Prosecution Insights
Last updated: August 15, 2026
Application No. 18/852,369

A SYSTEM FOR AMRS THAT LEVERAGES PRIORS WHEN LOCALIZING AND MANIPULATING INDUSTRIAL INFRASTRUCTURE

Final Rejection §102§103
Filed
Sep 27, 2024
Priority
Mar 28, 2022 — provisional 63/324,201 +1 more
Examiner
RINK, RYAN J
Art Unit
3619
Tech Center
3600 — Transportation & Electronic Commerce
Assignee
Seegrid Corporation
OA Round
2 (Final)
78%
Grant Probability
Favorable
3-4
OA Rounds
7m
Est. Remaining
89%
With Interview

Examiner Intelligence

Grants 78% — above average
78%
Career Allowance Rate
379 granted / 484 resolved
+26.3% vs TC avg
Moderate +11% lift
Without
With
+10.8%
Interview Lift
resolved cases with interview
Typical timeline
2y 5m
Avg Prosecution
17 currently pending
Career history
506
Total Applications
across all art units

Statute-Specific Performance

§101
7.3%
-32.7% vs TC avg
§103
44.6%
+4.6% vs TC avg
§102
15.8%
-24.2% vs TC avg
§112
27.3%
-12.7% vs TC avg
Black line = Tech Center average estimate • Based on career data from 484 resolved cases

Office Action

§102 §103
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 . DETAILED ACTION This is a Final Office Action on the merits. Claims 21-39 are currently pending and are addressed below. Response to Amendment The amendment filed 04/27/2026 has been entered. Claims 21-39 are currently pending. The previous 35 USC 112 rejection is overcome by Applicant’s amendments and comments. Response to Arguments Applicant's arguments filed 04/27/2026 have been fully considered but they are not persuasive. In response to Applicant's argument that the prior art fails to teach the pending claim limitations, the Examiner respectfully disagrees. Applicant argues, specifically, that Cesic et al. fails to teach a mobile robot configured for training within an environment. The Examiner respectfully disagrees. The Examiner notes that, broadly interpreted, any learning, adjusting, or updating within the environment would meet the claim limitations. Therefore, Cesic meets the claim limitation at least by updating the map from sensor data and adjusting a mission based on the data (see at least ¶0043, ¶0045, ¶0061-0065, ¶0078, ¶0084, ¶0091). Additionally, Cesic further teaches wherein models are trained using previous mission data (see at least ¶0037). These previous missions clearly meet the broadest reasonable interpretation of “training runs”. Therefore, the prior art of record meets the claim limitations and Applicant’s arguments are not persuasive. Claim Rejections - 35 USC § 102 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 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) 21-27 and 30-37 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Cesic et al. (US 2022/0121837). Regarding claim 21: Cesic teaches a system for localizing infrastructure (see at least abstract, ¶0002), comprising: a mobile robot configured for training within an environment (autonomous mobile robot 140, see at least Fig. 1, ¶0005, ¶0028, ¶0037); one or more sensors configured to collect sensor data while the mobile robot navigates the environment (see at least ¶0048); and a processor (processor 502) configured to process the sensor data to identify object parameters, determine an infrastructure indicated by the object parameters, and spatially register the infrastructure to a location within the environment to localize the infrastructure during a training run (Infrastructure including manipulable objects, such as pallets as defined in instant specification ¶007, see at least ¶0005, ¶0043, ¶0049, ¶0065). Regarding claim 22: Cesic further teaches wherein the processor is further configured to determine a pose of the mobile robot to localize the mobile robot (see at least ¶0072). Regarding claim 23: Cesic further teaches wherein the system further comprises a semantic database including a plurality of object models, wherein each object model embodies object parameters for a type of infrastructure, and the processor is further configured to access the object models to determine the infrastructure (see at least ¶0065, ¶0101). Regarding claim 24: Cesic further teaches wherein the semantic database groups infrastructure object models into types of infrastructure object models based on the object parameters (see at least ¶0054). Regarding claim 25: Cesic further teaches wherein the system is configured to be trained to manipulate infrastructure and to employ an infrastructure object model in that manipulation (see at least ¶0006, ¶0077). Regarding claim 26: Cesic further teaches wherein the mobile robotics platform is configured to employ an infrastructure object model other than the one it was trained to employ in the manipulation (multiple machine learning models, for identifying, determining pose, determining mission plans are employed, see at least ¶0047-0056, ¶0077, ¶0101). Regarding claim 27: Cesic further teaches wherein the system is configured to be trained to localize infrastructure and to employ an infrastructure object model in that localization (see at least ¶0057). Regarding claims 30-37, Cesic teaches a method as in claims 21-27 above. Claim Rejections - 35 USC § 103 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. Claims 28-29 and 38-39 are rejected under 35 U.S.C. 103 as being unpatentable over Cesic as applied to claims 21-27 above, in view of Horowitz et al. (US 20023/0192418). Regarding claims 28 and 38: Cesic teaches the limitations as above. Cesic further teaches a user interface for operating and monitoring the robot (see at least Figs. 6-9, ¶0034, ¶0040). Cesic does not explicitly teach the user interface allowing for operator inputs of attributes for training of the robot. Horowitz teaches a system and method of training and operating a robot in a facility for manipulation of objects in the facility, including a user interface responsive to operator inputs of attributes associated with infrastructure during training of the mobile robot, wherein the processor is further configured to store the attributes as priors associated with the localized infrastructure for use during runtime (see at least ¶0120). It would have been obvious to one of ordinary skill in the art at the time of filing of the invention to modify the robotic training and operation system and method as taught by Cesic by implementing a user-based supervised machine learning technique as taught by Horowitz in order to provide the expected result of customized, flexible learning, allowing a user to input relevant data to ensure a task, pose, classification, etc. is properly acquired. Regarding claims 29 and 39: The combination of Cesic and Horowitz teaches the limitations as in claims 28 and 38 above, but is silent as to the particular parameters provided by the user including dimensional attributes. It would have been obvious to one of ordinary skill in the art before the time of filing of the invention to modify the robotic control and learning system and method as taught by Cesic and Horowitz by including dimensional information of the object being trained upon in order to include relevant information for later classification/identification as well as determining manipulation strategies. Conclusion The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure. THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to RYAN J RINK whose telephone number is (571)272-4863. The examiner can normally be reached on M-F 8-5. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anna Momper can be reached on (571) 270-5788. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /Ryan Rink/Primary Examiner, Art Unit 3619
Read full office action

Prosecution Timeline

Sep 27, 2024
Application Filed
Jan 30, 2026
Non-Final Rejection mailed — §102, §103
Apr 27, 2026
Response Filed
Jun 17, 2026
Final Rejection mailed — §102, §103 (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

3-4
Expected OA Rounds
78%
Grant Probability
89%
With Interview (+10.8%)
2y 5m (~7m remaining)
Median Time to Grant
Moderate
PTA Risk
Based on 484 resolved cases by this examiner. Grant probability derived from career allowance rate.

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