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 Non-Final Office Action on the merits. Claims 21-39 are currently pending and are addressed below.
Response to Amendment
The amendment filed 08/17/2026 has been entered. Claims 21-39 are currently pending.
Response to Arguments
Applicant’s arguments with respect to claims 21-39 have been considered but are moot because the arguments do not apply to the combination of references and/or rationale being used in the current rejection.
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 21-27 and 30-37 are rejected under 35 U.S.C. 103 as being unpatentable over Cesic et al. (US 2022/0121837) in view of Passot et al. (2022/0083058).
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 during a training run (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 during the training run (see at least ¶0042-0043, ¶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), the processor being further configured to store the localized infrastructure as a prior for use by the mobile robot during a runtime subsequent to the training run (maps generated by the robot in previous runs, comprising information about objects and obstacles in the facility, see at least ¶0043).
Cesic does not explicitly teach the training run including an operator guiding the mobile robot through the environment.
Passot teaches a system and method of training a mobile robot to operate in an environment, including a mobile robot configured for training within an environment during a training run during a training run in which an operator guides the mobile robot through the environment and storing objects detected during the training run as a prior for use by the mobile during a runtime subsequent to the training run (operator demonstrates route, robot builds map during demonstration, robot subsequently performs autonomous navigation, see Fig. 2, S204, S208 ¶0148-0151, Fig. 12).
It would have been obvious to one of ordinary skill in the art at the time of filing of the invention to modify the system and method of navigating a robot through an environment to perform a task as taught by Cesic with the technique of utilizing operator-guided training as taught by Passot in order to efficiently facilitate a robot learning a demonstrated task in an environment, and then autonomously performing the demonstrated task.
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 and Passot 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 and Passot 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, Passot, 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, Passot, 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.
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, Khoi Tran can be reached on 5712726919. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Ryan Rink/Primary Examiner, Art Unit 3619
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