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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/4/2023 has been reconsidered by the examiner.
The information disclosure statement (IDS) submitted on 12/12/2025 has been considered by the examiner and an initialed copy of the IDS is hereby attached.
Response to Amendment
Applicant’s amendment filed on 12/12/2025 has been entered. Claims 1, 14 and 19 have been amended, and claims 21-23 have been canceled.
Response to Arguments
Applicant’s arguments with respect to claims 1, 14 and 19 under 35 USC 102(a)(1) 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 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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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 non-obviousness.
10. Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Pendleton (US 20230093601 A1), hereinafter Pendleton in view of Brunner et al (US 20200219264 A1), hereinafter Brunner.
Regarding claim 1, Pendleton discloses:
a method, comprising (Pendleton, para [0048], In an embodiment, a system comprises: at least one processor; and at least one non-transitory, computer-readable storage media comprising instructions that, upon execution of the instructions by the at least one processor, cause the vehicle to perform any of the preceding methods):
receiving a plurality of radar images from a plurality of radar sensors at a first time step (Pendleton, para [0030], In another aspect and/or embodiment, systems, methods, and computer program products described herein include or relate to a perception system of the autonomous system that identifies visibility-related factors related to one or more of the sensors, such as environmental conditions that would affect the sensors, detected sensor occlusion (e.g., by an object within the path of the sensor), blockage of the sensor, etc. The perception system uses these factors to generate a perception visibility model (PVM) related to the sensor detection capabilities. Based on the PVM, the perception system generates one or more maps. One such map is an occlusion map that indicates where an object is located that is occluding the sensors. Another such map is a PoD map related to the likelihood of a sensor being able to detect the presence of an object in a given location. In one embodiment, the PVM model, or portions thereof, is iterated toward a default model at pre-identified time intervals, if new data related to the sensors is not received by the perception system) Examiner interprets the pre-identified time intervals as a series of time steps,
the plurality of radar images corresponding to a plurality of views of a scene of a vehicle at the first time step (Pendleton, para [0030]),
generating at least one radar-based bounding box for an object in the scene of the vehicle based on the plurality of radar images (Pendleton, para [0184], Based on the identified constraint, constraint checking subsystem 1510 identifies stopping-reason 1520 which can be a stopping-reason as described above. Stopping-reason 1520 data can include, for example, an identifier (e.g., a label, bounding box) of an object, data related to a map location (e.g., a location of the vehicle), or some other data) and (further reference paras [0069-0070);
generating a set of radar-based object queries based on the at least one radar-based bounding box (Pendleton, para [0091], In some embodiments, perception system 402 receives data associated with at least one physical object (e.g., data that is used by perception system 402 to detect the at least one physical object) in an environment and classifies the at least one physical object. In some examples, perception system 402 receives image data captured by at least one camera (e.g., cameras 202a), the image associated with (e.g., representing) one or more physical objects within a field of view of the at least one camera. In such an example, perception system 402 classifies at least one physical object based on one or more groupings of physical objects (e.g., bicycles, vehicles, traffic signs, pedestrians, and/or the like). In some embodiments, perception system 402 transmits data associated with the classification of the physical objects to planning system 404 based on perception system 402 classifying the physical objects) and (para [0184]) Examiner interprets the detection or sensing of object/targets within a field of view as radar-based object queries ,
wherein each radar-based object query of the set of radar-based object queries comprises a first plurality of dimension features representing characteristics of an object (Pendleton, para [0147], Turning to FIG. 10, an example 1000 occlusion map is depicted. As noted above, the occlusion map is generated by PVM subsystem 820 and depicts areas that sensors 605 of vehicle 200 are occluded or blocked by an object in the vicinity of vehicle 200. The vicinity of vehicle 200 includes several objects of varying heights that form occlusion zones such as the low occlusion zone(s) 1030, the middle occlusion zone(s) 1035, and the tall occlusion zone(s) 1040. As used herein, the terms “low,” “middle,” and “tall” are used to distinguish relative heights with respect to one another. In one embodiment, low occlusion zone 1030 relates to a height between approximately ground level and approximately 1 meter off the ground. Middle occlusion zone 1035 relates to a height between approximately 1 meter and 2 meters off the ground. High occlusion zone 1040 relates to a height greater than approximately 2 meters. However, it will be understood that other embodiments have more or fewer occlusion zones, zones with different height parameters, etc.) and (further reference paras [0148-0149]) Examiner interprets shape, color, height as examples of dimension features representing characteristics of an object;
receiving a plurality of images from a plurality of image sensors at a second time step (Pendleton, para [0030]),
the plurality of images corresponding to a plurality of views of a scene of the vehicle at the second time step (Pendleton, para [0030]);
generating a plurality of feature maps based on the plurality of images (Pendleton, para [0093], In some embodiments, localization system 406 receives data associated with (e.g., representing) a location of a vehicle (e.g., vehicles 102) in an area. In some examples, localization system 406 receives LiDAR data associated with at least one point cloud generated by at least one LiDAR sensor (e.g., LiDAR sensors 202b). In certain examples, localization system 406 receives data associated with at least one point cloud from multiple LiDAR sensors and localization system 406 generates a combined point cloud based on each of the point clouds. In these examples, localization system 406 compares the at least one point cloud or the combined point cloud to two-dimensional (2D) and/or a three-dimensional (3D) map of the area stored in database 410. Localization system 406 then determines the position of the vehicle in the area based on localization system 406 comparing the at least one point cloud or the combined point cloud to the map. In some embodiments, the map includes a combined point cloud of the area generated prior to navigation of the vehicle. In some embodiments, maps include, without limitation, high-precision maps of the roadway geometric properties, maps describing road network connectivity properties, maps describing roadway physical properties (such as traffic speed, traffic volume, the number of vehicular and cyclist traffic lanes, lane width, lane traffic directions, or lane marker types and locations, or combinations thereof), and maps describing the spatial locations of road features such as crosswalks, traffic signs or other travel signals of various types. In some embodiments, the map is generated in real-time based on the data received by the perception system);
receiving a first set of object queries associated with the second time step (Pendleton, para [0091], In some embodiments, perception system 402 receives data associated with at least one physical object (e.g., data that is used by perception system 402 to detect the at least one physical object) in an environment and classifies the at least one physical object. In some examples, perception system 402 receives image data captured by at least one camera (e.g., cameras 202a), the image associated with (e.g., representing) one or more physical objects within a field of view of the at least one camera. In such an example, perception system 402 classifies at least one physical object based on one or more groupings of physical objects (e.g., bicycles, vehicles, traffic signs, pedestrians, and/or the like). In some embodiments, perception system 402 transmits data associated with the classification of the physical objects to planning system 404 based on perception system 402 classifying the physical objects),
wherein each object query of the first set of object queries comprises a second plurality of dimension features representing characteristics of an object (Pendleton, para [0091]) Examiner interprets classification, filtering or determining as examples of an object query;
enriching the first set of object queries associated with the second time step and the set of radar-based object queries associated with the first time step based on the plurality of feature maps associated with the second time step to generate a set of enriched object queries (Pendleton, para [0096], In some embodiments, perception system 402, planning system 404, localization system 406, and/or control system 408 implement at least one machine learning model (e.g., at least one multilayer perceptron (MLP), at least one convolutional neural network (CNN), at least one recurrent neural network (RNN), at least one auto encoder, at least one transformer, and/or the like). In some examples, perception system 402, planning system 404, localization system 406, and/or control system 408 implement at least one machine learning model alone or in combination with one or more of the above-noted systems. In some examples, perception system 402, planning system 404, localization system 406, and/or control system 408 implement at least one machine learning model as part of a pipeline (e.g., a pipeline for identifying one or more objects located in an environment and/or the like) Examiner interprets the machine learning model Multilayer Perceptron as what is used to enrich radar-based object queries by using sensor feature maps,
generating at least one bounding box for an object in the scene of the vehicle at the second time step based on the set of enriched object queries (Pendleton, para [0184]);
and causing the vehicle to be controlled based on the at least one bounding box (Pendleton, paras [0184]) and (para [0186], Additionally, stopping-reason 1520 can be provided to a timeout threshold generator 1525. Timeout threshold generator 1525 is configured to determine, based on stopping-reason 1520, a timeout until an intervention is to be requested. Specifically, timeout threshold generator 1525 is communicatively coupled with a timeout database 1530 that is configured to store timeout values related to different stopping-reasons. In one embodiment, the various timeout values are pre-defined, while in another embodiment the timeout database 1530 stores data that can be used by timeout threshold generator 1525 to calculate a timeout value based on the stopping-reason (e.g., based on a queue length of cars at an all-way stop, based on a number of pedestrians in a vicinity of the vehicle, etc.). As used herein, the timeout threshold is an amount of time the vehicle will wait before initiating at least one remedial action such as a request to an RVA, a MRM, a rerouting, etc)
Brunner discloses:
enriching the first set of object queries associated with the second time step and the set of radar-based object queries associated with the first time step based on the plurality of feature maps associated with the second time step to generate a set of enriched object queries (Brunner, para [0026], FIGS. 8A and 8B illustrate exemplary LiDAR frames captured at a first time step and a second time step, according to aspects of the disclosure) and (para [0047], The radar-camera sensor module 120 may detect one or more (or none) objects relative to the vehicle 100. In the example of FIG. 1, there are two objects, vehicles 130 and 140, within the horizontal coverage zones 150 and 160 that the radar-camera sensor module 120 can detect. The radar-camera sensor module 120 may estimate parameters (attributes) of the detected object(s), such as the position, range, direction, speed, size, classification (e.g., vehicle, pedestrian, road sign, etc.), and the like. The radar-camera sensor module 120 may be employed onboard the vehicle 100 for automotive safety applications, such as adaptive cruise control (ACC), forward collision warning (FCW), collision mitigation or avoidance via autonomous braking, lane departure warning (LDW), and the like); (further reference paras [0066-0070] regarding the sensor fusion architecture); and (further reference para [0096] regarding multiple time steps) Examiner interprets the deep neural networks and sensor fusion of the camera and radar sensor module as examples of enriching,
wherein enriching the first set of object queries and the set of radar-based object queries comprises using a decoder to generate the set of enriched object queries (Brunner, para [0050], One or more radar-camera sensor modules 120 are coupled to the OBC 200 (only one is shown in FIG. 2 for simplicity). In some aspects, the radar-camera sensor module 120 includes at least one camera 212, at least one radar 214, and an optional light detection and ranging (LiDAR) sensor 216. The OBC 200 also includes one or more system interfaces 220 connecting the processor(s) 206, by way of the data bus 208, to the radar-camera sensor module 120 and, optionally, other vehicle sub-systems (not shown).
[0051] The OBC 200 also includes, at least in some cases, a wireless wide area network (WWAN) transceiver 230 configured to communicate via one or more wireless communication networks (not shown), such as an NR network, an LTE network, a GSM network, and/or the like. The WWAN transceiver 230 may be connected to one or more antennas (not shown) for communicating with other network nodes, such as other vehicle UEs, pedestrian UEs, infrastructure access points, roadside units (RSUs), base stations (e.g., eNBs, gNBs), etc., via at least one designated RAT (e.g., NR, LTE, GSM, etc.) over a wireless communication medium of interest (e.g., some set of time/frequency resources in a particular frequency spectrum). The WWAN transceiver 230 may be variously configured for transmitting and encoding signals (e.g., messages, indications, information, and so on), and, conversely, for receiving and decoding signals (e.g., messages, indications, information, pilots, and so on) in accordance with the designated RAT) Examiner interprets the on-board computer OBC 200 as the decoder that is coupled to the radar-camera sensor fusion module 120,
wherein the set of enriched object queries comprises a third plurality of dimension features that are different from the first plurality of dimension features and the second plurality of dimension features (Brunner, para [0096], Referring to stage 940 of FIG. 9, FIG. 13 illustrates an adjacency graph 1310 at a first time step (frame 1960) and an adjacency graph 1320 at a second time step (frame 2033), according to aspects of the disclosure. Camera based tracking can be used to associate objects (e.g., vehicles 130/140) across frames (e.g., frames 1960 and 2033). The dimensions of an object (e.g., a vehicle) can be estimated when the object is near the ego vehicle and the LiDAR scan is good. Those dimensions can then be used in all subsequent frames containing the detected object. FIG. 13 illustrates the difference in the number of LiDAR points associated with the same two detected vehicles at a first time step (adjacency graph 1310) and a second time step 7.3 seconds later (adjacency graph 1320). As shown, there are many fewer LiDAR points associated with the two target vehicles in the second adjacency graph 1320 (frame 2033), where the target vehicles are further from the ego vehicle, than there are in the first adjacency graph 1310 (frame 1960), where the target vehicles are close to the ego vehicle. As such, it is beneficial to use the dimensions of the target vehicles calculated from the adjacency graph 131).
It would have been obvious to someone in the art prior to the effective filing date of the claimed invention to modify Pendleton with Brunner to incorporate the features of: wherein enriching the first set of object queries; and the set of radar-based object queries comprises using a decoder to generate the set of enriched object queries; and wherein the set of enriched object queries comprises a third plurality of dimension features that are different from the first plurality of dimension features and the second plurality of dimension features). Both arts are considered analogous arts as they both disclose autonomous vehicles system that include multimodal camera and LiDAR for object queries and radar-based queries respectively. The modification would render the predictable results of improved adaptive fusion, improved iterative refinement, and improved accuracy and tracking.
Regarding claim 2, Pendleton discloses:
` the method of claim 1 (Pendleton, para [0048]),
wherein generating a set of radar-based object queries based on the at least one radar-based bounding box comprises converting a vector associated with the at least one radar-based bounding box from a first vector having a first format to a second vector having a second format (Pendleton, para [0067], Cameras 202a include at least one device configured to be in communication with communication device 202e, AV compute 202f, and/or safety controller 202g via a bus (e.g., a bus that is the same as or similar to bus 302 of FIG. 3). Cameras 202a include at least one camera (e.g., a digital camera using a light sensor such as a charge-coupled device (CCD), a thermal camera, an infrared (IR) camera, an event camera, and/or the like) to capture images including physical objects (e.g., cars, buses, curbs, people, and/or the like). In some embodiments, camera 202a generates camera data as output. In some examples, camera 202a generates camera data that includes image data associated with an image. In this example, the image data may specify at least one parameter (e.g., image characteristics such as exposure, brightness, etc., an image timestamp, and/or the like) corresponding to the image. In such an example, the image may be in a format (e.g., RAW, JPEG, PNG, and/or the like). In some embodiments, camera 202a includes a plurality of independent cameras configured on (e.g., positioned on) a vehicle to capture images for the purpose of stereopsis (stereo vision). In some examples, camera 202a includes a plurality of cameras that generate image data and transmit the image data to AV compute 202f and/or a fleet management system (e.g., a fleet management system that is the same as or similar to fleet management system 116 of FIG. 1). In such an example, AV compute 202f determines depth to one or more objects in a field of view of at least two cameras of the plurality of cameras based on the image data from the at least two cameras. In some embodiments, cameras 202a is configured to capture images of objects within a distance from cameras 202a (e.g., up to 100 meters, up to a kilometer, and/or the like). Accordingly, cameras 202a include features such as sensors and lenses that are optimized for perceiving objects that are at one or more distances from cameras 202a).
Regarding claim 3, Pendleton discloses:
the method of claim 2 (Pendleton, para [0048]),
wherein individual object queries of the first set of object queries are vectors having the second format (Pendleton, para [0067]).
Regarding claim 4, Pendleton discloses:
the method of claim 2 (Pendleton, para [0048]),
wherein the set of enriched object queries comprises an enriched object query for each query of the set of radar- based object queries and each query of the first set of object queries (Pendleton, para [0091], In some embodiments, perception system 402 receives data associated with at least one physical object (e.g., data that is used by perception system 402 to detect the at least one physical object) in an environment and classifies the at least one physical object. In some examples, perception system 402 receives image data captured by at least one camera (e.g., cameras 202a), the image associated with (e.g., representing) one or more physical objects within a field of view of the at least one camera. In such an example, perception system 402 classifies at least one physical object based on one or more groupings of physical objects (e.g., bicycles, vehicles, traffic signs, pedestrians, and/or the like). In some embodiments, perception system 402 transmits data associated with the classification of the physical objects to planning system 404 based on perception system 402 classifying the physical object) and (para [0097]), Examiner notes that object queries such as camera image features may be processed by CNN for maps and each radar detection may be considered a query.
Regarding claim 5, Pendleton discloses:
the method of claim 4 (Pendleton, para [0048]),
wherein generating the at least one bounding box for an object in the scene of the vehicle at the second time step based on the set of enriched object queries comprises transforming individual vectors of the set of enriched object queries from the second format to the first format (Pendleton, para [0093, lines: 1-19], In some embodiments, localization system 406 receives data associated with (e.g., representing) a location of a vehicle (e.g., vehicles 102) in an area. In some examples, localization system 406 receives LiDAR data associated with at least one point cloud generated by at least one LiDAR sensor (e.g., LiDAR sensors 202b). In certain examples, localization system 406 receives data associated with at least one point cloud from multiple LiDAR sensors and localization system 406 generates a combined point cloud based on each of the point clouds. In these examples, localization system 406 compares the at least one point cloud or the combined point cloud to two-dimensional (2D) and/or a three-dimensional (3D) map of the area stored in database 410. Localization system 406 then determines the position of the vehicle in the area based on localization system 406 comparing the at least one point cloud or the combined point cloud to the map. In some embodiments, the map includes a combined point cloud of the area generated prior to navigation of the vehicle) Examiner notes that two-dimensional and three-dimensional are formats and multimodal may include cameras and LiDAR.
Regarding claim 6, Pendleton discloses:
the method claim 1 (Pendleton, para [0048]),
wherein generating a set of radar-based object queries is further based on a position embedding of the query and a context vector of the query (Pendleton, paras [0091] and [0096]) Examiner notes that MLP embeds raw data into learned query vectors and may be radar-based queries and also relates to cameras or LIDAR.
Regarding claim 7, Pendleton discloses:
the method of claim 6 further comprising calculating the position embedding of the query using an embed function on a center of the at least one radar-based bounding box (Pendleton, paras [0030] and [0096]) Examiner nots that the embed function is used within camera feature extracted by using convolutional neural networks (CNN) and spatially.
Regarding claim 8, Pendleton discloses:
the method of claim 6 further comprising calculating the context vector of the query using a multilayer perceptron embedding or a cos-sine position embedding (Pendleton, paras [0048] and [0096]).
Regarding claim 9, Pendleton discloses:
the method of claim 1 (Pendleton, para [0048]),
wherein enriching the first set of object queries and the set of radar-based object queries comprises enriching the first set of object queries based on the set of radar-based object queries Pendleton, paras [0048], [0091] and para [0096]).
Regarding claim 10, Pendleton discloses:
the method of claim 1 (Pendleton, para [0048]),
wherein enriching the first set of object queries and the set of radar-based object queries based on the plurality of feature maps comprises Pendleton, paras [0048], [0091] and para [0096]:
performing cross-attention computing functions between the first set of object queries and the plurality of feature maps (Pendleton, para [0066], Autonomous system 202 includes a sensor suite that includes one or more devices such as cameras 202a, LiDAR sensors 202b, radar sensors 202c, and microphones 202d. In some embodiments, autonomous system 202 can include more or fewer devices and/or different devices (e.g., ultrasonic sensors, inertial sensors, global positioning system (GPS) receivers (discussed below), odometry sensors that generate data associated with an indication of a distance that vehicle 200 has traveled, and/or the like). In some embodiments, autonomous system 202 uses the one or more devices included in autonomous system 202 to generate data associated with environment 100, described herein. The data generated by the one or more devices of autonomous system 202 can be used by one or more systems described herein to observe the environment (e.g., environment 100) in which vehicle 200 is located. In some embodiments, autonomous system 202 includes communication device 202e, autonomous vehicle compute 202f, and drive-by-wire (DBW) system 202h.) Examiner interprets the multimodal approach of cameras and LIDAR sensors as a form of cross-attention,
and performing cross-attention computing functions between the set of radar-based object queries and the plurality of feature maps (Pendleton, para [0093, lines: 1-19], In some embodiments, localization system 406 receives data associated with (e.g., representing) a location of a vehicle (e.g., vehicles 102) in an area. In some examples, localization system 406 receives LiDAR data associated with at least one point cloud generated by at least one LiDAR sensor (e.g., LiDAR sensors 202b). In certain examples, localization system 406 receives data associated with at least one point cloud from multiple LiDAR sensors and localization system 406 generates a combined point cloud based on each of the point clouds. In these examples, localization system 406 compares the at least one point cloud or the combined point cloud to two-dimensional (2D) and/or a three-dimensional (3D) map of the area stored in database 410. Localization system 406 then determines the position of the vehicle in the area based on localization system 406 comparing the at least one point cloud or the combined point cloud to the map. In some embodiments, the map includes a combined point cloud of the area generated prior to navigation of the vehicle).
Regarding claim 11, Pendleton discloses:
the method of claim 1 (Pendleton, para [0048]),
(Pendleton, para [0096]) discloses an auto encoder.
Brunner discloses:
wherein enriching the first set of object queries and the set of radar-based object queries comprises iterating through a defined number of layers of a decoder to generate the set of enriched object queries (Brunner, paras [0050-0051]) Examiner interprets the on-board computer OBC 200 as the decoder that is coupled to the radar-camera sensor fusion module 120.
It would have been obvious to someone in the art prior to the effective filing date of the claimed invention to modify Pendleton with Brunner to incorporate the features of: wherein enriching the first set of object queries and the set of radar-based object queries comprises iterating through a defined number of layers of a decoder to generate the set of enriched object queries. Both arts are considered analogous arts as they both disclose autonomous vehicles system that include multimodal camera and LiDAR for object queries and radar-based queries respectively. The modification would render the predictable results of augmented predictions of class, trajectory, and velocity; and improve object representation; and improve performance in occluded environments.
Regarding claim 12, Pendleton discloses:
the method of claim 1 (Pendleton, para [0048]),
wherein generating at least one bounding box for an object in the scene of the vehicle based on the first set of enriched object queries comprises generating a classification of an object type of individual bounding boxes (Pendleton, para [0091]),
and wherein causing the vehicle to be controlled based on the at least one bounding box comprises causing the vehicle to be controlled based on the classification (Pendleton, para [0184] and [0186]).
Regarding claim 13, Pendleton discloses:
the method of claim 1 (Pendleton, para [0048]),
wherein an interval between the first time step and the second time step is a defined time interval (Pendleton, para [0192], The process 1600 further includes examining, at 1610, constraints over each timestamp (e.g., over each timestamped iteration). Specifically, while there may be other constraints active that limit speed, spatial offset, etc., the crosswalk related constraint is identified as the primary constraint responsible for the immobility of vehicle 200 (zero speed constraint active at current position, other less relevant constraints potentially computed further along the path or if active at current position, dominated by the zero speed constraint thus similarly of lesser relevance). Based on metadata indicating a logical constraint type, including data related to an identified pedestrian and crosswalk, vehicle 200, and particularly constraint checking subsystem 1510, is configured to know stopping-reason 1520 for the current timestamp immobility).
Claim 14 is rejected under the same analysis as claim 1.
Claim 15 is rejected under the same analysis as claim 6.
Claim 16 is rejected under the same analysis as claim 7.
Claim 17 is rejected under the same analysis as claim 8.
Claim 18 is rejected under the same analysis as claim 9.
Claim 19 is rejected under the same analysis as claim 1.
Claim 20 is rejected under the same analysis as claim 6.
References Cited But Not Relied Upon
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure as thus:
Arora US 20230296757 A1 discloses a sensor data compression for map creation for autonomous systems
Breed et al US 20090140887 A1 discloses a mapping technique using probe vehicles that comprises sensor fusion ad neural networks (para [0266])
Breed et al US 20070152804 A1 discloses accident avoidance systems and methods that include sensor fusion and neural networks (paras [0019-0020] and predictions
Fontijine et al US 20210255304 A1 discloses radar deep learning that comprises a radar-vision fusion method and wherein radar and camera sensors maybe combined (fusion) (para [0040]) [0056 r/t predicted position at a time interval], pixel and impact of classification [0065], [0073] NN and timestamp of prediction of time prediction of objects etc.
Singh et al US 20240127597 A1 referenced to evaluate for double patenting
Li et al US 20210146963 A1 discloses object detection and queries that correspond with a time step (paras [0049] and [0054])
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 KIMBERLY JENKINS whose telephone number is (571)272-0404. The examiner can normally be reached Monday - Friday 8a-5p EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vladimir Magloire can be reached at 517.270.5144. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KIMBERLY JENKINS/Examiner, Art Unit 3648
/VLADIMIR MAGLOIRE/ Supervisory Patent Examiner, Art Unit 3648
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