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 .
DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03 February 2026 has been entered.
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.
Claims 1-4 and 6-17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Xu et al (US 10,438,371).
Xu et al disclose the following claimed features:
Regarding claim 1, a computer-implemented method for classifying scenarios of a virtual test (Figures 1-3), the method comprising: providing a first data set of sensor data (202, 204) of a travel of an ego vehicle captured by a plurality of vehicle-side surroundings detection sensors; transforming the first data set of sensor data into a single data-reduced second data set of sensor data (214), including the sensor data from each of the plurality of vehicle- side surroundings detection sensors (column 3, lines 45-65), by a first algorithm (212) or a multivariate data analysis method; applying a second machine learning algorithm (216) to the data-reduced second data set of sensor data for classifying scenarios comprised by the second data set (218); and outputting a third data set (224) having a plurality of classes representing a vehicle action;
wherein the first algorithm carries out a principle component analysis method, and wherein the principle component analysis method combines correlating first features of the plurality of vehicle-size surroundings detection sensors into a single data- reduced feature as a linear combination of values of the plurality of surroundings detection sensors (Figures 1 and 2).
Regarding claim 2, wherein the plurality of vehicle-side surroundings detection sensors includes an essentially identical field of vision in sections, a data set of a first surroundings detection sensor, a data set of a second surroundings detection sensor, and a data set of a third surroundings detection sensor comprising at least one same object (column 1, line 66 to column 2, line 23).
Regarding claim 3, wherein the first surroundings detection sensor is formed by a radar sensor, the second surroundings detection sensor is formed by a LIDAR sensor, and the third surroundings detection sensor is formed by a camera sensor (column 1, line 66 to column 2, line 23).
Regarding claim 4, wherein the first algorithm carries out a factor analysis method, a principle component analysis method, and/or a correspondence analysis method (Figure 2).
Regarding claim 6, wherein the second machine learning algorithm is formed by an artificial neural network, a size of an input layer being given by a number of second features of the data-reduced second data set, and a size of an output layer being given by a number of classes (Figure 2; column 5, line 4 to column 6, line 4).
Regarding claim 7, wherein a size of the input layer of the artificial neural network is identical to a size of the output layer of the artificial neural network (Figure 2; column 5, line 4 to column 6, line 4).
Regarding claim 8, wherein a number of hidden layers of the artificial neural network is smaller than the size of the input layer of the artificial neural network and the size of the output layer of the artificial neural network (Figure 2; column 5, line 4 to column 6, line 4).
Regarding claim 9, wherein the second machine learning algorithm carries out a multiclass classification, in which a probability is calculated for each class, and wherein the class having the highest probability is selected as a prediction (Figures 2 and 3).
Regarding claim 10, wherein a fourth data set having a logical scenario is generated based on the selected class representing the vehicle action (Figure 3).
Regarding claim 11, wherein the plurality of classes representing the vehicle action comprises at least one value of an acceleration operation, a braking operation, a change in direction and/or lane, a travel at a constant speed of the ego vehicle, a lane ID, and/or a time- or location-related condition for carrying out a vehicle action (column 14, lines 9-18).
Regarding claim 12, wherein, for the purpose of transforming the first data set of sensor data into a data-reduced second data set of sensor data, the first algorithm or the multivariate data analysis method comprises: a standardization of the first data set of sensor data of a travel of the ego vehicle captured by the plurality of vehicle-side surroundings detection sensors; a calculation of a covariance matrix from the standardized first data set; a determination of eigenvectors representing principle components; and a creation of a matrix made up of the determined eigenvectors for providing a data-reduced second data set (Figures 2 and 3).
Regarding claim 13, a computer-implemented method for providing a trained second machine learning algorithm (216) for classifying scenarios of a virtual test (Figures 4 and 5; column 8, lines 43-62), the method comprising: receiving a single data-reduced second data set of sensor data (214), including the sensor data from each of the plurality of vehicle- side surroundings detection sensors (column 3, lines 45-65), transformed by a first algorithm (212) or a multivariate data analysis method based on a first data set of sensor data (202, 204) of a travel of an ego vehicle captured by a plurality of vehicle-side surroundings detection sensors; receiving a third data set (224) having a plurality of classes representing a vehicle action; and training the second machine learning algorithm (216) by an optimization algorithm, which calculates an extreme value of a loss function for classifying scenarios of a virtual test (Figures 2 and 3); wherein the first algorithm carries out a principle component analysis method, and wherein the principle component analysis method combines correlating first features of the plurality of vehicle-size surroundings detection sensors into a single data- reduced feature as a linear combination of values of the plurality of surroundings detection sensors (Figures 1 and 2).
Regarding claim 14, a system for classifying scenarios of a virtual test, the system comprising: a plurality of vehicle-side surroundings detection sensors to provide a first data set of sensor data (202, 204) of a captured travel of an ego vehicle; a transformer to transform the first data set of sensor data into a single data-reduced second data set of sensor data (214), including the sensor data from each of the plurality of vehicle- side surroundings detection sensors (column 3, lines 45-64), by a first algorithm (212) or a multivariate data analysis method; an applicator to apply a second machine learning algorithm (216) to the data-reduced second data set of sensor data for classifying scenarios comprised by the second data set (218), the applicator being configured to output a third data set (224) having a plurality of classes representing a vehicle action (Figures 1-3); wherein the first algorithm carries out a principle component analysis method, and wherein the principle component analysis method combines correlating first features of the plurality of vehicle-size surroundings detection sensors into a single data- reduced feature as a linear combination of values of the plurality of surroundings detection sensors (Figures 1 and 2).
Regarding claim 15, a computer program including program code for carrying out the method when the computer program is executed on a computer (column 9, lines 10-17).
Regarding claim 16, a non-transitory computer-readable storage medium including program code for carrying out the method when executed on a computer (column 8, line 63 to column 9, line 8).
Regarding claim 17, wherein the single data-reduced second data set includes a linear combination of values of the plurality of vehicle-side surroundings detection sensors (column 3, lines 45-64).
Response to Arguments
Applicant's arguments filed 03 February 2026 have been fully considered but they are not persuasive. Applicant amended independent Claims 1, 13 and 14 by incorporating the limitation of claim 5 into these claims. However, this argument is found not persuasive since the limitation of claim 5 is disclosed or taught by Xu et al in Figures 1 and 2 as shown in the above detailed office action.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AN H DO whose telephone number is (571)272-2143. The examiner can normally be reached on M-F 7:5:30pm.
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/AN H DO/Primary Examiner, Art Unit 2853