DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 have been presented for examination based on the application filed on 12/22/2021.
Claim 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph.
Claims 1-2, 4-6, 8-9, 11-13, 15-16, 18-20 rejected under 35 U.S.C. 102(a)(1 ) as being anticipated by US PGPUB No. US 20190147610 A1 by Frossard; Davi Eugenio Nascimento et al.,
Claim(s) 3, 10 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over US PGPUB No. US 20190147610 A1 by Frossard; Davi Eugenio Nascimento et al., in view of US PGPUB No. US 20190259182 A1 by Sarukkai; Ramesh Rangarajan et al.
Claim(s) 7 & 14 are rejected under 35 U.S.C. 103 as being unpatentable over US PGPUB No. US 20190147610 A1 by Frossard; Davi Eugenio Nascimento et al., in view of US Patent No. US 11543830 B2 by Liang; Xiaodan et al..
This action is made Non-Final.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites:
1 An apparatus for training a kinematics model, comprising:
at least one memory; and
at least one processor coupled to the at least one memory, the at least one processor configured to:
analyze sensor data [B1] of a subject vehicle to identify a remote vehicle represented by the sensor data;
estimate, using the kinematics model [A], one or more predicted kinematic characteristics of the remote vehicle, wherein the one or more predicted kinematic characteristics are based on the sensor data of the subject vehicle;
determine, based on bag data [B2] associated with the remote vehicle, one or more ground-truth kinematic characteristics of the remote vehicle; and ..
As per [A], the claim purports to use the kinematic model however the model is never built, let alone trained. (1) It is unclear how the undefined and untrained model is used. (2) It is also unclear if the kinematic model is the model of the remote vehicle or the subject vehicle.
As per [B1] and [B2] its unclear where the bag data is computed from if not the sensor data. Or in other words it unclear what is the source of the bag data.
As per claim 2, it unclear how the error is computed. Claims 8 & 15 are rejected with similar rationale as claim 1.Claim 9 & 16 are rejected with similar rationale as claim 2.
Dependent claims 2-7, 9-14, 16-20 do not cure the deficiency of parent claims 1, 8 and 15 respectively and are rejected likewise.
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.
Claims 1-2, 4-6, 8-9, 11-13, 15-16, 18-20 rejected under 35 U.S.C. 102(a)(1 ) as being anticipated by US PGPUB No. US 20190147610 A1 by Frossard; Davi Eugenio Nascimento et al.,
Regarding Claims 1, 8 and 15
Frossard teaches (Claim 1) An apparatus for training a kinematics model (Frossard: Fig.3 & 8, Object Detection and Tracking System 302 having machine-learned model 304) / (Claim 8) 8. A computer-implemented method for training a kinematics model, (Frossard: Fig.7 flow) / (Claim 15) . A non-transitory computer-readable storage medium comprising at least one instruction for causing a computer or processor to (Frossard : : Fig.1, Fig.8 element 1014/1034, elements 1012/1032) / comprising: at least one memory (Frossard: Fig.1, Fig.8 element 1014/1034) ; and at least one processor coupled to the at least one memory, the at least one processor (Frossard : Fig.1, Fig.8 elements 1012/1032) configured to: analyze sensor data of a subject vehicle to identify a remote vehicle represented by the sensor data (Frossard: Fig.7 [0113]"... The training data may include sensor data such as image data, LIDAR data, RADAR data, etc. that has been annotated to indicate objects represented in the sensor data..." ; [0019] "... In example embodiments, the tracked objects may correspond to a predetermined group of classes, such as vehicles, pedestrians, bicycles, or other objects encountered within the environment of an autonomous vehicle or other system such as a user computing device....") ; estimate, using the kinematics model, one or more predicted kinematic characteristics of the remote vehicle, wherein the one or more predicted kinematic characteristics are based on the sensor data of the subject vehicle (Frossard: Fig.7 element 702-704 [0113]-[0114] "... [0113] At 702, training data can be provided to a machine-learned model that includes one or more first neural networks for object detection and one or more second neural networks for object matching.... The training data may include sensor data such as image data, LIDAR data, RADAR data, etc. that has been annotated to indicate objects represented in the sensor data ") [0020]) ; determine, based on bag data associated with the remote vehicle, one or more ground-truth kinematic characteristics of the remote vehicle (Frossard: [0113] "... The training data may include … any other suitable ground truth data that can be used to train the model for object detection, object matching, and object trajectory generation...."); and validate the one or more predicted kinematic characteristics of the remote vehicle using the one or more ground-truth kinematic characteristics of the remote vehicle (Frossard: Fig.7 element 706 [0115] "... [0115] At 706, one or more errors are detected in association with the trajectories generated at 504. Detecting the one or more errors may include determining a loss function that compares a generated trajectory with the ground truth data....") .
Regarding Claims 2, 9 and 16
Frossard teaches wherein to validate the one or more predicted kinematic characteristics of the remote vehicle, the at least one processor is configured to: calculate an error associated with the one or more predicted kinematic characteristics of the remote vehicle (Frossard: Fig.7 element 706-708 [0115]-[0116]) ; and update the kinematics model based on the error associated with the one or more predicted kinematic characteristics (Frossard: Fig.7 element 708-712 [0116]-[0118]) .
Regarding Claims 4, 11 & 18
Frossard teaches wherein the kinematics model is a machine-learning model (Frossard: Fig.3 showing the kinematic model as machine learning model 304 which generates the trajectories as output; Also see trajectory for each object as discussed in [0076] Fig.3; Fig.7 [0113]) .
Regarding Claims 5, 12 & 19
Frossard teaches wherein the sensor data of the subject vehicle includes Light Detection and Ranging (LiDAR) sensor data, camera data, radar data, or a combination thereof (Frossard: Fig.3 Lidar Point cloud as input data; RGB is image/camera data [0020]) .
Regarding Claims 6, 13 & 20
Frossard teaches wherein the one or more predicted kinematic characteristics of the remote vehicle comprises a velocity estimate, an acceleration estimate, or a combination thereof (Frossard: [0059]) .
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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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 31, 10 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over US PGPUB No. US 20190147610 A1 by Frossard; Davi Eugenio Nascimento et al., in view of US PGPUB No. US 20190259182 A1 by Sarukkai; Ramesh Rangarajan et al.
Regarding Claims 3, 10 and 17
Teachings of Frossard are shown in the respective parent claim(s) 1, 8 and 15.
Frossard teaches wherein the one or more ground-truth kinematic characteristics of the remote vehicle are recorded (Frossard: [0136]).
Frossard does not explicitly teach that ground truth is from the localization system of the remote vehicle.
Sarukkai teaches wherein the one or more ground-truth kinematic characteristics of the remote vehicle are recorded by a localization system of the remote vehicle (Sarukkai: [0038] "... [0038] In particular embodiments, a localization system 310 may calculate a differential of location readings (e.g., GPS coordinate) based on a machine-learning model 320....")[0045] "... [0045] FIG. 7 illustrates an example flow diagram 700 for training a machine-learning model 320 using ground-truth GPS locations, according to particular embodiments. In particular embodiments, the training may start at step 710. At step 710, the localization system 310 may input a plurality of training samples 400 to the integrated architecture 600...." in view of Fig.7 flow).
It would have been obvious to one (e.g. a designer) of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Sarukkai to Frossard to complement Frossard in detailing where the ground truth information is accurately gathered so that the common goal of updating the model as claimed in claim 1 can be achieved by both Frossard (Fig.7) and Sarukkai (Fig.7) . Additional motivation to combine would have been that Sarukkai & Frossard are analogous art to the instant claim in field of error calculation between the ground truth and machine learning based model so that the model can be accurately updated Frossard (Fig.7) and Sarukkai (Fig.7), in the same field of endeavor autonomous driving (Frossard ([0003]) and Sarukkai ([0001]) .
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Claim(s) 7 & 14 are rejected under 35 U.S.C. 103 as being unpatentable over US PGPUB No. US 20190147610 A1 by Frossard; Davi Eugenio Nascimento et al., in view of US Patent No. US 11543830 B2 by Liang; Xiaodan et al..
Regarding Claim 7 & 14
Teachings of Frossard are shown in the parent claim 7. F
Frossard does not teaches wherein the remote vehicle is an autonomous vehicle (AV).
Liang teaches wherein the remote vehicle is an autonomous vehicle (AV) (Liang: Claim 8 & 18 "... canonical representation and the prediction of the operating parameter comprises using a generative adversarial neural network (GAN) using a first database of real source images annotated with corresponding ground-truth operating parameters obtained from a real operating experience, wherein the real source images are images of real-world scenes related to the operation of other autonomous devices, and a second database of virtual source images annotated with corresponding ground-truth operating parameters obtained from the same operating experience, wherein the virtual source images are generated using computer graphics technology...." ) .
It would have been obvious to one (e.g. a designer) of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Liang to Frossard to complement Frossard in detailing where the ground truth information is gathered from thereby providing additional details (Liang: Claim 8 & 18). Additional motivation to combine would have been that Liang & Frossard are analogous art to the instant claim in field of autonomous driving (Liang: Abstract; Col.2 Lines 60-Col.3 Lines 18; Frossard: Abstract).
Conclusion
All claims are rejected.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Examiner’s Note: Examiner has cited particular columns and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in their entirety 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.
In the case of amending the claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
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Communication
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AKASH SAXENA whose telephone number is (571)272-8351. The examiner can normally be reached Mon-Fri, 7AM-3:30PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, RYAN PITARO can be reached on (571) 272-4071. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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AKASH SAXENA
Primary Examiner
Art Unit 2188
/AKASH SAXENA/Primary Examiner, Art Unit 2188 Saturday, May 17, 2025
1 Also see US 11543830 B2 by Liang; Xiaodan et al. Claim 8 at least which can be used in future rejections.