Office Action Predictor
Application No. 17/889,091

Method for Detecting Obstacle, Electronic Device, and Storage Medium

Non-Final OA §102§112
Filed
Aug 16, 2022
Examiner
RATCLIFFE, LUKE D
Art Unit
3645
Tech Center
3600 — Transportation & Electronic Commerce
Assignee
Beijing Baidu Netcom Science Technology Co., LTD.
OA Round
1 (Non-Final)
87%
Grant Probability
Favorable
1-2
OA Rounds
2y 11m
To Grant
99%
With Interview

Examiner Intelligence

87%
Career Allow Rate
1474 granted / 1688 resolved
Without
With
+12.2%
Interview Lift
avg trend
2y 11m
Avg Prosecution
44 pending
1732
Total Applications
career history

Statute-Specific Performance

§101
2.3%
-37.7% vs TC avg
§103
50.2%
+10.2% vs TC avg
§102
26.3%
-13.7% vs TC avg
§112
13.6%
-26.4% vs TC avg
Black line = Tech Center average estimate • Based on career data

Office Action

§102 §112
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. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Isam Alsomiri can be reached at 571-272-6970. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /LUKE D RATCLIFFE/Primary Examiner, Art Unit 3645
Read full office action

Prosecution Timeline

Aug 16, 2022
Application Filed
Dec 10, 2025
Non-Final Rejection — §102, §112
Mar 30, 2026
Response Filed

Precedent Cases

Applications granted by this same examiner with similar technology. Study what changed to get past this examiner.

Patent 12591049
TRANSMIT SIGNAL DESIGN FOR AN OPTICAL DISTANCE MEASUREMENT SYSTEM
2y 5m to grant Granted Mar 31, 2026
Patent 12590798
Multi-sensor depth mapping
2y 5m to grant Granted Mar 31, 2026
Patent 12585021
ADDRESSABLE PROJECTOR FOR DOT BASED DIRECT TIME OF FLIGHT DEPTH SENSING
2y 5m to grant Granted Mar 24, 2026
Patent 12578475
Processing Of Lidar Images
2y 5m to grant Granted Mar 17, 2026
Patent 12571893
DISTANCE MEASURING APPARATUS AND METHOD OF DETERMINING DIRT ON WINDOW
2y 5m to grant Granted Mar 10, 2026

AI Strategy Recommendation

Click below to generate an AI-powered prosecution strategy using examiner precedents, rejection analysis, and claim mapping.
Powered by AI — typically takes 5-10 seconds

Prosecution Projections

1-2
Expected OA Rounds
87%
Grant Probability
99%
With Interview (+12.2%)
2y 11m
Median Time to Grant
Low
PTA Risk
Based on 1688 resolved cases by this examiner