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 .
Information Disclosure Statement
IDS filed 6/20/24 is acknowledged, the reference therein relating to the general background of applicant’s invention.
Election/Restrictions
Claims6 and 16 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected species II, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 6/24/26.
U.S. patent 12,387,500 by Goel et al.
Claim Rejections - 35 USC § 102
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)(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.
1) Claim(s) 1-4 and 11-14 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by U.S. patent 12,387,500 by Goel et al.
2) Regarding claim 1, Goel teaches a vehicle control apparatus comprising: a first sensor configured to obtain first sensor data based on identifying an external object; a second sensor configured to obtain second sensor data based on identifying the external object (column 5, line 50; multiple sensors can be utilized to detect objects); a memory configured to store a first neural network model and a second neural network model (column 8, lines 17-32; machine learned models can be stored); and a processor (column 11, lines 26 and 27; a processor), wherein the processor is configured to: obtain first object data for predicting at least one of a first location of the external object, or a type of the external object, or combinations thereof based on entering first sensor data into the first neural network model; obtain second object data for predicting at least one of a second location of the external object, or the type of the external object, or combinations thereof based on entering the second sensor data into the first neural network model (figure 2; column 8, lines 34-53 and column 19, lines 41-52; objects are detected from various sensors [column 7, lines 33-40]; column 7, lines 60-65 discloses that the same model can be utilized for the various detections); select at least one of the first object data, or the second object data, or combinations thereof as input data to be entered into the second neural network model, based on at least one of a distance between the first location and the second location, first reliability of the first object data, or second reliability of the second object data, or combinations thereof (column 19, lines 41-52; combination of first and second object data can be selected for processing by another model based on confidence score thresholds); and output data including at least one of a final location of the external object, or a final type of the external object, or combinations thereof based on entering the input data into the second neural network model (column 9, lines 4-5; output can control vehicle).
3) Regarding claim 2, Goel teaches the vehicle control apparatus of claim 1, wherein the processor is configured to: select at least one of the first object data, or the second object data, or combinations thereof as the input data based on whether the first reliability and the second reliability exceed reference reliability (column 19, lines 41-52; combination of first and second object data can be selected for processing by another model based on confidence score thresholds).
4) Regarding claim 3, Goel teaches the vehicle control apparatus of claim 2, wherein the processor is configured to: identify the second reliability based on the first reliability exceeding the reference reliability; select the first object data and the second object data as the input data based on whether the second reliability exceeds the reference reliability; and select the first object data as the input data based on the second reliability being smaller than or equal to the reference reliability (column 19, lines 16-52; combination of first and second object data can be selected for processing by another model based on confidence score thresholds but can select a single output when only one model meets the confidence threshold).
5) Regarding claim 4, Goel teaches the vehicle control apparatus of claim 2, wherein the processor is configured to: identify the second reliability based on the first reliability being smaller than or equal to the reference reliability; select the second object data as the input data, based on whether the second reliability exceeds the reference reliability; remove the first object data and the second object data based on the second reliability being smaller than or equal to the reference reliability; and select third object data, which is based on sensor data obtained by a third sensor different from the first sensor and the second sensor, as the input data (column 19, lines 16-52; combination of first and second object data can be selected for processing by another model based on confidence score thresholds but can select a single output when only one model meets the confidence threshold).
6) Claims 11-14 are taught in the same manner as described in the rejection of claims 1-4 above.
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.
7) Claim(s) 5, 7, 15 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. patent 12,387,500 by Goel et al. as applied to claims 1 and 11 above, and further in view of U.S. patent application publication 2025/0284289 by Drews et al.
8) Regarding claim 5, Goel does not specifically teach the vehicle control apparatus of claim 1, wherein the processor is configured to: identify the first location, which is generated by the first object data and which indicates a center point of a first bounding box corresponding to the external object; identify the second location, which is generated by the second object data and which indicates a center point of a second bounding box corresponding to the external object; and determine whether a distance between the first location and the second location exceeds a reference distance.
Drews teaches the vehicle control apparatus of claim 1, wherein the processor is configured to: identify the first location, which is generated by the first object data and which indicates a center point of a first bounding box corresponding to the external object; identify the second location, which is generated by the second object data and which indicates a center point of a second bounding box corresponding to the external object; and determine whether a distance between the first location and the second location exceeds a reference distance (paragraphs 55 and 56; distance between bounding boxes in a sensor fusion system is determined, examiner notes that Mahalanobis distance utilizes a center point).
Goel and Drews are combinable because they are both from the vehicle sensor fusion field of endeavor.
It would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed to combine Goel with Drews to add distance between bounding boxes. The motivation for doing so would have been for an expression of modality confidence (paragraph 56). Therefore it would have been obvious to combine Goel with Drews to obtain the invention of claim 5.
9) Regarding claim 7, Drews (as combined with Goel in the rejection of claim 5 above) teaches the vehicle control apparatus of claim 5, wherein the processor is configured to: determine whether the first reliability and the second reliability exceed reference reliability, based on a fact that the distance between the first location and the second location is smaller than or equal to the reference distance (paragraphs 55 and 56; confidence in each modality can be obtained and compared to a reference).
10) Claims 15 and 17 are taught in the same manner as described in the rejection of claims 5 and 7 above.
11) Claim(s) 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. patent 12,387,500 by Goel et al. as applied to claims 1 and 11 above, and further in view of U.S. patent application publication 2023/0005173 by Tan et al.
12) Regarding claim 8, Goel teaches and train the second neural network model based on the overlapped area, or select at least one of the first object data, or the second object data, or combinations thereof as the input data (column 19, lines 16-52; combination of first and second object data can be selected for processing by another model based on confidence score thresholds).
Goel does not specifically teach the vehicle control apparatus of claim 1, wherein the processor is configured to: identify a first bounding box, which is generated by the first object data and which corresponds to the external object; identify a second bounding box, which is generated by the second object data and which corresponds to the external object; identify an area where the first bounding box overlaps the second bounding box.
Tan teaches the vehicle control apparatus of claim 1, wherein the processor is configured to: identify a first bounding box, which is generated by the first object data and which corresponds to the external object; identify a second bounding box, which is generated by the second object data and which corresponds to the external object; identify an area where the first bounding box overlaps the second bounding box (paragraph 126; intersection over union can be found for plurality of bounding boxes of identified objects).
Goel and Tan are combinable because they are both from the vehicle sensor fusion field of endeavor.
It would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed to combine Goel with Tan to add IoU of bounding boxes. The motivation for doing so would have been to determine a bounding box to utilize for an object (paragraph 126). Therefore it would have been obvious to combine Goel with Tan to obtain the invention of claim 8.
13) Claim 18 is taught in the same manner as described in the rejection of claim 8 above.
14) Claim(s) 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. patent 12,387,500 by Goel et al. as applied to claims 1 and 11 above, and further in view of U.S. patent application publication 2022/0350338 by Gyllenhammar et al.
15) Regarding claim 9, Goel does not specifically teach the vehicle control apparatus of claim 1, wherein the processor is configured to: identify data uncertainty for the input data based on the first sensor data and the second sensor data, which are entered into the first neural network model; identify model uncertainty for the second neural network model based on a third neural network model different from the second neural network model; and output the output data based on the input data, to which the data uncertainty is applied, and the second neural network model to which the model uncertainty is applied.
Gyllenhammar teaches the vehicle control apparatus of claim 1, wherein the processor is configured to: identify data uncertainty for the input data based on the first sensor data and the second sensor data, which are entered into the first neural network model; identify model uncertainty for the second neural network model based on a third neural network model different from the second neural network model; and output the output data based on the input data, to which the data uncertainty is applied, and the second neural network model to which the model uncertainty is applied (paragraphs 51 and 65; uncertainty models can be applied to object detections as well as sensor fusion data [i.e. the second network]).
Goel and Gyllenhammar are combinable because they are both from the vehicle sensor fusion field of endeavor.
It would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed to combine Goel with Gyllenhammar to add uncertainty models. The motivation for doing so would have been to provide a risk map defining a margin for error (paragraph 51). Therefore it would have been obvious to combine Goel with Gyllenhammar to obtain the invention of claim 9.
16) Claim 19 is taught in the same manner as described in the rejection of claim 9 above.
17) Claim(s) 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. patent 12,387,500 by Goel et al. as applied to claims 1 and 11 above, and further in view of U.S. patent application publication 2020/0025935 by Liang et al.
18) Regarding claim 10, Goel does not specifically teach the vehicle control apparatus of claim 1, wherein the second neural network model includes a multi-layer perceptron neural network.
Liang teaches the vehicle control apparatus of claim 1, wherein the second neural network model includes a multi-layer perceptron neural network (paragraph 48; sensor fusion can utilize multi-layer perceptron networks).
Goel and Liang are combinable because they are both from the vehicle sensor fusion field of endeavor.
It would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed to combine Goel with Liang to add multi-layer perceptron networks. The motivation for doing so would have been to comprehend the surrounding environment (paragraph 3). Therefore it would have been obvious to combine Goel with Liang to obtain the invention of claim 10.
19) Claim 20 is taught in the same manner as described in the rejection of claim 10 above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BENJAMIN O DULANEY whose telephone number is (571)272-2874. The examiner can normally be reached Mon-Fri 10-6.
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BENJAMIN O. DULANEY
Primary Examiner
Art Unit 2676
/BENJAMIN O DULANEY/Primary Examiner, Art Unit 2683
