DETAILED ACTION
Response to Amendment
Applicant's submission filed on July 2, 2025 has been entered.
Claim 1, 7, and 19 are amended.
Claim 11-12 and 20 are cancelled.
Claims 1-10, 13-19, and 21-23 are pending this application
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 1-5, 7, 9, 13-17, 19, and 22-23 are rejected under 35 U.S.C. 103 as being unpatentable over Bradley (US 2019/0220014 A1), in view of Levinson et al (WO 2019/195363 A1) and Smith (US 2018/0120842 A1).
Regarding Claim 1, Bradley teaches a system comprising [0076]:
an autonomous vehicle [0076];
a radar sensor on the autonomous vehicle [0076];
one or more processors [0075];
and one or more non-transitory computer readable media storing instructions executable by the one or more processors, wherein the instructions, when executed, cause the system to perform operations comprising [0075]:
receiving radar data captured by the radar sensor [0076]),
the radar data comprising a plurality of points having individual velocities and positions [0078];
providing the radar data to a machine learned model [0070];
receiving, as an output from the machine learned model [0083, 0146],
a representation of the radar data, the representation comprising a two-dimensional representation of a sensed object [0083, 0106, for using subsets of data associated with an object of interest, and 0146, 0176],
the two-dimensional representation including a sensed velocity and a sensed position of the sensed object [0078],
wherein the two-dimensional representation is based on three-dimensional radar data [0078-0080 for using three-dimensional radar data to determine the 2D image of the object];
and controlling, based at least in part on the estimated track, the autonomous vehicle relative to the estimated track [0083 for estimating location and speed, with 0088 for “The motion planning system 114 can provide the selected motion plan to a vehicle controller 116 that controls one or more vehicle controls 108 e.g., actuators or other devices that control gas flow, steering, braking, etc. to execute the selected motion plan”].
Bradley fails to explicitly teach a single representation of the radar data, the single representation comprising a two-dimensional representation of a sensed object, determining, based at least in part on the level of similarity meeting or exceeding a threshold tracked wherein the threshold is determined at least in part on a certainty of the first attribute.
Levinson has a method includes receiving a first signal from a first sensor, the first signal including data representing an environment (abstract) and teaches a single representation of the radar data, the single representation comprising a two-dimensional representation of a sensed object [0045 for using a point for sensing range]
determining, based at least in part on the level of similarity meeting or exceeding a threshold tracked wherein the threshold is determined at least in part on a certainty of the first attribute [0111 for determining certainty of classification (similarity thresholds), 0113].
It would have been obvious to a person of ordinary skill in the art before the effectivefilling date of the applicant’s invention to combine the object tracking of Bradley with the track estimation techniques of Levinson for the purpose to determine whether any differences in such parameters exist (Levinson, 0111).
Bradley fails to explicitly teach generating, based at least in part on comparing a first attribute of the two-dimensional representation of the sensed object and a second attribute of the existing two-dimensional representation of the tracked object, a level of similarity between the first attribute and the second attribute, determining, based at least in part on the level of similarity meeting or exceeding a threshold, that the sensed object is not being tracked.
Smith has control system of an autonomous vehicle can receive sensor data from a sensor array (abstract) and teaches generating, based at least in part on comparing a first attribute of the two-dimensional representation of the sensed object and a second attribute of the existing two-dimensional representation of the tracked object [0044 for comparing sensor data to determine a ghost object, also 0045-0047];
a level of similarity between the first attribute and the second attribute, determining, based at least in part on the level of similarity meeting or exceeding a threshold tracked [0046 for using attributes such as boundaries, walls, buildings with thresholds for red flags, and 0048-0049 for similarities of static/dynamic objects and using route (track) attributes such as exits, turns, traffic, to create flags and alerts (thresholds)],
that the sensed object is not being tracked [0050-0051 for determining if ghost objects should be tracked (not currently being tracked) and if to disregard the object or dynamically monitor the ghost object].
It would have been obvious to a person of ordinary skill in the art before the effectivefilling date of the applicant’s invention to combine the object tracking of Bradley with the untracked radar object techniques of Smith for the purpose to either disregard static ghost objects, or can monitor and track all detected ghost objects dynamically (Smith, 0050).
Regarding Claim 2, Bradley teaches the estimated track comprises one or more estimated future states of the sensed object [0086].
Regarding Claim 3, Bradley teaches the object data comprises a classification of the sensed object and generating the estimated track of the sensed object comprises predicting the one or more estimated future states of the sensed object based at least in part on the two- dimensional representation and the classification [0114, 0119].
Regarding Claim 4, Bradley teaches object data comprises a certainty associated with the two-dimensional representation [0059];
and the generating the estimated track is based at least in part on the certainty being equal to or exceeding a threshold certainty [0060].
Regarding Claim 5, Bradley teaches the generating the estimated track is based at least in part on a size of the two-dimensional representation, a distance of the two-dimensional representation from the autonomous vehicle, or a velocity associated with the two-dimensional representation [0060].
Regarding Claim 7, Bradley teaches a method comprising [0159]:
receiving a plurality of radar returns associated with an environment [0161, 0168];
providing the plurality of radar returns to a machine learned model [0080, 0161];
a representation, the representation comprising [0083, 0106, for using subsets of data associated with an object of interest, and 0146, 0176]:
receiving, as an output of the machine learned model, a two-dimensional representation of the sensed object based on the plurality of radar returns [0078, 0161];
receiving an existing a two-dimensional representation of a tracked object [0050 for updating (using existing) track data, and 0078-0080, and 0083 for using radar data to determine shape];
and generating, based on the two-dimensional representation of the sensed object and the comparison, an estimated track for the sensed object [0169, 0185]
and determining, based at least in part on the estimated track and additional sensor data from an additional sensor, an updated track [0083-0084, 0127].
Bradley fails to explicitly teach a single representation of the radar data, the single representation comprising a two-dimensional representation of a sensed object, determining, based at least in part on the level of similarity meeting or exceeding a threshold tracked wherein the threshold is determined at least in part on a certainty of the first attribute.
Levinson has a method includes receiving a first signal from a first sensor, the first signal including data representing an environment (abstract) and teaches a single representation of the radar data, the single representation comprising a two-dimensional representation of a sensed object [0045 for using a point for sensing range]
determining, based at least in part on the level of similarity meeting or exceeding a threshold tracked wherein the threshold is determined at least in part on a certainty of the first attribute [0111 for determining certainty of classification (similarity thresholds), 0113].
It would have been obvious to a person of ordinary skill in the art before the effectivefilling date of the applicant’s invention to combine the object tracking of Bradley with the track estimation techniques of Levinson for the purpose to determine whether any differences in such parameters exist (Levinson, 0111).
Bradley fails to explicitly teach generating, based at least in part on comparing a first attribute of the two-dimensional representation of the sensed object and a second attribute of the existing two-dimensional representation of the tracked object, a level of similarity between the first attribute and the second attribute, determining, based at least in part on the level of similarity meeting or exceeding a threshold, that the sensed object is not being tracked.
Smith has control system of an autonomous vehicle can receive sensor data from a sensor array (abstract) and teaches generating, based at least in part on comparing a first attribute of the two-dimensional representation of the sensed object and a second attribute of the existing two-dimensional representation of the tracked object [0044 for comparing sensor data to determine a ghost object, also 0045-0047];
a level of similarity between the first attribute and the second attribute, determining, based at least in part on the level of similarity meeting or exceeding a threshold tracked [0046 for using attributes such as boundaries, walls, buildings with thresholds for red flags, and 0048-0049 for similarities of static/dynamic objects and using route (track) attributes such as exits, turns, traffic, to create flags and alerts (thresholds)],
that the sensed object is not being tracked [0050-0051 for determining if ghost objects should be tracked (not currently being tracked) and if to disregard the object or dynamically monitor the ghost object].
It would have been obvious to a person of ordinary skill in the art before the effectivefilling date of the applicant’s invention to combine the object tracking of Bradley with the untracked radar object techniques of Smith for the purpose to either disregard static ghost objects, or can monitor and track all detected ghost objects dynamically (Smith, 0050).
Regarding Claim 9, Bradley teaches the two-dimensional representation includes at least one of a classification, a sensed velocity, or a sensed position [0050, 0055];
and the generating the estimated track is based at least in part on the classification corresponding to a predetermined object type, the sensed velocity meeting or exceeding a threshold velocity, or the sensed position being equal to or nearer than a threshold distance [0024, 0051].
Regarding Claim 13, Bradley teaches receiving additional data from one or more additional sensors, the one or more additional sensors comprising at least one of a lidar sensor, a time-of-flight sensor, or an imaging sensor [0077];
and identifying, in the additional data, a subset of the additional data associated with the sensed object, wherein the generating the estimated track is further based at least in part on the subset of the additional data [0076].
Regarding Claim 14, Bradley teaches the estimated track comprises one or more estimated future states of the sensed object [0030].
.
Regarding Claim 15, Bradley teaches the object data comprises a classification of the sensed object and generating the estimated track of the sensed object comprises predicting the one or more estimated future states of the sensed object based at least in part on the two- dimensional representation and the classification [0114, 0119].
Regarding Claim 16, Bradley the plurality of radar returns associated with the sensed object are associated with a first time, the method further comprising [0118]:
generating, based on additional radar returns associated with the sensed object and associated with a second time, a second two-dimensional representation of the sensed object [0119],
wherein the generating the estimated track is based at least in part on the second two- dimensional representation of the sensed object [0119].
Regarding Claim 17, Bradley teaches generating a trajectory for controlling an autonomous vehicle relative to the estimated track [0049, 0056];
and controlling the autonomous vehicle to travel based on the trajectory [0050, 0088].
Regarding Claim 19, Bradley teaches a non-transitory computer readable media storing instructions that, when executed by one or more processors, cause one or more the processors to perform operations comprising [0159]:
receiving a plurality of radar returns associated with an environment [0161];
providing the plurality of radar returns to a machine learned model [0162];
a representation, the representation comprising [0083, 0106, for using subsets of data associated with an object of interest, and 0146, 0176]:
receiving, as an output of the machine learned model, a two-dimensional representation of the sensed object based on the plurality of radar returns [0078, 0162];
and receiving an existing two-dimensional representation of a sensed object [0050 for updating (using existing) track data and 0161, 0169].
Bradley fails to explicitly teach a single representation of the radar data, the single representation comprising a two-dimensional representation of a sensed object, and a second confidence associated with the two-dimensional representation of the tracked object, and generating, based on at least in part on the first confidence and the second confidence, a comparison of the two-dimensional representation of the sensed object and the two-dimensional representation of the tracked object, wherein the comparison includes determining, based at least in part on the level of similarity between the two-dimensional representations meets or exceeds a threshold, wherein the threshold is determined based at least in part on a certainty of an attribute of the two dimensional representation.
Levinson has a method includes receiving a first signal from a first sensor, the first signal including data representing an environment (abstract) and teaches a single representation of the radar data, the single representation comprising a two-dimensional representation of a sensed object [0045 for using a point for sensing range],
the single representation comprising a two-dimensional representation of a sensed object, and a second confidence associated with the two-dimensional representation of the tracked object, and generating, based on at least in part on the first confidence and the second confidence, a comparison of the two-dimensional representation of the sensed object and the two-dimensional representation of the tracked object [0031, 0066],
wherein the comparison includes determining, based at least in part on the level of similarity between the two-dimensional representations meets or exceeds a threshold, wherein the threshold is determined based at least in part on a certainty of an attribute of the two dimensional representation [0111 for determining certainty of classification (similarity thresholds), 0113].
It would have been obvious to a person of ordinary skill in the art before the effectivefilling date of the applicant’s invention to combine the object tracking of Bradley with the track estimation techniques of Levinson for the purpose to determine whether any differences in such parameters exist (Levinson, 0111).
Bradley fails to explicitly teach determining, based at least in part on the comparison, that the sensed object is not being tracked; generating, based on the sensed object is not being tracked, an estimated track of the sensed object.
Smith has control system of an autonomous vehicle can receive sensor data from a sensor array (abstract) and teaches determining, based at least in part on the comparison, that the sensed object is not being tracked [0044 for comparing sensor data to determine a ghost object, also 0045-0047, 0050 for determining if ghost objects should be tracked (not currently being tracked) and if to disregard the object or dynamically monitor the ghost object];
generating, based on the sensed object is not being tracked, an estimated track of the sensed object [0050-0051].
It would have been obvious to a person of ordinary skill in the art before the effectivefilling date of the applicant’s invention to combine the object tracking of Bradley with the untracked radar object techniques of Smith for the purpose to either disregard static ghost objects, or can monitor and track all detected ghost objects dynamically (Smith, 0050).
Regarding Claim 22, Bradley teaches object data comprises a classification of the sensed object and generating of the estimated track of the sensed object comprises predicting one or more estimated future states of the sensed object based at least in part on the two-dimensional representation and the classification [0086, 0114, 0119].
Regarding Claim 23, Bradley teaches object data comprises a certainty associated with the two-dimensional representation [0059];
and the generating of the estimated track is based at least in part on the certainty being equal to or exceeding a threshold certainty [0060].
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Bradley (US 2019/0220014 A1), in view of Levinson et al (WO 2019/195363 A1) and Smith (US 2018/0120842 A1) as applied to claim 1 above, and further in view of Sakomoto et al (US 2016/0018219 Al).
Regarding Claim 6, Bradley fails to explicitly teach the determining that the sensed object is not being tracked is based at least in part on determining whether the two-dimensional representation is associated with track information associated with one or more tracked objects.
Sakamoto has an estimation apparatus includes a distance estimator (abstract) and teaches the determining that the sensed object is not being tracked is based at least in part on determining whether the two-dimensional representation is associated with track information associated with one or more tracked objects [0075-0076].
It would have been obvious to a person of ordinary skill in the art before the effectivefilling date of the applicant’s invention to combine the object tracking of Bradley with the untracked radar object techniques of Sakamoto for the purpose generating a new LKF tracker Q2 for each of the new targets and the observations are set as initial values of the state quantities (Sakamoto, 0076).
Claim 8 are rejected under 35 U.S.C. 103 as being unpatentable over Bradley (US 2019/0220014 A1), in view of Levinson et al (WO 2019/195363 A1) and Smith (US 2018/0120842 A1) as applied to claim 7 above in further view of Das Gupta (US 2021/0089041 A1).
Regarding Claim 8, Bradley fails to explicitly teach the two-dimensional representation includes a confidence associated with the two-dimensional representation; and the generating the estimated track is based at least in part on the confidence being equal to or above a threshold confidence value.
Das Gupta has methods for controlling an autonomous vehicle (abstract) and teaches the two-dimensional representation includes a confidence associated with the two-dimensional representation [0028, 0083];
and the generating the estimated track is based at least in part on the confidence being equal to or above a threshold confidence value [0030].
It would have been obvious to a person of ordinary skill in the art before the effectivefilling date of the applicant’s invention to combine the object tracking of Bradley with the confidence threshold values of Das Gupta for the purpose determines whether an ability of the autonomous vehicle to enter the interaction zone (Das Gupta, 0029).
Claim 10 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Bradley et al (US 2019/0220014 A1), in view of Levinson et al (WO 2019/195363 A1) and Smith (US 2018/0120842 A1) as applied to claims 1 and 7 above, and further in view of Wang (US 10,732,261 B1).
Regarding Claims 10 and 21, Bradley fails to explicitly teach wherein the two-dimensional representation is based solely on radar data.
Wang has a first predicted state data associated with a second time that is later than the first time, are generated based on the first track (abstract) and teaches wherein the two-dimensional representation is based solely on radar data [col 14, lines 5-25 for getting shape data from radar sensor].
It would have been obvious to a person of ordinary skill in the art before the effectivefilling date of the applicant’s invention to combine the object tracking of Bradley with the track estimation techniques of Wang for the purpose to determine size, a shape, a velocity of an object (Wang, col 14, lines 5-25).
Claims 18 is rejected under 35 U.S.C. 103 as being unpatentable over Bradley (US 2019/0220014 A1), in view of Levinson et al (WO 2019/195363 A1) and Smith (US 2018/0120842 A1) as applied to claim 7 above, and further in view of Sun (US 2021/0155266 A1).
Regarding Claim 18, Bradley teaches the plurality of radar returns are provided to a same layer of the machine learned model such that the machine learned model processes the plurality or radar returns [0029].
Bradley fails to explicitly teach having radar returns simultaneously.
Sun has an object trajectory prediction system (abstract) and teaches having radar returns simultaneously [0044, 0055].
It would have been obvious to a person of ordinary skill in the art before the effectivefilling date of the applicant’s invention to combine the object tracking of Bradley with the untracked radar object techniques of Sun for the purpose generating historic temporal data associated with a position of one or more obstacles relative to a navigational path of the vehicle (Sun, paragraph 44).
Response to Arguments
Applicant’s arguments with respect to claims 1-10, 13-19, and 21-23 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. Levinson teaches using confidence level and certainty to determine attribute similarity thresholds (classification) [Levinson, 0031, 0111].
The examiner acknowledges that this is a broader interpretation than Applicant’s.
However, examiners are not only allowed to apply broad interpretations, but are required to do so, as it reduces the possibility that the claims, once issued, will be interpreted more broadly than is justified. MPEP §2111. Patentability is determined by the “broadest reasonable interpretation
consistent with the specification” (MPEP §2111), not the narrowest reasonable interpretation. And Applicant does not have an explicit lexicographical statement in line with MPEP §2111.01
subsection IV requiring a specific interpretation of the relevant phrases which forces the examiner to interpret them only one way.
The express, implicit, and inherent disclosures of a prior art reference may be relied upon in the rejection of claims under 35 U.S.C. 102 or 103. "The inherent teaching of a prior art reference, a question of fact, arises both in the context of anticipation and obviousness." In re Napier, 55 F.3d 610, 613, 34 USPQ2d 1782, 1784 (Fed. Cir. 1995).
For applicant’s benefit, portions of the cited reference(s) have been cited to aid in the review of the rejection(s). While every attempt has been made to be thorough and consistent within the rejection it is noted that the PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, including disclosures that teach away from the claims. See MPEP 2141.02 VI.
“The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). A reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art, including non-preferred embodiments. Merck & Co. v.Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert. denied, 493 U.S. 975 (1989). See also Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005) See MPEP 2123.
Conclusion
THIS ACTION IS MADE FINAL. 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 SAMARINA MAKHDOOM whose telephone number is (703)756-1044. The examiner can normally be reached Monday – Thursdays from 8:30 to 5:30 pm eastern time.
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/SAMARINA MAKHDOOM/Examiner, Art Unit 3648 /S.M./
/William Kelleher/Supervisory Patent Examiner, Art Unit 3648