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 .
Response to Arguments
Applicant’s arguments with respect to claims 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.
Please see the new rejection below.
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) 1-8, 11-21 are rejected under 35 U.S.C. 103 as being unpatentable over Shmelkov (Incremental Learning of Object Detectors without Catastrophic Forgetting) in view of Li (RILOD: Near Real-Time Incremental Learning for Object Detection at the Edge).
Regarding claim 1, Shmelkov teaches a system comprising: a detection device comprising (figure 2):
detect and classify objects in the image using a dual-task classification model comprising: a pretrained detection branch configured with fixed parameters to classify predetermined objects (abstract and section 3.1); and
a trainable detection branch configured with learned parameters to classify new objects of interest, wherein the trainable detection branch is configured to operate in parallel with the pretrained detection branch (section 3.2 and figure 2);
update the trainable detection branch of the dual-task classification model with the received updated learned parameters while the fixed parameters of the pretrained detection branch remain frozen (section 3.2, frozen A and adapted B).
Li teaches a sensor configured to generate sensor data, wherein the sensor data comprises at least one image and a logic device, the logic device configured to (section 3, system overview);
Li teaches communicate object detection information to a control system in response to a user selection (section 3, system overview);
receive updated learned parameters based at least in part on the communicated object detection information (section 6.4, final model is transferred).
It would have been obvious prior to the effective filing date of the invention to one of ordinary skill in the art to include in Shmelkov the ability to transfer parameters and generate data as taught by Li. The reason is to allow the system to update specific data by user interaction.
Regarding claim 2, see Li, section 1, drones.
Regarding claim 3, see section 1 of Li, camera.
Regarding claims 4-5, see section 3.2 of Shmelkov, class labels and bounding boxes.
Regarding claims 6-8, see Li section 6 which teaches edge devices and updating.
Claim(s) 10 is rejected under 35 U.S.C. 103 as being unpatentable over Shmelkov (Incremental Learning of Object Detectors without Catastrophic Forgetting) in view of Li (RILOD: Near Real-Time Incremental Learning for Object Detection at the Edge) in further view of Zang (20160031559).
Regarding claim 10, see Zang, abstract and pars. 226-239 and 251.
It would have been obvious prior to the effective filing date of the invention to one of ordinary skill in the art to include in Shmelkov and Li the ability to have user give instruction for a UAV as taught by Zang in order to improve target detection.
Regarding claims 11-21, see the rejection of claims 1-8 and 10.
Conclusion
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/HADI AKHAVANNIK/Primary Examiner, Art Unit 2676