DETAILED ACTION
This is the first Office action on the merits and is responsive to the papers filed 09/09/2024. Claims 1-20 are currently pending and examined below.
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 01/14/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
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 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.
Claims 7-9, 12-13 and 15-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Graefe et al. (US 20190132709 A1; hereinafter Graefe).
Regarding claim 7, Graefe discloses:
An apparatus (Fig. 3: infrastructure equipment 61), comprising:
at least one memory ([0205] memory);
one or more sensors (Fig. 3: sensors 262);
at least one processor (Fig. 3: RTMS 300, [0205] processor) communicatively coupled to the at least one memory and the one or more sensors (see Fig. 3), and configured to:
obtain sensor information from the one or more sensors ([0056] “The object detector 305 is configured to receive sensor data from sensors 262 with the assistance of sensor-interface subsystem 310”);
determine a first set of object data based at least in part on the sensor information and an object recognition process ([0056] “the object detector 305 continuously tracks the observed objects 64, and determines vector information (e.g., travel direction, travel velocity/speed, travel acceleration, etc.) about the observed objects 64”);
generate a dynamic grid based at least in part on the sensor information ([0041] “the computing system of the infrastructure equipment 61 a, 61 b calculates a grid-based environment model that is overlaid on top of the observed coverage area 63.”, [0079] “by augmenting dynamic maps with sensor readings from the tracked objects 64”);
determine a second set of object data based at least in part on the dynamic grid ([0041] “The grid-based environment model allows the computing system of the infrastructure equipment 61 a, 61 b to target particular objects 64 in specific grid cells for purposes of requesting data from those targeted objects 64.”, [0079] “the map processing subsystem 309 uses known mechanisms to detect occlusions of the fixed sensors 262 or other reasons that reduce the completeness of environmental map 324, and selects sections or regions (e.g., grid cells) that correspond to the occluded area”); and
output an object track list based on a fusion of the first set of object data and the second set of object data ([0080] “After the sections or regions (e.g., grid cells) are selected, the object detector 305 consults a list of tracked objects 64 and creates a list of objects 64 that will (or are predicted to) pass through the selected section during a configurable time interval”).
Regarding claim 8, Graefe discloses:
wherein the one or more sensors include at least a camera and a radar sensor ([0040] “The individual sensors 262 may include various sensing capabilities, such as visual (e.g., image or video), radar, LiDAR, IR, ambient light, ultrasonic sensing; sound; etc.”).
Regarding claim 9, Graefe discloses:
wherein the at least one processor is further configured to identify clusters of dynamic grid cells in the dynamic grid ([0066] “the map segmenter 346 may be further configured to cluster the one or more objects 64 into the two or more map segments 325 based on respective locations of the one or more objects 64 and respective locations of the two or more segments in the environmental map 324”, [0079] “the map processing subsystem 309 uses known mechanisms to detect occlusions of the fixed sensors 262 or other reasons that reduce the completeness of environmental map 324, and selects sections or regions (e.g., grid cells) that correspond to the occluded area”).
Regarding claim 12, Graefe discloses:
wherein the at least one processor is further configured to generate an occlusion grid comprising occluded grid cells, and determine the second set of object data based at least in part on the occlusion grid ([0066] “the map segmenter 346 may be further configured to cluster the one or more objects 64 into the two or more map segments 325 based on respective locations of the one or more objects 64 and respective locations of the two or more segments in the environmental map 324”, [0079] “the map processing subsystem 309 uses known mechanisms to detect occlusions of the fixed sensors 262 or other reasons that reduce the completeness of environmental map 324, and selects sections or regions (e.g., grid cells) that correspond to the occluded area”).
Regarding claim 13, Graefe discloses:
wherein the at least one memory includes one or more machine learning models and the at least one processor is further configured to output indications of identified objects ([0056] “the object detector 305 continuously tracks the observed objects 64, and determines vector information (e.g., travel direction, travel velocity/speed, travel acceleration, etc.) about the observed objects 64”) based at least in part on the sensor information ([0056] “The object detector 305 is configured to receive sensor data from sensors 262 with the assistance of sensor-interface subsystem 310”) and the one or more machine learning models ([0056] “a machine learning (ML) object detection technique.., a deep learning object detection technique”).
Regarding claim 15, Graefe discloses:
wherein the object track list includes shape information to represent a detected object ([0106] “the object detector 305 creates or generates a sorted list of timestamps and object IDs.., which may be based on stored object data records 333 obtained from the object DB 330, which may indicate determined velocity/speed, position, direction, size, of the objects 64”).
Regarding claim 16, Graefe discloses:
wherein the object track list includes at least a location and a velocity of a detected object ([0106] “the object detector 305 creates or generates a sorted list of timestamps and object IDs.., which may be based on stored object data records 333 obtained from the object DB 330, which may indicate determined velocity/speed, position, direction, size, of the objects 64”).
Regarding claim 17, Graefe discloses:
wherein the at least one processor is further configured to receive map information and generate the dynamic grid based at least in part on the map information ([0066] “the map segmenter 346 may be further configured to cluster the one or more objects 64 into the two or more map segments 325 based on respective locations of the one or more objects 64 and respective locations of the two or more segments in the environmental map 324”, [0079] “the map processing subsystem 309 uses known mechanisms to detect occlusions of the fixed sensors 262 or other reasons that reduce the completeness of environmental map 324, and selects sections or regions (e.g., grid cells) that correspond to the occluded area”).
Regarding claim 18, Graefe discloses:
wherein the at least one processor is further configured to receive remote sensor information via a CV2X network communication ([0100] “the object 64 performs an initiation procedure (“init( )”) with the infrastructure equipment 61 wherein the object 64 establishes a connection with the infrastructure equipment 61 using a suitable V2X communication technology 250 and announces its intention to use maps data by sending a request package”) and generate the dynamic grid based at least in part on the remote sensor information ([0041] “the computing system of the infrastructure equipment 61 a, 61 b calculates a grid-based environment model that is overlaid on top of the observed coverage area 63.”, [0079] “by augmenting dynamic maps with sensor readings from the tracked objects 64”).
Regarding claim 19, Graefe discloses:
wherein the remote sensor information is provided by a roadside unit (RSU) (Fig. 2: the other one of the infrastructure equipment 61).
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 1-3, 6 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Graefe.
Regarding claim 1, Graefe discloses:
A method for generating an object track list (Fig. 3), comprising:
obtaining sensor information from one or more sensors on the vehicle ([0056] “The object detector 305 is configured to receive sensor data from sensors 262 with the assistance of sensor-interface subsystem 310”);
determining a first set of object data based at least in part on the sensor information and an object recognition process ([0056] “the object detector 305 continuously tracks the observed objects 64, and determines vector information (e.g., travel direction, travel velocity/speed, travel acceleration, etc.) about the observed objects 64”);
generating a dynamic grid based on an environment proximate to the vehicle based at least in part on the sensor information ([0041] “the computing system of the infrastructure equipment 61 a, 61 b calculates a grid-based environment model that is overlaid on top of the observed coverage area 63.”, [0079] “by augmenting dynamic maps with sensor readings from the tracked objects 64”);
determining a second set of object data based at least in part on the dynamic grid ([0041] “The grid-based environment model allows the computing system of the infrastructure equipment 61 a, 61 b to target particular objects 64 in specific grid cells for purposes of requesting data from those targeted objects 64.”, [0079] “the map processing subsystem 309 uses known mechanisms to detect occlusions of the fixed sensors 262 or other reasons that reduce the completeness of environmental map 324, and selects sections or regions (e.g., grid cells) that correspond to the occluded area”); and
outputting the object track list based on a fusion of the first set of object data and the second set of object data ([0080] “After the sections or regions (e.g., grid cells) are selected, the object detector 305 consults a list of tracked objects 64 and creates a list of objects 64 that will (or are predicted to) pass through the selected section during a configurable time interval”).
While the embodiment described in Fig. 3 of Graefe discloses a method of generating an object track list, this embodiment does not explicitly disclose generating an object track list in a vehicle.
However, an embodiment of Graefe described in [0108] teaches a method for generating an object track list in a vehicle ([0108] “The infrastructure equipment 700 (or “system 700”) may be implemented as the infrastructure equipment 61 discussed with regard to FIGS. 1-6..and/or any other element/device discussed herein. In other examples, the system 700 could be implemented in or by a UE.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Graefe’s embodiment described in Fig. 3 to implement in RSU to further incorporate Graefe’s embodiment described in [0108] to implement in a UE/vUE for the advantage of mobility which results in flexibility and convenience.
Regarding claim 2, Graefe discloses:
wherein the one or more sensors include at least a camera and a radar sensor ([0040] “The individual sensors 262 may include various sensing capabilities, such as visual (e.g., image or video), radar, LiDAR, IR, ambient light, ultrasonic sensing; sound; etc.”).
Regarding claim 3, Graefe discloses:
wherein determining the second set of object data includes identifying clusters of dynamic grid cells in the dynamic grid ([0066] “the map segmenter 346 may be further configured to cluster the one or more objects 64 into the two or more map segments 325 based on respective locations of the one or more objects 64 and respective locations of the two or more segments in the environmental map 324”, [0079] “the map processing subsystem 309 uses known mechanisms to detect occlusions of the fixed sensors 262 or other reasons that reduce the completeness of environmental map 324, and selects sections or regions (e.g., grid cells) that correspond to the occluded area”).
Regarding claim 6, Graefe discloses:
further comprising generating an occlusion grid comprising occluded grid cells, wherein determining the second set of object data is based at least in part on the occlusion grid ([0066] “the map segmenter 346 may be further configured to cluster the one or more objects 64 into the two or more map segments 325 based on respective locations of the one or more objects 64 and respective locations of the two or more segments in the environmental map 324”, [0079] “the map processing subsystem 309 uses known mechanisms to detect occlusions of the fixed sensors 262 or other reasons that reduce the completeness of environmental map 324, and selects sections or regions (e.g., grid cells) that correspond to the occluded area”).
Regarding claim 20, Graefe discloses:
An apparatus for generating an object track list (Fig. 3), comprising:
means for obtaining sensor information from one or more sensors on the vehicle ([0056] “The object detector 305 is configured to receive sensor data from sensors 262 with the assistance of sensor-interface subsystem 310”);
means for determining a first set of object data based at least in part on the sensor information and an object recognition process ([0056] “the object detector 305 continuously tracks the observed objects 64, and determines vector information (e.g., travel direction, travel velocity/speed, travel acceleration, etc.) about the observed objects 64”);
means for generating a dynamic grid based on an environment proximate to the vehicle based at least in part on the sensor information ([0041] “the computing system of the infrastructure equipment 61 a, 61 b calculates a grid-based environment model that is overlaid on top of the observed coverage area 63.”, [0079] “by augmenting dynamic maps with sensor readings from the tracked objects 64”);
means for determining a second set of object data based at least in part on the dynamic grid ([0041] “The grid-based environment model allows the computing system of the infrastructure equipment 61 a, 61 b to target particular objects 64 in specific grid cells for purposes of requesting data from those targeted objects 64.”, [0079] “the map processing subsystem 309 uses known mechanisms to detect occlusions of the fixed sensors 262 or other reasons that reduce the completeness of environmental map 324, and selects sections or regions (e.g., grid cells) that correspond to the occluded area”); and
means for outputting the object track list based on a fusion of the first set of object data and the second set of object data ([0080] “After the sections or regions (e.g., grid cells) are selected, the object detector 305 consults a list of tracked objects 64 and creates a list of objects 64 that will (or are predicted to) pass through the selected section during a configurable time interval”).
While the embodiment described in Fig. 3 of Graefe discloses an apparatus of generating an object track list, this embodiment does not explicitly disclose generating an object track list in a vehicle.
However, an embodiment of Graefe described in [0108] teaches an apparatus for generating an object track list in a vehicle ([0108] “The infrastructure equipment 700 (or “system 700”) may be implemented as the infrastructure equipment 61 discussed with regard to FIGS. 1-6..and/or any other element/device discussed herein. In other examples, the system 700 could be implemented in or by a UE.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Graefe’s embodiment described in Fig. 3 to implement in RSU to further incorporate Graefe’s embodiment described in [0108] to implement in a UE/vUE for the advantage of mobility which results in flexibility and convenience.
Claims 4-5 and 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Graefe in view of Nunn et al. (US 20230236030 A1; hereinafter Nunn).
Regarding claim 4, Graefe does not specifically disclose:
wherein the clusters of dynamic grid cells have similar velocities.
However, Nunn discloses:
wherein the clusters of dynamic grid cells have similar velocities ([0087] “for each grid cell of a map, the set of classification parameters may include one or more of:” [0088] “a detected individual speed in grid cells, per object class”).
Graefe and Nunn are considered to be analogous to the claimed invention because they are in the same field of road mapping. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Graefe’s road mapping to further incorporate Nunn’s road mapping for the advantage of further classifying grid cells which results a more reliable estimation of location of POI or road type (Nunn’s [0005]).
Regarding claim 5, Graefe does not specifically disclose:
wherein the clusters of dynamic grid cells have similar object classifications.
However, Nunn discloses:
wherein the clusters of dynamic grid cells have similar object classifications ([0087] “for each grid cell of a map, the set of classification parameters may include one or more of:” [0088] “a detected individual speed in grid cells, per object class”).
Graefe and Nunn are considered to be analogous to the claimed invention because they are in the same field of road mapping. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Graefe’s road mapping to further incorporate Nunn’s road mapping for the advantage of further classifying grid cells which results a more reliable estimation of location of POI or road type (Nunn’s [0005]).
Regarding claim 10, Graefe as currently modified does not specifically disclose:
wherein the clusters of dynamic grid cells have similar velocities.
However, Nunn discloses:
wherein the clusters of dynamic grid cells have similar velocities ([0087] “for each grid cell of a map, the set of classification parameters may include one or more of:” [0088] “a detected individual speed in grid cells, per object class”).
Graefe and Nunn are considered to be analogous to the claimed invention because they are in the same field of road mapping. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Graefe’s road mapping as currently modified to further incorporate Nunn’s road mapping for the advantage of further classifying grid cells which results a more reliable estimation of location of POI or road type (Nunn’s [0005]).
Regarding claim 11, Graefe as currently modified does not specifically disclose:
wherein the clusters of dynamic grid cells have similar object classifications.
However, Nunn discloses:
wherein the clusters of dynamic grid cells have similar object classifications ([0087] “for each grid cell of a map, the set of classification parameters may include one or more of:” [0088] “a detected individual speed in grid cells, per object class”).
Graefe and Nunn are considered to be analogous to the claimed invention because they are in the same field of road mapping. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Graefe’s road mapping as currently modified to further incorporate Nunn’s road mapping for the advantage of further classifying grid cells which results a more reliable estimation of location of POI or road type (Nunn’s [0005]).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Graefe in view of Alahmari et al. (US 20220058369 A1; hereinafter Alahmari).
Regarding claim 14, Graefe as currently modified does not specifically disclose:
wherein the at least one processor is further configured to output an Active Learning (AL) trigger based at least in part on a comparison of the first set of object data and the second set of object data, and to retrain the one or more machine learning models in response to the AL trigger.
However, Alahmari discloses:
wherein the at least one processor is further configured to output an Active Learning (AL) trigger based at least in part on a comparison of the first set of object data and the second set of object data, and to retrain the one or more machine learning models in response to the AL trigger ([0188] “The results of using Unet with initial training data labels generated using ASA only (i.e., baseline) shows high error rate compared to iterative deep learning and active deep learning due to the lower number of images available for training as shown in Table 8”).
Graefe and Alahmari are considered to be analogous to the claimed invention because they are in the same field of machine learning. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Graefe’s machine learning to further incorporate Alahmari’s machine learning for the advantage of active learning which results in faster and more efficient training of models (Alahmari’s Abstract).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAYSUN WU whose telephone number is (571)272-1528. The examiner can normally be reached Monday-Friday 8AM-5PM.
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/PAYSUN WU/Examiner, Art Unit 3665
/DONALD J WALLACE/Primary Examiner, Art Unit 3665