DETAILED ACTION
Claim Objections
A series of singular dependent claims is permissible in which a dependent claim refers to a preceding claim which, in turn, refers to another preceding claim.
A claim which depends from a dependent claim should not be separated by any claim which does not also depend from said dependent claim. It should be kept in mind that a dependent claim may refer to any preceding independent claim. In general, applicant's sequence will not be changed. See MPEP § 608.01(n). (see specifically claims 9 and 10)
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 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Referring to claims 1-20, the term “to-be-detected image feature” is unclear, specifically how feature extraction is performed on a “to-be-detected image feature”. The examiner is interpreting the “to-be-detected image feature” as a potential image feature that feature extraction is performed on. Appropriate correction or clarification is required.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed 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 Chen (20210056324).
Referring to claims 1, 11, and 19, Chen shows a method for detecting an obstacle (see figure 2), comprising:
acquiring point cloud data collected by a lidar sensor during detection of an obstacle (see figure 1 Ref S202);
converting the point cloud data into an obstacle feature map (see figure 1 Ref S204), wherein the obstacle feature map comprises a to-be-detected image feature (see paragraph 43-44 note the point cloud is converted to an image map that includes potential features and relationships), and the to-be-detected image feature comprises an obstacle feature (see the neural network that is used to extract potential obstacles also see paragraph 47 and 49);
performing feature extraction on the to-be-detected image feature in the obstacle feature map to obtain the obstacle feature (see paragraph 43-44 note the neural network model extracts unknown features and relationships of objects to determine the presence of an obstacle), wherein a weight value in the obstacle feature map of the obstacle feature is increased during the feature extraction (see paragraph 49), and the feature extraction is monitored based on monitoring data generated by point cloud distribution data corresponding to the point cloud data (the neural network feature extraction is monitored based on monitoring data due to the learning nature of a neural network paragraph 49. It is inherent with feature extraction in data that is generated by a point cloud corresponds to the distribution data of the point cloud); and
determining a target obstacle according to the obstacle feature (see the object recognition extracts features and is able to determine if an object is a movable and stationary object see figure 5-6 and paragraph 70-72 also see paragraphs 95-97 in conjunction with figure 11).
Referring to claims 2, 12, and 20, Chen shows the performing feature extraction on the to-be-detected image feature in the obstacle feature map to obtain the obstacle feature comprises:
dividing a process of performing the feature extraction on the to-be-detected image feature into an encoding processing stage and a decoding processing stage (see the segmentation of the front view as shown in paragraph 92-93);
performing primary extraction on the to-be-detected image feature based on an attention mechanism at the encoding processing stage to obtain an intermediate image feature (see paragraph 103 note the first neural network model); and
performing secondary extraction on the intermediate image feature under monitoring according to the monitoring data at the decoding processing stage, to obtain the obstacle feature (see paragraph 103 and 104 note the second neural network).
Referring to claims 6 and 16, Chen shows the determining a target obstacle according to the obstacle feature comprises:
using the obstacle feature as input data of an obstacle detection model, and receiving an output result of the obstacle detection model (see the neural network in paragraph 47-49); and
determining whether an object corresponding to the obstacle feature is the target obstacle based on the output result (see paragraph 47-49 note the neural network outputs a object as an obstacle based on feature recognition).
Referring to claims 7 and 17, Chen shows dividing the point cloud data into a plurality of lattices according to distance information; and
determining a point cloud range of the point cloud data according to distribution positions of the point cloud data in the lattices (see the segmentation performed in paragraph 91-93).
Referring to claims 8 and 18, Chen shows the converting the point cloud data into an obstacle feature map comprises:
dividing the point cloud data according to pixels to obtain divided point cloud data; and
performing post-processing manners on the divided point cloud data to obtain a plurality of obstacle features (see the projection of the results of the point cloud as shown in paragraph 94-98 also see figure 11).
Allowable Subject Matter
Claims 3-5, 9, 10, 13, 14, 15 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include 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