CTNF 17/416,017 CTNF 99042 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. DETAILED ACTION 07-42-04 AIA A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/26/2026 has been entered. Response to Amendments Claims 1, 4-13, and 16-21 remain pending in the application. Claims 1, 13, 20, and 21 have been amended. The amendment filed 04/15/2026 is sufficient to overcome the 103 rejections of claims 1, 4, 11-13, 16, and 18-21 over Bernat in view of Ha and Cichon, the 103 rejections of claims 5-9 over Bernat in view of Ha, Cichon, and Chen, and the 103 rejections of claims 10 and 17 over Bernat in view of Ha, Cichon, and Barry. Response to Arguments Argument 1 , regarding the 103 rejections, applicant argues that none of the cited art teaches an edge device that “directly controls operation of factory devices based on the predictions made by the retrained neural network executing on the hardware accelerator”. Applicant argues that the device taught by Cichon is strictly a notification-based device that does not directly control operation of any factory devices beyond notifying applications to initiate a shutdown procedure. Examiner notes that Cella et al (Pub. No.: US 20180284737 A1), hereafter Cella teaches the neural network being trained based on a library of sensor measurements collected from a group of factories (AI models are trained based on sensor data from an industrial environment, P0028)… wherein the data comprise locally collected sensor measurement data of a specific factory, the edge device being a component in a factory setting that acts as a controller at the specific factory, wherein the edge device receives the sensor measurement data, makes predictions based on the sensor measurement data and directly controls operation of factory devices based on the predictions made by the retrained neural network executing on the hardware accelerator (“The monitoring device 8120 may include a controller 8122. The controller 8122 may include a data acquisition circuit 8104, a data analysis circuit 8108, a MUX control circuit 8114, and a response circuit 8110”, P0629. “the response circuit 8110 may initiate a variety of actions based on the sensor status provided by the data analysis circuit 8108”, P0632. “Based on analysis of data about the industrial machine, such as those conditions that may be detected by the set of sensors, an action may be taken, such as notifying a user of a change in the condition, adjusting operating parameters, scheduling preventive maintenance”, P0534. Response circuit may take actions such as changing operating parameters of industrial machines based on a neural network’s analysis and prediction of operating states of industrial machines, P0894, P0826). The full prior art rejections are outlined below. 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. Claims 1, 4, 11-13, 16, and 18-21 are rejected under 35 U.S.C. 103 as being as being unpatentable over Guim Bernat et al (Pub.No.: US 20220358370 A1) hereafter Bernat in view of Ha et al (Pub.No.: US 20200117863A1), hereafter Ha and Cella et al (Pub. No.: US 20180284737 A1), hereafter Cella. Regarding claim 1 , Bernat teaches a method for training a neural network on a hardware accelerator of an edge device, the hardware accelerator comprising one or more communication buses (hardware accelerator communicates with the use of a bus, P0093, P0095) , a dedicated neural network processing unit, and one or more processors , the method comprising: deploying a domain independent portion of a neural network onto a dedicated neural network processing unit of the hardware accelerator of the edge device (ML model is executed by an edge computing platform which receives data from the edge device corresponding to the model. Type of accelerator/accelerator hardware is specified in the inference request sent to the edge computing platform, P0041-P0042)… deploying a domain dependent portion of the trained neural network onto one or more additional processors of the hardware accelerator of the edge device (“In some examples, execution of a particular model may be performed at more than one appliance or hardware implementation, more than one chassis or rack 440A, or even distributed across different racks or enclosures in independent power domains.”, P0039. In view of P0039, ML model is executed by an edge computing platform which receives data from the edge device corresponding to the model. Type of accelerator/accelerator hardware is specified in the inference request sent to the edge computing platform, P0041-P0042); retraining the domain dependent portion on the one or more additional processors of the hardware accelerator using data collected at the edge device to refine the neural network (“other forms of AI models (including machine learning) approaches and formats which are not neural networks may be employed”, P0042. Data is sent from an edge device to an edge computing platform through the edge gateway, and then the model can be executed at a particular platform type (dependent domain), or among multiple possible platforms (independent domain) P0039-P0044), wherein the dedicated neural network processing unit executes the domain independent portion of the trained neural network, wherein the one or more additional processors execute the domain dependent portion of the trained neural network connected to the domain independent portion over the communication buses (“In some examples, execution of a particular model may be performed at more than one appliance or hardware implementation, more than one chassis or rack 440A, or even distributed across different racks or enclosures in independent power domains.”, P0039. In view of P0039, ML model is executed by an edge computing platform which receives data from the edge device corresponding to the model. Type of accelerator/accelerator hardware is specified in the inference request sent to the edge computing platform, P0041-P0042) , and wherein the trained neural network is divided at the edge device using the one or more additional processors (Operations of the invention may be implemented in an electronic processing system where any collection of machines capable of executing the model can do so individually or jointly, such as with additional processors, P0086). Bernat does not appear to explicitly teach retraining the domain dependent portion. Ha teaches retraining the domain dependent portion (“In order to learn a new task (e.g., detecting fake alcohol), the neural network may inherit the common layers from a well-trained model (e.g., the baby formula model) and retrain only the task-specific layers”, P0190). Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Bernat and Ha before them, to include Ha’s specific teaching of retraining task-specific layers in a model in Bernat’s system of AI inference architecture with hardware acceleration. One would have been motivated to make such a combination of retraining task specific layers of a neural network (see Ha P0190), and receiving data by an edge device to produce other approaches to AI models, such as machine learning, with the edge computing platform (see Bernat P0042). Bernat in view of Ha does not appear to explicitly teach “the neural network being trained based on a library of sensor measurements collected from a group of factories;… wherein the data comprise locally collected sensor measurement data of a specific factory, the edge device being a component in a factory setting that acts as a controller at the specific factory, wherein the edge device receives the sensor measurement data, makes predictions based on the sensor measurement data and directly controls operation of factory devices based on the predictions made by the retrained neural network executing on the hardware accelerator”. Cella teaches the neural network being trained based on a library of sensor measurements collected from a group of factories (AI models are trained based on sensor data from an industrial environment, P0028)… wherein the data comprise locally collected sensor measurement data of a specific factory, the edge device being a component in a factory setting that acts as a controller at the specific factory, wherein the edge device receives the sensor measurement data, makes predictions based on the sensor measurement data and directly controls operation of factory devices based on the predictions made by the retrained neural network executing on the hardware accelerator (“The monitoring device 8120 may include a controller 8122. The controller 8122 may include a data acquisition circuit 8104, a data analysis circuit 8108, a MUX control circuit 8114, and a response circuit 8110”, P0629. “the response circuit 8110 may initiate a variety of actions based on the sensor status provided by the data analysis circuit 8108”, P0632. “Based on analysis of data about the industrial machine, such as those conditions that may be detected by the set of sensors, an action may be taken, such as notifying a user of a change in the condition, adjusting operating parameters, scheduling preventive maintenance”, P0534. Response circuit may take actions such as changing operating parameters of industrial machines based on a neural network’s analysis and prediction of operating states of industrial machines, P0894, P0826). Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Bernat, Ha, and Cella before them, to include Cella’s specific teaching of collecting sensor data in a factory setting to train a machine learning model with in Bernat’s system of AI inference architecture with hardware acceleration. One would have been motivated to make such a combination of adjusting operating parameters of industrial machines based on a machine learning model’s analysis (see Cella P0534, P0632), and receiving data by an edge device to produce other approaches to AI models, such as machine learning, with the edge computing platform (see Bernat P0042) to improve operation, reduce down time, preventive maintenance, failure prevention, (see Cella P0534). Regarding claim 13 , Bernat teaches an edge device connected to a remote computer system over a network, the edge device (Edge computing platform and edge device are connected to each other over the edge cloud, P0035, which is a network in view of P0022) comprising: a hardware accelerator comprising: one or more communication buses (hardware accelerator communicates with the use of a bus, P0093, P0095), dedicated neural network processing unit executing a domain independent portion of a trained neural network, …and one or more processors executing a domain dependent portion of the trained neural network connected to the domain independent portion over the communication buses (“In some examples, execution of a particular model may be performed at more than one appliance or hardware implementation, more than one chassis or rack 440A, or even distributed across different racks or enclosures in independent power domains.”, P0039. In view of P0039, ML model is executed by an edge computing platform which receives data from the edge device corresponding to the model. Type of accelerator/accelerator hardware is specified in the inference request sent to the edge computing platform, P0041-P0042), wherein the domain dependent portion is re-trained by the processors using data collected at the edge device to refine the trained neural network (“other forms of AI models (including machine learning) approaches and formats which are not neural networks may be employed”, P0042. Data is sent from an edge device to an edge computing platform through the edge gateway, and then the model can be executed at a particular platform type (dependent domain), or among multiple possible platforms (independent domain) P0039-P0044)… , and wherein the trained neural network is divided at the edge device using the additional processors (Operations of the invention may be implemented in an electronic processing system where any collection of machines capable of executing the model can do so individually or jointly, such as with additional processors, P0086). Bernat does not appear to explicitly teach wherein the domain dependent portion is re-trained. Ha teaches wherein the domain dependent portion is re-trained (“In order to learn a new task (e.g., detecting fake alcohol), the neural network may inherit the common layers from a well-trained model (e.g., the baby formula model) and retrain only the task-specific layers”, P0190). Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Bernat and Ha before them, to include Ha’s specific teachings of retraining task-specific layers in a model in Bernat’s system of AI inference architecture with hardware acceleration. One would have been motivated to make such a combination of retraining task-specific layers in model (see Ha P0190), and receiving data by an edge device to produce other approaches to AI models, such as machine learning, with the edge computing platform (see Bernat P0042). Bernat in view of Ha does not appear to explicitly teach “the neural network being trained based on a library of sensor measurements collected from a group of factories;… wherein the data comprise locally collected sensor measurement data of a specific factory, the edge device being a component in a factory setting that acts as a controller at the specific factory, wherein the edge device receives the sensor measurement data, makes predictions based on the sensor measurement data and directly controls operation of factory devices based on the predictions made by the retrained neural network executing on the hardware accelerator”. Cella teaches the neural network being trained based on a library of sensor measurements collected from a group of factories (AI models are trained based on sensor data from an industrial environment, P0028)… wherein the data comprise locally collected sensor measurement data of a specific factory, the edge device being a component in a factory setting that acts as a controller at the specific factory, wherein the edge device receives the sensor measurement data, makes predictions based on the sensor measurement data and directly controls operation of factory devices based on the predictions made by the retrained neural network executing on the hardware accelerator (“The monitoring device 8120 may include a controller 8122. The controller 8122 may include a data acquisition circuit 8104, a data analysis circuit 8108, a MUX control circuit 8114, and a response circuit 8110”, P0629. “the response circuit 8110 may initiate a variety of actions based on the sensor status provided by the data analysis circuit 8108”, P0632. “Based on analysis of data about the industrial machine, such as those conditions that may be detected by the set of sensors, an action may be taken, such as notifying a user of a change in the condition, adjusting operating parameters, scheduling preventive maintenance”, P0534. Response circuit may take actions such as changing operating parameters of industrial machines based on a neural network’s analysis and prediction of operating states of industrial machines, P0894, P0826). Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Bernat, Ha, and Cella before them, to include Cella’s specific teaching of collecting sensor data in a factory setting to train a machine learning model with in Bernat’s system of AI inference architecture with hardware acceleration. One would have been motivated to make such a combination of adjusting operating parameters of industrial machines based on a machine learning model’s analysis (see Cella P0534, P0632), and receiving data by an edge device to produce other approaches to AI models, such as machine learning, with the edge computing platform (see Bernat P0042) to improve operation, reduce down time, preventive maintenance, failure prevention, (see Cella P0534). Regarding claims 4 and 16 , Bernat in view of Ha and Cella teaches the limitations of claims 1 and 13 as outlined above. Bernat further teaches wherein the number of layers of the trained neural network included in the domain dependent portion is selected based on hardware characteristics of the edge device (Edge computing platform provides mapping of the AI model after receiving data from edge device. The mapping includes number of layers of the neural network, P0026). Regarding claims 11 and 18 , Bernat in view of Ha and Cella teaches the limitations of claims 1 and 13 as outlined above. Bernat further teaches wherein the additional processors of the hardware accelerator are graphical processing units (GPUs) (Accelerator hardware includes GPUs, P0044). Regarding claims 12 and 19 , Bernat in view of Ha and Cella teaches the limitations of claims 1 and 13 as outlined above. Bernat further teaches wherein the additional processors of the hardware accelerator are central processing units (CPUs) (Accelerator hardware includes CPUs, P0027). Regarding Claim 20 , Bernat teaches an edge device comprising a hardware accelerator comprising one or more communication buses (hardware accelerator communicates with the use of a bus, P0093, P0095) , a dedicated neural network processing unit, and one or more additional processors, the edge device configured to: receive a domain independent portion and a domain dependent portion of a neural network from a computer via a network (Neural network portions are communicated between edge device and computing platform, P0041-P0042. Edge computing platform and edge device are connected to each other over the edge cloud, P0035, which is a network in view of P0022)…, deploy the domain independent portion of the neural network onto a dedicated neural network processing unit of the hardware accelerator of the edge device, deploy the domain dependent portion of the neural network onto one or more additional processors of the hardware accelerator of the edge device (“In some examples, execution of a particular model may be performed at more than one appliance or hardware implementation, more than one chassis or rack 440A, or even distributed across different racks or enclosures in independent power domains.”, P0039. In view of P0039, ML model is executed by an edge computing platform which receives data from the edge device corresponding to the model. Type of accelerator/accelerator hardware is specified in the inference request sent to the edge computing platform, P0041-P0042),…, use the domain independent portion and the domain dependent portion to perform one or more tasks (“the use of the presently described service 430 may enable the performance of AI inference operations within a network fog or distributed collection of edge computing devices, platforms, and systems.”, P0035) …on the one or more additional processors of the hardware accelerator using data collected at the edge device to refine the neural network (“other forms of AI models (including machine learning) approaches and formats which are not neural networks may be employed”, P0042. Data is sent from an edge device to an edge computing platform through the edge gateway, and then the model can be executed at a particular platform type (dependent domain), or among multiple possible platforms (independent domain) P0039-P0044)... and wherein the trained neural network is divided at the edge device using the additional processors (Operations of the invention may be implemented in an electronic processing system where any collection of machines capable of executing the model can do so individually or jointly, such as with additional processors, P0086). Bernat does not appear to explicitly teach: train the domain dependent portion. Ha teaches train the domain dependent portion (“In order to learn a new task (e.g., detecting fake alcohol), the neural network may inherit the common layers from a well-trained model (e.g., the baby formula model) and retrain only the task-specific layers”, P0190). Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Bernat and Ha before them, to include Ha’s specific teachings of dividing a neural network into common layers and task-specific layers and training a neural network’s task-specific layers in Bernat’s system of AI inference architecture with hardware acceleration. One would have been motivated to make such a combination of dividing a neural network into common layers and task specific layers (see Ha P0190) and executing a neural network on a specific platform or among multiple platforms to serve and respond to multiple applications and meet ultra-low latency requirements for these applications (see Bernat P0044 and P0004). One would have been motivated to make such a combination of training task specific layers of a neural network (see Ha P0190), and receiving data by an edge device to produce other approaches to AI models, such as machine learning, with the edge computing platform (see Bernat P0042). Bernat in view of Ha does not appear to explicitly teach “the neural network being trained based on a library of sensor measurements collected from a group of factories;… wherein the data comprise locally collected sensor measurement data of a specific factory, the edge device being a component in a factory setting that acts as a controller at the specific factory, wherein the edge device receives the sensor measurement data, makes predictions based on the sensor measurement data and directly controls operation of factory devices based on the predictions made by the retrained neural network executing on the hardware accelerator”. Cella teaches the neural network being trained based on a library of sensor measurements collected from a group of factories (AI models are trained based on sensor data from an industrial environment, P0028)… wherein the data comprise locally collected sensor measurement data of a specific factory, the edge device being a component in a factory setting that acts as a controller at the specific factory, wherein the edge device receives the sensor measurement data, makes predictions based on the sensor measurement data and directly controls operation of factory devices based on the predictions made by the retrained neural network executing on the hardware accelerator (“The monitoring device 8120 may include a controller 8122. The controller 8122 may include a data acquisition circuit 8104, a data analysis circuit 8108, a MUX control circuit 8114, and a response circuit 8110”, P0629. “the response circuit 8110 may initiate a variety of actions based on the sensor status provided by the data analysis circuit 8108”, P0632. “Based on analysis of data about the industrial machine, such as those conditions that may be detected by the set of sensors, an action may be taken, such as notifying a user of a change in the condition, adjusting operating parameters, scheduling preventive maintenance”, P0534. Response circuit may take actions such as changing operating parameters of industrial machines based on a neural network’s analysis and prediction of operating states of industrial machines, P0894, P0826). Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Bernat, Ha, and Cella before them, to include Cella’s specific teaching of collecting sensor data in a factory setting to train a machine learning model with in Bernat’s system of AI inference architecture with hardware acceleration. One would have been motivated to make such a combination of adjusting operating parameters of industrial machines based on a machine learning model’s analysis (see Cella P0534, P0632), and receiving data by an edge device to produce other approaches to AI models, such as machine learning, with the edge computing platform (see Bernat P0042) to improve operation, reduce down time, preventive maintenance, failure prevention, (see Cella P0534). Regarding claim 21 , Bernat in view of Ha and Cella teaches the limitations of claim 20 as outlined above. Bernat also teaches: generate a new version of the domain dependent portion of the neural network onto one or more additional processors of the hardware accelerator of the edge device (edge computing platform may generate portion of the model and train the model based on data provided by the edge device, P0036, P0042). Executing forms of AI models, including machine learning, on additional processors of hardware accelerator using data collected at the edge device (“other forms of AI models (including machine learning) approaches and formats which are not neural networks may be employed”, P0042. Data is sent from an edge device to an edge computing platform through the edge gateway, and then the model can be executed at a particular platform type (dependent domain), or among multiple possible platforms (independent domain) P0039-P0044). Ha teaches: train the new version of the domain dependent portion (“In order to learn a new task (e.g., detecting fake alcohol), the neural network may inherit the common layers from a well-trained model (e.g., the baby formula model) and retrain only the task-specific layers”, P0190). 07-21-aia AIA Claim s 5-9 are rejected under 35 U.S.C. 103 as being unpatentable over Bernat in view of Ha, Cella, and Chen et al (Pub.No.: US 20190042867 A1) hereafter Chen . Regarding claim 5 , Bernat in view of Ha and Cella teaches the limitations of claim 1 as outlined above. Bernat does not appear to explicitly teach wherein the domain independent portion performs feature extraction on a set of input data and the domain dependent portion performs one or more image processing tasks on outputs for the domain independent portion. Chen teaches wherein the domain independent portion performs feature extraction on a set of input data and the domain dependent portion performs one or more image processing tasks on outputs for the domain independent portion (Feature extraction is done on images from a database, and these images are processed in the form of object detection and image classification, P0392). Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Bernat, Ha, Cella, and Chen before them, to include Chen’s specific teachings of feature extraction and image processing tasks in Bernat’s system of AI inference architecture with hardware acceleration. One would have been motivated to make such a combination of feature extraction and image processing tasks (see Chen P0392), and processing data at the edge device for object detection and classification (see Bernat P0038 and P0042). Regarding claim 6 , Bernat in view of Ha, Cella, and Chen teaches the elements of claim 5. Chen further teaches wherein the image processing tasking comprise one or more of object detection, object segmentation, image classification, or localization (Image classification is included in image processing, P0391). Regarding claim 7 , Bernat in view of Ha and Cella teaches the elements of claim 1 as outlined above. Bernat does not appear to explicitly teach wherein the neural network is trained using a first set of data that is not specific to a factory operating environment and the domain dependent portion of the neural network is retrained using a second set of data that is specific to the factory operating environment. Chen teaches wherein the neural network is trained using a first set of data that is not specific to a factory operating environment and the domain dependent portion of the neural network is retrained using a second set of data that is specific to the factory operating environment (Invention may be used in regards to inventory tracking and logistics, specifically in a factory setting, P0509). Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Bernat, Ha, Cella, and Chen before them, to include Chen’s specific teachings of tracking inventory and logistics in a factory setting in Bernat’s system of AI inference architecture with hardware acceleration. One would have been motivated to make such a combination of tracking inventory (see Chen P0509), and AI inference architecture for performing inference architecture with the use of AI in broadly applicable environments and fields not directly related to the invention (see Bernat P0030). Regarding claim 8 , Bernat in view of Ha, Cella, and Chen teaches the elements of claim 7 as outlined above. Chen further teaches wherein the first set of data and second set of data comprise image data (ML model includes use of visual (image) data, figure 38, P0344). Regarding claim 9 , Bernat in view of Ha, Cella, and Chen teaches the elements of claim 8 as outlined above. Chen further teaches wherein the first set of data and second set of data comprise audio data (ML model includes audio data, P0539) . 07-21-aia AIA Claim s 10 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Bernat in view of Ha, Cella, and Barry et al (Pub.No.: US 20150046673A1) hereafter Barry . Regarding claims 10 and 17 , Bernat in view of Ha and Cella teaches the elements of claims 1 and 13 as outlined above. Bernat does not appear to explicitly teach wherein the additional processors of the hardware accelerator are SHAVE vector processors. Barry teaches wherein the additional processors of the hardware accelerator are SHAVE vector processors (SHAVE vector processor is used to carry out computer vision calculations, P0062). Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Bernat, Ha, Cella, and Barry before them, to include Barry’s specific teachings of a SHAVE vector processor being used in Bernat’s system of AI inference architecture with hardware acceleration. One would have been motivated to make such a combination of a SHAVE vector processor consisting of RAM, DDR, and ASIC components (see Barry P0062), and a processor containing RAM, DDR, and ASIC components to communicate with system memory (see Bernat P0093, P0107). Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to ISHAN MOUNDI whose telephone number is (703)756-1547. The examiner can normally be reached 8:30 A.M. - 5 P.M.. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Ell can be reached at (571) 270-3264. 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. /I.M./Examiner, Art Unit 2141 /MATTHEW ELL/Supervisory Patent Examiner, Art Unit 2141 Application/Control Number: 17/416,017 Page 2 Art Unit: 2141 Application/Control Number: 17/416,017 Page 3 Art Unit: 2141 Application/Control Number: 17/416,017 Page 4 Art Unit: 2141 Application/Control Number: 17/416,017 Page 5 Art Unit: 2141 Application/Control Number: 17/416,017 Page 6 Art Unit: 2141 Application/Control Number: 17/416,017 Page 7 Art Unit: 2141 Application/Control Number: 17/416,017 Page 8 Art Unit: 2141 Application/Control Number: 17/416,017 Page 9 Art Unit: 2141 Application/Control Number: 17/416,017 Page 10 Art Unit: 2141 Application/Control Number: 17/416,017 Page 11 Art Unit: 2141 Application/Control Number: 17/416,017 Page 12 Art Unit: 2141 Application/Control Number: 17/416,017 Page 13 Art Unit: 2141 Application/Control Number: 17/416,017 Page 14 Art Unit: 2141 Application/Control Number: 17/416,017 Page 15 Art Unit: 2141 Application/Control Number: 17/416,017 Page 16 Art Unit: 2141 Application/Control Number: 17/416,017 Page 17 Art Unit: 2141 Application/Control Number: 17/416,017 Page 18 Art Unit: 2141 Application/Control Number: 17/416,017 Page 19 Art Unit: 2141 Application/Control Number: 17/416,017 Page 20 Art Unit: 2141 Application/Control Number: 17/416,017 Page 21 Art Unit: 2141
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