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 § 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) 11-15 and 17-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Laddah et al. (US 2022/0035376), cited in the IDS dated 10/7/24.
Regarding claim 11, Laddah discloses a method for predicting trajectories of objects in surroundings of a vehicle, the method comprising:
detecting, by environment sensors of the vehicle, raw sensor data of the surroundings of the vehicle (see paras 23, 57, and 68, vehicle 102 contains a plurality of sensors, such as radar, lidar, and cameras 114 to detect data about objects in the vehicle’s surroundings);
pre-processing the raw sensor data in a plurality of successive time intervals to generate object hypotheses (see paras 27-28, 31, 34, 36-37, 87, 101-102, and 112, raw sensor data is obtained over a certain time period and preprocessed);
segmenting, based on the determined object hypotheses, the raw sensor data and allocating the segmented raw sensor data to a respective object hypothesis of the object hypotheses (see paras 23, 68, and 73, individual objects in the vehicle 102 surrounding area is detected and features are extracted, such as location, position, size, speed, velocity, etc.);
converting, by a learning-based encoder block, the raw sensor data belonging to the respective object hypothesis into latent encodings and the latent encodings are allocated to the respective object hypothesis as a feature (see paras 23, 73-74, 87-90, and 111, sensor data is fed to a network model);
generating, in a merging block and from the individual object hypotheses, object hypotheses merged by learning-based clusters are generated from the individual object hypotheses and the allocated features (see paras 23, 27, 73-74, 94-95, 98, and 117, spatial and temporal features of the objects are fused);
forming, in a tracking block, tracks of the respective merged object hypotheses by generating allocations between the merged object hypotheses determined in a current time interval and merged object hypotheses determined in several previous time intervals (see paras 23, 73-74, 95, and 98, fused features are used to predict the trajectories of the objects based on time intervals); and
predicting the trajectories of the objects using the formed tracks of the respective merged object hypotheses (see paras 23, 40-41, 73-74, 98, 105, and 117-118, fused features are used to predict the trajectories of the objects).
Regarding claim 12, Laddah further discloses wherein the raw sensor data is recorded for a plurality of environment sensors of several sensor modalities, pre-processed individually for each of the plurality of environment sensors, and the latent encodings are determined from this individually for each of the plurality of environment sensors (see paras 27-28, 31, 34, 36-37, 87, 101-102, and 112, raw sensor data for each object is obtained over a certain time period and preprocessed).
Regarding claim 13, Laddah further discloses wherein trajectories of the merged object hypotheses predicted for a future point in time are compared to true trajectories of the merged object hypotheses determined at the future point in time to determine a prediction error, wherein the determined prediction error is propagated back to the encoder block, to the merging block and to the tracking block for training (see paras 127 and 131, back propagation of errors is utilized)
Regarding claim 14, Laddah further discloses wherein the prediction of the trajectories involves a transformer model, a recurrent neural network, or a graph neural network (see para 144, recurrent neural networks can be utilized).
Regarding claim 15, Laddah further discloses wherein the segmented raw data of an object hypothesis of a camera is converted into latent encodings with a convolutional neural network, wherein the weightings are learned in the convolutional neural network (see paras 23, 73-74, 87-90, 111, and 144, sensor data can be fed to a convolutional network model utilizing weightings).
Regarding claim 17, Laddah further discloses wherein for the learning-based formation of the merged object hypotheses, a paired measure of belonging is calculated between nodes in a graph, wherein a graph neural network is used for link prediction or edge classification, such that paired probabilities arise that nodes belong to a same object, wherein clusters of the individual nodes are formed based on the measure of belonging using a clustering algorithm (see paras 90-97, a graph algorithm is utilized).
Regarding claim 18, Laddah further discloses wherein the learning-based formation of the merged object hypotheses uses a learned graph clustering algorithm (see paras 90-97, a graph algorithm is utilized).
Regarding claim 19, Laddah further discloses wherein, for each of the learning-based clusters, information of all nodes is aggregated by pooling to produce, for each merged object hypothesis, an aggregated latent representation of the sensor data and an aggregated state (see paras 23, 27, 73-74, 94-95, 98, and 117, spatial and temporal features of the objects are fused).
Regarding claim 20, Laddah further discloses wherein the formation of the tracking blocks uses a graph neural network for the link prediction or the edge classification (see para 93, a graph algorithm is utilized).
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 16 is rejected under 35 U.S.C. 103(a) as being unpatentable over Laddah as applied to claim 11 above, and further in view of Rajpal et al. (US 2023/0054440).
Laddah does not disclose expressly wherein the segmented raw sensor data of an object hypothesis of a Lidar sensor are converted into latent encodings with a PointNet, wherein the weightings in the PointNet are learned.
Rajpal discloses wherein the segmented raw sensor data of an object hypothesis of a Lidar sensor are converted into latent encodings with a PointNet, wherein the weightings in the PointNet are learned (see paras 82-83, a PointNet model is used to classify objects detected by an autonomous driving vehicle).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the PointNet model, as described by Rajpal, with the system of Laddah.
The suggestion/motivation for doing so would have been to provide direct, efficient, and robust handling of the raw sensor data thereby reducing the need for conversion of data.
Therefore, it would have been obvious to combine Rajpal with Laddah to obtain the invention as specified in claim 16.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. To further show the state of the art please refer to the attached Notice of References Cited.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARK R MILIA whose telephone number is (571) 272-7408. The examiner can normally be reached Monday-Friday, 8am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Akwasi Sarpong can be reached at 571-270-3438. The fax number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARK R MILIA/ Primary Examiner, Art Unit 2681
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