DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
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.
Claim(s) 1-8, 11-16, 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Choi et al (US 20220204009 A1, hereinafter Choi), in view of Farabet et al. (US 20190303759 A1, hereinafter Farabet).
Regarding Claim 11, Choi teaches A system comprising (Choi, Fig. 1, Element 100, Computer system): a virtual driver of an autonomous system; and a computer processor (Choi, Fig. 1, Element 120 Processor(s)) executing a simulator causing the computer processor to perform operations comprising (Choi, Paragraph [0002], [0004], "Aspects of the disclosure provide for a method for simulating sensor data and evaluating sensor behavior in an autonomous vehicle" “the running of the simulation includes modeling the one or more detection devices based on
configuration characteristics or operational settings of a perception system of the given vehicle”); generating a digital twin of a real world scenario as a simulated environment state of a simulated environment (Choi, Paragraph [0053], "The server computing devices 410 may construct environment data <read on digital twin) for a simulation using the log data. For example, the server computing devices 410 may use log data to identify static scenery and perception objects in the area encompassing the given run.") iteratively, through a plurality of timesteps (Choi, Paragraph [0052], "The given run 601 may comprise the locations logged by the vehicle 100 during ten seconds of driving in the area 600.... the given run 601 is shown broken down into eleven vehicle locations L1-L11 at eleven timestamps T1-T11, one second apart from each other."): executing a sensor simulation model on the simulated environment state to obtain simulated sensor output (Choi, Abstract, "run a simulation of one or more detection devices on a simulated vehicle driving along the given run to obtain simulated sensor data", Paragraph [0057], To obtain simulated sensor data, the server computing devices 410 may run a simulation using one or more simulated detection devices of the perception system 172 and the constructed environment data. The simulation may include retracing rays transmitted from the one or more simulated detection devices and recompute intensities of the reflected rays off points in the constructed environment data."), obtaining, from the virtual driver of the autonomous system, at least one actuation action that is based on the simulated sensor output (Choi, Paragraph [0065], "methods for evaluating a simulation system configured to simulate behavior of one or more sensors in an autonomous vehicle"; [0037], "computing devices 110 may cause the vehicle to accelerate... decelerate... change direction"), [[ updating an autonomous system state of the autonomous system based on the at least one actuation action, modeling, using a plurality of actor models, a plurality of actors in the simulated environment according to the simulated environment state to obtain a plurality of actor actions, and updating the simulated environment state according to the plurality of actor actions and the autonomous system state; and ]] evaluating the virtual driver after updating the simulated environment state (Choi, Paragraph [0022], "The server computing devices 410 may extract one or more metrics from the details of the logged sensor data and the details of the simulated sensor data; [0062], Based on the one or more metrics 910, the server computing devices 410 or other one or more processors may perform an evaluation 920 of how the simulation performed").
But, Choi does not explicitly disclose updating an autonomous system state of the autonomous system based on the at least one actuation action, modeling, using a plurality of actor models, a plurality of actors in the simulated environment according to the simulated environment state to obtain a plurality of actor actions, and updating the simulated environment state according to the plurality of actor actions and the autonomous system state.
However, Farabet teaches a system comprising: a virtual driver of an autonomous system; and a computer processor executing a simulator causing the computer processor to perform operations comprising (Farabet, Paragraph [0005], "verifying autonomous machines using simulated environments. Systems and methods are disclosed for training, testing, and/or verifying one or more features of a real-world system-such as a software stack for use in autonomous vehicles and/or robots"): generating a digital twin of a real world scenario as a simulated environment state of a simulated environment (Farabet, Paragraph [0033], "re-simulation system that uses physical sensor data generated by vehicle(s) 102 in real-world environments to train, test, verify, and/or validate one or more DNNs..."; [0058], "The simulation system 400A may generate a simulated environment 410... may include features of a driving environment... in an effort to simulate a real-world environment accurately within the simulated environment 410."; 01 teaches generating a simulated environment (digital twin) based on physical sensor data from a real-world environment.) iteratively, through a plurality of timesteps (Farabet, Paragraph [0075], "simulation system 400 may use a distributed shared memory protocol to maintain the state of the global simulation... in real-time."; [0106], "software stack(s) 706... for a current time slice."; [0105], "generate and provide updated sensor inputs to the GPU platform 624. This process may repeat until a simulation is completed." D1 teaches maintaining the state of the simulation in real-time, implying iterative timesteps/time slices.); executing a sensor simulation model on the simulated environment state to obtain simulated sensor output (Farabet, Paragraph [0114), "generate virtual sensor data using the simulation data for each of the virtual sensors of the vehicle."; [0060], "ray-tracing may be used to simulate LIDAR sensor for accurate generation of LIDAR data."); obtaining, from the virtual driver of the autonomous system, at least one actuation action that is based on the simulated sensor output (Farabet, Paragraph (0066], "sensor data (and/or encoded sensor data) may be used by the software stack(s) 116... to perform one or more operations (e.g., generate one or more controls, route planning...) "; [0116]", computing, by one or more machine learning models"; [0129], "process sensor signals, and output operation commands"; [0105), "simulation engine 630 which may update the behavior of one or more of the virtual objects based on the operations and/or commands"); updating an autonomous system state of the autonomous system based on the at least one actuation action (Farabet, Paragraph [0088]", updating... one or more attributes of a virtual object within a simulated environment... based at least in part on the signal"; [0106), "driving commands <read on actuation actions> generated originally by the software stack(s) 706... passed to ego-object dynamics which may use custom or built-in dynamics to update the object state <read on autonomous system state >"), modeling, using a plurality of actor models, a plurality of actors in the simulated environment according to the simulated environment state to obtain a plurality of actor actions (Farabet, Paragraph [0053), "The Al objects... may be controlled using artificial intelligence... in a way that simulates... how corresponding real-world objects would behave the bot may be trained to act like a pedestrian"; [0091], "Al engine 608 that simulates traffic, pedestrians <read on plurality of actor models >), and updating the simulated environment state according to the plurality of actor actions and the autonomous system state (Farabet, Paragraph [0106], "The simulation system may use the object's state, commands, and/or information, in addition to using traffic Al, pedestrian Al, and/or other features of the simulation platform, to generate or update the simulated environment (e.g., to a current state)."); and evaluating the virtual driver after updating the simulated environment state (Farabet, Paragraph [0106], "KPI framework 710 may monitor and evaluate the current simulation"; [0107], "KPI evaluation component may evaluate the performance of the virtual object(s) ").
Farabet and Choi are analogous since both of them are dealing with simulation and evaluation of autonomous systems within simulated environments for testing or
verification purposes. Choi provided a way of constructing simulated environments from real-world data and executing sensor simulations to generate simulated sensor output for evaluating autonomous vehicle behavior. Farabet provided a way of performing closed-loop autonomous system simulation in which virtual sensors generate sensor data, software stacks generate control operations, virtual object states are updated, Al actors (traffic and pedestrians) are modeled, and the simulated environment is iteratively updated and evaluated through KPI frameworks. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate the autonomous system state updating, actor modeling, and iterative environment updating framework taught by Farabet into the modified invention of Choi such that the simulation system of Choi not only simulates sensor behavior along a logged run but also performs closed loop updates to the autonomous system state and surrounding actor behaviors within the simulated environment while enabling evaluation of virtual driver performance. The motivation is to improve the realism and completeness of autonomous vehicle simulation by enabling dynamic interaction between simulated actors, vehicle state updates, and environment evolution during evaluation, as discussed by Farabet in Paragraph [0005] (training, testing, and verifying autonomous machines using simulated environments) and Paragraph [0106] (updating simulated environment state using object states and Al actor behaviors).
Regarding Claim 1, it recites limitations similar in scope to the limitations of Claim 11 but as a method and the combination of Choi and Farabet teaches all the limitations as of Claim 11. Therefore is rejected under the same rationale.
Regarding Claim 2, the combination of Choi and Farabet teaches the invention in Claim 1.
The combination further teaches wherein the simulated sensor output replicates a format of input to the virtual driver from a plurality of sensors on the autonomous system in the real world (Choi, Paragraph [0059], "a simulation of a run 801 may be run in the constructed environment data 700. The run 801 for simulated vehicle 870 may match the vehicle locations over time of the given run 601 from the log data and/or the simulated run 701 for the log data. As shown in table 810, the timestamps T1-T11 and vehicle locations L1-L11 match that of table 710 in FIG. 7. The object pointsets for agent vehicle 720 based on the one or more simulated detection devices are P1'-P11' for each respective timestamp T1-T11. The object pointsets P1'-P11' may differ from the object pointsets P1-P11 due to differences between the simulated detection devices and the detection devices that collected the logged sensor data").
Farabet further teaches the same (Farabet, Abstract, ".virtual sensor data generated by virtual sensors within the simulated environment may be encoded to a format consistent with the format of the physical sensor data generated by the vehicle.", Paragraph [0096], "The data may be transmitted to the software stack(s) 116 such that the data may match (e.g., bit-to-bit) the data sent from a physical sensor of a physical vehicle..."; 01 teaches that the virtual sensor data is encoded to replicate the format (e.g., bit-to-bit match) of the input from physical sensors on the real-world autonomous system).
As explained in rejection of claim 1, the obviousness for combining of autonomous system of Farabet into Choi is provided above.
Regarding Claim 3, the combination of Choi and Farabet teaches the invention in Claim 2.
The combination further teaches wherein the simulated sensor output comprises a LIDAR sensor output (Choi, Paragraph [0053]; "The server computing devices 410 may construct environment data for a simulation using the log data. For example, the server computing devices 410 may use log data to identify static scenery and perception objects in the area encompassing the given run"; "[0021] The scaled mesh may include points from LIOAR data in the log data.", [0057], "The simulation may include retracing rays transmitted from the one or more simulated detection devices... recompute intensities of the reflected rays...").
Farabet further teaches the same (Farabet, Paragraph [0130] "The sensor data may be received from... LIOAR sensor(s) 1164...", [0097], "The sensor emulator 616 may emulate at least cameras, LIOAR sensors..."; 01teaches simulating LIOAR sensor output.).
As explained in rejection of claim 1, the obviousness for combining of autonomous system simulation of Farabet into Choi is provided above.
Regarding Claim 4, the combination of Choi and Farabet teaches the invention in Claim 2.
The combination further teaches generating an image from a perspective of a camera of the autonomous system; and presenting the image to the virtual driver (Choi, Paragraph [0055); "The logged sensor data may include camera image data"; "The perception logic may be used by the server computing devices 410 to determine first details regarding detection of objects during the given run").
Farabet further teaches the same (Farabet, Paragraph [0086], "In an example where the virtual sensor is a virtual camera, the simulation data may correspond to at least the data from the simulation necessary to generate a field of view of the virtual camera within the simulated environment 410.", [0062], "sensor data (e.g., image data) may be transmitted... between the simulator component(s) 402 and the vehicle simulator component(s) 406.", [0066], "sensor data (and/or encoded sensor data) may be used by the software stack(s) 116... executed on the vehicle hardware 104").
As explained in rejection of claim 1, the obviousness for combining of autonomous system of Farabet into Choi is provided above.
Regarding Claim 5, the combination of Choi and Farabet teaches the invention in Claim 1.
The combination further teaches modeling, using an actor model of the new actor, a behavior of the new actor in the real world scenario, wherein updating the simulated environment state accounts for the behavior of the new actor, and wherein the virtual driver reacts to the behavior of the new actor (Choi, Paragraph [0058], "The server computing devices 410 may then determine second details regarding detection of objects in the simulated sensor data using the perception logic in a same or similar manner as described above for the first details of the logged sensor data").
Choi does not explicitly disclose but Farabet teaches adding a new actor to the plurality of actors to create a mixed reality scenario from the real world scenario (Farabet, Paragraph [0059], "pre-recorded video may be augmented or changed to include additional pedestrians, obstacles, and/or the like, such that the virtual objects <read on adding new actors> ... may be tested against variations in the real-world data") and modeling, using an actor model of the new actor, a behavior of the new actor in the real world scenario, wherein updating the simulated environment state accounts for the behavior of the new actor, and wherein the virtual driver reacts to the behavior of the new actor (Farabet, Paragraph [0053], "The Al objects... may be controlled... in a way that simulates... how corresponding real-world objects would behave....the bot may be trained to act like a pedestrian...", [0006], "outputs may be used to control the virtual object within the simulated environment... to determine how the virtual object... may perform in any number of different situations."; it is noted the modeling the behavior of actors (acting like pedestrians) and having the virtual driver react to them).
Farabet and Choi are analogous since both of them are dealing with simulation of autonomous systems within virtual environments to evaluate and validate autonomous driving behavior based on simulated sensor inputs and control outputs. Choi provided a way of generating simulated sensor data from reconstructed environments and evaluating behavior of autonomous vehicle systems based on simulated runs. Farabet provided a way of updating autonomous system state based on actuation commands generated by a software stack and updating the simulated environment using object states and AI-controlled actors within a closed-loop simulation framework. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate closed-loop updating of autonomous system state and simulation environment based on actuation actions and actor behaviors taught by Fara bet into the modified invention of Choi such that the simulation system of Choi performs iterative updates of the autonomous system state and simulated environment according to actor actions and autonomous system dynamics during simulation execution.
Regarding Claim 6, the combination of Choi and Farabet teaches the invention in Claim 1.
The combination further teaches modifying, a behavior of an existing actor of the plurality of actors to create a mixed reality scenario from the real world scenario (Farabet, Paragraph [0059], "pre-recorded video may be augmented or changed to include additional pedestrians, obstacles, and/or the like, such that the virtual objects... may be tested against variations in the real-world data.", [0111], "the KPI evaluation component 802... may include an API to save the state of any ongoing simulation, change state or trigger behaviors... try to explore the space of potential dangerous scenarios."; [0159], "The PVA(s) may be designed and configured to accelerate computer vision algorithms for the advanced driver assistance systems (ADAS), autonomous driving, and/or augmented reality (AR) and/or virtual reality (VR) applications"); and modeling, using an actor model of the existing actor, the behavior of the existing actor in the real world scenario, wherein the updated autonomous system state accounts for the behavior of the existing actor, wherein updating the simulated environment state accounts for the behavior of the existing actor, and wherein the virtual driver reacts to the behavior of the existing actor (Farabet, Paragraph [0053], "In an example where an Al object (e.g., bot) in the simulated environment corresponds to a pedestrian, the bot may be trained to act like a pedestrian... (e.g., running, walking... jaywalking... failing to heed stop signs <read on behavior of the existing actor>).", "[0006] ...outputs may be used to control the virtual object within the simulated environment... to determine how the virtual object... may perform in any number of different situations").
Farabet and Choi are analogous since both of them are dealing with simulation of autonomous vehicle behavior within simulated environments for evaluating autonomous driving systems under varying environmental conditions. Choi provided a way of constructing simulation environments from logged real-world runs and performing sensor simulation to evaluate autonomous vehicle behavior. Farabet provided a way of modifying simulated scenarios, changing actor behaviors, and using Al-driven actor models to reproduce or alter real-world behaviors in order to test virtual objects under varied or dangerous conditions. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate behavior modification of existing actors and actor-model-based behavioral simulation taught by Farabet into the modified invention of Choi such that the simulated environment derived from real-world data in Choi can be modified into mixed-reality scenarios in which actor behaviors are modeled and influence autonomous system state updates and virtual driver reactions. The motivation is to increase testing coverage and robustness by enabling controlled variation of actor behaviors and exploration of dangerous or rare scenarios, as discussed by Farabet in Paragraph [0059) (augmenting recorded data with additional actors) and Paragraph [0111] (changing state or triggering behaviors to explore dangerous scenarios).
Regarding Claim 7, the combination of Choi and Farabet teaches the invention in Claim 1.
The combination further teaches training the virtual driver to modify the virtual driver based on evaluating the virtual driver (Farabet, Paragraph [0031], "The training sub-system 106 may train and/or test any number of machine learning models, including deep neural networks {DNNs)"; [0040]; "Model refinement, pruning, and/or fine tuning 130 may include updating the DNNs to further refine and improve the accuracy and efficacy of the DNNs."; [0042]; "Active learning may be used to identify the data that may be used to provide increased performance for the DNNs in additional or alternative situations or environments.").
Farabet and Choi are analogous since both of them are dealing with simulation-based evaluation of autonomous driving systems using virtual environments and machine learning-driven control models. Choi provided a way of evaluating autonomous vehicle behavior through simulated sensor execution and environment replay. Farabet provided a way of training and refining machine learning models (virtual drivers/software stacks) based on simulation outputs and evaluation results. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate training and refinement of the virtual driver based on evaluation results taught by Farabet into the modified invention of Choi such that the virtual driver evaluated in Choi's simulation can be iteratively updated and improved based on evaluation outcomes. The motivation is to improve the accuracy and performance of autonomous driving models through iterative learning and refinement based on simulated evaluations, as discussed by Farabet in Paragraphs [0031], [0040]. and [0042] describing training, finetuning, and active learning of machine learning models.
Regarding Claim 8, the combination of Choi and Farabet teaches the invention in Claim 1.
The combination further teaches performing adversarial training of the virtual driver through modifying a behavior of the plurality of actors based on the evaluating the virtual driver (Farabet, Paragraph [0111], "KPI evaluation component 802... may include an API to... change state or trigger behaviors... try to explore the space of potential dangerous scenarios.", [0006], "The simulated environment may be generated to create difficult to navigate, dangerous, unsafe, and/or otherwise unpredictable situations for the virtual object to navigate."; "As a result, previously untested scenarios (e.g., due to safety concerns, difficulty of reproduction, etc.) may be tested, repeated, and improved upon within the simulated environment").
Farabet and Choi are analogous since both of them are dealing with simulation environments used to evaluate autonomous driving behavior under varied environmental and actor conditions. Choi provided a way of replaying real-world runs in simulation for evaluating autonomous vehicle perception and behavior. Farabet provided a way of deliberately modifying actor behaviors and generating challenging or dangerous scenarios to stress-test and improve virtual driver performance within simulated environments. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate adversarial-style scenario generation through modification of actor behavior taught by Farabet into the modified invention of Choi such that the virtual driver evaluated in Choi's simulation is trained under challenging or adversarial actor behaviors to improve robustness and performance. The motivation is to expose the virtual driver to difficult or unpredictable situations in order to improve safety and robustness through repeated testing and improvement, as discussed by Farabet in Paragraph [0006] (creating difficult or unsafe situations for testing) and Paragraph [0111] (changing behaviors to explore dangerous scenarios).
Regarding Claim 12, it recites limitations similar in scope to the limitations of Claim 2 and therefore is rejected under the same rationale.
Regarding Claim 13, it recites limitations similar in scope to the limitations of Claim 5 and therefore is rejected under the same rationale.
Regarding Claim 14, it recites limitations similar in scope to the limitations of Claim 6 and therefore is rejected under the same rationale.
Regarding Claim 15, it recites limitations similar in scope to the limitations of Claim 7 and therefore is rejected under the same rationale.
Regarding Claim 16, it recites limitations similar in scope to the limitations of Claim 8 and therefore is rejected under the same rationale.
Regarding Claim 19, it recites limitations similar in scope to the limitations of claim 1 and the combination of Choi and Farabet teaches all the limitations as of Claim 1. And Choi discloses these features can be implemented on a computer readable storage medium (Choi, Paragraph [0005], “Other aspects of the disclosure provide for a nontransitory, tangible computer-readable medium on which computer-readable instructions of a program are stored. The instructions, when executed by one or more computing devices, cause the one or more computing devices to perform a method for implementing a simulation for sensor data for an autonomous vehicle”).
Regarding Claim 20, it recites limitations similar in scope to the limitations of Claim 5 and therefore is rejected under the same rationale.
Claim(s) 9-10, 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Choi et al (US 20220204009 A1, hereinafter Choi), in view of Farabet et al. (US 20190303759 A1, hereinafter Farabet) as applied to Claim 1, 11 above respectively and further in view of Wang et al. (US 20210253118 A1, hereinafter Wang).
Regarding Claim 9, the combination of Choi and Farabet teaches the invention in Claim 1.
The combination does not explicitly disclose but Wang teaches modeling, using a
latency model, a plurality of latencies of the virtual driver executing on the autonomous
system in the real world scenario (Wang, Paragraph [00191], "generating the
discrete-time dynamic model further comprises introducing a time-latency term
for the subsystem, the time-latency term representing a time between
transmitting of a command by the ADV controller and an initial execution of the
command by the ADV subsystem controller"; Paragraph [0005]," Time-latency
can be attributable to data collection and processing time needed to identify
objects surrounding the vehicle and attributable to computing of a control
command needed to stay on the planned trajectory").
Wang and Choi are analogous since both of them are dealing with autonomous
vehicle control in which control commands are computed and executed and system
timing affects control performance. Choi provided a way of operating a virtual driver /
autonomous control in a real world driving scenario. Wang provided a way of
introducing a "time-latency term" into a model. Therefore, it would have been obvious to
one of ordinary skill in the art before the effective filing date of the claimed invention was
made to incorporate the time-latency term modeling taught by Wang into modified
invention off Choi such that the simulation framework accounts for time delays
associated with sensing, computation, and actuation during operation of the
autonomous driving stack, thereby enabling the virtual driving system to more
realistically reflect execution timing effects that occur in real-world autonomous vehicle
control.
Regarding Claim 10, the combination of Choi, Farabet and Wang teaches the
invention in Claim 9.
The combination further teaches wherein the plurality of latencies comprises a
sensor latency of transmitting sensor output to the virtual driver, a computer hardware
latency of the virtual driver executing on the autonomous system, and an autonomous
system latency of updating the autonomous system state based on the at least one
actuation action (Wang, Paragraph [0005], "Time-latency can be attributable to
data collection and processing time needed to identify objects surrounding the
vehicle and attributable to computing of a control command needed to stay on
the planned trajectory."; Paragraph [0029], "A time-latency in physical actuation
represents a difference between a time issuing of a command from the controller
111 and the time that the subsystem controller outputs a command to begin the
physic actuation.").
Wang and Choi are analogous since both of them are dealing with autonomous
vehicle control systems that process sensor data, compute control commands, and
actuate vehicle subsystems. Choi provided a way of running the virtual driver /
autonomous control. Wang provided a way of characterizing delay contributors including
delay attributable to "data collection and processing", "computing of a control command", and "physical actuation". Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate these modeled latencies taught by Wang into modified invention off Choi
such that the modeled simulation incorporates multiple categories of execution delay,
including delays associated with sensor data delivery, processing by computing
hardware, and downstream vehicle state updates resulting from control commands,
thereby improving realism of autonomous system operation timing within the simulation
Regarding Claim 17, it recites limitations similar in scope to the limitations of Claim 9 and therefore is rejected under the same rationale.
Regarding Claim 18, it recites limitations similar in scope to the limitations of Claim 10 and therefore is rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20200218253 A1 ADVANCED CONTROL SYSTEM WITH MULTIPLE CONTROL PARADIGMS
US 11163320 B1 Processing of multispectral sensors for autonomous flight
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YUJANG TSWEI whose telephone number is (571)272-6669. The examiner can normally be reached 8:30am-5:30pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kent Chang can be reached on (571) 272-7667. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/YuJang Tswei/Primary Examiner, Art Unit 2614