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 .
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.
Claims 1, 2, 15 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Agarwal (US Pub. 2022/0051489) in view of Uncertainty evaluation of object detection algorithms for autonomous vehicles, by Peng et al.
With respect to claim 1, Agrawal discloses An image dataset processing method (see figure 2), comprising: acquiring an initial autonomous driving image dataset, wherein the initial autonomous driving image dataset comprises a plurality of initial autonomous driving images and a plurality of annotations corresponding to the plurality of initial autonomous driving images, (see figure 1, ego car taking pictures of person, tree and building numerical 110, 112, 114; see pargarph 0038 and 0039, wherein sensor data is stored and annotated);
inputting the initial autonomous driving image dataset into a first autonomous driving detection model to obtain a first image detection result and inputting the initial autonomous driving image dataset into a second autonomous driving detection model to obtain a second image detection result, (see figure 5, numerical 504 and 506 is read as first and second detection model, to obtain the first and second detection results, see paragraph 0009);
comparing the first image detection result with the second image detection result to obtain a comparison result, wherein the first image detection result serves as a reference detection result, (see paragraph 0042-0044, wherein the results of first perception algorithm and second perception algorithm are compared and first is read as the reference);
[determining a comparison type based on the comparison result, wherein the comparison type comprises true positive, false positive, and false negative];
determining evaluation scores of the plurality of initial autonomous driving images [based on the comparison type], (see paragraph 0042-0044, wherein confidence scores are determined and then those confidence scores of the first perception algorithm and second perception algorithm are compared); and
selecting a target autonomous driving image dataset from the initial autonomous driving image dataset based on the evaluation scores of the plurality of initial autonomous driving images and adjusting at least one annotation in the target autonomous driving image dataset, (see figure 7, and paragraph 0061 for details), as claimed.
However, Agarwal fails to explicitly disclose determining a comparison type based on the comparison result, wherein the comparison type comprises true positive, false positive, and false negative; and determining evaluation scores of the plurality of initial autonomous driving images based on the comparison type, as claimed.
Peng teaches determining a comparison type based on the comparison result, wherein the comparison type comprises true positive, false positive, and false negative; and determining evaluation scores of the plurality of initial autonomous driving images based on the comparison type, (see figure 4, and section 2.4 Evaluation method for object detection page 245 right hand column from last five lines to page 246 first two lines), as claimed.
It would have been obvious to one ordinary skilled in the art the effective date of invention to combine the two references as they are analogous because they are solving similar problem of object detection for the autonomous vehicle using image analysis. Teaching of Peng to use a confusion matrix to evaluate the results can be incorporated into Agarwal’s system as suggested in paragraph 0043, to compare the results of first and second perception algorithm, for suggestion, and modifying the system will yields more accurate object detection system (see Abstract of Peng), for motivation.
With respect to claim 2, combination of Agarwal and Peng further discloses wherein, each of the plurality of initial autonomous driving images comprises at least one target object; the first image detection result comprises first detection box information of at least one of the at least one target object, a respective first category corresponding to each of the at least one of the at least one of target object, a first confidence level of the first category, and a first area corresponding to the first detection box information; the second image detection result comprises second detection box information of at least one of the at least one target object, a respective second category corresponding to each of the at least one of the at least one target object, a second confidence level of the second category, and a second area corresponding to the second detection box information; the comparison result comprises a category comparison result; and comparing the first image detection result with the second image detection result to obtain the comparison result comprises: comparing the first category with the second category to obtain the category comparison result, wherein the category comparison result comprises category match and category mismatch, (see Agarwal paragraph 0026, wherein the bounding boxes are used to identify the pedestrian, vehicle, vegetation and buildings), as claimed.
With respect to claim 15, combination of Agarwal and Peng further discloses wherein selecting the target autonomous driving image dataset from the initial autonomous driving image dataset based on the evaluation scores of the plurality of initial autonomous driving images comprises: sorting the plurality of initial autonomous driving images based on the evaluation scores of the plurality of initial autonomous driving images to obtain a sorted initial autonomous driving image dataset; and selecting the target autonomous driving image dataset from the sorted initial autonomous driving image dataset, wherein the target autonomous driving image dataset comprises a preset number of target autonomous driving images, (see Agarwal paragraph 0055, wherein the sensor data is ranked which is read as sorting the data), as claimed.
Claims 17 and 19 are rejected for the same reasons as set forth in the rejections for claims 1 and 2, because claims 17 and 19 are claiming subject matter of similar scope as claimed in claims 1 and 2. Furthermore, see Agarwal figure 2 for electronic device, as claimed.
Claim 18 is rejected for the same reasons as set forth in the rejections for claim 1, because claim 18 is claiming subject matter of similar scope as claimed in claim 1. Furthermore, see Agarwal figure 1 numerical 126 Memory, for storage medium, as claimed.
Allowable Subject Matter
Claims 3-14 and 20-21 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
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/VIKKRAM BALI/Primary Examiner, Art Unit 2663
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