CTNF 18/158,535 CTNF 80697 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. Claims 1-20 are pending in the application. Examiner’s Note: The examiner has cited particular passages including column and line numbers, paragraphs as designated numerically and/or figures as designated numerically in the references as applied to the claims below 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 claims, other passages, paragraphs and figures of any and all cited prior art references may apply as well. It is respectfully requested from the applicant, in preparing an eventual response, to fully consider the context of the passages, paragraphs and figures as taught by the prior art and/or cited by the examiner while including in such consideration the cited prior art references in their entirety as potentially teaching all or part of the claimed invention. MPEP 2141.02 VI: “PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, INCLUDING DISCLOSURES THAT TEACH AWAY FROM THE CLAIMS." Information Disclosure Statement 06-52 AIA The information disclosure statement (IDS) submitted on 01/24/2023 was filed after the mailing date of the first office acti on. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. 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. Claim 8 recites a system comprising: one or more computer readable storage media storing program instructions and one or more processors which, in response to executing the program instructions, are configured to: (a) obtain industrial activity data comprising steps of industrial activities performed by robotic machines on an industrial floor; (b) obtain robotic machine data comprising configuration information of the robotic machines associated with performing the steps of the industrial activities; (c) input the industrial activity data and the robotic machine data to a machine learning model to analyze the steps of the industrial activities in view of the configuration information of the robotic machines to generate a production plan that aggregates performance of selected steps by the robotic machines; and (d) configure the robotic machines on the industrial floor according to the production plan generated by the machine learning model. Step 1 : The claim recites a system comprising a combination of concrete devices (storage, processors), and therefore is a machine, which is a statutory category of invention. Step 2A Prong one : The claim recites a series of steps from a to d: Obtain data, input data into a machine learning model, analyzing data to generate a production plan, and configure the machine according to the plan. The limitation of obtain data, input data into a machine learning model, analyzing data to generate a production plan, and configure the machine according to the plan, as drafted, is a process that under its broadest reasonable interpretation, covers performance of limitation in the mind but for the recitation of generic computer components (storage and processors). That is nothing in the claim element precludes the step from practically being performed in the mind with the help of pen and paper. For example, the series of steps of obtaining industrial activity data, obtaining robotic configuration data, analyzing the data to generate a production plan, and configuring machines according to the plan can be performed by a human supervisor mentally evaluating tasks and machine capabilities and creating a schedule. If the claim limitation, under its broadest interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Step 2A, Prong Two : Besides the abstract ideas, the claim recites the additional element of one or more processors, one or more computer readable storage media storing program, and configure the robotic machines on the industrial floor according to the production plan generated by the machine learning model. The processors and storage media are cited at a high level of generality and perform their ordinary functions of executing instructions and storing data that they represent no more than mere instructions to apply the judicial exceptions of a computer. As such, they are nothing more than an attempt to generally link the use of judicial exceptions to the technological environment of a computer (see MPEP 2106.05(f)). The step of “configure the robotic machines on the industrial floor according to the production plan” merely applies the generated plan to machines at a high level of abstraction without reciting a specific machine configuration techniques, a specific control algorithm, an improvement to robotic operation, or a specific technical solution to a technological problem. The claim does not recite a particular manner of improving robotic control or machine functionality, but instead merely uses generic computing components to perform data analysis and then instruct machines according to the result. Even when viewed in combination, these elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception. Step 2B : The additional elements, individually and in combination, do not amount to significantly more than the abstract idea itself. The claim merely recites generic computer component (storage and processor), generic machine learning model, and generic robotic machines performing their ordinary functions. There is no indication that the claimed machine learning model is unconventional, nor that the configuration of robotic machines is performed in a non-conventional manner. Even when considered in combination, these additional elements represent mere instruction to apply an exception which do not provides an inventive concept. The claim is not eligible. Regarding independent claims 1 and 15, they are directed to a method and instruction, respectively, to implement the system as set forth in claim 8. They recite a series of steps substantially similar to claim 1 and do not correct the issues set forth above. The claims are likewise not eligible. Regarding claim 2, the claim depends on claim 1 and recites the same abstract idea and the additional elements of creating a training dataset from historical tracking data of the robotic machines performing the industrial activities; and training the machine learning model to evaluate the steps of the industrial activities in view of the configuration information of the robotic machines to generate the production plan. The elements directed to training the machine learning model constitutes a mathematical concept, which is category of abstract idea. The machine learning training operations are fundamentally mathematical in nature. The fact that such operations may be computationally complex or impractical to perform entirely in the mind does not remove them from the “Mathematical Concepts” category of abstract ideas. Mathematical optimization remains abstract even when executed by computer. The element does not improve the functioning of the machine learning model, improve the functioning of the robotic machines. Instead, the machine learning model is recited at a high level of generality and is used as a black-box analytical tool to process data and generate a plan. The element creating a training dataset from historical tracking data of the robotic machines performing the industrial activities constitutes data gathering and organization for use in the mathematical training process, which is insignificant extra-solution activity (see MPEP 2106(g)). Accordingly, claim 2 recites a mathematical concept and thus an abstract idea. The claim is not eligible. Regarding claim 3, the claim depends on claim 1 and recites the same abstract idea and the additional elements of executing a simulation of the robotic machines performing the industrial activities according to the production plan to determine an effectiveness of the production plan. The element merely direct to a human performing the calculation of the plan and evaluate its effectiveness. The claim recites an abstract idea (mathematical simulation and evaluation). The claim is not eligible. Regarding claim 4, the claim depends on claim 1 and recites the same abstract idea and the additional elements of determining a degree of commonality between the steps of the industrial activities; and selecting steps having a high degree of commonality for performance aggregation. The recites an abstract idea (mental comparison and classification). The claim is not eligible. Regarding claim 5, the claim depends on claim 1 and recites the same abstract idea and the additional elements of identifying a sequence of steps of the industrial activities that reduces the performance of the nonvalue-added steps by the robotic machines; and selecting the sequence of steps for performance aggregation. The claim recites an abstract idea (workflow optimization and organizing activity). The claim is not eligible. Regarding claim 6, the claim depends on claim 1 and recites the same abstract idea and the additional elements of identifying industrial activity volume and step volume. The claim recites an abstract idea (Mental process and Mathematical calculation). The claim is not eligible. Regarding claim 7, the claim depends on claim 1 and recites the same abstract idea and the additional elements of identifying patterns in the steps of the industrial activities. The claim recites an abstract idea (Mental process). The claim is not eligible. Regarding claim 9, the claim depends on claim 8 and recites the same abstract idea and the additional elements of perform physical distance collaboration analysis for the robotic machines to identify sets of robotic machines that are available to collaborate on one or more steps of one or more industrial activities. The claim directed to a Mathematical concept (distance calculation, spatial analysis) and Mental processes (grouping based on proximity and availability). Thus, the claim recites an abstract idea and is not eligible. Regarding claim 10, the claim depends on claim 8 and recites the same abstract idea and the additional elements of execute a simulation of the robotic machines performing the industrial activities according to the production plan to determine an effectiveness of the production plan. The element merely direct to a human performing the calculation of the plan and evaluate its effectiveness. The claim recites an abstract idea (mathematical simulation and evaluation). The claim is not eligible. Regarding claim 11, the claim depends on claim 8 and recites the same abstract idea and the additional elements of determine a degree of commonality between the steps of the industrial activities; and select steps having a high degree of commonality for performance aggregation. The recites an abstract idea (mental comparison and classification). The claim is not eligible. Regarding claim 12, the claim depends on claim 8 and recites the same abstract idea and the additional elements of identify a sequence of steps of the industrial activities that reduces the performance of the nonvalue-added steps by the robotic machines; and select the sequence of steps for performance aggregation. The claim recites an abstract idea (workflow optimization and organizing activity). The claim is not eligible. Regarding claim 13, the claim depends on claim 8 and recites the same abstract idea and the additional elements that similar to claim 6 which was found to be an abstract idea. The claim is not eligible. Regarding claim 14, the claim depends on claim 8 and recites the same abstract idea and the additional elements that similar to claim 7 which was found to be an abstract idea. The claim is not eligible. Regarding claims 16-20, the claims depend on claim 15 and recites the same abstract idea and the additional elements that similar to claim 2-7, respectively, which was found to be an abstract idea. Thus, the claims are not eligible. Claim Rejections - 35 USC § 102 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. 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15-03-aia AIA Claim(s) 1-3, 5-10, 12-16, 18-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Mazur et al. U.S. Patent No. 11,874,655 (“Mazur”) . Regarding claim 8, Mazur discloses a system comprising: one or more computer readable storage media storing program instructions and one or more processors which, in response to executing the program instructions [ SEE fig. 1 and 2 ], are configured to: obtain industrial activity data comprising steps of industrial activities performed by robotic machines [ SEE fig. 3 ] on an industrial floor; one area 16 may include a sub-assembly production process and another area 16 may include a core production process . In another example, each area 16 may be related to a different operation being performed in the manufacturing process . For instance, in a jelly bean manufacturing system, the areas 16 may include a jelly bean making area, a packaging area, a water filtration area, and the like . In yet another example, the area may include a production line in which a particular industrial process may be performed. Referring back to the jelly bean manufacturing system example, the production line may include a cooking line in which the jelly beans may be created, a sorting line where the jelly beans may be sorted according to a respective flavor, and a packaging line where the sorted jelly beans may be packaged into boxes or the like . [col. 4 lines 1-16, see also col. 4 lines 24-34] The components 20 may also be related to various industrial equipment such as mixers, machine conveyors, tanks, skids, specialized original equipment manufacturer machines, and the like . The components 20 may also be associated with devices used by the equipment such as scanners, gauges, valves, flow meters, and the like. In one embodiment, every aspect of the component 20 may be controlled or operated by a single controller (e.g., control system) [col. 4 lines 48-56; read further col. 7 lines 53-67; col. 10 lines 13-35 ] obtain robotic machine data comprising configuration information of the robotic machines associated with performing the steps of the industrial activities; the system may be configured to fetch data pertaining to components and/or parts based on identifying information about components and/or parts and the stored OEM/supplier information . This may include, for example, product manuals, warranty information, recall information, service/maintenance guides , product specifications , product dimensions, replacement specifications, compatibility information, and so forth. The registry may also include location information about specific components/parts . Location information may be in the form of global location according to some coordinate system (e.g., GPS coordinates, quadrant/location within a facility or on a floor), or location relative to other components (spatially, within a process flow, etc.) within the industrial automation system . Once data has been collected, mappings between industrial automation systems, components, parts, and so forth may be generated. These registries may then be used to manage components within the industrial automation system [col. 4 lines 30-48] In certain embodiments, the respective control system may determine how each component 20 may relate to a respective cell 18 or area 16 based on data received from each respective component 20 . For instance, a control system of a first component 20 may receive data from multiple other components 20, such as a motor for a conveyer belt and a compressor for some industrial automation device . Upon receiving the data from a second component 20 that corresponds to the motor for the conveyer belt, the control system of the first component 20 may determine that the second component 20 is associated with some cell 18, which may be part of some area 16, based on a speed in which the motor may be operating . That is, the control system of the first component 20 may refer to information, such as system design parameters for the industrial automation system 10, and determine where the motor is located by identifying a motor with operating parameters, as specified by the system design parameters, having a substantially similar speed as the received speed . In certain embodiments, the speed at which the motor may be operating may not be sufficient to identify a particular motor if other motors in the industrial automation system 10 are operating at the same speed. As such, the control system may identify a motor by monitoring a speed profile (i.e., speed curve over time) of each motor in the industrial automation system 10. Additional ways in which a control system may identify particular components 20 may include monitoring an operating mode (e.g., running/stopped/paused) of each component 20, examining network related information (e.g. IP addresses, MAC addresses, sub-net masks, or a combination of any of these, etc.) associated with each component 20 [col. 8 lines 7-38] At block 502, industrial automation system component data is received. The data may include, for example, design elements, a design file, a project file, information about characteristics of the various components, or parts of components, of the industrial automation system, such as part number, serial number, model number, OEM, supplier, vendor, catalog number, vendor/supplier/OEM contact information , etc., information about how the various components of the industrial automation system are configured, information about how the various components of the industrial automation system work in concert to perform one or more industrial automation processes, operational data, desired results of the one or more industrial automation processes, acceptable values or ranges for various parameters of the industrial automation system or the one or more industrial automation processes , compatibility information, and so forth . [col. 13 line 60 to col. 14 line 10] input the industrial activity data and the robotic machine data to a machine learning model to analyze the steps of the industrial activities in view of the configuration information of the robotic machines to generate a production plan that aggregates performance of selected steps by the robotic machines; and In any case, after analyzing the data associated with each component 20, the control system of the first component 20 may determine its relationship with other components 20 of the industrial automation system with respect to the various scopes or hierarchical levels of the industrial automation system 10. By understanding the relationship to other components 20 with respect to various scopes of the industrial automation system 10, the control system of the first component 20 may become aware of conditions occurring in processes, areas 16, or cells 18 that may directly or indirectly affect the operations of the first component 20 . [col. 8 lines 47-58] The filling and sealing station 210 may function at an optimal rate when the washed cans and bottles enter the filling and sealing station 210 in a steady, uniform stream. However, if the transition between the conveyor section 204 and the aligning conveyor section 208 is erratic or faster than desired, the filling and sealing station 210 may not function at an optimal rate. As such, optimizing performance parameters (e.g., speed, size, function, position/arrangement, or quantity) of the conveyor sections (i.e., conveyor section 204 or aligning conveyor section 208) may be beneficial to the efficiency of the packaging factory 200 . [col. 8 lines 50-61; read further col. 8 line 63 col. 9 line 14 ] In one embodiment, the industrial control system 22 may determine the categories or scopes of the industrial automation system 10 based on a factory diagram or specification that describes the various processes employed by the industrial automation system 10 and the components 20 used for the respective processes. In another embodiment, each control system for each component 20 may include information indicating the function of the component 20, a location of the component 20 with respect to the industrial automation system 10, a part of a manufacturing process that the component 20 is associated with, or the like . Here, each respective control system of each respective component 20 may send this information to the industrial control system 22 or to other control systems of nearby components 20. The control system that receives the information may then determine how the component 20 that transmitted the information may relate to the various scopes of the industrial automation system 10, how the component 20 that received the information may be related to the component 20 that transmitted the information with respect to the various scopes of the industrial automation system 10, and the like . In certain embodiments, each control system may send information related to the scopes of the industrial automation system 10, information detailing a relationship between each scope of the industrial automation system 10, information detailing a relationship between each component 20 in the industrial automation system with respect to each scope of the industrial automation system 10, and the like to a database 300, which may be accessible by each control system as a centralized database or a database distributed between a number of machines, computers, or the like . [col. 11 lines 25] At block 512, machine learning may be utilized to analyze the model and generate one or more recommendations . Recommendations may include, for example, adjusting operational parameters, performing service/maintenance, replacing a part, automatically or manually ordering a replacement part, automatically or manually scheduling maintenance/service, using different product/material, providing hyperlinks to useful resources (e.g., written or video instructions to how to perform a service operation, a recalibration, troubleshoot, etc.), and so forth. In some embodiments, the recommendation may include an adjustment to operating parameters (e.g., to increase the lifespan of the part, component, and/or device), or a recommendation for specific replacement parts to increase the lifespan of the part, component, and/or device (e.g., replace with a higher durometer part at next service). As discussed above with respect to FIG. 2, the recommendation may include adjusting operating parameters to optimize the way in which two components interact with one another . For example, in the packaging factory shown and described with regard to FIG. 3, conveyor sections may transport cans and bottles between stations. A first conveyor section from one or more first OEMs may deliver bottles to a first station made up of components from one or more second OEMs, which may, receive the bottles, perform some first operations (e.g., filling and sealing) on the bottles, and provide the bottles to a second conveyor section from one or more third OEMs. The second conveyor section may then provide the bottles to a second station made up of components from one or more fourth OEMs, which may, receive the bottles, perform some second operations (e.g., sterilizing) on the bottles, and provide the bottles to a third conveyor section from one or more fifth OEMs. Because the system utilizes components from many different suppliers/OEMs, getting the various components to operate in concert with one another to perform the industrial automation process may be challenging. Accordingly, the system may use machine learning to generate recommendations to adjust various operational parameters (e.g., motor speeds, flow rates, and so forth) based on the models, the retrieved external data, and information stored in the registry, to optimize operation of the various components . [col. 14 line 52 to col. 15 line 26] configure the robotic machines on the industrial floor according to the production plan generated by the machine learning model. As such, the control system of the first component 20 may adjust its operations and send commands to other components 20 to adjust their respective operations to compensate or minimize negative consequences that may occur due to the conditions in the areas 16, the cells 18, or the like . For example, production capacity of upstream or downstream cells being automatically adjusted by control systems in the respective cells by monitoring production levels of the cells adjacent to or related to the respective control system. As a result, the control systems may optimize production of the industrial automation system 10 by reducing the effects of bottlenecks cells that may lead to over or under production . In another example, sections of a conveyor used to transport materials may start adjusting their respective speeds based on other sections of the conveyor or production variances associated with the area 16, the cells 18, or the entire factory 12 . In yet another example, the control system of the first component 20 may take into account energy consumption data associated with a second component to adjust the operation of the first component 20 (e.g. go to a lower energy consumption mode to maintain overall consumption constant, etc.). Additionally, after each component 20 becomes aware of the presence or existence of another component 20, some of the components 20 may negotiate and determine an optimal production rates for each component 20 based on pre-determined criteria such as energy consumption/rates, production mix, production levels, and the like . [col. 8 lines 58-67 and col. 9 lines 1-17] Regarding claim 1, the claim is directed to the method of steps to implement the system as set forth in claim 8. Therefore, it is rejected on the same basis as set forth hereinabove. Regarding claim 2, Mazur discloses the system may use machine learning to generate recommendations to adjust various operational parameters (e.g., motor speeds, flow rates, and so forth) based on the models, the retrieved external data, and information stored in the registry, to optimize operation of the various components [ col. 14 line 52 to col. 15 line 26 ]. Therefore, Mazur discloses creating a training dataset from historical tracking data of the robotic machines performing the industrial activities; and training the machine learning model to evaluate the steps of the industrial activities in view of the configuration information of the robotic machines to generate the production plan. Regarding claim 3, Mazur discloses a simulation of the robotic machines performing the industrial activities according to the production plan to determine an effectiveness of the production plan [ Model is generated and simulate – col. 14 lines 48-51 ]. Regarding claim 5, Mazur discloses identifying a sequence of steps of the industrial activities that reduces the performance of the nonvalue-added steps by the robotic machines; and selecting the sequence of steps for performance aggregation [ reducing the effects of bottlenecks – col. 8 line 63 to col. 9 line 2; col. 9 line 50 to col. 10 line 13 ]. Regarding claim 6, Mazur discloses identifying industrial activity volume and step volume [ col. 10 lines 26-36 - in certain embodiments, the sensors 226 may include sensors configured to measure the rate of bottles or containers per minute (BPM) entering or leaving a machine component (i.e., stations 204, 206, 208, 214, 216, 218 or 220), or the rate of accumulation of bottles on a portion of a conveyor section (e.g., conveyor section 204 or 212 ). In general, any sensors 226 capable of measuring a parameter value of interest relating to the beverage packaging process of the packaging factory 200 (e.g., rate, pressure, speed, accumulation, density, distance, position/arrangement, quantity, size, and so forth) may be used. ]. Regarding claim 7, Mazur discloses identifying patterns in the steps of the industrial activities [col. 10 lines 57-65; col. 11 lines 19-56; col. 14 lines 1-5 - information about how the various components of the industrial automation system are configured, information about how the various components of the industrial automation system work in concert to perform one or more industrial automation processes ]. Regarding claim 9, Mazur discloses perform physical distance collaboration analysis for the robotic machines to identify sets of robotic machines that are available to collaborate on one or more steps of one or more industrial activities [ col. 10 lines 32-36; col. 35-52 -The mappings may indicate relationships between the same or similar parts/components, parts/components that interact with one another, parts/components that are near each other (e.g., disposed within a given distance of one another), parts/components that pass product to one another or are otherwise adjacent to one another in the process flow, parts/components that are from common OEMs/suppliers, parts/components that are on similar maintenance schedules, and so forth. At block 510, performance of one or more industrial automation processes as performed by the industrial automation system is modeled ]. Regarding claim 10, Mazur discloses execute a simulation of the robotic machines performing the industrial activities according to the production plan to determine an effectiveness of the production plan [Model is generated and simulate – col. 14 lines 48-51]. Regarding claim 12, Mazur discloses identify a sequence of steps of the industrial activities that reduces the performance of the nonvalue-added steps by the robotic machines; and select the sequence of steps for performance aggregation [ reducing the effects of bottlenecks – col. 8 line 63 to col. 9 line 2; col. 9 line 50 to col. 10 line 13 ]. Regarding claim 13, Mazur discloses identifying industrial activity volume and step volume [ col. 10 lines 26-36 ]. Regarding claim 14, Mazur discloses identify patterns in the steps of the industrial activities [ col. 10 lines 57-65; col. 11 lines 19-56; col. 14 lines 1-5 ]. Regarding claim 15-16, 18-20, the claims are directed to the program instructions to implement the system as set forth in claims 8, 10, and 12-14. Therefore, they rejected on the same basis as set forth hereinabove . Allowable Subject Matter Claims 4, 11, 17 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action, and including all of the limitations of the base claim and any intervening claims. 13-03-01 AIA The following is a statement of reasons for the indication of allowable subject matter: Claims 4, 11, 17 are considered allowable since, when reading the claims in light of the specification, none of the references of record alone or in combination disclose or suggest the combination of subject matter specified in the dependent claim(s) “determining a degree of commonality between the steps of the industrial activities; and selecting steps having a high degree of commonality for performance aggregation.” Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US Pub. No. 2021/0304036 to Willison et al. teach systems and methods relate to training models for predicting one or more properties of the manufacturing assembly line, such as a production level. The disclosed systems and methods may also relate to optimizing the configuration of a manufacturing assembly line or evaluating faults in a manufacturing assembly line. Specifically, Willison et al. teach the method involves operating a processor to: receive cell data associated with at least one cell during an operation of an active manufacturing assembly line, the cell data including, for each cell, at least one input state of that cell and a cell position of that cell within the active manufacturing assembly line; receive line production data associated with the cell data, the line production data being representative of a production level of the active manufacturing assembly line in association with the respective cell data; determine one or more production associations between the cell data of each cell and the production level of the active manufacturing assembly line; evaluate the one or more production associations to identify one or more critical production associations to the operation of the active manufacturing assembly line. Any inquiry concerning this communication or earlier communications from the examiner should be directed to VINCENT HUY TRAN whose telephone number is (571)272-7210. The examiner can normally be reached M-F 7:00-4:00. 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, Kamini S Shah can be reached at 571-272-2279. 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. VINCENT H TRAN Primary Examiner Art Unit 2115 /VINCENT H TRAN/Primary Examiner, Art Unit 2115 Application/Control Number: 18/158,535 Page 2 Art Unit: 2115 Application/Control Number: 18/158,535 Page 3 Art Unit: 2115 Application/Control Number: 18/158,535 Page 4 Art Unit: 2115 Application/Control Number: 18/158,535 Page 5 Art Unit: 2115 Application/Control Number: 18/158,535 Page 6 Art Unit: 2115 Application/Control Number: 18/158,535 Page 7 Art Unit: 2115 Application/Control Number: 18/158,535 Page 8 Art Unit: 2115 Application/Control Number: 18/158,535 Page 9 Art Unit: 2115 Application/Control Number: 18/158,535 Page 10 Art Unit: 2115 Application/Control Number: 18/158,535 Page 11 Art Unit: 2115 Application/Control Number: 18/158,535 Page 12 Art Unit: 2115 Application/Control Number: 18/158,535 Page 13 Art Unit: 2115 Application/Control Number: 18/158,535 Page 14 Art Unit: 2115 Application/Control Number: 18/158,535 Page 15 Art Unit: 2115 Application/Control Number: 18/158,535 Page 16 Art Unit: 2115