DETAILED ACTION
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 Status
This action is in response to the application filed on 05/31/2024. Claims 1-20 are pending and examined below.
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.
Claims 1-6, 12-16, 19, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 20230133152 A1 (“Barnehama”).
Regarding claim 1, Bernehama teaches receiving, by a processor of a mobile robot, at least one image including a set of objects (see at least [0021]);
processing the at least one image using a trained machine learning model to assign a quality metric to a first object of the set of objects in the at least one image (see at least [0058]); and
controlling the mobile robot to perform an action based, at least in part, on the quality metric assigned to the first object (see at least [0059]).
Regarding claim 2, Bernehama teaches the set of objects includes a set of boxes, the trained machine learning model includes a box detection model, and processing the at least one image to assign a quality metric to a first object of the set of objects comprises processing the at least one image using the box detection model (see at least [0101]).
Regarding claim 3, Bernehama teaches the box detection model is configured to detect two dimensional box faces or three dimensional shapes (see at least [0076]).
Regarding claim 4, Bernehama teaches determining an extent of damage to the first object; and assigning the quality metric based on the extent of damage to the first object (see at least [0073]).
Regarding claim 5, Bernehama teaches determining an extent of damage to the first object comprises categorizing the extent of damage into two or more categories of damage (see at least [0073]).
Regarding claim 6, Bernehama teaches controlling the mobile robot to provide an indication to a user that the first object cannot be effectively grasped by the mobile robot when the quality metric is less than a threshold value (see at least [0073]).
Regarding claim 12, Bernehama teaches determining an order of grasping objects from the set of objects based, at least in part, on the quality metric (see at least [0076]).
Regarding claim 13, Bernehama teaches determining to grasp a second object of the set of objects prior to grasping the first object when the quality metric assigned to the first object is less than a threshold value (see at least [0128]).
Regarding claim 14, Bernehama teaches determining to grasp the first object first when the quality metric assigned to the first object is less than a threshold value (see at least [0087]).
Regarding claim 15, Bernehama teaches controlling the mobile robot to move the first object from a first location to a second location at a speed determined based, at least in part, on the quality metric (see at least [0055]).
Regarding claim 16, Bernehama teaches controlling the mobile robot to move the first object from a first location to a second location through a trajectory determined based, at least in part, on the quality metric (see at least [0054]).
Regarding claim 19, Bernehama teaches receive at least one image including a set of objects; and process the at least one image using a trained machine learning model to assign a quality metric to a first object of the set of objects in the at least one image (see at least [0058]); and
a controller configured to control the mobile robot to perform an action based, at least in part, on the quality metric assigned to the first object (see at least [0059]).
Regarding claim 20, Bernehama teaches receiving at least one image including a set of objects; processing the at least one image using a trained machine learning model to assign a quality metric to a first object of the set of objects in the at least one image (see at least [0058]); and
controlling a mobile robot to perform an action based, at least in part, on the quality metric assigned to the first object (see at least [0059]).
Claim Rejections - 35 USC § 103
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, 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 7-10 are rejected under 35 U.S.C. 103 as being unpatentable over US 20230133152 A1 (“Barnehama”) in view of US 20230415345 A1 (“Zizka”).
Regarding claim 7, Barnehama is not explicit on selecting, based on the quality metric associated with the first object, a grasping strategy for grasping the first object; and controlling the mobile robot to grasp the first object based on the grasping strategy, however,
Zizka discloses selecting, based on the quality metric associated with the first object, a grasping strategy for grasping the first object; and controlling the mobile robot to grasp the first object based on the grasping strategy (see at least [0119]).
One of ordinary skill in the art would have been motivated to combine the system disclosed by Barnehama with the various inventive mobile robots that provide on the spot retrieval, picking, and/or placement of items to facilitate order fulfillment disclosed by Zizka in order to provide a simple way to scale automation particularly if the fulfillment center is modified over time (e.g., the layout of the center is changed to accommodate more or less stock items) (Zizka, [0006]).
Regarding claim 8, Barnehama is not explicit on selecting a grasping strategy for grasping the first object comprises selecting a second face of the first object to grasp when the quality metric assigned to the first object indicates that a first face of the first object has a quality less than a threshold value, and controlling the mobile robot to grasp the first object based on the grasping strategy comprises controlling the mobile robot to grasp the second face of the first object, however,
Zizka discloses selecting a grasping strategy for grasping the first object comprises selecting a second face of the first object to grasp when the quality metric assigned to the first object indicates that a first face of the first object has a quality less than a threshold value, and controlling the mobile robot to grasp the first object based on the grasping strategy comprises controlling the mobile robot to grasp the second face of the first object (see at least [0121])
One of ordinary skill in the art would have been motivated to combine the system disclosed by Barnehama with the various inventive mobile robots that provide on the spot retrieval, picking, and/or placement of items to facilitate order fulfillment disclosed by Zizka in order to provide a simple way to scale automation particularly if the fulfillment center is modified over time (e.g., the layout of the center is changed to accommodate more or less stock items) (Zizka, [0006]).
Regarding claim 9, Barnehama is not explicit on selecting one or more locations on the first object to grasp the first object, and controlling the mobile robot to grasp the first object based on the grasping strategy comprises controlling the mobile robot to grasp the first object at the one or more locations, however,
Zizka discloses selecting one or more locations on the first object to grasp the first object, and controlling the mobile robot to grasp the first object based on the grasping strategy comprises controlling the mobile robot to grasp the first object at the one or more locations (see at least [0098]).
One of ordinary skill in the art would have been motivated to combine the system disclosed by Barnehama with the various inventive mobile robots that provide on the spot retrieval, picking, and/or placement of items to facilitate order fulfillment disclosed by Zizka in order to provide a simple way to scale automation particularly if the fulfillment center is modified over time (e.g., the layout of the center is changed to accommodate more or less stock items) (Zizka, [0006]).
Regarding claim 10, Barnehama is not explicit on selecting a grasping technique based on the quality metric assigned to the first object, and controlling the mobile robot to grasp the first object based on the grasping strategy comprises controlling the mobile robot to grasp the first object using the grasping technique, however,
Zizka discloses selecting a grasping technique based on the quality metric assigned to the first object, and controlling the mobile robot to grasp the first object based on the grasping strategy comprises controlling the mobile robot to grasp the first object using the grasping technique(see at least [0097]).
One of ordinary skill in the art would have been motivated to combine the system disclosed by Barnehama with the various inventive mobile robots that provide on the spot retrieval, picking, and/or placement of items to facilitate order fulfillment disclosed by Zizka in order to provide a simple way to scale automation particularly if the fulfillment center is modified over time (e.g., the layout of the center is changed to accommodate more or less stock items) (Zizka, [0006]).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over US 20230133152 A1 (“Barnehama”) in view of US 20230415345 A1 (“Zizka”) in further view of US 20210157312 A1 (“Cella”).
Regarding claim 11, Barnehama in view of Zizka is not explicit on selecting a pinch grasp technique when the quality metric assigned to the first object is less than a threshold value, however,
Cella discloses selecting a pinch grasp technique when the quality metric assigned to the first object is less than a threshold value (see at least [1985]).
One of ordinary skill in the art would have been motivated to combine the system disclosed by Barnehama in view of Zizka with the an intelligent digital twin system disclosed by Cella for collecting, discovering, capturing, disseminating, managing, and processing information about industrial machines, including factual information (such as about internal structures, parts and components), operational information and procedural information, including know-how and other information (Cella, [0019]).
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over US 20230133152 A1 (“Barnehama”) in view of US 20190168787 A1 (“Messinger”).
Regarding claim 17, Barnehama is not explicit on determining, a parametric shape and dynamics of the trajectory based, at least part, on the quality metric, however
Messinger discloses determining, a parametric shape and dynamics of the trajectory based, at least part, on the quality metric (see at least [0153]).
One of ordinary skill in the art would have been motivated to combine the system disclosed by Barnehama with the inspection system and method disclosed by Messinger because improved systems and methods for managing inspections may be desirable (Messinger, [0021]).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over US 20230133152 A1 (“Barnehama”) in view of US 20210090694 A1 (“Colley”).
Regarding claim 18, Barnehama is not explicit on controlling the mobile robot to grasp the first object, and the method further comprises: detecting a change in an estimated mass of the first object while grasping the first object; selecting an image including the first object captured prior to grasping the first object; receiving an annotated version of the image; and retraining the trained machine learning model using the annotated version of the image, however,
Colley discloses controlling the mobile robot to grasp the first object, and the method further comprises: detecting a change in an estimated mass of the first object while grasping the first object (see at least [1947]);
selecting an image including the first object captured prior to grasping the first object; receiving an annotated version of the image; and retraining the trained machine learning model using the annotated version of the image (see at least [1948]).
One of ordinary skill in the art would have been motivated to combine the system disclosed by Barnehama with the method and system for storing user application programs and micro-service programs disclosed by Colley in order to control a robot (Colley, [1948]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATHEW FRANKLIN GORDON whose telephone number is (408)918-7612. The examiner can normally be reached Monday - Friday, 7:00 - 5:00 PST.
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/MATHEW FRANKLIN GORDON/Primary Examiner, Art Unit 3665
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