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 Status
This action is in response to the application filed on 06/11/2025. Claims 1-20 are pending and examined below.
Claim Rejections - 35 USC § 102
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, 4-5, 7-10, 13-14, and 16-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 20220164602 A1 (“Frtunikj”).
Regarding claim 1, Frtunikj teaches one processor in communication with at least one memory, the at least one processor programmed to receive a training data set, the training data set including one or more images and one or more labels associated with the one or more images (see at least [0028]);
train a machine learning model based on the training data set, the machine learning model configured to generate a change in pose of a vehicle based upon a single input image (see at least [0020]); and
transmit the machine learning model to at least one autonomous vehicle, wherein operation of the at least one autonomous vehicle is controlled at least in part using the machine learning model (see at least [0020]).
Regarding claim 4, Frtunikj teaches the one or more labels each include a ground truth indicating a correct prediction associated with a corresponding image of the one or more images (see at least [0054]).
Regarding claim 5, Frtunikj teaches the one or more labels include one or more of a distance traveled, a pitch, or a roll (see at least [0051]).
Regarding claim 7, Frtunikj teaches determine whether an accuracy of the machine learning model reaches an accuracy threshold (see at least [0026]).
Regarding claim 8, Frtunikj teaches transmit the machine learning model to the autonomous vehicle in response to the machine learning model reaching the accuracy threshold (see at least [0031]).
Regarding claim 9, Frtunikj teaches train the machine learning model to generate the change in pose further based on metadata of the single input image (see at least [0048]).
Regarding claim 10, Frtunikj teaches receiving a training data set, the training data set including one or more images and one or more labels associated with the one or more images (see at least [0028]);
training the machine learning model based on the training data set, the machine learning model configured to generate a change in pose of a vehicle based upon a single input image; and (see at least [0020])
transmitting the machine learning model to the at least one autonomous vehicle, wherein operation of the at least one autonomous vehicle is controlled at least in part using the machine learning model (see at least [0046]).
Regarding claim 13, Frtunikj teaches the one or more labels each include a ground truth indicating a correct prediction associated with a corresponding image of the one or more images (see at least [0054]).
Regarding claim 14, Frtunikj teaches the one or more labels include one or more of a distance traveled, a pitch, or a roll (see at least [0051]).
Regarding claim 16, Frtunikj teaches determining whether an accuracy of the machine learning model reaches an accuracy threshold (see at least [0026]).
Regarding claim 17, Frtunikj teaches transmitting the machine learning model to the autonomous vehicle in response to the machine learning model reaching the accuracy threshold (see at least [0026]).
Regarding claim 18, Frtunikj teaches training the machine learning model to generate the change in pose further based on metadata of the single input image (see at least [0048]).
Regarding claim 19, Frtunikj teaches receive a training data set, the training data set including one or more images and one or more labels associated with the one or more images (see at least [0028]);
train a machine learning model based on the training data set, the machine learning model configured to generate a change in pose of a vehicle based upon a single input image (see at least [0020]); and
control operation of the autonomous vehicle at least in part using the machine learning model (see at least [0020]).
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.
Claims 6 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over US 20220164602 A1 (“Frtunikj”) in view of US 20230148384 A1 (“Liu”).
Regarding claim 6, Frtunikj is not explicit on determine a difference between a prediction output by the machine learning model and a label according to a loss function; and adjust one or more parameters or weights of the machine learning model based on the determined difference using back-propagation techniques, however,
Liu discloses determine a difference between a prediction output by the machine learning model and a label according to a loss function (see at least [0063]); and
adjust one or more parameters or weights of the machine learning model based on the determined difference using back-propagation techniques (see at least [0052]).
One of ordinary skill in the art would have been motivated to combine the system disclosed by Frtunikj with the field of digital image processing and in particular to a method, apparatus and system for configuring a machine learning model for use in estimating optical flow maps for digital image data disclosed by Liu because optical flow estimation (OFE) has become an important task for video image processing (Liu, [0003]).
Regarding claim 15, Frtunikj is not explicit on determining a difference between a prediction output by the machine learning model and a label according to a loss function; and adjusting one or more parameters or weights of the machine learning model based on the determined difference using back-propagation techniques, however,
Liu discloses determining a difference between a prediction output by the machine learning model and a label according to a loss function (see at least [0063]); and
adjusting one or more parameters or weights of the machine learning model based on the determined difference using back-propagation techniques (see at least [0052]).
One of ordinary skill in the art would have been motivated to combine the system disclosed by Frtunikj with the field of digital image processing and in particular to a method, apparatus and system for configuring a machine learning model for use in estimating optical flow maps for digital image data disclosed by Liu because optical flow estimation (OFE) has become an important task for video image processing (Liu, [0003]).
Claims 2-3, 11-12, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over US 20220164602 A1 (“Frtunikj”) in view of US 20240362572 A1 (“Arora”).
Regarding claim 2, Frtunikj is not explicit on generate the change in pose based on an amount of blur of the single input image, however,
Arora discloses generate the change in pose based on an amount of blur of the single input image (see at least [0077]).
One of ordinary skill in the art would have been motivated to combine the system disclosed by Frtunikj with a method of maintaining trailer asset data in a database of a yard is presented, the method including comparing an update time difference for one or more database entries to a predetermined data refresh threshold so that processing of location data and/or range finding data, asset ID functions, rectification, association, and so forth, may all be performed on cloud services, by external servers, or otherwise performed offboard the ego vehicle (Arora, [0179]).
Regarding claim 3, Frtunikj is not explicit on train the machine learning model based in part on blurred objects in the one or more images of the training data set, however,
Arora discloses train the machine learning model based in part on blurred objects in the one or more images of the training data set (see at least [0077]).
Regarding claim 11, Frtunikj is not explicit on generate the change in pose based on an amount of blur of the single input image, however,
Arora discloses generate the change in pose based on an amount of blur of the single input image (see at least [0077]).
One of ordinary skill in the art would have been motivated to combine the system disclosed by Frtunikj with a method of maintaining trailer asset data in a database of a yard is presented, the method including comparing an update time difference for one or more database entries to a predetermined data refresh threshold so that processing of location data and/or range finding data, asset ID functions, rectification, association, and so forth, may all be performed on cloud services, by external servers, or otherwise performed offboard the ego vehicle (Arora, [0179]).
Regarding claim 12, Frtunikj is not explicit on training the machine learning model based in part on blurred objects in the one or more images of the training data set, however,
Arora discloses training the machine learning model based in part on blurred objects in the one or more images of the training data set (see at least [0077]).
One of ordinary skill in the art would have been motivated to combine the system disclosed by Frtunikj with a method of maintaining trailer asset data in a database of a yard is presented, the method including comparing an update time difference for one or more database entries to a predetermined data refresh threshold so that processing of location data and/or range finding data, asset ID functions, rectification, association, and so forth, may all be performed on cloud services, by external servers, or otherwise performed offboard the ego vehicle (Arora, [0179]).
Regarding claim 20, Frtunikj is not explicit on generate the change in pose based on an amount of blur of the single input image, and wherein the at least one processor is programmed to train the machine learning model based in part on blurred objects in the one or more images of the training data set, however,
Arora discloses generate the change in pose based on an amount of blur of the single input image, and wherein the at least one processor is programmed to train the machine learning model based in part on blurred objects in the one or more images of the training data set [0077]).
One of ordinary skill in the art would have been motivated to combine the system disclosed by Frtunikj with a method of maintaining trailer asset data in a database of a yard is presented, the method including comparing an update time difference for one or more database entries to a predetermined data refresh threshold so that processing of location data and/or range finding data, asset ID functions, rectification, association, and so forth, may all be performed on cloud services, by external servers, or otherwise performed offboard the ego vehicle (Arora, [0179]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATHEW FRANKLIN GORDON whose telephone number is (408)918-7612. The examiner can normally be reached Monday - Friday, 7:00 - 5:00 PST.
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/MATHEW FRANKLIN GORDON/Primary Examiner, Art Unit 3665
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