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 .
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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Claims 1-4, 6-13, 15-20 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-18 of U.S. Patent No. 12271790 (reference patent). Although the claims at issue are not identical, they are not patentably distinct from each other as discussed below.
Regarding instant claim 1, it is not patentably distinct from reference claim 1 because instant claim 1 is anticipated by reference claim 1. While reference claim 1 provides additional details regarding parameters and the generation of the second track, each limitation of the instant claim is taught by the reference claim.
Regarding instant claim 2, it is not patentably distinct from reference claim 2 because instant claim 2 is anticipated by reference claim 2. Each limitation of the instant claim is taught by the reference claim.
Regarding instant claim 3, it is not patentably distinct from reference claim 6 because instant claim 3 is anticipated by reference claim 6. Each limitation of the instant claim is taught by the reference claim.
Regarding instant claim 4, it is not patentably distinct from reference claim 7 because instant claim 4 is anticipated by reference claim 7. Each limitation of the instant claim is taught by the reference claim.
Regarding instant claim 6, it is not patentably distinct from reference claim 1 because instant claim 6 is anticipated by reference claim 1. While reference claim 1 provides additional details regarding parameters and the generation of the second track, each limitation of the instant claim is taught by the reference claim.
Regarding instant claim 7, it is not patentably distinct from reference claim 3 because instant claim 7 is anticipated by reference claim 3. Each limitation of the instant claim is taught by the reference claim.
Regarding instant claim 8, it is not patentably distinct from reference claim 4 because instant claim 8 is anticipated by reference claim 4. Each limitation of the instant claim is taught by the reference claim.
Regarding instant claim 9, it is not patentably distinct from reference claim 5 because instant claim 9 is anticipated by reference claim 5. Each limitation of the instant claim is taught by the reference claim.
Regarding instant claim(s) 10-13, 15-18,
Claim(s) 10-13, 15-18 is/are system claims corresponding to method claim(s) 1-4, 6-9, respectively. Accordingly, the Examiner’s remarks with respect to claim(s) 10-13, 15-18 are substantially the same as those made above with respect to claim(s) 1-4, 6-9.
Regarding instant claim(s) 19-20,
Claim(s) 19-20 is/are non-transitory computer-readable medium claims corresponding to method claim(s) 1-2 respectively. Accordingly, the Examiner’s remarks with respect to claim(s) 19-20 are substantially the same as those made above with respect to claim(s) 1-2.
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 (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 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) 1-4, 6-7, 10-13, 15-16, 19-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 20200272160 A1 to Djuric.
Regarding claim 1,
Djuric teaches:
A method comprising:
obtaining a first track associated with an object; ([0102] – “FIG. 2 shows an example of a kinematics model 200 that includes an x axis 202, an x position 204, a y axis 206, a y position 208, an object center 210, a front wheel 212, a front wheel to center distance 214, a rear wheel 216, a rear wheel to center distance 218, a heading 220, a velocity vector 222, an angle 224, and a steering angle 226”. [0109] – “state data 302 can include… one or more states of one or more detected objects and/or tracked objects in the environment including the location, acceleration, heading, trajectory, and/or velocity of the one or more tracked object… for example, the one or more states of one or more objects in an environment over a plurality of time intervals” or Fig. 3 – state data 302 Examiner notes that first track may correspond to, e.g., kinematics model 200 or to state data 302 obtained in a different manner than from a sensor (e.g., from map data as discussed in [0109]) or at a time interval prior to the sensor measured state data 302 corresponding to measurement data (also discussed in [0109].)
generating, based on the first track, a first set of parameters; ([0102] – “FIG. 2 shows an example of a kinematics model 200 that includes an x axis 202, an x position 204, a y axis 206, a y position 208, an object center 210, a front wheel 212, a front wheel to center distance 214, a rear wheel 216, a rear wheel to center distance 218, a heading 220, a velocity vector 222, an angle 224, and a steering angle 226. [0103] – “kinematics model 200 can be used to encode information including the kinematics of an object… the kinematics model 200 can be embedded in a machine-learning tracking and kinematics model that is used to determine various aspects of object motion including the velocity and heading of the object over time.” [0109] – “state data 302 can include: map data that indicates roads, crosswalks, lane directions, the locations of objects in an environment, and/or other information associated with the state of an environment; one or more states of one or more detected objects and/or tracked objects in the environment including the location, acceleration, heading, trajectory, and/or velocity of the one or more tracked objects;”)
obtaining measurement data from one or more sensors; ([0109] – “the one or more tracked objects can include one or more objects external to the vehicle 108 that are detected by the one or more sensors 114.”)
extracting a first set of features from the measurement data; ([0035] – “Further, the vehicle computing system can generate the state data based at least in part on the one or more sensor outputs ” [0109] – “state data 302 can include… tracked objects in the environment including the location, acceleration, heading, trajectory, and/ or velocity of the one or more tracked objects; the acceleration of a tracking object in the environment; and the steering angle of the tracking object in the environment… for example, the one or more states of one or more objects in an environment over a plurality of time intervals”)
inputting the first set of features to a machine learning model; ([0109] – “In this example, the machine-learned tracking and kinematics model 304 can receive the state data 302 as an input.”)
executing the machine learning model to output a second set of parameters, ([0110] – “Each of the plurality of layers of the machine-learned tracking and kinematics model 304 can: receive an input (e.g., the state data which can include information associated with the state of an environment as input for the first layer and one or more feature maps as input for the remaining layers subsequent to the first layer); perform some operations on the input (e.g., evaluate one or more variables, attributes, and/or aspects of the input based at least in part on one or more parameters); and generate an output based on the operations performed on the input (e.g., generate a predicted motion of a tracking object and one or more tracked objects external to the tracking object)”) wherein the machine learning model has been trained by calculating an adjustment loss of using sample data representing a ground truth track, ([0131-132] – “By way of example, the loss can be based on evaluation of a loss function associated with the accuracy of the trajectories determined by the machine-learned tracking and kinematics model relative to ground-truth trajectories of the objects”) and updating the machine learning model based on the adjustment loss; ([0131] – “After each of the iterations of performing operations on the training data and determining the loss, the computing system can adjust parameters of the machine learned tracking and kinematics model based at least in part on the loss.”)
generating, based on the first set of parameters [0103] – “kinematics model 200”) and the second set of parameters, a second track associated with the object; ([0110] – “Based at least in part on the input of the unconstrained output data 306, the one or more kinematics layers 308 can generate the constrained output data 310. The constrained output data 310 can include information associated with the predicted motion of the tracking object and the one or more tracked objects external to the tracking object. For example, the constrained output data can include one or more trajectories of the one or more tracked objects in the environment. The one or more trajectories of the one or more tracked objects in can include predicted locations of one or more tracked objects in the environment.” Examiner notes that because second set of parameters is based on first set of parameters, any second track generated based on the second set of parameters is also based on the first set of parameters.) and
providing the second track to an autonomous vehicle control system for autonomous control of a vehicle. (Figs. 5-7; [0137-138] – “The predicted state of the object can be used to perform one or more operations including operations associated with motion planning for a vehicle (e.g. , determining a travel path for a vehicle that does not intersect any of the one or more predicted locations of the one or more objects).” [0140] – “At 602, the method 600 can include determining one or more control inputs for each of the one or more objects based at least in part on the one or more trajectories of each of the one or more objects. The one or more control inputs can include an input to one or more control devices of an object (e.g., a control device of a vehicle).”)
Regarding claim 2,
Djuric teaches:
The method of claim 1, (see rejection of claim above)
wherein the first track is associated with a first time, ([0102] – “FIG. 2 shows an example of a kinematics model 200 that includes an x axis 202, an x position 204, a y axis 206, a y position 208, an object center 210, a front wheel 212, a front wheel to center distance 214, a rear wheel 216, a rear wheel to center distance 218, a heading 220, a velocity vector 222, an angle 224, and a steering angle 226”. [0109] – “state data 302 can include… one or more states of one or more detected objects and/or tracked objects in the environment including the location, acceleration, heading, trajectory, and/or velocity of the one or more tracked object… for example, the one or more states of one or more objects in an environment over a plurality of time intervals” Examiner notes that first track may correspond to, e.g., state data 302 obtained at a time interval prior to the sensor measured state data 302 corresponding to measurement data.) and the measurement data ([0035] – “Further, the vehicle computing system can generate the state data based at least in part on the one or more sensor outputs ” [0109] – “state data 302 can include… tracked objects in the environment including the location, acceleration, heading, trajectory, and/ or velocity of the one or more tracked objects; the acceleration of a tracking object in the environment; and the steering angle of the tracking object in the environment… for example, the one or more states of one or more objects in an environment over a plurality of time intervals”) and the second track are associated with a second time that is later than the first time. ([0126] – “machine-learned tracking and kinematics model, which has been trained to receive the input and generate an output including one or more trajectories of the one or more objects at one or more subsequent time intervals following the plurality of time intervals.”)
Regarding claim 3,
Djuric teaches:
The method of claim 1, (see rejection of claim above)
wherein the first set of features comprise at least one of (1) track or automotive vehicle (AV) metadata, (2) lidar points, (3) radar points, or (4) Hough points. ([0041] – “In some embodiments, the state data can be based at least in part on one or more outputs from one or more sensors including at least one of one or more light detection and ranging devices (LiDAR), one or more radar devices, one or more sonar devices, one or more thermal sensors, and/or one or more image sensors.”)
Regarding claim 4,
Djuric teaches:
The method of claim 1, (see rejection of claim above)
wherein the adjustment loss comprises at least one of translation loss, angle loss, or extent loss. ([0130] – “evaluation and minimization of one or more loss functions that include one or more parameters associated with the tracked objects (e.g., parameters associated with motion characteristics and/or physical dimensions of the tracked objects).”) ([0102] – “FIG. 2 shows an example of a kinematics model 200 that includes an x axis 202, an x position 204, a y axis 206, a y position 208, an object center 210, a front wheel 212, a front wheel to center distance 214, a rear wheel 216, a rear wheel to center distance 218, a heading 220, a velocity vector 222, an angle 224, and a steering angle 226”)
Regarding claim 6,
Djuric teaches:
The method of claim 1, (see rejection of claim above)
wherein the first set of parameters defines a first box, (Figs. 2-4; [0102] – “FIG. 2 shows an example of a kinematics model 200 that includes an x axis 202, an x position 204, a y axis 206, a y position 208, an object center 210, a front wheel 212, a front wheel to center distance 214, a rear wheel 216, a rear wheel to center distance 218, a heading 220, a velocity vector 222, an angle 224, and a steering angle 226. [0103] – “kinematics model 200 can be used to encode information including the kinematics of an object… the kinematics model 200 can be embedded in a machine-learning tracking and kinematics model that is used to determine various aspects of object motion including the velocity and heading of the object over time.”) and the second set of parameters defines a second box. (Figs. 2-4; [0115] – “Furthermore, the motion of the object 414 can be determined based at least in part on use of the machine-learned tracking and kinematics model 304 that is depicted in FIG. 3 and/or the machine-learned tracking and kinematics models described in the methods 500-800 that are depicted in FIGS. 5-8.” [0111-115] – “shows the environment 402, the object 404, the heading 406, the object tracks 408, the environment 412, the object 414, the heading 416, and the object tracks 418. The object 404 and the object 414 can include one or more attributes and/or capabilities of the vehicle 108 that is depicted in FIG. 1.”)
Regarding claim 7,
Djuric teaches:
The method of claim 6, (see rejection of claim above)
wherein the second set of parameters define the second box to be consistent with the measurement data. ([0109] – “In this example, the machine-learned tracking and kinematics model 304 can receive the state data 302 as an input.”)
Regarding claim(s) 10-13, 15-16,
Claim(s) 10-13, 15-16 is/are system claims corresponding to method claim(s) 1-4, 6-7, respectively. Accordingly, the Examiner’s remarks and application of the prior art with respect to claim(s) 10-13, 15-16 are substantially the same as those made above with respect to claim(s) 1-4, 6-7.
Regarding claim(s) 19-20,
Claim(s) 19-20 is/are non-transitory computer-readable medium claims corresponding to method claim(s) 1-2 respectively. Accordingly, the Examiner’s remarks and application of the prior art with respect to claim(s) 19-20 are substantially the same as those made above with respect to claim(s) 1-2.
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.
Claim(s) 5, 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20200272160 A1 to Djuric in view of US 20220268964 A1 to Choi.
Regarding claim 5,
Djuric teaches:
The method of claim 1, (see rejection of claim above)
Djuric does not explicitly teach the additional elements of the claim.
However, Choi teaches:
wherein the machine learning model has been trained by calculating afirst loss function and a second loss function using the sample data representing the ground truth track, and summing the loss functions using weights to calculate the adjustment loss. ([0042] – “In this case, the combination of the first loss function and the second loss function may be expressed as a sum of the first loss function and the second loss function to which a predetermined weight is applied. For example, in consideration of the fact that the MSE loss of the first loss function is larger than the MAE loss of the second loss function, in order to adjust a relative weight of the two loss functions, the loss function expressed with the sum of the first loss function and the second loss function in which a specific weight is multiplied may be used for training the deep learning model. ”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have applied Choi’s known technique to Djuric’s known method ready for improvement to yield predictable results. Such a finding is proper because (1) Djuric teaches a base technique of training a model based on ground truth and loss functions; (2) Choi teaches a specific technique of summing weighted loss functions; (3) one of ordinary skill in the art would have recognized that applying the known technique would have yielded predictable results and resulted in an improved system; and (4) no additional findings based on the Graham factual inquiries are necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness (See MPEP 2143).
A modification of the combination of Djuric in view of Choi to use translation, angle, and extent as parameters of the loss function would have been obvious to try as one of a finite number of identified, predictable solutions with a reasonable expectation of success. Such a finding is proper because (1) at the time of the invention, there had been a recognized problem or need in the art, in this case a need to choose parameters of the loss function; (2) there are a finite number of identified, predictable solutions, Djuric teaches [0130] – “evaluation and minimization of one or more loss functions that include one or more parameters associated with the tracked objects ( e.g., parameters associated with motion characteristics and/or physical dimensions of the tracked objects).”; (3) one of ordinary skill in the art could have pursued the known potential solutions with a reasonable expectation of success; and (4) no additional findings based on the Graham factual inquiries are necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness (See MPEP 2143).
Regarding claim(s) 14,
Claim(s) 14 is/are system claims corresponding to method claim(s) 5, respectively. Accordingly, the Examiner’s remarks and application of the prior art with respect to claim(s) 14 are substantially the same as those made above with respect to claim(s) 5.
Allowable Subject Matter
Claims 8-9, 17-18 objected to as being dependent upon a rejected base claim, but would be allowable if double patent rejections are overcome and if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is an examiner’s statement of reasons for indicating allowable subject matter: The closest prior art of record (US 20200272160 A1 to Djuric, US 20190079526 A1 to Vallespi-Gonzalez)) neither teaches nor fairly renders obvious the combinations set forth in claims 8-9, 17-18. See analysis regarding claims 8-9 below. Claim(s) 17-18 recite similar limitation(s) to claims 8-9, respectively and is/are indicated as allowable subject matter for similar reasons.
Regarding claim 8,
Djuric teaches:
The method of claim 6, (see rejection of claim above)
The prior art of record does not teach, in combination with the remaining elements of the claim:
wherein the second set of parameters comprise at least one of (1) an angle to rotate the first box such that a heading of the first box matches the second box or (2) a translation from the first box to the second box.
Regarding claim 9,
Djuric teaches:
The method of claim 6, (see rejection of claim above)
The prior art of record does not teach, in combination with the remaining elements of the claim:
wherein the second set of parameters comprise a translation from the first box to the second box, and the translation represents a distance between a particular portion of the first box and a particular portion of the second box.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JULIANA CROSS whose telephone number is (571)272-8721. The examiner can normally be reached Mon-Fri 9am-5pm Pacific time.
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/JULIANA CROSS/Examiner, Art Unit 3648
/BRADY W FRAZIER/Primary Examiner, Art Unit 3648
