DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-20 were pending for examination in Application No. 18/520,098, filed November 27th, 2023. In the remarks and amendments received on February 12th, 2026, claims 1-6, 8, 11-17, and 19-20 are amended, claim 7 is cancelled, and claim 21 is added. Accordingly, claims 1-6 and 8-21 are pending for examination in the application.
Response to Arguments
Applicant’s arguments filed February 12th, 2026, with respect to the rejection of claim 1, have been fully considered but are moot because the arguments do not apply to the new combination of references, facilitated by Applicant’s newly submitted amendments being used in the current rejection.
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.
Claim(s) 1-6, 10, and 12-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sano et al. (US-20180189599-A1), and further in view of Hu et al. (US-20240420344-A1).
Regarding claim 1, Sano teaches:
A method comprising:
based at least on sensor data (from “external sensor “10B) obtained in a perspective view using one or more sensors disposed on a machine navigating within an environment (Fig. 6, “mobile object” 10, the vehicle, navigates through the environment using “external sensor” 10B),
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(generating) one or more values (“existence probability p’ (r)”) indicating whether one or more locations within the environment are associated with a nearest boundary of an object (“closest position to the mobile object 10”) in one or more directions from the machine (see this citation in view of Fig. 7: “the calculation unit 20E derives, for each angular direction φ, α according to the first distance and the second distance of a detection point P which exists in the closest position to the mobile object 10 in the corresponding angular direction φ. Then, the calculation unit 20E calculates, for each angular direction φ, the existence probability p′ (r) for each distance r from the mobile object 10 along each of the lines extending along the respective angular directions φ,” Para [0126]);
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determining, based at least on the one or more values, that a location (“partitioned region B’8(B’)”) of the one or more locations is associated with the nearest boundary of the object (in view of Fig. 9: “among the plurality of regions B′ arranged along this angular direction φ (e.g., a region B′1 to a region B′11), a partitioned region B′8(B′) is a region B′ at the distance r.sub.0 that includes a detection point P closest to the mobile object 10,” Para [0146]);
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and causing, based at least on the location associated with the nearest boundary of the object, the machine to perform one or more operations (“To automatically drive the mobile object 10, the power control unit 10G controls the power unit 10H on the basis of information that can be acquired from the external sensor 10B and the internal sensor 10C, and existence probability information derived from processing,” Para [0031]).
Sano is not relied upon to teach the following limitations. Hu, however, further teaches:
generating, by one or more machine learning models (“neural network prediction model”)
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Hu is considered to be analogous to the claimed invention because they are in the same field of predicting and assisting autonomous vehicle trajectories. 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 incorporated the teachings of Hu into Sano for the benefit of enhanced and better object/obstacle detection.
Regarding claim 2, the rejection of claim 1 is incorporated herein. Sano in view of Hu teaches the method of claim 1, and the combination further teaches:
wherein the determining that the location is associated with of the nearest boundary of the object comprises:
processing first data that represents the one or more outputs using the one or more machine learning models (Hu, “the neural network prediction model is configured to predict… an occupancy grid map,” Para [0043]);
and generating, using the one or more machine learning models and based at least on the processing, an output (Hu, “Each pixel in the occupancy grid map corresponding to the target object represents a location on the ground, and a value of each pixel indicates a probability that other vehicles, pedestrians, or cyclists may be present at the location,” Para [0044]) indicating that the location is associated with the nearest boundary of the object (Sano, in view of Sano’s Fig. 9: “among the plurality of regions B′ arranged along this angular direction φ (e.g., a region B′1 to a region B′11), a partitioned region B′8(B′) is a region B′ at the distance r.sub.0 that includes a detection point P closest to the mobile object 10,” Para [0146]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Hu into Sano for the benefit of enhanced and better object/obstacle detection.
Regarding claim 3, the rejection of claim 1 is incorporated herein. Sano in view of Hu teaches the method of claim 1, and Sano further teaches:
wherein the determining that the location is associated with the nearest boundary of the object comprises:
determining that the location (Fig. 9, “B’8”) is associated with a highest value from the one or more values (Fig. 9, at location B’8, existence probability is found to be the highest);
and determining, based at least on the location being associated with the highest value, that the location is associated with the nearest boundary of the object (“The detection point P is the one which exists in the closest position to the mobile object 10,” Para [0159]).
Regarding claim 4, the rejection of claim 1 is incorporated herein. Sano in view of Hu teaches the method of claim 1, and the combination further teaches:
the one or more locations (“distance r”) include a plurality of locations (Sano, Fig. 9, at each distance r, or “location”, an existence probability is known);
the one or more values include a plurality of values (Sano, Fig. 9, for each distance r, an existence probability is known);
and the one or more outputs include a grid map of the environment surrounding the machine (Hu, Fig. 8), the grid map being partitioned into a plurality of portions (“pixel”) associated with the plurality of locations (each pixel represents a certain amount of area at a certain location), and the plurality of values (“probability”) are associated with the plurality of locations (Hu, “Each pixel in the occupancy grid map corresponding to the target object represents a location on the ground, and a value of each pixel indicates a probability that other vehicles, pedestrians, or cyclists may be present at the location,” Para [0044]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Hu into Sano for the benefit of enhanced and better object/obstacle detection.
Regarding claim 5, the rejection of claim 1 is incorporated herein. Sano in view of Hu teaches the method of claim 1, and Sano further teaches:
the one or more values associated with the one or more locations include at least:
one or more first values (Figs. 10B, D, and F, “existence probability values” to the left of the peaks) associated with one or more first distances (Figs. 10B, D, and F, x values to the left of r0) from the machine, the one or more first distances (values to the left of “r0”) corresponding to one or more second locations of the one or more locations (Figs. 10A, C, and E, values to the left of point “P”/values closer to mobile object 10);
a second value (Figs. 10B, D, “existence probability value” at the peak of the graph) associated with a second distance (Figs. 10B and D, “r0”) from the machine, the second distance corresponding to the location of the one or more locations (Figs. 10A, C, and E, values at point “P”); and
one or more third values (Figs. 10B, D, and F, “existence probability values” to the right of the peaks) associated with one or more third distances (Figs. 10B, D, and F, x values to the right of r0) from the machine, the one or more third distances corresponding to one or more third locations of the one or more locations (Figs. 10A, C, and E, values to the right of point “P”/values further from mobile object 10); and
the second value is greater than the one or more first values and the one or more third values (Figs. 10A-10F, “second value” represents a peak on the graphs, which is greater than values to the right or left of the peak, which are the “first” and “third” values).
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Regarding claim 6, the rejection of claim 1 is incorporated herein. Sano in view of Hu teaches the method of claim 1, and Sano further teaches:
generating an obstacle map representing the environment (Sano, Fig. 12), the obstacle map indicating at least the location associated with the nearest boundary of the object (Sano, Fig. 12 shows the closest point, point P, at any given angle), wherein the causing the machine to perform the one or more operations is based at least on the obstacle map (Sano, “To automatically drive the mobile object 10, the power control unit 10G controls the power unit 10H on the basis of information that can be acquired from the external sensor 10B and the internal sensor 10C, and existence probability information derived from processing,” Para [0031]).
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Regarding claim 10, the rejection of claim 1 is incorporated herein. Sano in view of Hu teaches the method of claim 1, and Sano further teaches:
wherein the sensor data comprises one or more of: image data generated using the machine (“The external sensor 10B is a photographing apparatus, a distance sensor (a millimeter wave radar, a laser sensor), or a sonar sensor or an ultrasonic sensor that detects an object-using sound waves, for example,” Para [0042]);
LiDAR data generated using the machine;
RADAR data generated using the machine;
or ultrasonic data generated using the machine.
Claims 12-17 and 19 are system and processor claims that correspond to method claims 1-6. The rejection of claims 1-6 applies to claims 12-17 and 19.
Regarding claim 18, the rejection of claim 12 is incorporated herein. Sano in view of Hu teaches the method of claim 12, and Sano further teaches:
wherein the system is comprised in at least one of:
a control system for an autonomous or semi-autonomous machine (Sano, “the mobile object 10 is… capable of automatically traveling (autonomously traveling) without driving operation by a human,” Para [0028]);
a perception system for an autonomous or semi-autonomous machine;
a system for performing simulation operations;
a system for performing digital twin operations;
a system for performing light transport simulation;
a system for performing collaborative content creation for 3D assets;
a system for performing deep learning operations;
a system implemented using an edge device;
a system implemented using a robot;
a system for performing conversational AI operations;
a system implementing one or more large language models (LLMs);
a system for performing generative AI operations;
a system for generating synthetic data;
a system incorporating one or more virtual machines (VMs);
a system implemented at least partially in a data center;
or a system implemented at least partially using cloud computing resources.
Examiner’s Note: Claim 18 as recited is treated as a “field of use” or “intended use” limitation and therefore carries no patentable weight although it has been examined in view of Sano in view of Hu above. The system as recited has been examined as evidenced in claim 12 above. With respect to the enumerated environments that said system is “comprised in”, the specification as disclosed merely mentions these environments as preferred intended use environments without specific details that warrant said system comprised in these environments resulted in a novel and non-obvious structural change to the system. Reference to MPEP 2112.01 is also made for applicant’s attention.
Claim 20 is a processor claim that corresponds to system claim 18. The rejection of claim 18 therefore applies to claim 20. Additionally, claim 20 is treated as a “field of use” or “intended use” claim and thus the examiner’s note for claim 18 also applies to claim 20.
Regarding claim 21, the rejection of claim 19 is incorporated herein. Sano in view of Hu teach the processor of claim 19, and Sano further teaches:
wherein the values include at least:
one or more first values for one or more first locations of the locations (existence probability at point P), the one or more first values indicating that the one or more nearest boundaries of the one or more objects are located at the one or more first locations (Fig. 9, a high existence probability value indicates that an object exists at distance r from mobile object 10, which is a location);
and one or more second values for one or more second locations of the locations (existence probability at values to the left of r0), the one or more second values indicating that the one or more nearest boundaries of the one or more objects are not located at the one or more second locations (Fig. 9, a low existence probability value indicates that an object does not or very likely does not exist at distances to the left of r0/distances closer to the mobile object 10, which is a location).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sano et al. (US-20180189599-A1) in view of Hu et al. (US-20240420344-A1) as applied to claim 1 above, and further in view of Chiu et al. (US-20200184718-A1).
Regarding claim 8, the rejection of claim 1 is incorporated herein. Sano in view of Hu teaches the method of claim 1, and Hu further teaches:
generating, by the one or more neural networks (“the neural network prediction model is configured to predict… an occupancy grid map,” Para [0043]) and based at least on the one or more first values and the one or more second values, the one or more outputs representing, from the top-down perspective of the environment, the one or more values (“probability”) indicating whether the one or more locations within the environment are associated with the nearest boundary of an object (“a value of each pixel indicates a probability that other vehicles, pedestrians, or cyclists may be present at the location,” Para [0044]) in the one or more directions from the machine (Fig. 8, Ego-Vehicle Predicted Occupancy Grid Map has values in all directions from the vehicle).
Hu is not relied upon to teach the following limitations. Chiu, however, further teaches:
generating, using the one or more neural networks (“One architecture employs individual deep neural networks for each modality and map the input into a feature representation,” Para [0029]) and based at least on a first portion of the sensor data corresponding to a first sensor modality (“3D point cloud”), first data (3D point cloud from a first sensor on a platform with a first modality,” Para [0005]) representing one or more first values associated with the one or more locations (“generating at least one first space semantic label and at least one first space semantic label uncertainty associated with at least one first space point in the 3D point cloud,” Para [0005]);
generating, using the one or more neural networks and based at least on a second portion of the sensor data corresponding to a second sensor modality (“2D image”), second data (“a 2D image from a second sensor on the platform with a second modality different from the first modality,” Para [0005]) representing one or more second values associated with the one or more locations (“generating at least one second space semantic label and at least one second space semantic label uncertainty associated with at least one second space point in the 2D image,” Para [0005]).
Chiu is considered to be analogous to the claimed invention because they are both in the same field of vehicle obstacle perception using sensors. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to have incorporated the teachings of Chiu into Sano and Hu for the benefit of a wider field of view to detect obstacles around the vehicle.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Hu into Sano for the benefit of enhanced and better object/obstacle detection.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sano et al. (US-20180189599-A1) in view of Hu et al. (US-20240420344-A1) as applied to claim 1 above, and further in view of You et al. (US-20180164832-A1).
Regarding claim 9, the rejection of claim 1 is incorporated herein. Sano in view of Hu teaches the method of claim 1, but are not completely relied upon to teach the following limitations. You, however, further teaches generating, using the one or more machine learning (Hu, “raster image data is processed by a neural network prediction model,” Para [0034]) and based at least on the sensor data (You, “front image”), at least one of:
a height map indicating one or more heights associated with the one or more locations (You, “generating a height map of the front image by transforming the generated depth map,” Para [0007]); or one or more uncertainty values associated with the one or more locations.
You is considered to be analogous to the claimed invention because they are both in the same field of avoiding obstacles in a vehicle’s field of view. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to have incorporated the teachings of You into Sano and Hu for the benefit of more accurately determining if an obstacle exists in a field of view.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Hu into Sano for the benefit of enhanced and better object/obstacle detection.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sano et al. (US-20180189599-A1) in view of Hu et al. (US-20240420344-A1) as applied to claim 1 above, and further in view of Lee et al. (US-20200294310-A1) and Kentley-Klay (US-20200064842-A1).
Regarding claim 11, the rejection of claim 1 is incorporated herein. Sano in view of Hu teach the method of claim 1, but are not relied upon to teach the following limitations. Lee, however, further teaches wherein the one or more machine learning models are trained using at least:
input data (object detector is trained using “training images,” Para [0057]) that includes at least
and ground truth data (“ground-truth data for a parking space”) representing at least one or more
Lee fails to teach the following limitations as further claimed. Kentley-Klay, however, further teaches “second” sensor data, machines, environments, values, locations, boundaries, and objects (“In some examples, the first vehicle 104(1) may migrate testing and/or training to second vehicle 104(2) by transmitting… an updated model 408 (which may include a trained target ML model) and/or an experimental model 410 to the second vehicle 104(2),” Para [0066]).
Lee is considered to be analogous to the claimed invention because they are both in the field of object detection in autonomous vehicles. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to have incorporated the teachings of Lee into Sano and Hu for the benefit of up-to-date training information for more reliable object avoidance.
Kentley-Klay is considered to be analogous to the claimed invention because they are both in the field of vehicle object avoidance. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to have incorporated the teachings of Kentley-Klay into Sano, Hu, and Lee for the benefit of up-to-date training information for more reliable object avoidance.
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 RACHEL A OMETZ whose telephone number is (571)272-2535. The examiner can normally be reached 6:45am-4:00pm ET Monday-Thursday, 6:45am-1:00pm ET every other Friday.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vu Le can be reached at 571-272-7332. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Rachel Anne Ometz/Examiner, Art Unit 2668 3/25/26
/VU LE/Supervisory Patent Examiner, Art Unit 2668
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