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 § 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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over US 20240265707 A1 (“Peppoloni”) in view of US 20230071446 A1 (“Narayana”).
Regarding claim 1, Peppoloni discloses a first sensor configured to capture images; and one or more processors, wherein the one or more processors are programmed (see at least [0013])
receive a first image from the first sensor, the first image captured by the first sensor during movement of the autonomous vehicle (see at least [0022]);
control operation of the autonomous vehicle based on the generated change in pose of the autonomous vehicle (see at least [0062]).
Peppoloni is not explicit on execute a first machine learning model using the first image to generate a change in pose of the autonomous vehicle, the first machine learning model trained to output changes in pose of autonomous vehicles based on blurring in individual images, however
Narayana discloses execute a first machine learning model using the first image to generate a change in pose of the autonomous vehicle, the first machine learning model trained to output changes in pose of autonomous vehicles based on blurring in individual images (see at least [0127]).
One of ordinary skill in the art would have been motivated to combine the system disclosed by Peppoloni with the techniques for automatically generating mapping information for a defined area via analysis of visual data of photos of the area disclosed by Narayana in order to have a single data structure split into multiple data structures and/or by having multiple data structures consolidated into a single data structure (Narayana, 0181]).
Regarding claim 2, Peppoloni discloses determine a global position of the autonomous vehicle based on the generated change in pose of the autonomous vehicle (see at least [0102]); and
control operation of the autonomous vehicle further based on the determined global position (see at least [0062]).
Regarding claim 3, Peppoloni discloses determine the global position of the autonomous vehicle by: identifying an initial position of the autonomous vehicle; and adjusting the initial position of the autonomous vehicle based on the change in pose output by the first machine learning model (see at least [0150]).
Regarding claim 4, Peppoloni discloses execute the first machine learning model using only the first image as input to generate the change in pose of the autonomous vehicle (see at least [0105]).
Regarding claim 5, Peppoloni discloses encode one or more timestamps into one or more pixels of the first image (see at least [0018]); and
execute the first machine learning model using the first image encoded with the one or more timestamps (see at least [0071]).
Regarding claim 6, Peppoloni is not explicit on output changes in pose of autonomous vehicles based on blurred objects in individual images, however,
Narayana discloses output changes in pose of autonomous vehicles based on blurred objects in individual images (see at least [0127]).
One of ordinary skill in the art would have been motivated to combine the system disclosed by Peppoloni with the techniques for automatically generating mapping information for a defined area via analysis of visual data of photos of the area disclosed by Narayana in order to have a single data structure split into multiple data structures and/or by having multiple data structures consolidated into a single data structure (Narayana, 0181]).
Regarding claim 7, Peppoloni discloses generate the change in pose of the autonomous vehicle including one or more of a distance traveled of the autonomous vehicle during capture of the first image, a yaw of the autonomous vehicle during capture of the first image, a pitch of the autonomous vehicle during capture of the first image, or a roll of the autonomous vehicle during capture of the first image (see at least [0169]).
Regarding claim 8, Peppoloni discloses a plurality of sensors each configured to capture images of an environment surrounding the autonomous vehicle, the plurality of sensors comprising the first sensor; and wherein the one or more processors are programmed (see at least [0062])
receive a plurality of images from the plurality of sensors, the plurality of images including the first image (see at least [0065]); and
execute a plurality of machine learning models, the plurality of machine learning models including the first machine learning model, using the plurality of images as input to generate a plurality of changes in pose of the autonomous vehicle, each of the plurality of machine learning models receiving a different image of the plurality of images as a respective single input and generating a change in pose of the autonomous vehicle based on the respective single input (see at least [0067]); and
control operation of the autonomous vehicle based on the plurality of changes in pose of the autonomous vehicle (see at least [0062]).
Regarding claim 9, Peppoloni discloses at least some of the plurality of machine learning models are configured to have identical weights or parameters (see at least [0105]).
Regarding claim 10, Peppoloni discloses select a trajectory for the autonomous vehicle based on the generated change in pose (see at least [0102]); and
control the autonomous vehicle based on the trajectory (see at least [0062]).
Regarding claim 11, Peppoloni discloses the machine learning model includes an encoder and a plurality of decoders, each of the plurality of decoders configured to generate a different type of output based on embeddings generated from images, and wherein the one or more processors are programmed to execute the machine learning model (see at least [0073])
executing the encoder using the first image as input to generate an embedding; and executing the decoder of the plurality of decoders to generate the change in pose of the autonomous vehicle (see at least [0105]).
Regarding claim 12, Peppoloni discloses receiving a first image from a first sensor of the autonomous vehicle, the first image captured by the first sensor during movement of the autonomous vehicle (see at least [0022]);
controlling operation of the autonomous vehicle based on the generated change in pose of the autonomous vehicle (see at least [0062]).
Peppoloni is not explicit on executing a first machine learning model using the first image as to generate a change in pose of the autonomous vehicle, the first machine learning model trained to output changes in pose of autonomous vehicles based on blurring in individual images, however,
Narayana discloses executing a first machine learning model using the first image as to generate a change in pose of the autonomous vehicle, the first machine learning model trained to output changes in pose of autonomous vehicles based on blurring in individual images (see at least [0127]).
One of ordinary skill in the art would have been motivated to combine the system disclosed by Peppoloni with the techniques for automatically generating mapping information for a defined area via analysis of visual data of photos of the area disclosed by Narayana in order to have a single data structure split into multiple data structures and/or by having multiple data structures consolidated into a single data structure (Narayana, 0181]).
Regarding claim 13, Peppoloni discloses determining a global position of the autonomous vehicle based on the generated change in pose of the autonomous vehicle (see at least 0102]); and
controlling operation of the autonomous vehicle further based on the determined global position (see at least [0062]).
Regarding claim 14, Peppoloni discloses identifying an initial position of the autonomous vehicle; and adjusting the initial position of the autonomous vehicle based on the change in pose output by the first machine learning model (see at least [0150]).
Regarding claim 15, Peppoloni discloses the first machine learning model is executed using only the first image as input to generate the change in pose of the autonomous vehicle (see at least [0105]).
Regarding claim 16, Peppoloni discloses encoding one or more timestamps into one or more pixels of the first image (see at least [0018]); and
executing the first machine learning model using the first image encoded with the one or more timestamps (see at least [0071]).
Regarding claim 17, Peppoloni is not explicit on the first machine learning model is trained to output changes in pose of autonomous vehicles based on blurred objects in individual images, however,
Narayana Discloses the first machine learning model is trained to output changes in pose of autonomous vehicles based on blurred objects in individual images (see at least [0127]).
One of ordinary skill in the art would have been motivated to combine the system disclosed by Peppoloni with the techniques for automatically generating mapping information for a defined area via analysis of visual data of photos of the area disclosed by Narayana in order to have a single data structure split into multiple data structures and/or by having multiple data structures consolidated into a single data structure (Narayana, 0181]).
Regarding claim 18, Peppoloni discloses generating the change in pose of the autonomous vehicle including one or more of a distance traveled of the autonomous vehicle during capture of the first image, a yaw of the autonomous vehicle during capture of the first image, a pitch of the autonomous vehicle during capture of the first image, or a roll of the autonomous vehicle during capture of the first image (see at least [0169]).
Regarding claim 19, Peppoloni discloses one or more processors in communication with a first sensor configured to capture images, the one or more processors programmed (see at least [0022])
receive a first image from the first sensor, the first image captured by the first sensor during movement of the autonomous vehicle (see at least [0022]);
control operation of the autonomous vehicle based on the generated change in pose of the autonomous vehicle (see at least [0062]).
Peppoloni is not explicit on execute a first machine learning model using the first image as to generate a change in pose of the autonomous vehicle, the first machine learning model trained to output changes in pose of autonomous vehicles based on blurring in individual images, however,
Narayana discloses execute a first machine learning model using the first image as to generate a change in pose of the autonomous vehicle, the first machine learning model trained to output changes in pose of autonomous vehicles based on blurring in individual images (see at least 0127]).
One of ordinary skill in the art would have been motivated to combine the system disclosed by Peppoloni with the techniques for automatically generating mapping information for a defined area via analysis of visual data of photos of the area disclosed by Narayana in order to have a single data structure split into multiple data structures and/or by having multiple data structures consolidated into a single data structure (Narayana, 0181]).
Regarding claim 20, Peppoloni discloses determine a global position of the autonomous vehicle based on the generated change in pose of the autonomous vehicle (see at least [0102]); and
control operation of the autonomous vehicle further based on the determined global position (see at least [0062]).
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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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Christian Chace can be reached at (571) 272-4190. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MATHEW FRANKLIN GORDON/Primary Examiner, Art Unit 3665
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