Prosecution Insights
Last updated: May 04, 2026
Application No. 18/972,163

METHODS AND APPARATUS FOR PROVIDING MAPS FOR USE WITH AUTONOMY SYSTEMS

Non-Final OA §102
Filed
Dec 06, 2024
Priority
Dec 11, 2023 — provisional 63/608,594
Examiner
REFAI, RAMSEY
Art Unit
3664
Tech Center
3600 — Transportation & Electronic Commerce
Assignee
Nuro, Inc.
OA Round
1 (Non-Final)
50%
Grant Probability
Moderate
1-2
OA Rounds
2y 7m
Est. Remaining
62%
With Interview

Examiner Intelligence

Grants 50% of resolved cases
50%
Career Allowance Rate
326 granted / 652 resolved
-2.0% vs TC avg
Moderate +12% lift
Without
With
+12.5%
Interview Lift
resolved cases with interview
Typical timeline
4y 0m
Avg Prosecution
16 currently pending
Career history
668
Total Applications
across all art units

Statute-Specific Performance

§101
28.1%
-11.9% vs TC avg
§103
26.6%
-13.4% vs TC avg
§102
25.8%
-14.2% vs TC avg
§112
14.7%
-25.3% vs TC avg
Black line = Tech Center average estimate • Based on career data from 652 resolved cases

Office Action

§102
DETAILED ACTION Responsive to the claims filed on December 6, 2024. Claims 1-20 are presented. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Specification The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed. 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)(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. Claims 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Joubert et al (US 11,938,963). As per claim 1, Joubert et al teach a method comprising: obtaining sensor data from a plurality of sensors, the plurality of sensors being onboard a vehicle (see at least column 8, lines 35-53; sensors on vehicle); obtaining prior map data from a server, the server being offboard with respect to the vehicle (see at least column 23, lines 1-24, column 10, line 57-column 11, line 26; map data obtained from remove vehicle services via network); processing the sensor data using a first arrangement to generate processed sensor data, the first arrangement being located onboard the vehicle (see at least fig 1 (120); column 8, lines 35-53; primary vehicle control system); and generating an inferred context map using a map prediction arrangement located onboard the vehicle, wherein generating the inferred context map includes processing the processed sensor data and the prior map data using the map prediction arrangement (see at least figs 4, 5, 9, and corresponding text; augmented digital map is created by fusing sensor data and map data). As per claim 2, Joubert et al teach wherein processing the sensor data using the first arrangement to generate the processed sensor data includes: generating a bird’s-eye-view (BEV) representation of the sensor data using the first arrangement, wherein the processed sensor data includes the BEV representation (see at least figs 13-14 and corresponding text). As per claim 3, Joubert et al teach wherein the plurality of sensors includes at least one camera and at least one lidar, the sensor data including camera data obtained from the at least one camera and lidar data obtained from the at least one lidar (see at least fig 1 (130)), and wherein processing the sensor data using the first arrangement to generate the processed sensor data further includes: concatenating the camera data and the lidar data to generate concatenated sensor data using the first arrangement, wherein generating the BEV representation of the sensor data includes processing the concatenated sensor data (see at least figs 13-14 and corresponding text). As per claim 4, Joubert et al teach providing the inferred context map to an autonomy system onboard the vehicle, wherein the autonomy system is arranged to generate at least one vehicle command to control the vehicle (see at least column 26, line 62-column 27, line 7). As per claim 5, Joubert et al teach identifying at least one feature in the processed sensor data; determining whether the at least one feature is included in the prior map data; identifying a discrepancy when it is determined that the at least one feature is not included in the prior map data, wherein generating the inferred context map using the map prediction arrangement includes adding the at least one feature to the inferred map when the discrepancy is identified (see at least column 26, lines 7-29). As per claim 6, Joubert et al teach wherein the map prediction arrangement includes a machine learning model (see at least column 26, lines 30-43). As per claim 7, Joubert et al teach wherein obtaining the sensor data includes obtaining the sensor data in real-time as the vehicle operates (see at least column 26, lines 46-62). As per claim 14, Joubert et al teach a vehicle comprising: a chassis; a sensor system carried on the chassis; a communications system carried on the chassis (see at least column 7, lines 45-67); one or more tangible non-transitory, computer-readable media carried on the chassis; and logic encoded in the one or more tangible non-transitory, computer-readable media for execution and when executed operable to: obtain sensor data from the sensor system (see at least column 8, lines 35-53; sensors on vehicle); obtain prior map data from a server using the communications system, the server being offboard with respect to the vehicle, process the sensor data to generate processed sensor data (see at least column 23, lines 1-24, column 10, line 57-column 11, line 26; map data obtained from remove vehicle services via network); and generate an inferred context map, wherein the logic operable to generate the inferred context map includes logic operable to process the processed sensor data and the prior map data (see at least figs 4, 5, 9, and corresponding text; augmented digital map is created by fusing sensor data and map data). Claims 8-13 and 15-20 contain similar limitations as the claims above and therefore are rejected under similar rationale. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ramsey Refai whose telephone number is (313)446-4867. The examiner can normally be reached M-F 9am-5pm 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, Kito Robinson can be reached at (571) 270-3921. 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. RAMSEY REFAI Primary Examiner Art Unit 3664 /RAMSEY REFAI/Primary Examiner, Art Unit 3664
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Prosecution Timeline

Dec 06, 2024
Application Filed
Apr 18, 2026
Non-Final Rejection — §102 (current)

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

1-2
Expected OA Rounds
50%
Grant Probability
62%
With Interview (+12.5%)
4y 0m (~2y 7m remaining)
Median Time to Grant
Low
PTA Risk
Based on 652 resolved cases by this examiner. Grant probability derived from career allowance rate.

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