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 Amendment
Claims 1-20 remain pending. Claims 1, 8, 15 have been amended.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-6, 8-13, and 15-19 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Vig (US 11790668 B2).
With respect to claim 1, Vig teaches A method for detecting a portion of an environment of a vehicle (
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col 1 lines 46-52), comprising:
generating, using one or more sensors coupled to a vehicle (see figure 2 element 210), environmental data from an environment of the vehicle, wherein the environmental data comprises one or more of the following:
ground LiDAR data from the environment (
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col 2 clause 4);
camera data from the environment (
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col 2 clause 4); and
path data corresponding to a change in position of one or more other vehicles within the environment (
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col 2 clause 2);
inputting the environmental data into a machine learning model (col 24 lines 6-37) trained to generate a heatmap (col 20 lines 19-42);
and using a processor (col 1-col2 clause 1):
based on the environmental data, determining a portion of the environment, wherein the portion of the environment comprises an area having a likelihood, greater than a minimum threshold, of being adjacent to one or more pavement markings (col 19 lines 57-67 and col 20 lines 1-18),
wherein the portion of the environment is exclusive of the one or more pavement markings; and generating the heatmap (col 20 lines 36-42),
wherein the heatmap corresponds to the portion of the environment (col 20 lines 36-42).
With respect to claim 2, Vig teaches the method of claim 1, wherein the one or more sensors comprise one or more of the following: one or more LiDAR systems (see col 9 lines 10-11); one or more cameras (see col 9 lines 10-11); and one or more RADAR systems (see col 9 lines 10-11).
With respect to claim 3, Vig teaches the method of claim 1, wherein the ground LiDAR data comprises a 2-dimensional grouping of data points within the environment (col 12 lines 21-40).
With respect to claim 4, Vig teaches the method of claim 1, wherein generating ground LiDAR data comprises:
capturing, using one or more LiDAR systems, 3-dimensional LiDAR data from the environment (col 12 lines 21-40); and
distilling the 3-dimensional LiDAR data to the ground lidar data (col 21 lines 56-67).
With respect to claim 5, Vig teaches the method of claim 1, wherein: the one or more sensors comprise one or more cameras configured to generate one or more images (col 9 lines 10-11), and
the camera data comprises one or more images (col 2 clause 4).
With respect to claim 6, Vig teaches the method of claim 1, wherein: the processor is configured to run the machine learning model (col 24 lines 6-37 and col 16 lines 13-25), and the machine learning model comprises a neural network (col 24 lines 6-37).
With respect to claim 8, Vig teaches all limitations in consideration of claim 1, due to the substantial similarities between claim 1 and claim 8, with claim 8 being directed towards a system version of claim 1. Vig further teaches a vehicle (see figure 2), an image module (see figure 2 element 210), and a processor (col 16 lines 13-25).
With respect to claim 9, Vig teaches the system of claim 8 and all other limitations in consideration of claim 2, due to the substantial similarity between claims 9 and 2.
With respect to claim 10, Vig teaches the system of claim 8 and all other limitations in consideration of claim 3, due to the substantial similarity between claims 10 and 3.
With respect to claim 11, Vig teaches the system of claim 8 and all other limitations in consideration of claim 4, due to the substantial similarity between claims 11 and 4.
With respect to claim 12, Vig teaches the system of claim 8 and all other limitations in consideration of claim 5, due to the substantial similarity between claims 12 and 5.
With respect to claim 13, Vig teaches the system of claim 8 and all other limitations in consideration of claim 6, due to the substantial similarity between claims 13 and 6.
With respect to claim 15, Vig teaches all limitations in consideration of claim 1, due to the substantial similarities of claim 15 and claim 1, with claim 15 being directed towards a system enabled to do the functions of claim 1. Vig additionally teaches an imaging device comprising one or more cameras (see figure 2), the imaging device coupled to a vehicle (see col 16 lines 13-25), wherein the one or more cameras are configured to capture an image depicting an environment within view of the one or more cameras (see col 2 clause 4); and
a computing device (see figure 3), including a processor (col 16 lines 13-25) and a memory, coupled to the vehicle, configured to store programming instructions that (col 17 lines 6-26).
With respect to claim 16, Vig teaches the system of claim 15 and all other limitations in consideration of claim 2, due to the substantial similarity between claims 16 and 2.
With respect to claim 17, Vig teaches the system of claim 15 and all other limitations in consideration of claim 3, due to the substantial similarity between claims 17 and 3.
With respect to claim 18, Vig teaches the system of claim 15 and all other limitations in consideration of claim 4, due to the substantial similarity between claims 18 and 4.
With respect to claim 19, Vig teaches the system of claim 15 and all other limitations in consideration of claim 6, due to the substantial similarity between claims 19 and 6.
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.
Claims 7, 14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Vig as applied to claims 1, 8, and 15, respectfully, and further in view of Throsby (JP 2022539245 A).
With respect to claim 7, Vig teaches the method of claim 1, but does not explicitly teach any further limitations. Throsby further teaches the method of claim 1, wherein generating path data corresponding to a change in position of the one or more other vehicle in the environment comprises: using the processor (“…processor…” page 9 paragraph 5 line 1): identifying, using image recognition (“object detection, segmentation, and/or classification. In some examples, the perceptual component 222 detects the presence of entities in proximity to the vehicle 202 and/or entity types (e.g., cars, pedestrians, bicycles, animals, buildings, trees, road surfaces, curbs, sidewalks, unknown, etc.)” page 9 paragraph 7 lines 1-4), a first position of one of the one or more other vehicles at a first time (“Example 126 represents a first multi-channel image …first channel 132 may represent a bounding box, position, extent (eg, length and width), etc. of autonomous vehicle 106 and/or object 108 in the environment.” Page 7 paragraph 3 lines 1-4); identifying, using image recognition, a second position of the one of the one or more other vehicles at a second time (“Example 128 represents a second multi-channel image associated with second candidate action 118 . In some examples, some aspects of example 128 may be equal to some aspects of example 126. For example, example 128 can include first channel 132” page 7 paragraph 4 lines 1-3), wherein the second time is after the first time (“As can be appreciated, examples 126 and 128 can include multiple multi-channel images representing the environment at various points in time within the environment. For example, examples 126 and/or 128 may represent the history of autonomous vehicle 106 and object 108 (and other objects such as pedestrians and vehicles) over the past 4 seconds at 0.5 second intervals, although any Instances of numbers and periods can be used to represent the environment” page 8 paragraph 2); determining a change in position between the first position and the second position (“For example, an image may represent an object as a two-dimensional bounding box representing the position of the object within the environment, as well as the object's extent (e.g., object length and width) and object classification (e.g., vehicle, pedestrian, etc.). can be represented. Motion information, such as velocity information, can be represented as a velocity vector associated with a bounding box, although other representations are envisioned.” Page 3 paragraph 5 lines 6-10, motion information); and generating a visual representation of the change in position (“Sensor data and any data based on sensor data can be represented in a top-down view of the environment. For example, an image may represent an object as a two-dimensional bounding box representing the position of the object within the environment, as well as the object's extent (e.g., object length and width) and object classification (e.g., vehicle, pedestrian, etc.). can be represented. Motion information, such as velocity information, can be represented as a velocity vector associated with a bounding box, although other representations are envisioned.” Page 3 paragraph 5 lines 5-10).
Throsby is analogous art in the same field of endeavor as the claimed invention. Throsby is directed towards sensor coupled vehicles that gather environmental data (“Sensors of a first vehicle (such as an autonomous vehicle) can capture sensor data of the environment, which can include a second vehicle or objects away from the vehicle such as pedestrians.” Page 2 paragraph 4 lines 2-4). A person of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to combine Vig and Throsby by utilizing Throsby’s time based environmental, with the expectation that doing so would lead to improvements in decision making (pathing, navigation, etc.) based on enhanced complex reasoning capabilities created by understanding the behavior and intent of objects in the environment around the sensing vehicle (“Autonomous driving in dense urban environments is challenging because of the complex reasoning often used to resolve multidirectional interactions between objects. This reasoning can be time sensitive and constantly evolving. The techniques described herein are intended for driving scenarios, which may include, but are not limited to, urban intersections without traffic lights. At these junctions, multiple objects (vehicles, pedestrians, cyclists, etc.) often compete for the same shared space, so predicting object intent is useful for successfully navigating intersections.” Throsby Description of Embodiments paragraph 2 lines 1-6).
With respect to claim 14, Vig teaches the system of claim 8, and in view of Throsby, all additional limitations in consideration of claim 7, due to the substantial similarities between claims 7 and 14.
With respect to claim 20, Vig teaches the system of claim 15, and in view of Throsby, all additional limitations in consideration of claim 7, due to the substantial similarities between claims 7 and 20
Response to Arguments
Applicant's arguments filed 03/06/2026 have been fully considered.
With respect to applicant’s arguments, in regards to the independent claims 1, 8, and 15, due to
the amendment, the examiner has provided an updated rejection using newly found prior art. Because this art is neither Cohen, Throsby, nor Hosoya , the examiner respectfully finds all associated arguments moot. However, the examiner accordingly finds applicant’s argument that the amendment overcomes the previously used prior art persuasive.
With respect to the dependent claims, because all independent claims remain rejected, the examiner finds applicant’s arguments regarding their allowability moot and additionally their arguments regarding the previously applied prior art combinations moot.
Due to the amount of changes made, the examiner declines the request for interview.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REBECCA C WILLIAMS whose telephone number is (571)272-7074. The examiner can normally be reached M-F 7:30am - 4:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew W Bee can be reached at (571)270-5183. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/REBECCA COLETTE WILLIAMS/Examiner, Art Unit 2677
/ANDREW W BEE/Supervisory Patent Examiner, Art Unit 2677
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