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
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) 12-15, 17-18, and 20-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yoo et al (US 20200294257) in view of Fontijne et al (US 20210255304).
Regarding claim 12, 17, and 22, Yoo teaches a computer-implemented method for determining a classification for an object (abs, “fuse 2D and 3D object detection results for classifying objects”), the method comprising the following steps: providing pixels of a radar image that are assigned to the object; and providing a point cloud (abs, “a point cloud may be filtered to include only points”), wherein the point cloud includes at least one point that represents a radar reflection assigned to the object (para 31), through at least one property assigned to the object (para 31); extracting first features that characterize the object from the pixels (para 31, “locations and corresponding image-space locations of LIDAR data, RADAR data, and/or other depth data may be known, or determined, using intrinsic and/or extrinsic parameters”); extracting second features that characterize the object from the point cloud (para 29 and 31 and 67, “one or more of the layers may include an input layer. The input layer may hold values associated with the input (e.g., vectors, tensors, etc. corresponding to sensor data, voxelized sensor data, feature vectors, etc.). For example, when the sensor data is an image(s), the input layer may hold values representative of the raw pixel values of the image(s) as a volume (e.g., a width, W, a height, H, and color channels, C (e.g., RGB), such as 32×32×3), and/or a batch size, B (e.g., where batching is use”); and determining the classification of the object depending on the first features and the second features (para 67 and claim 16), a signal for controlling at least one actuator of a driver assistance mechanism or an autonomous vehicle is determined depending on the classification (para 163 “ When the AEB system detects a hazard, it typically first alerts the driver to take corrective action to avoid the collision and, if the driver does not take corrective action, the AEB system may automatically apply the brakes in an effort to prevent, or at least mitigate, the impact of the predicted collision “), control a physical operation of the at least one actuator of the driver assistance mechanism or the autonomous vehicle based on the signal. (para 80 and 81, “The controller(s) 736 may provide the signals for controlling one or more components and/or systems of the vehicle 700 in response to sensor data received from one or more sensors (e.g., sensor inputs)”).
Regarding claim 12, 17, and 22, Fontijne teaches concatenating the first features and the second features into an input variable for a third neural network (para 83, “The radar and LiDAR features are then concatenated (1114) for further processing by a neural network (1116) for object detection (1118).”); operating the third neural network to determine the classification of the object depending on the input variable including the concatenated first features and second features (para 51, “ach feature 344 within a respective cell 342 can be identified as having up to four parameters: range, Doppler, azimuth, and elevation. This is called a radar frame. As an example, a feature 344 within a cell 342 may be the signal-to-noise ratio (SNR) computed by a constant false alarm rate (CFAR) algorithm. However, it should be understood that other methods may be used to target and identify features 344 within a cell 342.”). It would have been obvious to modify Yoo to include concatenating the first features and the second features into an input variable for a third neural network and operating the third neural network to determine the classification of the object depending on the input variable including the concatenated first features and second features because it is merely a substitution of a well-known method to determine an object features of Yoo with the method to determine if an object features of to yield a predictable method to determine if an object features
Regarding claim 13 and 20, Yoo teaches the pixels are mapped to the first features using a first neural network trained for mapping the pixels to the first features, wherein the point cloud is mapped to the second features using a second neural network trained to map the point cloud to the second features (claim 16 and fig 6), and wherein an input variable is determined depending on the first features and the second features, wherein the input variable is mapped to the classification using a third neural network trained to map the input variable to the classification (para 47 and fig 6).
Regarding claim 14 and 21, Yoo teaches the first neural network and the second neural network and the third neural network are trained independently of one another or that at least two of the first, second, and third neural networks are trained jointly (para 28 and 30).
Regarding claim 15 and 18, Yoo teaches raw data for determining the radar image are sensed by at least one sensor, and wherein the radar image is determined depending on the raw data sensed (para 24).
Response to Arguments
Applicant’s arguments with respect to claim(s) 12-15, 17-18, and 20-22 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TIMOTHY A BRAINARD whose telephone number is (571)272-2132. The examiner can normally be reached Monday - Friday 8:30 a.m.-5 p.m.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Resha Desai can be reached at (571) 270-7792. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
TIMOTHY A. BRAINARD
Primary Examiner
Art Unit 3648
/TIMOTHY A BRAINARD/Primary Examiner, Art Unit 3648
Prosecution Insights