstanding debts, etc.), credit history (e.g., credit score, credit reports, and information about any bankruptcies, foreclosures, and other credit issues, etc.), and property information (e.g., the address and purchase price of the home of interest, as well as information about any other real estate the borrower owns)”). Regarding Claim 7 , Freed further teaches: The system of Claim wherein the data analysis engine includes a resource advancement worthiness verification machine learning (ML) model configured to receive data related to resource advancement worthiness of a resource advancement requestor and based on the data, verify the resource advancement worthiness of the resource advancement requestor (paragraph 39: “according to one embodiment, the generative AI may be used to generate potential mortgage offers based on input data and the underlying model. In such an embodiment, a borrower or lender may be able to upload to platform 100 whatever documentation and information they may currently possess and which is associated with information necessary to apply for a loan and the generative AI can generate an individually tailored mortgage loan estimate (e.g., including loan terms such as length, interest rate, amortization schedule, and/or the like) for the borrower using on the data provided”. And, paragraph 48: “Data acquisition engine 300 may receive borrower information and documents associated with applying for a loan. Some of the information and documents that may be obtained by platform 100 can include, but is not limited to, personal information (e.g., name, social security number, date of birth, address, phone number, email address, health information, etc.), employment and income information (e.g., current and previous employers, length of employment, and income documentation such as pay stubs, W-2s, and tax returns), assets and liabilities (e.g., bank statements, investment account statements, and information about any outstanding debts, etc.), credit history (e.g., credit score, credit reports, and information about any bankruptcies, foreclosures, and other credit issues, etc.), And, “Data acquisition engine 300 may utilize one or more machine learning algorithms to automatically validate obtained data”. The credit score and report will indicate the resource advancement worthiness of the resource advancement requestor, that is the credit worthiness of the borrower). Regarding Claim 8 , Freed further teaches: The system of Claim wherein the data analysis engine includes a resource advancement target assessment machine learning (ML) model configured to receive data related to a resource advancement target that is a basis for the resource advancement and, based on the data, assess a value of the resource advancement target (paragraph 48: “Data acquisition engine 300 may receive borrower information and documents associated with applying for a loan. Some of the information and documents that may be obtained by platform 100 can include, but is not limited to, personal information (e.g., name, social security number, date of birth, address, phone number, email address, health information, etc.), employment and income information (e.g., current and previous employers, length of employment, and income documentation such as pay stubs, W-2s, and tax returns), assets and liabilities (e.g., bank statements, investment account statements, and information about any outstanding debts, etc.), credit history (e.g., credit score, credit reports, and information about any bankruptcies, foreclosures, and other credit issues, etc.), and property information (e.g., the address and purchase price of the home of interest, as well as information about any other real estate the borrower owns). Data acquisition engine 300 may utilize one or more machine learning algorithms to automatically validate obtained data”. The bank statements, investment account statements, and information about any outstanding debts, as well as information about any other real estate the borrower owns will provide a value of the resource advancement target, that is the net worth of the borrower). Claims 9-16 are similar to Claims 1-8 and are rejected under the same rationale as stated above for those claims. (The last limitation of Claim 11 on the “modification” also being taught by Freed, see paragraph 41: “the platform identifies invalid data and informs the borrower/lender via the UI where the borrower/lender is allowed to correct the data, and then the platform publishes the updated data onto the appropriate LOS or intended recipient platform”). Claims 17-20 are similar to Claims 1-4 and are rejected under the same rationale as stated above for those claims. (The last limitation of Claim 19 on the “modification” also being taught by Freed, see paragraph 41: “the platform identifies invalid data and informs the borrower/lender via the UI where the borrower/lender is allowed to correct the data, and then the platform publishes the updated data onto the appropriate LOS or intended recipient platform”). Examiner's Note : The Examiner cites particular pages, sections, columns, line numbers, and/or paragraphs in the references as applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in its entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner and the additional related prior arts made of record that are considered pertinent to applicant's disclosure to further show the general state of the art. The Examiner's interpretations in parenthesis are provided with the cited references to assist the applicants to better understand how the examiner interprets the prior art to read on the claims. Such comments are entirely consistent with the intent and spirit of compact prosecution . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892 for the relevant prior art where for example Dhar, US 2002/0040339 A1, teaches accepting on-line loan applications, processing the applications automatically, and generating loan offers based on the loan application and credit worthiness . Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVE MISIR whose telephone number is (571)272-5243. The examiner can normally be reached M-R 8-5 pm, F some hours. 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, Abdullah Al Kawsar can be reached at 5712703169. 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. /DAVE MISIR/Primary Examiner, Art Unit 2127 Application/Control Number: 18/372,394 Page 2 Art Unit: 2127 Application/Control Number: 18/372,394 Page 3 Art Unit: 2127 Application/Control Number: 18/372,394 Page 4 Art Unit: 2127 Application/Control Number: 18/372,394 Page 5 Art Unit: 2127 Application/Control Number: 18/372,394 Page 6 Art Unit: 2127 Application/Control Number: 18/372,394 Page 7 Art Unit: 2127 Application/Control Number: 18/372,394 Page 8 Art Unit: 2127 Application/Control Number: 18/372,394 Page 9 Art Unit: 2127 Application/Control Number: 18/372,394 Page 10 Art Unit: 2127 Application/Control Number: 18/372,394 Page 11 Art Unit: 2127 Application/Control Number: 18/372,394 Page 12 Art Unit: 2127 Application/Control Number: 18/372,394 Page 13 Art Unit: 2127 Application/Control Number: 18/372,394 Page 14 Art Unit: 2127 Application/Control Number: 18/372,394 Page 15 Art Unit: 2127 Application/Control Number: 18/372,394 Page 16 Art Unit: 2127 Application/Control Number: 18/372,394 Page 17 Art Unit: 2127 Application/Control Number: 18/372,394 Page 18 Art Unit: 2127 Application/Control Number: 18/372,394 Page 19 Art Unit: 2127 Application/Control Number: 18/372,394 Page 20 Art Unit: 2127 Application/Control Number: 18/372,394 Page 21 Art Unit: 2127 Application/Control Number: 18/372,394 Page 22 Art Unit: 2127
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