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 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.
Claim(s) below is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Deng, Junyuan, et al. "Nerf-loam: Neural implicit representation for large-scale incremental lidar odometry and mapping." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2023 (Herein after Deng).
Referring to claims 1 and 12, Deng shows a system and corresponding method (see abstract as well as figure 4), comprising:
a computer that includes a processor and a memory (see paragraph below figure 9), the memory including instructions executable by the processor to:
generate a set of points from a measurement scan obtained by a lidar sensor (see figure 4 note the input is a LIDAR scan);
generate an expected termination distance of the set of points based on a neural implicit representation of the set of points (see Neural Odometry section 4.2);
compute a loss function that includes a relatively low margin correlated with a variance or standard deviation of a training distribution centered at a learned point of the set of points based on the expected termination distance of the learned point, the learned point being learned by the neural implicit representation See section 3 note the novel lossfunction to realize more suitable neural SDF for LIDAR data)
generate a keyframe from the set of points (see section 4.4 note the key-scan); and
generate a pose of the lidar sensor based on the keyframe (see paragraph 4.4 note the mesh and poses are generated from the refined key-scan).
Referring to claim 2, Deng shows the instructions to generate the pose of the lidar sensor additionally include instructions to:
modify the pose of the lidar sensor to align with the neural implicit representation of the set of points (see section 4.4 note the continuous modification of the SDF that is queried to generate the final mesh and poses).
Referring to claims 3 and 17, Deng shows the instructions to compute the loss function includes instructions to:
assign a relatively high margin correlated with a variance or standard deviation of a training distribution centered at an unlearned point of the set of points based on the unlearned point being unlearned by the neural implicit representation (see section 3 note the Neural SDF value and the training of the SDF pairs that includes using the ground point and non-ground point SDF optimization).
Referring to claims 4 and 18, Deng shows transmit the generated pose of the lidar sensor to an autonomous vehicle driving application (see the introduction as well as figure 1).
Referring to claims 5 and 19, Deng shows execute motion planning by the autonomous vehicle driving application based on the generated pose (see the introduction).
Referring to claim 13, Deng shows generate a mesh representation of the measurement scan based on the neural implicit representation of the set of points (see figure 4 note the final mesh).
Referring to claim 15, Deng shows the neural implicit representation includes expected weights along rays terminating at the set of points (see figure 3 also see training the SDF pairs).
Allowable Subject Matter
Claims 6-12 and 20 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
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LUKE D RATCLIFFE whose telephone number is (571)272-3110. The examiner can normally be reached M-F 9:00AM-5:00PM EST.
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/LUKE D RATCLIFFE/Primary Examiner, Art Unit 3645
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