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 § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claim 1 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement.
The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Parent specification (see Application 17/936,363) and provisional application (see 63/249,566) do not disclose the claimed subject matter.
Claim 1 recites:
1. A method comprising:
receiving a first image data set and a second sensor data set, the first image data set being based on images of a first environment, the second data set being measurements taken by sensors of the first environment, the first image data set having a first number of first channels, the second sensor data set having a second number of second channels, the second number of second channels being greater than the first number of first channels;
training a first image model using the first image data set, the first image model including a plurality of layers, a first layer of the plurality of layers receiving predetermined dimensions and the first number of first channels of the first image data set, subsequent layers of the plurality of layers receiving the output of previous layers, weights of the layers being determined through training the first image model;
pre-training a second sensor model using the second sensor data set and the weights of the layers that were determined through training the first image model, the pre-training the second sensor model comprising:
dividing output of a convolution of predetermined dimensions of the second data set and the second number of second channels to generate different divided outputs to provide to a second plurality of branches,
for each of the second plurality of branches:
selecting a third number of the second number of channels, the third number of the second channels being equal to the first number of first channels, applying each layer of the plurality of layers using the weights of the layers that were determined through training the first model to the particular divided output of that particular branch,
determining a particular weight for the particular branch to apply to the output of a last layer of the plurality of layers to generate a first particular branch output;
fine-tune training the second sensor model using the second sensor data set using the particular weights of the particular branches and defining weights for the plurality of layers, the fine-tune training of the second sensor model comprising: dividing output of the convolution of predetermined dimensions of the second data set and the second number of second channels to generate different divided outputs to provide to the second plurality of branches,
for each of the second plurality of branches:
applying a first layer of the plurality of layers to the particular divided output of that particular branch,
concatenating output of each particular branch and applying a second layer of the plurality of layers to at least a portion of the concatenated output, determining a particular weight for each layer of each branch based oon the training of the second model,
determining a particular weight for the particular branch to apply to the output of a last layer of the plurality of layers to generate a second particular branch output;
generating the second model for similar sensors of the second data set, the second model including weights determined in the fine-tune training.
Parent application references simulated LIDAR and references:
The classification module 220 may be a neural network that performs semantic segmentation. The classification module 220 may label objects by assigning a classification code to each point (e.g., class codes as defined by the American Society for Photogrammetry and Remote Sensing (ASPRS)).
The classification module 220 may apply a deep-learning approach that considers every individual point and assigns its class label by considering the spatial relationships between the target point and its nearest neighbors, with nearest neighbors re-calculated at each layer of the network.
The classification module 220 may have the capacity for adding new object types using the same fundamental network architecture. Individual objects may be segmented out of the cloud using this as a starting point. In some embodiments, the classification module 220 uses a cascade of individual classifiers that all use a combination of supervised and unsupervised methods. In various embodiments, the cascade of binary classifiers enables the classification module 220 to use the natural priors of the world (true incidence rate) and the true/false positive rate to maximize overall accuracy.
The parent specification lacks description of convolutional fine tuning, concatenation of branches:
dividing output of a convolution of predetermined dimensions of the second data set and the second number of second channels to generate different divided outputs to provide to a second plurality of branches,
for each of the second plurality of branches:
selecting a third number of the second number of channels, the third number of the second channels being equal to the first number of first channels, applying each layer of the plurality of layers using the weights of the layers that were determined through training the first model to the particular divided output of that particular branch,
determining a particular weight for the particular branch to apply to the output of a last layer of the plurality of layers to generate a first particular branch output;
fine-tune training the second sensor model using the second sensor data set using the particular weights of the particular branches and defining weights for the plurality of layers, the fine-tune training of the second sensor model comprising: dividing output of the convolution of predetermined dimensions of the second data set and the second number of second channels to generate different divided outputs to provide to the second plurality of branches,
for each of the second plurality of branches:
applying a first layer of the plurality of layers to the particular divided output of that particular branch,
concatenating output of each particular branch and applying a second layer of the plurality of layers to at least a portion of the concatenated output, determining a particular weight for each layer of each branch based oon the training of the second model,
determining a particular weight for the particular branch to apply to the output of a last layer of the plurality of layers to generate a second particular branch output;
generating the second model for similar sensors of the second data set, the second model including weights determined in the fine-tune training.
Prior Art
Manivasagam (US 2020/0301799) disclose simulated LIDAR:
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The cited art does not disclose concatenation in the context of claim 1.
Additional prior art relevant to Applicant’s disclosure but not relied upon:
Dolan (US 2020/0184027) discloses simulated LIDAR.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RYAN M GRAY whose telephone number is (571)272-4582. The examiner can normally be reached on Monday through Friday, 9:00am-5:30pm (EST).
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/RYAN M GRAY/Primary Examiner, Art Unit 2611
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