DETAILED ACTION
Claims 1-20 are considered in this office action. Claims 1-20 are pending examination.
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 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 of this title, 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 8-9, 17 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Camus (WO2005/060640A2) in view of Chavan et al. (COLLIDE-PRED: PREDICTION OF ON-ROAD COLLISIONS FROM SURVEILLANCE VIDEOS) and herein after will be referred as Camus and Chavan respectively.
Regarding Claim 1, Camus teaches a method (Fig.4 Para [0023]: “FIG. 4 depicts a flow diagram of a method 400 of operating the collision avoidance system 102”) for determining a collision candidate object for a tracking target object, performed by a computing device that includes a processor and a memory, the method comprising:
obtaining position-related information for a tracking target object at a target time point (Para [0040]: “In order to make a determination in collision detector 310 as to the type of object that is in danger of imminent collision with vehicle 100, the classification computed in step 415 is communicated to collision detector 310. In effect, the object being tracked by object tracker 308 is handed off to collision detector 310. This handoff allows for the association of a detected object between object tracker 308 and collision detector 310. The objects may be associated between the two systems by location, e.g., within a range. The objects may also be associated by matching additional information such as range rate, closing velocity, velocity vector and collision impact point, height, width and/or length, major-axis orientation, classification, edges and/or bounding box, image statistics such as image edge density or image contrast, or depth map statistics such as depth variation or depth map density.”);
determining a first collision candidate object group for the tracking target object at the target time point based on position-related information on each of a plurality of objects at a first time point prior to the target time point and position-related information on the tracking target object at the target time point (Para [0045]: “In step 430, a safety measure based on the classification from step 415 is activated using collision detector 310. If a tracked object is determined to be in a position that could possibly involve a collision with the vehicle 100, object information is provided to the driver and an alarm, or avoidance or damage or injury mitigation mechanism is initiated. The avoidance or damage or injury mitigation mechanism thresholds are adjusted based on the classification of the object. Finally, the method 400 terminates at step 435.”);
Chavan teaches determining a second collision candidate object group for the tracking target object at the target time point based on position-related information on each of a plurality of objects at a second time point prior to the first time point and position-related information on the tracking target object at the target time point (Page 2 Section 3.2: “The next part of collide-pred is tracking of the moving objects. For the sake of tracking, we used the method SiamRPN [11]. The moving objects, identified by YOLO are then given as input to SiamRPN, where the tracking of n individual object(s) is carried out. Therefore, we are going to have n number of different trajectories (t), over P no. of previous frames, as the output from this block for a certain frame fi. It is shown in Eqn. (2)”);
and determining at least one object included in both the first collision candidate object group and the second collision candidate object group as a collision candidate object for the tracking target object at the target time point (Page 2 Section 3.3-3.4: “3.3. Object Trajectory Prediction (2) The trajectories of the moving objects are then fed as the input to the transformer to extract out the estimated trajectory (𝜏̂) over Q no. of future frames. Therefore, the outcome of this block is n no. of different trajectories as well. The only difference from the previous one is here future trajectory information will be available. The outcome is expressed here in Eqn. (3). 3.4. Collision Prediction (3) This is most crucial part of this work, where we have come up with a solution. As we know the future trajectories of the moving objects are the input in this block. Each of the 𝜏 :𝑗 = 1, . . , 𝑛 consumes Q no. of different locations. We derived a solution of judiciously combining the information of past and future trajectories for decision making. As we can say, if there is a huge variation in the true location of the object than its predicted value, it could be treated as a sudden change in motion, which in return increases the probability of accident/ collision.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Camus to incorporate the teachings of Chavan to include determining a second collision candidate object group for the tracking target object at the target time point based on position-related information on each of a plurality of objects at a second time point prior to the first time point and position-related information on the tracking target object at the target time point and determining at least one object included in both the first collision candidate object group and the second collision candidate object group as a collision candidate object for the tracking target object at the target time point. Doing so would optimize object tracking and avoiding the collision.
Similarly claims 19 and 20 rejected on the similar rational.
Regarding Claim 2, Camus in view of Chavan teaches method of claim 1.
Camus teaches the position-related information corresponding to the tracking target object or at least one object among the plurality of objects includes at least one of position range determination information or velocity information for an object corresponding to the position-related information (Para [0040] : “In order to make a determination in collision detector 310 as to the type of object that is in danger of imminent collision with vehicle 100, the classification computed in step 415 is communicated to collision detector 310. In effect, the object being tracked by object tracker 308 is handed off to collision detector 310. This handoff allows for the association of a detected object between object tracker 308 and collision detector 310. The objects may be associated between the two systems by location, e.g., within a range. The objects may also be associated by matching additional information such as range rate, closing velocity, velocity vector and collision impact point, height, width and/or length, major-axis orientation, classification, edges and/or bounding box, image statistics such as image edge density or image contrast, or depth map statistics such as depth variation or depth map density.”).
Regarding Claim 3, Camus in view of Chavan teaches method of claim 2.
Camus teaches the position range determination information includes at least one of position estimation information or position error information for an object corresponding to the position-related information, and the existence range of an object corresponding to the position-related information, being centered at an estimated position according to the position estimation information, includes the maximum error range according to the position error information (Para [[0045] : “ In step 430, a safety measure based on the classification from step 415 is activated using collision detector 310. If a tracked object is determined to be in a position that could possibly involve a collision with the vehicle 100, object information is provided to the driver and an alarm, or avoidance or damage or injury mitigation mechanism is initiated. The avoidance or damage or injury mitigation mechanism thresholds are adjusted based on the classification of the object. Finally, the method 400 terminates at step 435.”).
Regarding Claim 8, Camus in view of Chavan teaches method of claim 3.
Camus teaches the existence range is configured to increase as time elapses from the measurement time point of the position estimation information (Para [0045]).
Regarding Claim 9, Camus in view of Chavan teaches method of claim 3.
Camus teaches the determining of the first collision candidate object group includes determining a first time point existence area of a collision candidate object for a tracking target object at the target time point; and determining objects positioned within the first time point existence area at the first time point among the plurality of objects as a first collision candidate object group (Para [0045]).
Regarding Claim 17, Camus in view of Chavan teaches method of claim 1.
Chavan teaches the determining of the second collision candidate object group includes: determining a second time point existence area of a collision candidate object for a tracking target object at the target time point; and determining objects positioned within the second time point existence area at the second time point among the plurality of objects as a second collision candidate object group (Section 3.2-3.4 Page 2-3).
Allowable Subject Matter
Claims 4-7, 10-16 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
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Van Heukelom et al. (US2024/0400103A1) teaches techniques for predicting and avoiding collisions with objects detected in an environment of a vehicle are discussed herein. A vehicle computing device can implement a model that receives a set of potential reference trajectories for a vehicle to follow at a future time. The model can determine a tracking trajectory for the vehicle to follow while changing between a first reference trajectory and a second reference trajectory. The model may be implemented in connection with a parallel processing unit to determine points defining the tracking trajectory that represent spatial and temporal differences. The tracking trajectory can be used by the vehicle computing device for predicting vehicle actions by the vehicle computing device to control the vehicle.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ABDHESH K JHA whose telephone number is (571)272-6218. The examiner can normally be reached M-F:0800-1700.
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/ABDHESH K JHA/Primary Examiner, Art Unit 3668