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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 6/11/2025 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claim 1-20 rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-20 of U.S. Patent No. 12358524. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims are obvious broadening of scope with the change of predicted future velocities being changed to plurality of actor motion characteristics.
Conforms with 35 USC § 101
The presently examined claims were evaluated for a 101 Alice type rejection. The conclusion from going through the Alice/Mayo test is that the independent claims are integrated into a practical application (or cannot be performed merely with the human mind) and are therefore patent eligible under 35 U.S.C. 101. See MPEP §2106, subsection III and MPEP §2106.04, subsection II(A).
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1)/102(a)(2) as being anticipated by US 20190382007 A1 hereinafter Casa.
As to claim 1, Casa discloses a computer-implemented method, [Casa: abstract] comprising:
obtaining sensor data descriptive of an actor in an environment of an autonomous vehicle [Casa: 0076, #802] and at least a portion of the environment of the autonomous vehicle that does not include the actor, [Casa: 0005 “The multiple outputs include at least one detection output indicative of zero or more objects detected within the surrounding environment of the autonomous vehicle”] the sensor data comprising at least one sweep of the environment of the autonomous vehicle; [Casa: 0025]
processing the sensor data with a multi-head machine-learned perception model to generate a detection of the actor, [Casa: See Headers Fig. 6, HeaderD, HeaderI, HeaderR] the multi-head machine-learned perception model comprising a plurality of output heads respectively configured to output an actor motion characteristic of a plurality of actor motion characteristics; [Casa: See Headers Fig. 6, HeaderD, HeaderI, HeaderR – gives trajectory 0095]
determining a motion trajectory for the autonomous vehicle based on the detection and the plurality of actor motion characteristics; and [Casa: 0140 #808 motion planning unit]
controlling the autonomous vehicle based at least in part on the motion trajectory. [Casa: 0141 #810 vehicle controlling unit]
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Fig. 6 Casas
As to claim 2, Casa discloses further comprising: fusing the sensor data from two or more distinct sensor modalities into a common representation of the sensor data, [Casa: 0089 “(iv) this representation also facilitates the fusion of LIDAR and map features as both are defined in bird's eye view”] wherein processing the sensor data is based on the common representation of the sensor data, and the sensor data is captured from two or more distinct sensor modalities. [Casa: 0089]
As to claim 3, Casa discloses wherein processing the sensor data further comprises generating, by the multi-head machine-learned perception model, one or more uncertainty scores respectively associated with the plurality of actor motion characteristics. [Casa: 0079 score associated with object type and location in an image that is associated with actor motion characteristics. E.g., Starting point of motion.]
As to claim 4 and 18, Casa discloses further comprising, prior to determining the motion trajectory for the autonomous vehicle, processing the plurality of actor motion characteristics and the one or more uncertainty scores with a machine-learned object tracker model configured to generate one or more second velocity outputs, [Casa: 0075, 0081, 0091 bounding boxes incorporate the uncertainty] the second velocity outputs comprising data descriptive of velocities of the actor at one or more discrete future timesteps. [Casa: 0075, 0081, 0091 bounding boxes incorporate the uncertainty]
As to claim 5 and 19, Casa discloses comprising aligning, by the machine- learned object tracker model, the plurality of actor motion characteristics to a motion model respective to a class of the actor to generate the one or more second velocity outputs. [Casa: 0075, bounding boxes around prediction movement of objects. 0079 objects belong to predetermined classes]
As to claim 6, Casa discloses wherein the machine-learned object tracker model is configured to smooth the plurality of actor motion characteristics to generate the one or more second velocity outputs, and wherein the one or more second velocity outputs comprise smoothed velocity outputs. [Casa: 0096 “A weighted smooth L1 loss can be applied to the regression targets associated to the positive samples only. More precisely, the loss over the regression target set R.sub.t can be applied, which for t∈[1, T] (forecasting) does not include the bounding box size as previously explained.” The bounding boxes are predicted trajectories. ]
As to claim 7, Casa discloses wherein the machine-learned object tracker model comprises a multi-view tracker model [Casa: 0066] and the multi-head machine-learned perception model comprises a multi-view perception model. [Casa: 0088]
As to claim 8, Casa discloses wherein the plurality of actor motion characteristics are respectively associated with one or more discrete future time steps. [Casa: 0030]
As to claim 9, Casa discloses wherein the plurality of actor motion characteristics are determined in increments up to a prediction end time occurring at a given amount of time after a current time associated with the sensor data. [Casa: 0030]
As to claim 10, Casa discloses comprising: determining bounding box data associated with the actor based on the multi-head machine- learned perception model, [Casa: 0075, bounding boxes around prediction movement of objects.] wherein the multi-head machine-learned perception model is configured to regress instantaneous velocities of the actor; and regressing the plurality of actor motion characteristics by the multi-head machine-learned perception model. [Casa: 0075, “For instance, object intention determination system 200 can be configured to output three types of variables in a single forward pass corresponding to: detection scores for vehicle and background classes, high level actions' probabilities corresponding to discrete intention, and bounding box regressions in the current and future timesteps to represent the intended trajectory.”]
As to claim 11, Casa discloses wherein the sensor data comprises a plurality of sweeps of the environment of the autonomous vehicle. [Casa: 0089]
As to claim 12, Casa discloses wherein the plurality of actor motion characteristics comprise at least one of: instantaneous velocity, future velocity, acceleration, heading, bounding box information, classification, or angular velocity. [Casa: 0030 future trajectory in a bounding box]
As to claim 13 and 16, Casa discloses wherein the multi-head machine-learned perception model comprises a backbone network comprising a plurality of model layers coupled to the plurality of output heads, wherein the backbone network is configured to process the sensor data and provide the sensor data to the plurality of output heads. [Casa: Fig. 6, 0092-0093]
As to claim 14 and 17, Casa discloses wherein the backbone network is configured to perform one or more data manipulation functions. [Casa: Fig. 6, 0092-0093]
As to claim 15, Casa discloses an autonomous vehicle control system, comprising: [Casa: abstract]
one or more processors; [Casa: 0044]
and one or more non-transitory computer-readable media storing executable instructions that cause the one or more processors to perform operations comprising: [Casa: 0044]
For the remaining limitations of claim 15 see claim 1 for citations.
As to claim 20, Casa discloses an autonomous vehicle, comprising: [Casa: abstract]
one or more processors; [Casa: 0044]and
one or more non-transitory computer-readable media storing executable instructions that cause the one or more processors to perform operations comprising: [Casa: 0044]
For the remaining limitations of claim 20 see claim 1 for citations.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 11017550 B2 Systems and methods for detecting and tracking objects are provided. In one example, a computer-implemented method includes receiving sensor data from one or more sensors. The method includes inputting the sensor data to one or more machine-learned models including one or more first neural networks configured to detect one or more objects based at least in part on the sensor data and one or more second neural networks configured to track the one or more objects over a sequence of sensor data. The method includes generating, as an output of the one or more first neural networks, a 3D bounding box and detection score for a plurality of object detections. The method includes generating, as an output of the one or more second neural networks, a matching score associated with pairs of object detections. The method includes determining a trajectory for each object detection.
US 10809361 B2 Systems and methods for detecting and classifying objects proximate to an autonomous vehicle can include a sensor system and a vehicle computing system. The sensor system includes at least one LIDAR system configured to transmit ranging signals relative to the autonomous vehicle and to generate LIDAR data. The vehicle computing system receives the LIDAR data from the sensor system. The vehicle computing system also determines at least a range-view representation of the LIDAR data and a top-view representation of the LIDAR data, wherein the range-view representation contains a fewer number of total data points than the top-view representation. The vehicle computing system further detects objects of interest in the range-view representation of the LIDAR data and generates a bounding shape for each of the detected objects of interest in the top-view representation of the LIDAR data.
The examiner has pointed out particular references contained in the prior art of record in the body of this action for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. Applicant should consider the entire prior art as applicable as to the limitations of the claims. It is respectfully requested from the applicant, in preparing the response, to consider fully the entire references as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Inquiry
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FREDERICK M BRUSHABER whose telephone number is (313)446-4839. The examiner can normally be reached Monday-Friday 8am-5pm.
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/FREDERICK M BRUSHABER/
Primary Examiner
Art Unit 3665
/FREDERICK M BRUSHABER/Primary Examiner, Art Unit 3665
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