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 .
Response to Arguments
Applicant’s arguments with respect to claims 1 and 12 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Objections
Claims 1 and 12 are objected to because of the following informalities:
In claim 1, line 7, “array with of M by N rows and columns” should read “array with M by N rows and columns”.
In claim 12, line 9, “array with of M by N rows and columns” should read “array with M by N rows and columns”.
Appropriate correction is required.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 2, 4, 9-13, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Siebert et al. (U.S. Publication No. 2021/0347383; hereinafter Siebert) and further in view of Ono (U.S. Publication No. 2022/0292365; hereinafter Ono).
Regarding claim 1, Siebert teaches a method for controlling automated vehicle acceleration and braking (Siebert: Par. 86; i.e., the vehicle computing system 604 may include one or more system controllers 626, which may be configured to control … propulsion, braking… of the vehicle 602),
the method comprising: obtaining an image using at least one vehicle camera of a host vehicle (Siebert: Par. 18; i.e., the objects may be detected based on sensor data from sensors (e.g., cameras…) of the vehicle);
extracting machine learning model feature inputs based on the obtained image, by supplying the obtained image to multiple visual transformer layers to generate the machine learning model feature inputs (Sibert: Par. 35; i.e., the vehicle computing system may receive the sensor data and may determine a type of object 104 (e.g., classify the type of object), such as … a pedestrian… the object type may be input into a model; Par. 36; i.e., a machine learned model 108 (e.g., the model 108); Siebert: Par. 97; i.e., an exemplary neural network is a biologically inspired technique which passes input data through a series of connected layers to produce an output… a neural network may utilize machine learning);
processing the obtained image with a separate object detection model to detect one or more objects in the obtained image, the one or more objects including at least one pedestrian (Siebert: Par. 19; i.e., a model and/or computing device may receive the sensor data and may determine a type of object (e.g., classify the type of object), such as, for example, whether the object is … a pedestrian);
assigning attention weights to regions of the obtained image according to locations of the one or more objects in the obtained image (Siebert: Par. 41; i.e., the discretized representation 114 comprises multiple cells, such as cell 126 and cell 128. Each cell can comprise a probability that the pedestrian 104 will be at a location of the cell in the future; as displayed in Figure 3, the darker cells have higher probabilities assigned);
combining the attention weights with corresponding ones of the machine learning model feature inputs according to the regions of the obtained image (Siebert: Par. 41; i.e., the model 108 may determine that the cell 126 is associated with the crosswalk 106A and that the cell 128 is associated with the crosswalk 106B, and output the predicted trajectories 110A and 110B based at least in part on probabilities associated with respective cell locations; the probabilities and feature inputs are combined in the model to generate predicted trajectories);
executing a machine learning model to generate a crossing intention prediction output associated with the at least one pedestrian (Siebert: Par. 13; i.e., the object may be a pedestrian and the machine learned model may determine that the pedestrian intends to enter a crosswalk (e.g., crosswalk intention));
and in response to the crossing intention prediction output exceeding a crossing intention threshold, controlling automatic braking of the host vehicle according to a location of the at least one pedestrian (Siebert: Par. 14; i.e., the one or more intentions determined by the model may be associated with a trajectory of the object… a first trajectory having a first weight (e.g., 70%) for comprising the first intention … and a second weight (e.g., 30%) for comprising the second intention … a vehicle computing system of an autonomous vehicle may receive the output from the model (e.g., the trajectories, the weights, and the intentions) and determine a candidate trajectory to control the autonomous vehicle; Par. 86; i.e., the vehicle computing system 604 may include one or more system controllers 626, which may be configured to control … braking… of the vehicle 602).
Siebert does not explicitly teach wherein the machine learning model feature inputs are arranged in a three-dimensional array with of M by N rows and columns, and C channels.
However, in the same field of endeavor, Ono teaches wherein the machine learning model feature inputs are arranged in a three-dimensional array with of M by N rows and columns, and C channels (Ono: Par. 82; i.e., the convolutional arithmetic processor 50 performs the convolutional arithmetic process of a specific convolutional layer in the convolutional neural network on the numerical value stored in the storage device 48. Here, numerical values of input of the convolutional layer are a three-dimensional array including a row and a column and a channel).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Siebert to have further incorporated wherein the machine learning model feature inputs are arranged in a three-dimensional array with of M by N rows and columns, and C channels, as taught by Ono. Doing so would reduce the required memory capacity and reduce manufacturing costs (Ono: Par. 82; i.e., since the number of numerical values required to be stored in the storage device 48 is reduced as compared with the conventional method, the size of the memory required for the storage device 48 can be reduced as compared with the conventional method. As a result, the manufacturing cost can be advantageously reduced).
Regarding claim 2, Siebert in view of Ono teaches the method according to claim 1. Siebert further teaches wherein assigning the attention weights includes: assigning a first intensity value to a first region of the obtained image corresponding to the at least one pedestrian; and assigning a second intensity value to a second region of the obtained image which does not correspond to the at least one pedestrian, and the first intensity value is greater than the second intensity value (Siebert: Par. 41; i.e., the discretized representation 114 comprises multiple cells, such as cell 126 and cell 128. Each cell can comprise a probability that the pedestrian 104 will be at a location of the cell in the future; as displayed in Figure 3, the darker cells have higher probabilities assigned due to their association with a predicted location of the pedestrian, while the white cells have a low to zero probability of being the predicted pedestrian location).
Regarding claim 4, Siebert in view of Ono teaches the method according to claim 1. Siebert further teaches wherein: the machine learning model includes a multilayer perceptron; and executing the machine learning model includes generating the crossing intention prediction output according to an output of the multilayer perceptron (Siebert: Par. 97; i.e., an exemplary neural network is a biologically inspired technique which passes input data through a series of connected layers to produce an output… a neural network may utilize machine learning; Par. 13; i.e., the object may be a pedestrian and the machine learned model may determine that the pedestrian intends to enter a crosswalk).
Regarding claim 9, Siebert in view of Ono teaches the method according to claim 1. Siebert further teaches supplying training data and testing data to the machine learning model (Siebert: Par. 60; i.e., the vehicle computing system may store sensor data associated with actual location of an object and use this data as training data to train the model 108);
comparing multiple crossing intention prediction outputs of the machine learning model, based on the training data, to labeled crossing intention outputs of the testing data (Siebert: Par. 28; i.e., corresponding data may be input into the model to determine an output (e.g., an intent, a trajectory, a weight, and so on) and a difference between the determined output and the actual action by the object may be used to train the model);
determining whether an accuracy of a comparison is greater than or equal to a specified accuracy threshold; adjusting parameters of the machine learning model and retraining the machine learning model, in response to a determination that the accuracy of the comparison is less than the specified accuracy threshold; and saving the machine learning model for use in generating crossing intention prediction output, in response to a determination that the accuracy of the comparison is greater than or equal to the specified accuracy threshold (Siebert: Par. 20; i.e., a computing device may compare an output of the machine learned model to ground truth data and alter one or more parameters of the model based at least in part on the comparing; Par. 125; i.e., one or more parameters of the model may be updated, altered, and/or augmented to train the model. In some instances, the output from the model 108 can be compared against training data (e.g., ground truth representing labelled data) for use in training. Based at least in part on the comparison, parameter(s) associated with the model 108 can be updated).
Regarding claim 10, Siebert in view of Ono teaches the method according to claim 1. Siebert further teaches wherein the image includes at least a forty-five degree field of view from the at least one vehicle camera (Siebert: Par. 37; i.e., the top down representation 112 can represent an area around the vehicle 102… the area can be based at least in part on an area visible to sensors (e.g., a sensor range); the sensor range is at least 45 degrees because the top down representation represents an area surrounding the vehicle).
Regarding claim 11, Siebert in view of Ono teaches the method according to claim 10. Siebert further teaches wherein the one or more objects include at least one of a crosswalk, a traffic light, or another vehicle (Siebert: Par. 18; i.e., the autonomous vehicle may detect objects using one or more sensors while navigating in the environment. The objects may include … dynamic objects such as other vehicles).
Regarding claim 12, Siebert teaches a vehicle control system for controlling vehicle braking based on vehicle camera image processing (Siebert: Par. 86; i.e., the vehicle computing system 604 may include one or more system controllers 626, which may be configured to control … propulsion, braking… of the vehicle 602),
the vehicle control system comprising: at least one vehicle camera configured to obtain an image from a front of a host vehicle (Siebert: Par. 18; i.e., the objects may be detected based on sensor data from sensors (e.g., cameras…) of the vehicle);
and a vehicle control module of the host vehicle, the vehicle control module configured to: extract machine learning model feature inputs based on the obtained image by supplying the obtained image to multiple visual transformer layers to generate the machine learning model feature inputs (Sibert: Par. 35; i.e., the vehicle computing system may receive the sensor data and may determine a type of object 104 (e.g., classify the type of object), such as … a pedestrian… the object type may be input into a model; Par. 36; i.e., a machine learned model 108 (e.g., the model 108); Siebert: Par. 97; i.e., an exemplary neural network is a biologically inspired technique which passes input data through a series of connected layers to produce an output… a neural network may utilize machine learning);
processing the obtained image with a separate object detection model to detect one or more objects in the obtained image, the one or more objects including at least one pedestrian (Siebert: Par. 19; i.e., a model and/or computing device may receive the sensor data and may determine a type of object (e.g., classify the type of object), such as, for example, whether the object is … a pedestrian);
assign attention weights to regions of the obtained image according to locations of the one or more objects in the obtained image (Siebert: Par. 41; i.e., the discretized representation 114 comprises multiple cells, such as cell 126 and cell 128. Each cell can comprise a probability that the pedestrian 104 will be at a location of the cell in the future; as displayed in Figure 3, the darker cells have higher probabilities assigned);
combine the attention weights with corresponding ones of the machine learning model feature inputs according to the regions of the obtained image (Siebert: Par. 41; i.e., the model 108 may determine that the cell 126 is associated with the crosswalk 106A and that the cell 128 is associated with the crosswalk 106B, and output the predicted trajectories 110A and 110B based at least in part on probabilities associated with respective cell locations; the probabilities and feature inputs are combined in the model to generate predicted trajectories);
execute a machine learning model to generate a crossing intention prediction output associated with the at least one pedestrian (Siebert: Par. 13; i.e., the object may be a pedestrian and the machine learned model may determine that the pedestrian intends to enter a crosswalk (e.g., crosswalk intention));
and in response to the crossing intention prediction output exceeding a crossing intention threshold, control automatic braking of the host vehicle according to a location of the at least one pedestrian (Siebert: Par. 14; i.e., the one or more intentions determined by the model may be associated with a trajectory of the object… a first trajectory having a first weight (e.g., 70%) for comprising the first intention … and a second weight (e.g., 30%) for comprising the second intention … a vehicle computing system of an autonomous vehicle may receive the output from the model (e.g., the trajectories, the weights, and the intentions) and determine a candidate trajectory to control the autonomous vehicle; Par. 86; i.e., the vehicle computing system 604 may include one or more system controllers 626, which may be configured to control … braking… of the vehicle 602).
Siebert does not explicitly teach wherein the machine learning model feature inputs are arranged in a three-dimensional array with of M by N rows and columns, and C channels.
However, in the same field of endeavor, Ono teaches wherein the machine learning model feature inputs are arranged in a three-dimensional array with of M by N rows and columns, and C channels (Ono: Par. 82; i.e., the convolutional arithmetic processor 50 performs the convolutional arithmetic process of a specific convolutional layer in the convolutional neural network on the numerical value stored in the storage device 48. Here, numerical values of input of the convolutional layer are a three-dimensional array including a row and a column and a channel).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Siebert to have further incorporated wherein the machine learning model feature inputs are arranged in a three-dimensional array with of M by N rows and columns, and C channels, as taught by Ono. Doing so would reduce the required memory capacity and reduce manufacturing costs (Ono: Par. 82; i.e., since the number of numerical values required to be stored in the storage device 48 is reduced as compared with the conventional method, the size of the memory required for the storage device 48 can be reduced as compared with the conventional method. As a result, the manufacturing cost can be advantageously reduced).
Regarding claim 13, Siebert in view of Ono teaches the system according to claim 12. Siebert further teaches wherein the vehicle control module is configured to assign the attention weights by: assigning a first intensity value to a first region of the obtained image corresponding to the at least one pedestrian; and assigning a second intensity value to a second region of the obtained image which does not correspond to the at least one pedestrian, and the first intensity value is greater than the second intensity value (Siebert: Par. 41; i.e., the discretized representation 114 comprises multiple cells, such as cell 126 and cell 128. Each cell can comprise a probability that the pedestrian 104 will be at a location of the cell in the future; as displayed in Figure 3, the darker cells have higher probabilities assigned due to their association with a predicted location of the pedestrian, while the white cells have a low to zero probability of being the predicted pedestrian location).
Regarding claim 15, Siebert in view of Ono teaches the system according to claim 12. Siebert further teaches wherein: the machine learning model includes a multilayer perceptron; and the vehicle control module is configured to execute the machine learning model by generating the crossing intention prediction output according to an output of the multilayer perceptron (Siebert: Par. 97; i.e., an exemplary neural network is a biologically inspired technique which passes input data through a series of connected layers to produce an output… a neural network may utilize machine learning; Par. 13; i.e., the object may be a pedestrian and the machine learned model may determine that the pedestrian intends to enter a crosswalk).
Claims 3 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Siebert in view of Ono and further in view of Pan et al. (U.S. Publication No. 2021/0179097; hereinafter Pan).
Regarding claim 3, Siebert in view of Ono teaches the method according to claim 2, but does not teach wherein combining the attention weights and the machine learning model feature inputs includes generating a weighted sum of the machine learning model feature inputs, according to the attention weights.
However, in the same field of endeavor, Pan teaches wherein combining the attention weights and the machine learning model feature inputs includes generating a weighted sum of the machine learning model feature inputs, according to the attention weights (Pan: Par. 54; i.e., the spatial aggregation determination module 404 is configured to determine the aggregated lane encoding based on a weighted sum of the lane encoding for each of the one or more lanes, wherein a weight for each of the one or more lanes is based on the attention score of the lane).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Siebert to have further incorporated wherein combining the attention weights and the machine learning model feature inputs includes generating a weighted sum of the machine learning model feature inputs, according to the attention weights, as taught by Pan. Doing so would allow the system to more accurately predict the intention of the pedestrian (Pan: Par. 60; i.e., the method may accurately predict the trajectory or intended movement of the moving obstacles).
Regarding claim 14, Siebert in view of Ono teaches the system according to claim 13, but does not teach wherein the vehicle control module is configured to assign the attention weights and the machine learning model feature inputs by generating a weighted sum of the machine learning model feature inputs, according to the attention weights.
However, in the same field of endeavor, Pan teaches wherein the vehicle control module is configured to assign the attention weights and the machine learning model feature inputs by generating a weighted sum of the machine learning model feature inputs, according to the attention weights (Pan: Par. 54; i.e., the spatial aggregation determination module 404 is configured to determine the aggregated lane encoding based on a weighted sum of the lane encoding for each of the one or more lanes, wherein a weight for each of the one or more lanes is based on the attention score of the lane).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Siebert to have further incorporated wherein the vehicle control module is configured to assign the attention weights and the machine learning model feature inputs by generating a weighted sum of the machine learning model feature inputs, according to the attention weights, as taught by Pan. Doing so would allow the system to more accurately predict the intention of the pedestrian (Pan: Par. 60; i.e., the method may accurately predict the trajectory or intended movement of the moving obstacles).
Claims 6-8 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Siebert in view of Ono and further in view of Wang et al. (U.S. Patent No. 11810366; hereinafter Wang).
Regarding claim 6, Siebert in view of Ono teaches the method according to claim 1, but does not teach wherein executing the machine learning model includes: obtaining multiple key values according to the machine learning model feature inputs; executing a classification query according to the machine learning model feature inputs; and correlating a classification query output with the multiple key values.
However, in the same field of endeavor, Wang teaches wherein executing the machine learning model includes: obtaining multiple key values according to the machine learning model feature inputs; executing a classification query according to the machine learning model feature inputs; and correlating a classification query output with the multiple key values (Wang: Col. 7, lines 20-29; i.e., inputting the matrix into the multi-head attention neural network (Transformer) for extraction of local image features of pedestrians… with regard to a query matrix, a key matrix and a value matrix present in each of the image block vector sequences, obtaining an attention score matrix by matrix multiplying the query matrix and the key matrix).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Siebert to have further incorporated wherein executing the machine learning model includes: obtaining multiple key values according to the machine learning model feature inputs; executing a classification query according to the machine learning model feature inputs; and correlating a classification query output with the multiple key values, as taught by Wang. Doing so would allow for enhanced pedestrian recognition (Wang: Col. 5, lines 18-20; i.e., local features of pedestrians are enhanced to improve the recognition rate of pedestrian).
Regarding claim 7, Siebert in view of Ono and Wang teaches the method according to claim 6. Wang further teaches wherein executing the machine learning model includes: combining the attention weights with a correlation of the classification query output and the multiple key values (Wang: Col. 7, lines 51-62; i.e., calculating a multi-head attention weight through the activation function… calculating a single attention: Head=Attention(Quey, Key, Value); 5, multi-head attention; the attention weights are combined with the query output and key values);
and executing a normalized exponential function on a combination of the attention weights and the correlation of the classification query output and the multiple key values, to generate the crossing intention prediction output (Wang: Col. 8, lines 10-13; i.e., inputting the images into the three-channel image convolutional neural network, where the input images are weighted and combined in a convolution depth direction; Col. 9, lines 56-58; i.e., using the activation function (Softmax) to map the probability distribution of pedestrians into categories to recognize pedestrians).
Regarding claim 8, Siebert in view of Ono and Wang teaches the method according to claim 7. Wang further teaches wherein executing the machine learning model includes: combining an output of the normalized exponential function with the multiple key values to generate an embedding vector; and supplying the embedding vector to a multilayer perceptron to generate the crossing intention prediction output (Wang: Col. 9, lines 31-37; i.e., inputting the output of multi-head attention, the output of channel convolution and the output of spatial convolution into a multi-layer perceptron, where the local image features of pedestrians are mapped to parallel branches through the linear layer to conduct feature fusion calculation, so as to obtain the enhanced local image features of pedestrians; Col. 9, lines 56-58; i.e., using the activation function (Softmax) to map the probability distribution of pedestrians into categories to recognize pedestrians).
Regarding claim 17, Siebert in view of Ono teaches the system according to claim 12, but does not teach wherein the vehicle control module is configured to executing the machine learning model by: obtaining multiple key values according to the machine learning model feature inputs; executing a classification query according to the machine learning model feature inputs; and correlating a classification query output with the multiple key values.
However, in the same field of endeavor, Wang teaches wherein the vehicle control module is configured to executing the machine learning model by: obtaining multiple key values according to the machine learning model feature inputs; executing a classification query according to the machine learning model feature inputs; and correlating a classification query output with the multiple key values (Wang: Col. 7, lines 20-29; i.e., inputting the matrix into the multi-head attention neural network (Transformer) for extraction of local image features of pedestrians… with regard to a query matrix, a key matrix and a value matrix present in each of the image block vector sequences, obtaining an attention score matrix by matrix multiplying the query matrix and the key matrix).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Siebert to have further incorporated wherein the vehicle control module is configured to executing the machine learning model by: obtaining multiple key values according to the machine learning model feature inputs; executing a classification query according to the machine learning model feature inputs; and correlating a classification query output with the multiple key values, as taught by Wang. Doing so would allow for enhanced pedestrian recognition (Wang: Col. 5, lines 18-20; i.e., local features of pedestrians are enhanced to improve the recognition rate of pedestrian).
Regarding claim 18, Siebert in view of Ono and Wang teaches the system according to claim 17. Wang further teaches wherein the vehicle control module is configured to executing the machine learning model by: combining the attention weights with a correlation of the classification query output and the multiple key values (Wang: Col. 7, lines 51-62; i.e., calculating a multi-head attention weight through the activation function… calculating a single attention: Head=Attention(Quey, Key, Value); 5, multi-head attention; the attention weights are combined with the query output and key values);
and executing a normalized exponential function on a combination of the attention weights and the correlation of the classification query output and the multiple key values, to generate the crossing intention prediction output (Wang: Col. 8, lines 10-13; i.e., inputting the images into the three-channel image convolutional neural network, where the input images are weighted and combined in a convolution depth direction; Col. 9, lines 56-58; i.e., using the activation function (Softmax) to map the probability distribution of pedestrians into categories to recognize pedestrians).
Regarding claim 19, Siebert in view of Ono and Wang teaches the system according to claim 18. Wang further teaches wherein the vehicle control module is configured to executing the machine learning model by: combining an output of the normalized exponential function with the multiple key values to generate an embedding vector; and supplying the embedding vector to a multilayer perceptron to generate the crossing intention prediction output (Wang: Col. 9, lines 31-37; i.e., inputting the output of multi-head attention, the output of channel convolution and the output of spatial convolution into a multi-layer perceptron, where the local image features of pedestrians are mapped to parallel branches through the linear layer to conduct feature fusion calculation, so as to obtain the enhanced local image features of pedestrians; Col. 9, lines 56-58; i.e., using the activation function (Softmax) to map the probability distribution of pedestrians into categories to recognize pedestrians).
Regarding claim 20, Siebert in view of Ono and Wang teaches the system according to claim 19. Siebert further teaches wherein: the image includes at least a forty-five degree field of view from the at least one vehicle camera (Siebert: Par. 37; i.e., the top down representation 112 can represent an area around the vehicle 102… the area can be based at least in part on an area visible to sensors (e.g., a sensor range); the sensor range is at least 45 degrees because the top down representation represents an area surrounding the vehicle);
and the one or more objects include at least one of a crosswalk, a traffic light, or another vehicle (Siebert: Par. 18; i.e., the autonomous vehicle may detect objects using one or more sensors while navigating in the environment. The objects may include … dynamic objects such as other vehicles).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRANDON Z WILLIS whose telephone number is (571)272-5427. The examiner can normally be reached Weekdays 8:00-5:30.
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 D. Bishop can be reached at (571) 270-3713. 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.
/B.Z.W./Examiner, Art Unit 3661
/Erin D Bishop/Supervisory Patent Examiner, Art Unit 3665
Prosecution Insights