DETAILED ACTION
Claims 2-21 are pending.
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 §§ 706.02(l)(1) - 706.02(l)(3) 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).
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Claims 2-21 is/are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-18 of U.S. Patent No. 11,816,585. Although the claims at issue are not identical, they are not patentably distinct from each other because the instant application recites similar features and limitations as the patented application. Both the instant application and patent present features for objection detection using sensors and machine learning modules.
Present Application 19/259,524
Patent 11,816,585
2. A system comprising:
one or more cameras;
one or more processors coupled to the one or more cameras, the one or more processors configured to:
implement a first trained machine learned model configured to:
receive first image data from the one or more cameras; and
based on the first image data and at a first time, detect new object data not previously detected by the system; and
implement a second trained machine learned model configured to:
receive second image data from the one or more cameras and prior object data previously detected by the first trained machine learned model; and
based on the second image data and the prior object data previously detected and at a time prior to the first time, determine a location for the prior object data.
1. A method implemented by a system of one or more processors, the method comprising:
obtaining a plurality of images at a threshold frequency, the images being obtained from one or more image sensors positioned about a vehicle;
determining, based on the images, location information associated with objects classified in the images,
wherein determining location information is based on analyzing the images via a first machine learning model at the threshold frequency,
wherein a subset of the images is analyzed via a second machine learning model at less than the threshold frequency,
wherein the first machine learning model is configured to periodically receive output information from the second machine learning model, the received output information being input into the first machine learning model in combination with a first image of the plurality of images, and the received output information being usable to increase an accuracy of determining location information associated with objects classified in the first image,
wherein the plurality of images represents a sequence of images comprising at least the first image, and wherein prior to completion of the analysis by the second machine learning model, the first image is analyzed via the first machine learning model; and
outputting the determined location information, wherein the determined location information is configured for use in autonomous driving of the vehicle.
.
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.
Claim(s) 1, 4-5, 7-9, 11-12, 14-16, 18-19, 21 is/are rejected under 35 U.S.C. 102A(1) as being anticipated by Hess et al. (US 2020/0125845).
Claim 2, Hess teaches a system comprising:
one or more cameras (i.e. optical cameras) (p. 0011);
one or more processors coupled to the one or more cameras (p. 0024), the one or more processors configured to:
implement a first trained machine learned model (i.e. machine learning modules) (p. 0023) configured to:
receive first image data from the one or more cameras (i.e. variety of images for training data) (p. 0023-0025); and
based on the first image data (i.e. set of images at a location or position) and at a first time, detect new object data not previously detected by the system (i.e. object) (p. 0023- 0025).
implement a second trained machine learned model (i.e. machine learning modules for tracking) (fig. 2A-C) configured to:
receive second image data (i.e. second image at a different time) from the one or more cameras and prior object data (i.e. crosswalk sign) previously detected by the first trained machine learned model (fig. 2A-C; p. 0036-0039); and
based on the second image data and the prior object data previously detected and at a time prior to the first time (i.e. crosswalk detected in first image at a distance), determine a location for the prior object data (i.e. at a second time the crosswalk sign is at a different location determined by a second set of images) (p. 0035-0039).
Claim 4, Hess teaches the system of claim 2, wherein the second image data includes the first image data (i.e. at a second time the crosswalk sign is at a different location determined by a second set of images) (p. 0035-0039).
Claim 5, Hess teaches The system of claim 2, wherein a portion of the second image data is not included in the first image data (i.e. each set of images are slightly different in order to accurately track the object) (fig. 2A-C; p. 0036-0039).
Claim 7, Hess teaches the system of claim 2, wherein the second model is a tracker or a detector (i.e. each set of images are slightly different in order to accurately track the object) (fig. 2A-C; p. 0036-0039).
Claim 8, Hess teaches The system of claim 2, wherein the second model is further configured to determine a location based on received inertial measurement unit information or global satellite system information (i.e. vehicle may communicate with each other and also through Internet) (p. 0050-0051).
Claim 9 is analyzed and interpreted as method of claim 1.
Claim 11 is analyzed and interpreted as method of claim 4.
Claim 12 is analyzed and interpreted as method of claim 5.
Claim 14 is analyzed and interpreted as method of claim 7
Claim 15 is analyzed and interpreted as method of claim 8.
Claim 16 recites “Non-transitory computer storage media storing instructions that when executed by a system of one or more processors, cause the one or more processors” to perform the steps of claim 1. Hess teaches Non-transitory computer storage media storing instructions that when executed by a system of one or more processors, cause the one or more processors” to perform the steps of claim 1 (p. 0003).
Claim 18 recites “Non-transitory computer storage media storing instructions that when executed by a system of one or more processors, cause the one or more processors” to perform the steps of claim 3. Hess teaches Non-transitory computer storage media storing instructions that when executed by a system of one or more processors, cause the one or more processors” to perform the steps of claim 3 (p. 0003).
Claim 19 recites “Non-transitory computer storage media storing instructions that when executed by a system of one or more processors, cause the one or more processors” to perform the steps of claim 4. Hess teaches Non-transitory computer storage media storing instructions that when executed by a system of one or more processors, cause the one or more processors” to perform the steps of claim 4 (p. 0003).
Claim 21 recites “Non-transitory computer storage media storing instructions that when executed by a system of one or more processors, cause the one or more processors” to perform the steps of claim 7. Hess teaches Non-transitory computer storage media storing instructions that when executed by a system of one or more processors, cause the one or more processors” to perform the steps of claim 7 (p. 0003).
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.
Claim(s) 3, 10, 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hess et al. (US 2020/0125845) in view of Kang et al. (US 2020/0077023).
Claim 3, Hess is not entirely clear in teaching the system of claim 2, wherein the second model is not trained to output new objects.
Kang teaches the system of claim 2, wherein the second model is not trained to output new objects (i.e. machine leaning is implemented to optimize present images not detect new ones) (p. 0121).
Therefore, it would have been obvious to one of ordinary skill in the are before the effective filing date of the present invention to have provided image optimization as taught by Kang to the system of Hess to provide stabilization of motion images (p. 0121).
Claim 10 is analyzed and interpreted as method of claim 2.
Claim 17 recites “Non-transitory computer storage media storing instructions that when executed by a system of one or more processors, cause the one or more processors” to perform the steps of claim 2. Hess teaches Non-transitory computer storage media storing instructions that when executed by a system of one or more processors, cause the one or more processors” to perform the steps of claim 2 (p. 0003).
Claim(s) 6, 13, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hess et al. (US 2020/0125845) in view of Tauschinsky et al. (US 2020/0033831).
Claim 6, Hess is silent regarding The system of claim 2, wherein the first model and the second model operate at different sampling rates.
Tauschinsky teaches The system of claim 2, wherein the first model and the second model operate at different sampling rates (i.e. machine learning receives different sampling rates as training data for detection) (p. 0031-0034).
Therefore, it would have been obvious to one of ordinary skill in the are before the effective filing date of the present invention to have provided different sampling rates as taught by Tauschinsky to the system of Hess to provide accurate outlier detection (p. 0037).
Claim 13 is analyzed and interpreted as method of claim 6.
Claim 20 recites “Non-transitory computer storage media storing instructions that when executed by a system of one or more processors, cause the one or more processors” to perform the steps of claim 6. Hess teaches Non-transitory computer storage media storing instructions that when executed by a system of one or more processors, cause the one or more processors” to perform the steps of claim 6 (p. 0003).
Conclusion
Claims 2-21 are rejected.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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US 20250316093 A1 YERAKARAJU; SURESH KUMAR et al. – real-time object detection
US 12258045 B2 Ucar; Seyhan et al. – vehicle network with machine learning
US 20250037285 A1 Chen; Yitao et al. – efficient segmentation with edge computing
US 20250005447 A1 VEER; Sushant et al. – combining learning based and rule based machine learning object detection
US 11036240 B1 Irschara; Arnold et al. – aerial vehicle navigation through machine learning
US 20210110045 A1 BUESSER; Beat et al. – robust training modules
US 20190354786 A1 Lee; Tencia et al. – light state detection with machine learning
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MUSHFIKH I. ALAM
Primary Examiner
Art Unit 2426
/MUSHFIKH I ALAM/ Primary Examiner, Art Unit 2426 6/1/2026
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