DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice on Prior Art Rejections
2. 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 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.
Status of Claims
3. This Office Action is in response to the applicant's arguments/remarks filed July 14, 2026. Claims 1, 3-7,9,11,13-17, and 19- 20 are amended, new claims 21 and 22 are added, and claims 2, 12, and 18 are cancelled. Claims 1, 3-11, 13-17, and 19-22 are presently pending and are presented for examination.
Response to Arguments/Remarks
4. 35 USC § 101 rejection. Applicant’s arguments/remarks filed July 14, 2026 regarding the 35 USC § 101 rejection have been fully considered. Applicant’s arguments/remarks are persuasive. Accordingly, the 35 USC § 101 rejection is withdrawn.
5. 35 USC § 103 rejection. Applicant’s arguments/remarks filed July 14, 2026 regarding the previous 35 USC § 103 rejection have been fully 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. The applicant’s arguments are only directed to new added amendments and not the prior rejection of record. Based on the new features of the claims presented in the amendments, further search and/or consideration was required to examine the amended claims, so a new 35 USC § 103 ground(s) of rejection is made further in view of Bernal et al, US 9,405,974, presented in this Final Office Action. Therefore, the prior 35 USC § 103 rejection is withdrawn in view of the new added features in the amended claims.
Some additional documents defining the general state of the art that describe object orientation determination from map include:
US 2021/0157004: AUTONOMOUS SCANNING AND MAPPING SYSTEM
US 2023/0386163: OBJECT LEVEL DATA AUGMENTATION SCHEME FOR TRAINING OBJECT DETECTORS
US 2023/0177804: END-TO-END VEHICLE PERCEPTION SYSTEM TRAINING
US 2021/0181758: OBJECT DETECTION AND TRACKING
Therefore, for the above reasons, the examiner maintains rejection over claims 1, 3-11, 13-17, and 19-22.
Claim Rejections - 35 USC § 103
6. 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 of this title, 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.
7. Claims 1, 3-11, 13-17, and 19-22 are rejected under 35 U.S.C 103 as being unpatentable over Vallespi-Gonzalez et al, US 2019/0079526, in view of Levinson et al. US 2020/0098394, further in view of Bernal et al, US 9,405,974, hereinafter referred to as Vallespi-Gonzalez, Levinson, and Bernal, respectively.
Regarding claim 1, Vallespi-Gonzalez discloses a method comprising:
identifying a plurality of objects based on sensor data associated with an environment (See at least fig 1-10, ¶ 67, 68, 78, 84, 86, 89, 66, 3, “autonomous vehicles, can receive sensor data based on the state of the environment through which the vehicle travels. The sensor data can be used to determine the state of the environment around the vehicle.”);
obtaining, using at least one processor, a map parameter indicative of an expected orientation of an object in the environment (See at least fig 1-10, ¶ 5, 7, 67, 68, 78, 84, 86, 89, 66, 21, “determining an estimated set of physical dimensions of the one or more objects (e.g., physical dimensions including an estimated length, width, and height) and one or more orientations ( e.g., one or more headings, directions, and/or bearings) of the one or more objects associated with a vehicle ( e.g., within range of an autonomous vehicle's sensors) based on one or more states”);
grouping, using the at least one processor, a set of objects of the plurality of objects based on group parameter (See at least fig 1-10, ¶ 7, 21, 33, 34, 36, 37, 38, 39, 40, 41, 47, 6, “The operations can also include determining, based in part on the object data and a machine learned model, one or more characteristics of the one or more objects. The one or more characteristics can include an estimated set of physical dimensions of the one or more objects”), (The examiner notes that grouping is equivalent to obtaining object data of at least two objects as presented in the prior art);
determining, using the at least one processor, an orientation of the set of objects based on the expected orientation indicated by the map parameter (See at least fig 1-10, ¶ 6, 7, 8, 21, 22, 23, 39, 41, 43, 44, 47, 5, “based in part on the estimated set of physical dimensions of the one or more objects, one or more orientations corresponding to the one or more objects. The one or more orientations can be relative to the location of the autonomous vehicle.”); and
generating, using the at least one processor, object detection data associated with the set of objects based on the sensor data and the determined orientation of the set of objects, wherein the object detection data is indicative of one or more spatial features of the set of objects (See at least fig 1-10, ¶ 6, 7, 8, 21, 22, 23, 39, 41, 43, 44, 47, 116, 39, 51, “determine object characteristics (e.g., orientation, shape, dimensions) for detected objects. An autonomy system can include numerous different components (e.g., perception, prediction, and/or optimization) that jointly operate to determine a vehicle's motion plan”);
and causing an autonomous vehicle to navigate based on the object detection data (See at least fig 1-10, ¶ 6, 7, 8, 21, 22, 39, 41, 43, 44, 47, 116, 39, 51, 23, “Based on the orientations of the objects, the vehicle can change its course or increase/reduce its velocity so that the vehicle and the objects can safely navigate around each another.”).
Vallespi-Gonzalez fails to explicitly disclose grouping, using the at least one processor, a set of objects of the plurality of objects based on group parameter.
However, Levinson teaches grouping, using the at least one processor, a set of objects of the plurality of objects based on group parameter (See at least fig 1-7, ¶ 28, 94, 108, 109, 110, 111, 112, 114, 106, “determine parameters associated with respective groups of objects associated with sensor data. The process 700 may include comparing various parameters to determine whether one or more differences exist between such parameters”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Vallespi-Gonzalez and include grouping, using the at least one processor, a set of objects of the plurality of objects based on group parameter as taught by Levinson because it would allow the perception system identifying one or more errors associated with one or more of the determined groups of objects (Levinson ¶ 30).
Vallespi-Gonzalez fails to explicitly disclose an expected orientation of an object in the environment.
However, Bernal teaches an expected orientation of an object in the environment (See at least fig 1-10, Col 11, lines 53-67, “the expected size and orientation of the objects can be learned overtime by performing object detection repeatedly and storing the pixel size 150 and orientation 152 of the detected objects 140 as a function of their location 148, e.g., the object information 154 of the associated data storage device 128. FIG. SA shows a pseudocolored object size map corresponding to the camera 134 and scene used in the experimental setup and obtained via calibration.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Vallespi-Gonzalez and include an expected orientation of an object in the environment as taught by Bernal because it would allow to achieve robust and computationally efficient tracking that has object orientation and size awareness (Col 2, lines 9-15).
Regarding claim 3, Vallespi-Gonzalez discloses the method of claim 1, wherein grouping the set of objects comprises: determining, distances between objects in the set of objects based on the sensor data; and clustering, the objects to form a group based on the distances between the objects (See at least fig 1-10, ¶ 6, 7, 8, 21, 22, 39, 41, 43, 44, 47, 5, 23, 59, 62, 92, 97, 30, “The plurality of classified features can be based in part on the output from one or more sensors that have captured a plurality of training objects ( e.g., actual objects used to train the machine learned model) from various angles and/or distances in different environments (e.g., urban areas, suburban areas, rural areas, heavy traffic, and/or light traffic) and/or environmental conditions”).
Regarding claim 4, Vallespi-Gonzalez discloses the method of claim 1, wherein determining the orientation of the set of objects based on the map parameter comprises: extracting one or more line patterns associated with the set of objects based on the sensor data and the group parameter. (See at least fig 1-10, ¶ 6, 7, 8, 21, 22, 23, 39, 41, 43, 44, 47, 116, 39, 82, “determined characteristics of the object 410 to previously classified features that correspond to the features detected by the sensors including the size, coloring, and movement patterns”).
Regarding claim 5, Vallespi-Gonzalez discloses the method of claim 4, wherein determining the orientation of the set of obiects based on the map parameter comprises: discarding one or more lines associated with the set of objects based on the one or more line patterns (See at least fig 1-10, ¶ 6, 7, 8, 21, 22, 23, 39, 41, 43, 44, 47, 116, 39, 82, 120, “the one or more orientations of the one or more objects (e.g., the one or more orientations of the one or more objects determined at 812 in FIG. 8) can be based in part on characteristics of the one or more bounding shapes (e.g., the one or more bounding shapes generated at 908) including a length, a width, a height, or a center-point associated with the one or more bounding
shapes.”).
Regarding claim 6, Vallespi-Gonzalez discloses the method of claim 4, wherein determining the orientation of the set of obiects based on the map parameter comprises: aligning an object among the set of objects based on the one or more line patterns; and determining the orientation of the set of obiects based on the alignment. (See at least fig 1-10, ¶ 6, 7, 8, 22, 39, 41, 43, 44, 47, 5, 23, 21, “determining an estimated set of physical dimensions of the one or more objects (e.g., physical dimensions including an estimated length, width, and height) and one or more orientations ( e.g., one or more headings, directions, and/or bearings) of the one
or more objects associated with a vehicle ( e.g., within range of an autonomous vehicle's sensors) based on one or more states”).
Regarding claim 7, Vallespi-Gonzalez discloses the method of claim 1, further comprising: determining, using the at least one processor, at least one ground-truth object in the environment based on the sensor data; determining, using the at least one processor, object orientation data indicative of ground-truth orientation of the at least one ground-truth object based on the sensor data; determining, using the at least one processor, a confidence parameter indicative of a difference in orientation between the object orientation data and the orientation of the set of objects based on a comparison of the object orientation data and the orientation of the set of objects; and updating, using the at least one processor, the orientation of the set of objects based on the confidence parameter (See at least fig 1-10, ¶ 6, 7, 8, 22, 39, 41, 43, 44, 47, 5, 23, 21, 49, 106, 116, 115, “comparing one or more characteristics of the one or more objects to a plurality of classified features associated with the plurality of training objects. The one or more characteristics of the one or more objects can include the properties, conditions, or qualities of the one or more objects based in part on the object data including the temperature, shape, texture, velocity, acceleration, and/or physical dimensions (e.g., length, width, and/or height) of the one or more objects and/or portions of the one or more objects”).
Regarding claim 8, Vallespi-Gonzalez discloses the method of claim 1, further comprising: discarding, based on the group parameter and the map parameter, a group from the object detection data (See at least fig 1-10, ¶ 6, 7, 8, 21, 22, 23, 39, 41, 43, 44, 47, 116, 39, 82, 120, 27, “the differences in correct classification output between a machine learned model (that outputs the one or more objects classification labels) and a set of classified object labels associated with a plurality of training objects that have previously been correctly identified, can be processed using an error loss function (e.g., a cross entropy function) that can determine a set of probability distributions based on the same plurality of training objects”).
Regarding claim 9, Vallespi-Gonzalez discloses the method of claim 1, further comprising: modifying, using the at least one processor, the object detection data with map layer information based on the map parameter and/or the group parameter (See at least fig 1-10, ¶ 6, 7, 8, 21, 22, 23, 39, 41, 43, 44, 47, 5, “based in part on the estimated set of physical dimensions of the one or more objects, one or more orientations corresponding to the one or more objects. The one or more orientations can be relative to the location of the autonomous vehicle.”).
Regarding claim 10, Vallespi-Gonzalez discloses the method of claim 1, further comprising: performing, using the at least one processor, a non-maximum suppression scheme on the object detection data based on the group parameter (See at least fig 1-10, ¶ 6, 7, 8, 21, 22, 23, 39, 41, 43, 44, 47, 5, 32, “The one or more computing processes can include the classification ( e.g., allocation or sorting into different groups or categories) of the physical outputs from the sensor device, based in part on one or more classification criteria (e.g., a size, shape, velocity, or acceleration associated with an object).”).
Regarding claim 11, Vallespi-Gonzalez discloses the method of claim 1, further comprising: tracking, using the at least one processor, one or more objects in the environment based on the orientation of the set of obiects and the sensor data (See at least fig 1-10, ¶ 67, 68, 78, 84, 86, 89, 66, 33, “The machine learned model can compare the object data to the classifier data based in part on sensor outputs captured from the detection of one or more classified objects (e.g., thousands or millions of objects) in a variety of environments or conditions. Based on the comparison, the vehicle computing system can determine one or more characteristics of the one or more objects”)
Regarding claim 13, Vallespi-Gonzalez discloses the method of claim 1, further comprising: determining, using the at least one processor, a labelling of the set of objects in the environment based on the orientation of the set of objects and the sensor data (See at least fig 1-10, ¶ 6, 7, 8, 21, 22, 23, 39, 41, 43, 44, 47, 116, 39, 82, 120, 27, 26, “The vehicle computing system can access a machine learned model that has been generated and/or trained in part using classifier data including a plurality of classified features and a plurality of classified object labels associated with training data that can be based on, or associated with, a plurality of training objects”).
Regarding claim 14, Vallespi-Gonzalez discloses the method of claim 1, further comprising: updating, using the at least one processor, a machine-learning model based on the orientation of the set of objects (See at least fig 1-10, ¶ 6, 7, 8, 21, 22, 39, 41, 43, 44, 47, 116, 39, 82, 120, 27, 26, 23, “The vehicle can use any combination of the object data and/or the machine learned model to determine physical dimensions and/or orientations that correspond to the objects (e.g., the dimensions or orientations of other vehicles within a predetermined area). The orientations of the objects can be used in part to determine when objects have a trajectory that will intercept the vehicle as the object travels along its trajectory”).
Regarding claim 15, Vallespi-Gonzalez discloses the method of claim 14, wherein updating the machine-learning model comprises: inputting into the machine-learning model one or more of: the orientation of the set of objects, one or more object parameters, the group parameter, and the map parameter; outputting, an updated orientation of the set of objects from the machine-learning model; and recursively applying the updated orientation of the set of objects in place of the orientation of the set of objects (See at least fig 1-10, ¶ 6, 7, 8, 21, 22, 39, 41, 43, 44, 47, 116, 39, 82, 120, 27, 26, 23, 49, “the machine learned model can be more easily adjusted ( e.g., via re-fined training) than a rules-based system (e.g., requiring re-written rules) as the vehicle computing system is periodically updated to calculate advanced
object features. This can allow for more efficient upgrading of the vehicle computing system, leading to less vehicle downtime.”).
Regarding claim 16, Vallespi-Gonzalez discloses the method of claim 1, further comprising: obtaining, using the at least one processor, one or more estimated object parameters; comparing, using the at least one processor, the one or more estimated object parameters and the orientation of the set of objects; determining, using the at least one processor, a differential parameter indicative of a difference in orientation between the one or more estimated object parameters and the orientation of the set of objects based on the comparing; and updating, using the at least one processor, the orientation of the set of objects[[data]] based on the differential parameter (See at least fig 1-10, ¶ 6, 7, 8, 21, 22, 23, 39, 41, 43, 44, 47, 116, 39, 82, 120, 27, 115, “The comparison of the one or more characteristics of the one or more objects to the plurality of classified features associated with the plurality of training objects can include the determination of values for each of the one or more characteristics and comparing the values to one or more values associated with the plurality of classified features associated with the plurality of training objects. Based in part on the comparison the vehicle computing system can determine differences and similarities between the one or more characteristics of the one or more objects and the plurality of classified features associated
with the plurality of training objects”).
Regarding claim 17, Vallespi-Gonzalez discloses the method of claim 1, further comprising: estimating, using the at least one processor, a probability distribution of the orientation of the set of objects based on the map parameter and/or the group parameter; and determining, using the at least one processor, the object detection data based on the probability distribution of the orientation of the set of objects (See at least fig 1-10, ¶ 6, 7, 8, 21, 22, 23, 39, 41, 43, 44, 47, 116, 39, 82, 120, 27, “the differences in correct classification output between a machine learned model (that outputs the one or more objects classification labels) and a set of classified object labels associated with a plurality of training objects that have previously been correctly identified, can be processed using an error loss function (e.g., a cross entropy function) that can determine a set of probability distributions based on the same plurality of training objects”).
Regarding claim 19, Vallespi-Gonzalez discloses a non-transitory computer readable medium comprising instruction stored thereon that, when executed by at least one processor, cause the at least one processor to carry out operations comprising:
identifying a plurality of objects based on sensor data associated with an environment (See at least fig 1-10, ¶ 67, 68, 78, 84, 86, 89, 66, 3, “autonomous vehicles, can receive sensor data based on the state of the environment through which the vehicle travels. The sensor data can be used to determine the state of the environment around the vehicle.”);
obtaining a map parameter indicative of an expected orientation of an object in the environment (See at least fig 1-10, ¶ 5, 7, 67, 68, 78, 84, 86, 89, 66, 21, “determining an estimated set of physical dimensions of the one or more objects (e.g., physical dimensions including an estimated length, width, and height) and one or more orientations ( e.g., one or more headings, directions, and/or bearings) of the one or more objects associated with a vehicle ( e.g., within range of an autonomous vehicle's sensors) based on one or more states”);
grouping a set of objects of the plurality of objects based on group parameter (See at least fig 1-10, ¶ 7, 21, 33, 34, 36, 37, 38, 39, 40, 41, 47, 6, “The operations can also include determining, based in part on the object data and a machine learned model, one or more characteristics of the one or more objects. The one or more characteristics can include an estimated set of physical dimensions of the one or more objects”), (The examiner notes that grouping is equivalent to obtaining object data of at least two objects as presented in the prior art);
determining an orientation of the set of objects based on the expected orientation indicated by the map parameter (See at least fig 1-10, ¶ 6, 7, 8, 21, 22, 23, 39, 41, 43, 44, 47, 5, “based in part on the estimated set of physical dimensions of the one or more objects, one or more orientations corresponding to the one or more objects. The one or more orientations can be relative to the location of the autonomous vehicle.”); and
generating object detection data associated with the set of objects based on the sensor data and the determined orientation of the set of objects, wherein the object detection data is indicative of one or more spatial features of the set of objects (See at least fig 1-10, ¶ 6, 7, 8, 21, 22, 23, 39, 41, 43, 44, 47, 116, 39, 51, “determine object characteristics (e.g., orientation, shape, dimensions) for detected objects. An autonomy system can include numerous different components (e.g., perception, prediction, and/or optimization) that jointly operate to determine a vehicle's motion plan”);
and causing an autonomous vehicle to navigate based on the object detection data (See at least fig 1-10, ¶ 6, 7, 8, 21, 22, 39, 41, 43, 44, 47, 116, 39, 51, 23, “Based on the orientations of the objects, the vehicle can change its course or increase/reduce its velocity so that the vehicle and the objects can safely navigate around each another.”).
Vallespi-Gonzalez fails to explicitly disclose grouping, using the at least one processor, a set of objects of the plurality of objects based on group parameter.
However, Levinson teaches grouping, using the at least one processor, a set of objects of the plurality of objects based on group parameter (See at least fig 1-7, ¶ 28, 94, 108, 109, 110, 111, 112, 114, 106, “determine parameters associated with respective groups of objects associated with sensor data. The process 700 may include comparing various parameters to determine whether one or more differences exist between such parameters”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Vallespi-Gonzalez and include grouping, using the at least one processor, a set of objects of the plurality of objects based on group parameter as taught by Levinson because it would allow the perception system identifying one or more errors associated with one or more of the determined groups of objects (Levinson ¶ 30).
Vallespi-Gonzalez fails to explicitly disclose an expected orientation of an object in the environment.
However, Bernal teaches an expected orientation of an object in the environment (See at least fig 1-10, Col 11, lines 53-67, “the expected size and orientation of the objects can be learned overtime by performing object detection repeatedly and storing the pixel size 150 and orientation 152 of the detected objects 140 as a function of their location 148, e.g., the object information 154 of the associated data storage device 128. FIG. SA shows a pseudocolored object size map corresponding to the camera 134 and scene used in the experimental setup and obtained via calibration.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Vallespi-Gonzalez and include an expected orientation of an object in the environment as taught by Bernal because it would allow to achieve robust and computationally efficient tracking that has object orientation and size awareness (Col 2, lines 9-15).
Regarding claim 20, Vallespi-Gonzalez discloses a system, comprising at least one processor and at least one memory storing instructions thereon that, when executed by the at least one processor, cause the at least one processor to perform operations comprising:
identifying a plurality of objects based on sensor data associated with an environment (See at least fig 1-10, ¶ 67, 68, 78, 84, 86, 89, 66, 3, “autonomous vehicles, can receive sensor data based on the state of the environment through which the vehicle travels. The sensor data can be used to determine the state of the environment around the vehicle.”);
obtaining a map parameter indicative of an expected orientation of an object in the environment (See at least fig 1-10, ¶ 5, 7, 67, 68, 78, 84, 86, 89, 66, 21, “determining an estimated set of physical dimensions of the one or more objects (e.g., physical dimensions including an estimated length, width, and height) and one or more orientations ( e.g., one or more headings, directions, and/or bearings) of the one or more objects associated with a vehicle ( e.g., within range of an autonomous vehicle's sensors) based on one or more states”);
grouping a set of objects of the plurality of objects based on group parameter (See at least fig 1-10, ¶ 7, 21, 33, 34, 36, 37, 38, 39, 40, 41, 47, 6, “The operations can also include determining, based in part on the object data and a machine learned model, one or more characteristics of the one or more objects. The one or more characteristics can include an estimated set of physical dimensions of the one or more objects”), (The examiner notes that grouping is equivalent to obtaining object data of at least two objects as presented in the prior art);
determining an orientation of the set of objects based on the expected orientation indicated by the map parameter (See at least fig 1-10, ¶ 6, 7, 8, 21, 22, 23, 39, 41, 43, 44, 47, 5, “based in part on the estimated set of physical dimensions of the one or more objects, one or more orientations corresponding to the one or more objects. The one or more orientations can be relative to the location of the autonomous vehicle.”); and
generating object detection data associated with the set of objects based on the sensor data and the determined orientation of the set of objects, wherein the object detection data is indicative of one or more spatial features of the set of objects (See at least fig 1-10, ¶ 6, 7, 8, 21, 22, 23, 39, 41, 43, 44, 47, 116, 39, 51, “determine object characteristics (e.g., orientation, shape, dimensions) for detected objects. An autonomy system can include numerous different components (e.g., perception, prediction, and/or optimization) that jointly operate to determine a vehicle's motion plan”);
and causing an autonomous vehicle to navigate based on the object detection data (See at least fig 1-10, ¶ 6, 7, 8, 21, 22, 39, 41, 43, 44, 47, 116, 39, 51, 23, “Based on the orientations of the objects, the vehicle can change its course or increase/reduce its velocity so that the vehicle and the objects can safely navigate around each another.”).
Vallespi-Gonzalez fails to explicitly disclose grouping, using the at least one processor, a set of objects of the plurality of objects based on group parameter.
However, Levinson teaches grouping, using the at least one processor, a set of objects of the plurality of objects based on group parameter (See at least fig 1-7, ¶ 28, 94, 108, 109, 110, 111, 112, 114, 106, “determine parameters associated with respective groups of objects associated with sensor data. The process 700 may include comparing various parameters to determine whether one or more differences exist between such parameters”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Vallespi-Gonzalez and include grouping, using the at least one processor, a set of objects of the plurality of objects based on group parameter as taught by Levinson because it would allow the perception system identifying one or more errors associated with one or more of the determined groups of objects (Levinson ¶ 30).
Vallespi-Gonzalez fails to explicitly disclose an expected orientation of an object in the environment.
However, Bernal teaches an expected orientation of an object in the environment (See at least fig 1-10, Col 11, lines 53-67, “the expected size and orientation of the objects can be learned overtime by performing object detection repeatedly and storing the pixel size 150 and orientation 152 of the detected objects 140 as a function of their location 148, e.g., the object information 154 of the associated data storage device 128. FIG. SA shows a pseudocolored object size map corresponding to the camera 134 and scene used in the experimental setup and obtained via calibration.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Vallespi-Gonzalez and include an expected orientation of an object in the environment as taught by Bernal because it would allow to achieve robust and computationally efficient tracking that has object orientation and size awareness (Col 2, lines 9-15).
Regarding claim 21, Vallespi-Gonzalez discloses the method of claim 1, wherein causing an autonomous vehicle to navigate based on the object detection data comprises training a machine-learning model using the object detection data and using the trained machine-learning model to control navigation of the autonomous vehicle (See at least fig 1-10, ¶ 6, 7, 8, 21, 22, 39, 41, 43, 44, 47, 49, 116, 39, 82, 120, 27, 26, 23, “The vehicle can use any combination of the object data and/or the machine learned model to determine physical dimensions and/or orientations that correspond to the objects (e.g., the dimensions or orientations of other vehicles within a predetermined area). The orientations of the objects can be used in part to determine when objects have a trajectory that will intercept the vehicle as the object travels along its trajectory”).
Regarding claim 22, Vallespi-Gonzalez discloses the method of claim 1, wherein determining the orientation of the set of objects based on the expected orientation indicated by the map parameter comprises: determining an orientation of a first object of the set of objects based on the sensor data; and modifying the determined orientation of the first object in accordance with the expected orientation (See at least fig 1-10, ¶ 6, 7, 8, 21, 22, 23, 39, 41, 43, 44, 47, 5, “based in part on the estimated set of physical dimensions of the one or more objects, one or more orientations corresponding to the one or more objects. The one or more orientations can be relative to the location of the autonomous vehicle.”).
Vallespi-Gonzalez fails to explicitly disclose the expected orientation.
However, Bernal teaches the expected orientation (See at least fig 1-10, Col 11, lines 53-67, “the expected size and orientation of the objects can be learned overtime by performing object detection repeatedly and storing the pixel size 150 and orientation 152 of the detected objects 140 as a function of their location 148, e.g., the object information 154 of the associated data storage device 128. FIG. SA shows a pseudocolored object size map corresponding to the camera 134 and scene used in the experimental setup and obtained via calibration.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Vallespi-Gonzalez and include the expected orientation as taught by Bernal because it would allow to achieve robust and computationally efficient tracking that has object orientation and size awareness (Col 2, lines 9-15).
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LUIS MARTINEZ whose email is luis.martinezborrero@uspto.gov and telephone number is (571)272-4577. The examiner can normally be reached on Monday-Friday 8:30AM-5:00PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, HUNTER LONSBERRY can be reached on (571)272-7298. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LUIS A MARTINEZ BORRERO/Primary Examiner, Art Unit 3665
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