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 .
This office action is in response to applicant amendment/remarks filed 07/02/2026. Claims 4, 11, and 18 have been amended. No claims have been cancelled and no claims have been newly added. Accordingly, claims 1-20 are pending.
Response to Arguments
Applicant’s arguments, see pages 1-2 filed 07/02/2026, with respect to the 35 U.S.C. 112(b) rejection with regards to the “medium length of distance” of claims 4, 11, and 18 have been fully considered and are persuasive since the applicant have amended the claim to include a “median” length of distance. The 35 U.S.C. 112(b) rejection of claims 4, 11, and 18 has been withdrawn.
Applicant’s arguments, see pages 2-5 filed 07/02/2026, with respect to the rejection(s) of claims 1-20 under 35 U.S.C. 103 have been fully considered and are persuasive, specifically to the previous combination with regards to the Tripathy reference. Furthermore, the applicant’s arguments that Nagy does not use an inference model to generate a predictive graph flow and that the first/beginning, intermediate, and second/end point in Nagy is erroneous is persuasive. However, Nagy still discloses the first/beginning, intermediate, and second/end point as shown in the new mapping below. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Nagy et al. US20200056895A1, Schubert et al. US20190042859A1, and Krumm et al. US20100305850A1, in a second non-final rejection.
Examiner Note: The limitations with regards to the primary reference Nagy has been remapped, since a new 35 U.S.C. 103 combination with regards to Schubert and Krumm requires remapping of the primary reference that uses another embodiment to disclose the claimed matter.
Claim Rejections - 35 USC § 103
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.
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, 3, 6-8, 10, 13-15, 17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Nagy et al. US20200056895A1 (henceforth Nagy) in view of Schubert et al. US20190042859A1 (henceforth Schubert) and Krumm et al. US20100305850A1 (henceforth Krumm).
Regarding claim 1,
Nagy discloses:
A non-transitory computer-readable medium having computer-executable instructions embodied thereon that, when executed by computing hardware, cause the computing hardware to perform operations comprising: (See at least Para. 0131-0133.)
identifying a first point that is a current location of an autonomous vehicle; (See at least Para. 0100, “The diagram 700 shows a set of candidate vehicle start points 704A, 704B” and Claim 3, “wherein the first candidate vehicle start point is a current location of the autonomous vehicle”. Therefore, the first point is a current location of an autonomous vehicle.)
Generating a first set of route portions associated with a second point to generate a flow graph comprising directional vectors (Para. 0021, “route components can have directionality and connectivity”) from a plurality of intermediate points to the second point, and each of the plurality of intermediate points represents a beginning of at least one of the routes; (See at least Fig. 9 and Para. 0103, “the first stage end points are candidate vehicle end points 708A, 708B, 708C, 708D, 708E. The first stage start points are candidate waypoints 706A, 706B, 706C” and Para. 0107, “The vehicle autonomy system can continue to execute the path planning algorithm to generate candidate routes until it finds the best candidate route traversing each of the stage start points”. Further see Para. 0108 “this includes finding the best route that traverses from the stage algorithm start point 902 to the candidate waypoint 706A (e.g., candidate route 906), the best route that traverses from the algorithm start point 902 to the candidate way point 706B (e.g., candidate route 908), and the best route that traverses from the algorithm start point 902 to the candidate waypoint 706C (e,g,, candidate route 910)”. The candidate routes 906, 908, 910 are the first set of route portions, the second point is one of 708A-708E, and the intermediate points are points 706A-706C, which represents the beginning (i.e. first stage start points) of at least one of the routes.)
identifying a second set of route portions from the first point to one or more of the plurality of intermediate points; (See at least Fig. 10, and Para. 0110, “ The previous stage start points (e.g., the set of candidate waypoints 706A, 706B, 706C) become the second stage end points. The second stage start points are the candidate vehicle start points 704A, 704B.” Fig. 10 shows a second set of route portions from the first point to one or more of the plurality of intermediate points.)
generating a set of routes from combinations of the first set of route portions and the second set of route portions, wherein each of the set of routes connects the first point to the second point using at least one of the plurality of intermediate points; selecting, based at least in part on selection criteria, a primary route from the set of routes;
(See at least Para. 0112, “the vehicle autonomy system determines the best route between the second stage algorithm start point and the second stage algorithm end point. If, at operation 806, it is determined that the current stage is the last stage, the vehicle autonomy system determines an overall best route from the best route determined at operation 804 and one or more corresponding candidate routes from a previous stage or stages. For example, the portion of the best route between a current stage end point and the current stage algorithm end point is replaced by the portion of the corresponding candidate route from the previous stage that included the same point” and Para. 0113, “the overall best route is found by replacing the portion of the route 1005 between the candidate waypoint 706C and the second stage algorithm end point is replaced with the portion of the candidate route 910 between the candidate way point 706C and the candidate vehicle end point 708C. This results in an overall best route 710, shown in FIG. 7.” A set of routes is generated from a combination of the first set of route portions (i.e. the portion that runs from the middle stops to the second point in Fig. 9) and the second set of route portions (i.e. the portions that run from the vehicle’s starting place to the middle stops, wherein each of the set of routes connects the first point to the second point using at least one of the plurality of intermediate points. The best route 710 (i.e. the primary route) is therefore selected.)
generating navigation instructions for the primary route; and communicating the navigation instructions to the autonomous vehicle to cause the autonomous vehicle to execute the navigation instructions and travel from the first point to the second point.
(See at least Para. 0092, “the vehicle autonomy system controls the vehicle along the best route determined at operation 408 from the vehicle start point towards the vehicle end point.” Further see at least Para. 0052-0053, “ The vehicle autonomy system 302 sends commands to control the one or more vehicle controls 307 to operate the vehicle 300 according to the route.”, wherein the autonomous vehicle has a navigator system to determine a motion plan for controlling the vehicle, which includes communicating the navigation instructions to the autonomous vehicle to execute the navigation instructions.)
Nagy does not specifically state generating a first set of route portions by processing historical data associated with a second point using an inference model to generate a flow graph comprising directional vectors representing predicted routes taken and wherein the historical data comprises data previously captured during performance of routes utilized to reach the second point. However, Schubert teaches:
generating a first set of route portions by processing historical data associated with a second point using an inference model to generate a flow graph comprising directional vectors representing predicted routes taken and wherein the historical data comprises data previously captured during performance of routes utilized to reach the second point. (See at least Para. 0027, “in order to determine a navigation path for a segment of a target location, a machine learning model may be used. The model may have been trained based on previously determined navigation paths for similar locations. When data representing a segment of a target location is input, the model may output a distribution of possible navigation paths for the vehicle to follow”. A machine learning model that has been trained based on historical data is an inference model. Further see Para. 0027, “the distribution of possible navigation paths may be represented as a grid representing a portion of the target location where each cell of the grid on a possible navigation path is highlighted”. The ANN of Fig. 8 (i.e. the inference model) is used to generate the flow graph. See Para. 0111, “FIG. 8 illustrates an example structure of an ANN 800 that may be used to determine a navigation path for a segment of a target location“ and Para. 0114, “the output nodes may correspond to a sequence of vectors indicating a direction of travel from the previous output node.” A first set of route portions are generated by processing historical data associated with a second point using an inference model to generate a flow graph comprising directional vectors, wherein the historical data comprises data previously captured during performance of routes utilized to reach the second point. Additionally see Para. 0094, wherein the historical data comprises data previously captured utilized to reach the second point.)
It would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to have modified Nagy to incorporate the teachings of Schubert to include “generating a first set of route portions by processing historical data associated with a second point using an inference model to generate a flow graph comprising directional vectors representing predicted routes taken wherein the historical data comprises data previously captured during performance of routes utilized to reach the second point” since “a vehicle's autonomous control system may sometimes lack the ability to safely navigate and complete tasks without human assistance, especially in unfamiliar locations” (Para. 0021, Schubert) and “an autonomous vehicle may encounter an environment layout including physical obstacles or surfaces that the vehicle is unable to confidently navigate. In such situations, the vehicle's control system may cause the vehicle to perform operations that a human would typically avoid. For example, the control system may navigate the vehicle through areas that a package recipient may view as off-limits, such as gardens, private walkways, or lawns. In some cases, the control system may not be able to confidently identify a viable navigation path for the vehicle at all. Thus, although autonomous and semi-autonomous vehicles can present some potential benefits that may improve elements of the shipping process” (Para. 0022, Schubert). This would create a more robust navigation system for autonomous vehicles such that they can confidently navigate and improve navigational elements of a shipping process.
Furthermore, Nagy routes the vehicle to drop-off points using map data describing roadways and segments, and Schubert discloses that an autonomous control system “lack the ability to safely navigate and complete tasks without human assistance, especially in unfamiliar locations” (Para. 0021, Schubert) and “the control system may navigate the vehicle through areas that a package recipient may view as off-limits, such as gardens, private walkways, or lawns. In some cases, the control system may not be able to confidently identify a viable navigation path for the vehicle at all” (Para. 0022, Schubert), in which Schubert solves by generating paths from historical data of prior traversals to that location. It would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to generate Nagy’s route portions using Schubert’s inference model (that includes this historical data) in order to route the vehicle along the path that has already been shown to succeed at that location.
Additionally, a person having ordinary skill in the art would have a reasonable expectation of success in combining the teachings of Nagy and Schubert. The claimed invention is merely a combination of known elements and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable.
Nagy and Schubert do not specifically state that the historical data comprises time series data. However, Krumm teaches:
the historical data comprises time series data. (See at least Para. 0054, wherein the historical GPS data comprises time series data.)
It would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to have modified Nagy and Schubert to incorporate the teachings of Krumm to include “the historical data comprises time series data” since “the time related data can identify the direction of the traces that is comprised of the time stamped nodes” (Para. 0054, Krumm) and “raw positioning data (e.g., GPS data points) can be collected, such as from a plurality of vehicles, which mark traces of vehicular travel. In this embodiment, a location of the traces can be clarified, such as into more coherent groups, using adjustment forces that are related to the traces. From these groups of clarified traces, a graph line can be created by merging the traces, for example, into graph of nodes and edges representing the road network. In this embodiment, the graph may be suitable for planning vehicle travel routes” (Para. 0006, Krumm). Using historical time series data would create a more robust system for planning vehicle travel routes since “roadways and other vehicular travel routes can often change due to construction, new roads, or other conditions, for example” (Para. 0001, Krumm). Additionally, a person having ordinary skill in the art would have a reasonable expectation of success in combining the teachings of Nagy, Schubert, and Krumm. The claimed invention is merely a combination of known elements and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable.
Regarding claim 3,
Nagy discloses:
wherein the selection criteria comprise identifying a route from the set of routes having a shortest time duration to traverse from the first point to the second point. (See at least Para. 0021, wherein the estimated driving time is included in the cost (wherein a longer driving time is a higher cost). Further see Para. 0091, “the vehicle autonomy system applies a path planning algorithm to find the lowest cost and/or highest reward combination of route components between the algorithm start point and the algorithm end point. In the example of FIG. 5, the best route 509 is from the algorithm start algorithm 502 to the candidate vehicle start point 506C, to the candidate vehicle end point 508A, and finally to the algorithm end point 504.” The lowest cost includes a shortest driving time.)
Regarding claim 6,
Nagy, Schubert, and Krumm discloses the limitations as recited in claim 1 above,
Schubert discloses the inference model (Fig. 8, ANN 800) but does not specifically state applying an uncertainty constraint to the time series data to remove data used in generating one or more of the set of routes having a certainty that does not satisfy a threshold. However, Krumm teaches:
applying an uncertainty constraint to the time series data to remove data used in generating one or more of the set of routes having a certainty that does not satisfy a threshold. (See at least Para. 0050, “there may be random outlier traces that result from errors, such as GPS signal errors, driving errors, or other potential recording errors. These trace segments typically will map to graph edges that have a small trace volume (e.g., one trace forms the graph-line). Further, road segments that are traveled merely a few times will also typically be mapped to graph edges having a small trace volume (e.g., one to three traces form the graph-line). In this aspect, for example, these graph-line segments may be considered "unreliable" when they have a low trace volume forming the graph line. In one embodiment merging related traces into a graph line can comprise removing edges from the graph line that have a number of traces that meets a desired trace volume threshold.” Data is removed that is used in generating one or more of the set of routes having a certainty that does not satisfy a threshold (i.e. unreliable data is removed).)
It would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to have modified Nagy and Schubert to incorporate the teachings of Krumm to include “the historical data comprises time series data” since “the time related data can identify the direction of the traces that is comprised of the time stamped nodes” (Para. 0054, Krumm) and “raw positioning data (e.g., GPS data points) can be collected, such as from a plurality of vehicles, which mark traces of vehicular travel. In this embodiment, a location of the traces can be clarified, such as into more coherent groups, using adjustment forces that are related to the traces. From these groups of clarified traces, a graph line can be created by merging the traces, for example, into graph of nodes and edges representing the road network. In this embodiment, the graph may be suitable for planning vehicle travel routes” (Para. 0006, Krumm). Using historical time series data would create a more robust system for planning vehicle travel routes since “roadways and other vehicular travel routes can often change due to construction, new roads, or other conditions, for example” (Para. 0001, Krumm). Additionally, a person having ordinary skill in the art would have a reasonable expectation of success in combining the teachings of Nagy, Schubert, and Krumm. The claimed invention is merely a combination of known elements and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable.
Regarding claim 7,
Nagy, Schubert, and Krumm discloses the limitations as recited in claim 1 above.
Schubert further teaches:
wherein the inference model is a two-dimensional Gaussian model.
(See at least Para. 0120, “Gauss-Jacobi backpropagation, Gauss-Seidel backpropagation” which represent two-dimensional gaussian models for weighing values between interconnected nodes based on prior navigation paths.)
It would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to have modified Nagy to incorporate the teachings of Schubert to include “wherein the inference model is a two-dimensional Gaussian model” since “ANN 800 may be trained to determine navigation paths that are likely similar to navigation paths that a human would select. Thus, the delivery system may autonomously select navigation paths that are likely to be successful without direct involvement of a human user.” (See Para. 0123, Schubert). This would create a more robust system for determining navigation paths for generating a flow graph. Additionally, a person having ordinary skill in the art would have a reasonable expectation of success in combining the teachings of Nagy and Schubert. The claimed invention is merely a combination of known elements and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable.
Regarding claim 8,
Nagy, Schubert, and Krumm discloses the limitations as recited in claim 1 above, and is therefore rejected under the same rejection and obviousness rational.
Regarding claim 10,
Nagy, Schubert, and Krumm discloses the limitations as recited in claim 3 above, and is therefore rejected under the same rejection and obviousness rational.
Regarding claim 13,
Nagy, Schubert, and Krumm discloses the limitations as recited in claim 6 above, and is therefore rejected under the same rejection and obviousness rational.
Regarding claim 14,
Nagy, Schubert, and Krumm discloses the limitations as recited in claim 7 above, and is therefore rejected under the same rejection and obviousness rational.
Regarding claim 15,
Nagy, Schubert, and Krumm discloses the limitations as recited in claim 1 above, and is therefore rejected under the same rejection and obviousness rational.
Nagy further discloses:
generating a second set of route portions based at least in part on segmenting map data to generate segmented map data and comparing the segmented map data to the flow graph to identify an area shared by the segmented map data and the flow graph that corresponds to the plurality of intermediate points, wherein each of the second set of route portions is from a beginning point to one or more of the plurality of intermediate points. (See at least Fig. 5 and Para. 0021-0022, “A route component is a section or segment of roadway on which the autonomous vehicle can travel. Route components can have directionality and connectivity. The directionality of a route component describes a direction in which the autonomous vehicle can traverse the route component.” Further see at least Para. 0034-0036.)
Regarding claim 17,
Nagy, Schubert, and Krumm discloses the limitations as recited in claim 3 above, and is therefore rejected under the same rejection and obviousness rational.
Regarding claim 20,
Nagy, Schubert, and Krumm discloses the limitations as recited in claim 6 above, and is therefore rejected under the same rejection and obviousness rational.
Claims 2, 9, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Nagy, Schubert, and Krumm further in view of Okuyama US20180218602A1.
Regarding claim 2,
Nagy, Schubert, and Krumm discloses the limitations as recited in claim 1 above.
Nagy does not specifically state wherein the selection criteria comprise identifying a route in the set of routes having a shortest distance for navigating from the first point to the second point. However, Okuyama teaches:
wherein the selection criteria comprise identifying a route in the set of routes having a shortest distance for navigating from the first point to the second point
(See at least Para. 0005, “The route searching device extracts a route with a shortest distance”. Further see Fig. 3, wherein the shortest route is from a position P of the vehicle V (i.e. a first point) to destination Q (i.e. a second point).)
It would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to have modified Nagy to incorporate the teachings of Okuyama to include “wherein the selection criteria comprise identifying a route in the set of routes having a shortest distance for navigating from the first point to the second point” in order to “ to provide a route searching device and a route searching method which can improve efficiency and accuracy of lane-basis route searching” (Para. 0004, Okuyama). This would create an improved and more robust pathing system for vehicles. Additionally, a person having ordinary skill in the art would have a reasonable expectation of success in combining the teachings of Nagy and Okuyama. The claimed invention is merely a combination of known elements and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable.
Regarding claim 9,
Nagy, Schuber, Krumm and Okuyama discloses the limitations as recited in claim 2 above, and is therefore rejected under the same rejection and obviousness rational.
Regarding claim 16,
Nagy, Schuber, Krumm and Okuyama discloses the limitations as recited in claim 2 above, and is therefore rejected under the same rejection and obviousness rational.
Claims 4, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Nagy, Schubert, and Krumm further in view of Jordens et al. US20170219714A1 (henceforth Jordens).
Regarding claim 4,
Nagy, Schubert, and Krumm discloses the limitations as recited in claim 1 above. Nagy discloses a selecting criteria comprising identifying a route from the set of routes having the lowest cost (para. 0022) but does not specifically state wherein the selection criteria comprise at least one of a median length of distance or a mean length of distance to traverse among lengths of distance to traverse for the set of routes.
However, Jordens teaches:
wherein the selection criteria comprise identifying a route from the set of routes having at least one of a median length of distance or a mean length of distance to traverse among lengths of distance to traverse for the set of routes. (See at least Para. 0037, “The desired trajectory 31 may be a spatial mean value for all ascertained or measured trajectories 32-a and 32-b, or a median.“)
It would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to have modified Nagy to incorporate the teachings of Jordens to include “wherein the selection criteria comprise identifying a route from the set of routes having at least one of a median length of distance or a mean length of distance to traverse among lengths of distance to traverse for the set of routes” since “the formation of a median can lessen an influence of extreme driving maneuvers” (Para. 0037, Jordens). This would create an improved and more robust pathing system for vehicles. Additionally, a person having ordinary skill in the art would have a reasonable expectation of success in combining the teachings of Nagy and Jordens. The claimed invention is merely a combination of known elements and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable.
Regarding claim 11,
Nagy, Schubert, Krumm, and Jordens discloses the limitations as recited in claim 4 above, and is therefore rejected under the same rejection and obviousness rational.
Regarding claim 18,
Nagy, Schubert, Krumm, and Jordens discloses the limitations as recited in claim 4 above, and is therefore rejected under the same rejection and obviousness rational.
Claims 5, 12, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Nagy, Schubert, and Krumm further in view of Paglieroni et al. US20200117211A1 (henceforth Paglieroni).
Regarding claim 5,
Nagy, Schubert, and Krumm discloses the limitations as recited in claim 1 above, including an inference model (See ANN 800 of Schubert). Schubert does not specifically state wherein the model mathematically applies an attractive force to the second point to affect flow of the directional vectors.
However, Paglieroni teaches:
wherein the model mathematically applies an attractive force to the second point to affect flow of the directional vectors.
(See at least Fig. 1 and Para. 0018, “FIG. 1 illustrates one configuration of field lines. The electric field lines for a configuration of four positive charges 102 and one large negative charge 101 is illustrated. The field lines radiate away from the positive charges and converge to the negative charge.” An attractive force (i.e. to the second point 101) is applied to affect flow of the directional vectors. The attractive force is applied mathematically, by using equation 3 (i.e. a mathematical equation) as shown in Para. 0018.)
It would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to have modified Nagy and Schubert to incorporate the teachings of Paglieroni to include “wherein the model mathematically applies an attractive force to the second point to affect flow of the directional vectors” in order to “travel direction for a vehicle to travel to a target or destination location so that the objects are avoided based on an attract-repel model” (Para. 0014, Paglieroni). This would create an improved and more robust pathing system for vehicles. Additionally, a person having ordinary skill in the art would have a reasonable expectation of success in combining the teachings of Nagy, Schubert and Paglieroni. The claimed invention is merely a combination of known elements and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable.
Regarding claim 12,
Nagy, Schubert, Krumm and Paglieroni discloses the limitations as recited in claim 5 above, and is therefore rejected under the same rejection and obviousness rational.
Regarding claim 19,
Nagy, Schubert, Krumm and Paglieroni discloses the limitations as recited in claim 5 above, and is therefore rejected under the same rejection and obviousness rational.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GABRIEL J LAMBERT whose telephone number is (571)272-4334. The examiner can normally be reached M-F 10:00 am- 6:00 pm MDT.
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, Erin Piateski can be reached at (571) 270-7429. 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.
/Erin M Piateski/Supervisory Patent Examiner, Art Unit 3669
/G.J.L./
Examiner
Art Unit 3669
