DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
Applicants Amendments filed on June 26, 2026, has been entered and made of record.
Currently pending Claim(s): 1-20
Independent Claim(s): 1, 12
Amended Claim(s): 1, 12
Canceled Claim(s): 4, 15
Response to Arguments
This office action is responsive to the Applicant’s Arguments/Remarks Made in an Amendment
received on June 26, 2026.
In view of amendments filed on June 26, 2026, the Applicant has amended independent Claim 1 to recite the additional imitation of Claim 4, which was indicated as allowable in the previous Office Action. However, the claim limitations recite “at least one of the following conditions …” then proceeds to list four different conditions (e.g., distribution, width, ratio, and/or class information). Under the broadest reasonable interpretation, only one of the conditions is needed to satisfy the claim limitation. Yun teaches the class information condition. The amended claim limitation adds further limitations to the “ratio of the width” condition. This wherein condition cannot be found the prior art. Yet, since the conditions are read in the alternative, Yun’s teaching of the class information condition meets the bounds of the claims under the broadest reasonable interpretation (MPEP 2111.01).
Thus, for the reasons cited above, the Examiner has maintained the rejection of Claims 1 and 12 over Yun and Zhu. The Examiner has also maintained the previous rejections of the dependent claims 2-3, 6-11, 13-14, and 17-20.
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 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 nonobviousness.
Claims 1, 6-7, 10-12, are 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Yun (US Pub No 20220185268), hereinafter Yun, in view of Zhu et al. (US Pat No 9097800), hereinafter Zhu.
As to Claim 1, Yun discloses an object recognition apparatus comprising
a light detection and ranging (LIDAR) device (see Fig. 1, roof lidar 132);
a camera (see Fig. 1, front camera 112);
a radar (see Fig. 1 , front radar 11 and rear/front side radars 113);
and a processor (see Fig. 4, track mergence module, and see paragraph [0032], “As used herein, the terms “unit”, “module”, and “block” may be implemented as software or hardware (e.g., a processor)”), wherein the processor is configured to:
identify a LIDAR track obtained through the LIDAR device and corresponding to an object outside of a host vehicle (see paragraph [0049, “a situation in which a roof lidar track having a small size with respect to one target is additionally generated”, and see Fig. 6, lidar only track FT2);
identify a fusion track obtained through at least one of the radar or the camera and corresponding to the object (see paragraph [0055], “FT1 indicates a fusion track generated by the DOF unit 131”, see FT1 in Fig. 6, and see Fig. 1, where the DOF unit obtains input from cameras and radars);
and determining that at least one of the following conditions is satisfied: class information of the object according to the fusion track satisfies a class condition (see paragraphs [0060 – 0062], “In the first phenomenon, deletion variables for the roof lidar-only track FT2, which must be deleted, among a plurality of tracks, are as below: FT1 corresponds to a passenger vehicle or a commercial vehicle also used as a passenger vehicle, having a height equal to or greater than 1.5 m”, where determining that FT1 corresponds to a vehicle is determining class information).
Yun fails to teach generating a synthetic fusion track including at least one of a longitudinal position, a lateral position, a width, a length, or a heading represented by the fusion track. Instead, if a condition is met, Yun teaches that a lidar track may be deleted (see paragraph [0060]), and that the fusion track may be used instead.
However, in an analogous art, Zhu teaches an automated vehicle that generates a LIDAR track and fusion track (see Col. 5, lines 18-20, “Moreover, in some examples, an autonomous vehicle includes both a LIDAR device and a RADAR device that are each scanned continuously throughout a scanning zone” )
that generates a synthetic fusion track including at least one of a longitudinal position, a lateral position, a width, a length, or a heading represented by the fusion track (see paragraph Col. 20, lines 38-45 “In the system 600, the controller 610 operates the LIDAR device 620 and the RADAR device 630 to characterize the radio-reflectivity of light-reflecting features indicated by a Scan with the LIDAR device 620. The controller 610 interprets the output from the RADAR device 630 to associate the light-reflective features with either solid materials or not solid materials and output the map of solid objects 612 in the environment surrounding the system 600”, where the map includes the longitudinal and lateral positions of the RADAR (fusion) track). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the synthetic fusion track taught by Zhu with the method taught by Yun. The motivation for doing so would be to remove false longitudinal and lateral positions caused by LIDAR detecting exhaust gas. Zhu teaches in Col. 4, lines 35-42, “For example, a cloud of exhaust gas or aggregate drops of water in rain or in a splash of water from a puddle may be detected by a LIDAR and appear in a 3-D point map with either a well-defined shape, or an amorphous shape. Thus, a LIDAR detector is sensitive to non-solid reflective features, even though such features may be safely disregarded for object avoidance”. Thus, it would have been obvious to combine the synthetic fusion track taught by Zhu with the teachings of Yun in order to obtain the invention as claimed in Claim 1.
As to Claim 6 , Yun in view of Zhu teaches that the processor is configured to determine that the class information satisfies the class condition based on determining that the class information indicates an automobile, wherein the class information is indicative of a type of the object according to the fusion track (see Yun, paragraphs [0060 – 0062], “In the first phenomenon, deletion variables for the roof lidar-only track FT2, which must be deleted, among a plurality of tracks, are as below: FT1 corresponds to a passenger vehicle or a commercial vehicle also used as a passenger vehicle, having a height equal to or greater than 1.5 m”, where the vehicle is the class, and FT1 is the fusion track).
As to Claim 7, Yun fails to explicitly teach that the processor is configured to identify the class information based on an image obtained through the camera. However, Zhu teaches that images obtained by a camera can be used to identify class information (see Col. 8 lines 32-36, “The computer vision system 140 can process and analyze images captured by camera 130 to identify objects and/or features in the environment surrounding vehicle 100. The detected features/objects can include traffic signals, road way boundaries, other vehicles, pedestrians, and/or obstacles, etc.”). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the object detection taught by Zhu with the teachings of Yun. The motivation for doing so would be to identify obstacles so that they can be avoided. Zhu teaches in Col. 8 lines 45-51, “The navigation and pathing system 142 is configured to determine a driving path for the vehicle 100. For example, the navigation and pathing system 142 can determine a series of speeds and directional headings to effect movement of the vehicle along a path that substantially avoids perceived obstacles”. Thus, it would have been obvious to combine the object recognition taught by Zhu with the teachings of Yun in order to obtain the invention as claimed in Claim 7.
As to Claim 10, Yun teaches when it is identified that i) the object enters a host lane where the host vehicle is located based on a heading of the LIDAR track including the LIDAR points and ii) the object does not enter the host lane based on a heading of the fusion track, identify that the object does not enter the host lane (see paragraphs [0077 -0078], “Referring to FIG. 9, among two tracks FT1 and FT2 of a target vehicle 910 driving in a lane adjacent to a lane in which a host vehicle 990 drives, when this situation is determined only based on the actual fusion track FT1 of the target vehicle 910, it is determined that the target vehicle 910 driving in the adjacent lane does not deviate from the adjacent lane but, when this situation is determined based on the roof lidar-only track FT2 in addition to the actual fusion track FT1 of the target vehicle 910, it is determined that the target vehicle 910 driving in the adjacent lane invades the drive lane of the host vehicle 900, and thereby, the host vehicle 900 is autonomously braked and may thus cause erroneous braking. In the present disclosure, even in a situation in which the first phenomenon occurs, it is determined that the target vehicle 910 driving in the adjacent lane does not deviate from the adjacent lane and thus the host vehicle 900 may not be braked”). However, Yun fails to teach the LIDAR track includes the LIDAR points caused by exhaust gas emitted from the object.
However, Zhu teaches identifying LIDAR points caused by exhaust gas (see Col. 5, lines 6-14, “In some embodiments, a processor-based system including a scanning LIDAR and a RADAR device use the RADAR device to determine whether LIDAR-indicated objects are solid objects. Both water vapor and exhaust plumes are not significantly detected in radio. A RADAR device can then be used to check whether a LIDAR-indicated reflective feature is also detected in radio. If the feature is present in the RADAR scan, the feature is determined to be solid. If the feature is not present in the RADAR scan, the feature is determined to not be solid” ). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the exhaust gas detection taught by Zhu with the method taught by Yun. The motivation for doing so would be to reduce unnecessary vehicle maneuvers caused by exhaust gas detection. Zhu teaches in Col. 4, lines 56-63, “Without the ability to distinguish between solid reflective features and non-solid reflective features, autonomous vehicles can make undesirable maneuvers to the detriment of passenger safety, fuel economy, etc. For example, in cold weather, the exhaust of a car within the scanning zone of the LIDAR can appear reflective, and cause an automatic object avoidance system to unnecessarily maneuver the vehicle around the exhaust.” Thus, it would have been obvious to combine the exhaust gas detection taught by Zhu with the teachings of Yun in order to obtain the invention as claimed in Claim 1.
As to Claim 11, Yun in view of Zhu teaches the object recognition apparatus of claim 1, wherein:
the camera includes a front side view camera configured to obtain an image in front of the host vehicle (see Yun, Fig.1, front camera 112);
the radar includes at least one of a front radar configured to obtain a radar point in front of the host vehicle, a front corner radar configured to obtain a radar point at a front corner of the host vehicle, or a rear corner radar configured to obtain a radar point at a rear corner of the host vehicle (see Yun, Fig. 1, front radar 111);
and the processor is configured to obtain the fusion track through at least one of the front side view camera, the front radar, the front corner radar, or the rear corner radar object (see paragraph [0055], “FT1 indicates a fusion track generated by the DOF unit 131”, see FT1 in Fig. 6, and see Fig 1, where the DOF unit obtains input from cameras and radars).
As to Claim 12, Claim 12 claims an object recognition method comprising the same steps taught in Claim 1. Thus, the rejection and rationale are analogous to that of Claim 1.
As to Claim 17, Claim 17 claims the same limitation as Claim 6 and is dependent on a similarly rejected independent claim. Therefore, the rejection and rationale are analogous to that made in Claim 6.
As to Claim 18, Claim 18 claims the same limitation as Claim 7 and is dependent on a similarly rejected independent claim. Therefore, the rejection and rationale are analogous to that made in Claim 7.
Claims 2 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Yun (US Pub No 20220185268) in view of Zhu et al. (US Pat No 9097800), and further in view of Bokyo et al. (US Pat No 8818609), hereinafter Bokyo.
As to Claim 2, Yun in view of Zhu fails to teach the processor is configured to determine that the distribution shape of the LIDAR points satisfies the distribution condition based on determining that the distribution shape of the LIDAR points included in the LIDAR track is not a specified shape identified as being unaffected by exhaust gas.
However, Bokyo teaches an autonomous driving system (see Col. 1, lines 1-22) which can identify distribution shapes that correspond to exhaust gas (see paragraphs Col. 19, lines 27-37, “The bounding box corresponds to a predicted exterior surface of the point cloud indicated object, in this case, a vehicle located in front of the autonomous vehicle. Of course, the bounding “box” can generally take the form of a multi-sided closed shape defining the predicted outer boundaries of the object. When a bounding box of a vehicle is tracked over time, portions of the vehicle that do not fit into the bounding box are more likely to be an exhaust plume because solid structures will usually be persistently present in the scans, and will cause the bounding box to take them into account.”, where the specified shape is the bounding box for the car). Thus, it would have obvious to one of ordinary skill in the art before ethe effective fling date of the claimed invention to combine the distribution shape taught by Bokyo with the teachings of Yun and Zhu. The motivation for doing so would be to easily identify exhaust gas and reduce errors in autonomous driving. Bokyo teaches in Col 3., lines 47-52, “For example, in cold weather, the exhaust of a car within the scanning zone of the LIDAR can appear reflective, and cause an automatic object avoidance system to unnecessarily maneuver the vehicle around the exhaust, even though such maneuvers are not warranted by a collision hazard.” Thus, it would have been obvious to combine the exhaust distribution shape recognition taught by Bokyo with the teachings of Yun and Zhu in order to obtain the invention as claimed in Claim 8.
As to Claim 9, Yun in view of Zhu fails to teach determining that the LIDAR points include LIDAR points caused by exhaust gas emitted from the object based on determining that at least one of the following conditions is satisfied: the distribution shape satisfies the distribution condition, the at least one of width satisfies the width condition, the ratio satisfies the ratio condition, or the class information satisfies the class condition; and generate the synthetic fusion track based on determining that the LIDAR points include the LIDAR points caused by the exhaust gas emitted from the object.
Yun teaches the class condition, but fails to teach determining that the LIDAR points include exhaust gas based on the class condition. Zhu teaches determining if LIDAR points include exhaust gas, but does not use any of the claimed conditions to determine if the LIDAR points include exhaust gas.
However, Bokyo teaches that LIDAR points can be identified by exhaust by examining their distributions (see paragraphs Col. 19, lines 27-37, “The bounding box corresponds to a predicted exterior surface of the point cloud indicated object, in this case, a vehicle located in front of the autonomous vehicle. Of course, the bounding “box” can generally take the form of a multi-sided closed shape defining the predicted outer boundaries of the object. When a bounding box of a vehicle is tracked over time, portions of the vehicle that do not fit into the bounding box are more likely to be an exhaust plume because solid structures will usually be persistently present in the scans, and will cause the bounding box to take them into account.”, where the specified shape is the bounding box for the car). Thus, it would have obvious to one of ordinary skill in the art before ethe effective fling date of the claimed invention to combine the distribution shape taught by Bokyo with the teachings of Yun and Zhu. The motivation for doing so would be to easily identify exhaust gas and reduce errors in autonomous driving, as taught by Bokyo in Col 3., lines 47-52.
As to Claim 20, Claim 20 claims the same limitation as Claim 9 and is dependent on a similarly rejected independent claim. Therefore, the rejection and rationale are analogous to that made in Claim 9.
Claim(s) 3 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Yun (US Pub No 20220185268) in view of Zhu et al. (US Pat No 9097800), and further in view of Einecke et al. (US Pub No 20130335569), hereinafter Einecke.
As to Claim 3, Yun in view of Zhu fails to explicitly teach that the width of the fusion track satisfies the width condition based on determining that the width of the fusion track falls within a width range specified to distinguish whether the object is an automobile.
However, in an analogous art, Einecke teaches a vehicle detection system that utilizes radar and cameras (see paragraph [0084]), that uses width to determine if an object is a vehicle (see paragraph [0085], “ The objects to be detected are preferably vehicles. In case of vehicle identification, the region of interest can preferably be rectangular with a predefined width and height reflecting typical vehicle values. As an example, a width of 1.8 m and a height of 1.4 m can be chosen”). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the width taught by Einecke with the teachings of Yun and Zhu. The motivation for doing so would be to identify only necessary objects and disregard objects that do not correspond to vehicles (see paragraph [0081], “ This method step can be used for identifying moving or stationary objects. This step can be carried out e.g. if the only objects that should be identified and processed are non-stationary i.e. non-static objects like moving vehicles. Other elements in the environment of the host vehicle like houses or trees will automatically be disregarded”). Thus, it would have been obvious to combine the width taught by Einecke in order to obtain the invention as claimed in Claim 3.
As to Claim 14, Claim 14 claims the same limitation as Claim 3 and is dependent on a similarly rejected independent claim. Therefore, the rejection and rationale are analogous to that made in Claim 3.
Claim(s) 8 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Yun (US Pub No 20220185268) in view of Zhu et al. (US Pat No 9097800), and further in view of Matsubara et al. (JP 2017129446), hereinafter Matsubara.
As to Claim 8 , Yun in view of Zhu fails to teach explicitly teach a flag corresponding to a distribution shape other than an L-shaped shape or an I-shaped shape based on determining that the distribution shape is not the L-shaped shape and is not the I-shaped shape; and determine that the distribution shape satisfies the distribution condition based on identifying that the flag corresponding to the LIDAR track is a specified flag indicating a distribution shape corresponding to the distribution shape other than the L-shaped shape and the I-shaped shape.
However, in an analogous art, Matsubara teaches an object detection device (see paragraphs [0001-0002]teaches that a flag may be assigned to a shape other than an L-shaped shape (see paragraph [0024], “The shape model fitting unit 13 votes the measurement point P in the input cluster in the L-shaped grid”, see L-shaped cluster in Fig. 2, and see [0026], “The shape model fitting unit 13 outputs a flag indicating whether the parameters of the estimated shape model and the parameters of the cluster and shape model used for updating the corresponding shape model have converged”, where the when the shape has not converged, an L-shaped.
Matsubara further teaches that this flag can be used to determine whether a shape condition indicating the presence of a vehicle (see paragraph [0030], “The object detecting unit 15 detects an object around the subject vehicle V based on the tracking result of the shape model in the shape model tracking unit 14. The detection result of the object in the object detection unit 15 can be used, for example, in a travel control device of the vehicle or to inform the driver of the presence of the object”, where the tracking result is the flag ).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the flag taught by Matsubara with the teachings Yun and Zhu. The motivation for doing so would be to increase object detection accuracy. Matsubara teaches in paragraph [0031], “Further, the shape model fitting unit 13 excludes clusters in which the movement direction of the cluster is different from the movement direction of the shape model by a predetermined direction threshold value or more, from the fitting to the shape model. By fitting the cluster to the shape model in this way, even if the measurement points of the detection target are clustered into a plurality of clusters, the object detection device 100 can accurately be applied to the shape model, and can be detected accurately.” Thus, it would have been obvious to combine the flag taught by Matsubara with the teachings of Yun and Zhu in order to obtain the invention as claimed in Claim 8.
As to Claim 19, Claim 19 claims the same limitation as Claim 8 and is dependent on a similarly rejected independent claim. Therefore, the rejection and rationale are analogous to that made in Claim 8.
Allowable Subject Matter
The following is a statement of reasons for the indication of allowable subject matter:
Yun and Zhu fail to teach a ratio condition, wherein determining that the ratio satisfies the ratio condition based on determining that the ratio of the width of the LIDAR track to the width of the fusion track falls within a ratio range specified to identify whether the LIDAR track is affected by exhaust gas. Yun teaches that the ratio between the overlap of two fusion tracks may be calculated, but fails to teach a ratio between the width of a lidar track and the width of a fusion track. Zhu fails to explicitly teach a width for either the fusion or LIDAR track. Bokyo teaches identifying exhaust gas based on a shape condition, but fails to explicitly teach a width of a LIDAR or radar track. Einecke teaches a width of a fusion track may be used to identify a vehicle, but fails to explicitly teach a LIDAR track. Einecke additionally fails to teach identifying exhaust gas. Matsubara teaches identifying vehicle shapes, and teaches identifying a width for the shapes, but fails to explicitly teach identifying exhaust gas, or identifying a ratio between the width of a fusion and the width of a LIDAR track.
The closest prior art of the record (Jia US Pub No 20230230255) discloses obtaining a radar track and a track from a separate sensor system, and comparing the width of each track to determine to create a fusion track. However, Jia only teaches that a discrepancy between width is found, and fails to explicitly teach a ratio. Furthermore, Jia fails to teach that a ratio condition can be used to identify whether the LIDAR track is affected by exhaust gas. The instant application has specifically stated that a ratio, not merely a ‘discrepancy’ or a difference between the width, must be used in order to account for possible errors of the sensor fusion system (see paragraph [0086] of the instant application).
Dallabetta et al. (DE 102021116859) teaches obtaining a width of a LIDAR track, and comparing the width to a threshold value to determine if an object is exhaust gas or a vehicle. However, Dallabetta fails to teach that a ratio between a LIDAR track and a sensor fusion track is obtained.
King et al. (US Pat No 12271790) teaches obtaining the width of a fusion track using LIDAR and radar and teaches a width/length ratio that can be used to scale the fusion track. However, King fails to teach calculating a ratio between a width of a fusion track and a width of a LIDAR track. King additionally fails to teach that this ratio can be used to identify exhaust gas.
As to Claim 5, Einecke teaches determine that the width of the fusion track satisfies the width condition based on determining that the width of the fusion track is greater than a width value specified to distinguish whether the object is an automobile. However, all the previously cited references fail to teach determine that the ratio satisfies the ratio condition based on determining that the ratio of the width of the LIDAR track to the width of the fusion track is greater than a ratio value specified to identify whether the LIDAR track is affected by exhaust gas.
Claims 15 contains similar limitations that were not found in the prior art. However, Claim 1 recites “at least one of the following conditions …” then proceeds to list four different conditions (e.g., distribution, width, ratio, and/or class information). Under the broadest reasonable interpretation, only one of the conditions is needed to satisfy the claim limitation. Yun teaches the class information condition. The amended claim limitation adds further limitations to the “ratio of the width” condition. This wherein condition cannot be found the prior art. Yet, since the conditions are read in the alternative, Yun’s teaching of the class information condition meets the bounds of the claims under the broadest reasonable interpretation (MPEP 2111.01).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SOUMYA THOMAS whose telephone number is (571)272-8639. The examiner can normally be reached M-F 8:30-5:00.
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/S.T./Examiner, Art Unit 2664
/JENNIFER MEHMOOD/Supervisory Patent Examiner, Art Unit 2664
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