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 .
Election/Restrictions
Restriction to one of the following inventions is required under 35 U.S.C. 121:
I. Claims 1-14, drawn to a method of operating a robot system, classified in B25J 9/1661.
II. Claims 15-20, drawn to a method of generating a touch heatmap for an object, classified in B25J 9/163.
The inventions are independent or distinct, each from the other because:
Inventions I and II are directed to related processes. The related inventions are distinct if: (1) the inventions as claimed are either not capable of use together or can have a materially different design, mode of operation, function, or effect; (2) the inventions do not overlap in scope, i.e., are mutually exclusive; and (3) the inventions as claimed are not obvious variants. See MPEP § 806.05(j). In the instant case, the inventions as claimed have materially different functions and effects as Invention I is drawn to the operation of a robot system while Invention II is drawn to generating a touch heatmap of an object. Furthermore, the two processes are not mutually exclusive to each other. Invention I does not exclusively require the heatmaps it uses to be generated through the method of Invention II as the heatmap may be obtained through more manual methods or other automated methods. Invention II does not exclusive generate heatmaps for the sole purpose of Invention I and the heatmaps generated may be used for other control methods. Furthermore, the inventions as claimed do not encompass overlapping subject matter and there is nothing of record to show them to be obvious variants.
Restriction for examination purposes as indicated is proper because all the inventions listed in this action are independent or distinct for the reasons given above and there would be a serious search and/or examination burden if restriction were not required because one or more of the following reasons apply:
(i) the inventions have acquired a separate status in the art in view of their different classification; (ii) the inventions have acquired a separate status in the art due to their recognized divergent subject matter; and/or (iii) the inventions require a different field of search (e.g., searching different classes/subclasses or electronic resources, or employing different search strategies or search queries).
Applicant is advised that the reply to this requirement to be complete must include (i) an election of an invention to be examined even though the requirement may be traversed (37 CFR 1.143) and (ii) identification of the claims encompassing the elected invention.
The election of an invention may be made with or without traverse. To reserve a right to petition, the election must be made with traverse. If the reply does not distinctly and specifically point out supposed errors in the restriction requirement, the election shall be treated as an election without traverse. Traversal must be presented at the time of election in order to be considered timely. Failure to timely traverse the requirement will result in the loss of right to petition under 37 CFR 1.144. If claims are added after the election, applicant must indicate which of these claims are readable upon the elected invention.
Should applicant traverse on the ground that the inventions are not patentably distinct, applicant should submit evidence or identify such evidence now of record showing the inventions to be obvious variants or clearly admit on the record that this is the case. In either instance, if the examiner finds one of the inventions unpatentable over the prior art, the evidence or admission may be used in a rejection under 35 U.S.C. 103 or pre-AIA 35 U.S.C. 103(a) of the other invention.
During a telephone conversation with Thomas Mahon on 04/24/2026 a provisional election was made without traverse to prosecute the invention of group I, claims 1-14. Affirmation of this election must be made by applicant in replying to this Office action. Claims 15-20 withdrawn from further consideration by the examiner, 37 CFR 1.142(b), as being drawn to a non-elected invention.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-14 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-12 and 14-15 of copending Application No. 18799731 (reference application).
Claims 1-12 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-4, 7, 9-12, and 16 of copending Application No. 18799719 (reference application).
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented. The accompanying tables below show the similarities and differences (bolded, wherein applicable) between the instant application and copending applications.
Although conflicting claims are not identical, they are not patentably distinct from each other because removing inherent and/or unnecessary limitation(s)/step(s) or adding an element and its function would be within the level of one of ordinary skill in the art. It is well settled that the adding or deleting of an element and its function(s) in the claim of the present application are an obvious expedient if the remaining elements perform the same function as before. In re Karlson, 136 USPQ 184 (CCPA 1963). Also note Ex parte Rainu, 168 USPQ 375 (Bd. App. 1969). Omission of a referenced element or step whose function is not needed would be obvious to one of ordinary skill in the art. Examiner further notes wherein although the claims are not identical (slightly broader and in a method form), they are commensurate in scope to the claim limitations provided in the issued U.S. Patent, and likewise would anticipate the currently provided claim limitations.
Table 1: Comparison of claims in Instant Application 18799726 vs. Application 18799731
Claims of Instant Application 18799726
Claims of Application 18799731
Claim 1. A method for operating a robot system including a robot body, at least one sensor, and a robot controller including at least one processor and at least one non-transitory processor-readable storage medium storing a library of object models and a library of associated touch heatmaps, the method comprising:
Claim 1. A robot control module comprising at least one non-transitory processor-readable storage medium storing a library of object models, a library of touch heatmaps associated with the library of object models, and processor-executable instructions or data that, when executed by at least one processor of a processor-based system, cause the processor-based system to:
capturing, by the at least one sensor, sensor data including a representation of an object;
capture, by at least one sensor carried by a robot body of the processor-based system, sensor data including a representation of an object;
accessing, by the robot controller in the library of object models, an object model representation of the object based on the sensor data;
access, in the library of object models, an object model representation of the object based on the sensor data;
accessing, by the robot controller in the library of touch heatmaps, a touch heatmap associated with the object model, the touch heatmap indictive of at least one touch region of the object model; and
access, in the library of touch heatmaps, a touch heatmap associated with the object model, the touch heatmap indictive of at least one touch region of the object model; and
controlling, by the robot controller, the at least one end effector to grasp the object based on the at least one touch region of the object model.
control, by the at least one processor, the at least one end effector to grasp the object based on the at least one touch region of the object model.
Claim 2. The method of claim 1, wherein:
Claim 2. The robot control module of claim 1, wherein:
the at least one non-transitory processor-readable storage medium further stores a library of grasp primitives;
the at least one non-transitory processor-readable storage medium further stores a library of grasp primitives;
the method further comprises accessing, by the robot controller, a first grasp primitive from the library of grasp primitives; and
the processor-executable instructions or data further cause the at least one processor to access a first grasp primitive from the library of grasp primitives; and
controlling the end effector to grasp the object further comprises controlling the at least one end effector to grasp the object in accordance with the first grasp primitive.
the processor-executable instructions or data which cause the at least one processor to control the at least one end effector to grasp the object further cause the at least one processor to control the at least one end effector to grasp the object in accordance with the first grasp primitive.
Claim 3. The method of claim 2, wherein the touch heatmap is further indicative of at least one grasp primitive including the first grasp primitive for grasping the object model at the at least one touch region.
Claim 3. The robot control module of claim 2, wherein the touch heatmap is further indicative of at least one grasp primitive including the first grasp primitive for grasping the object model at the at least one touch region.
Claim 4. The method of claim 2, further comprising selecting, by the robot controller, the first grasp primitive from the library of grasp primitives based on the touch heatmap.
Claim 4. The robot control module of claim 2, wherein the processor-executable instructions or data further cause the at least one processor to select the first grasp primitive from the library of grasp primitives based on the touch heatmap.
Claim 5. The method of claim 1, wherein:
Claim 5. The robot control module of claim 1, wherein:
the touch heatmap is indicative of a plurality of touch regions of the object model;
the touch heatmap is indicative of a plurality of touch regions of the object model;
the method further comprises selecting, by the robot controller, a subset of touch regions from the plurality of touch regions; and
the processor-executable instructions or data further cause the at least one processor to select a subset of touch regions from the plurality of touch regions; and
controlling the at least one end effector to grasp the object comprises controlling the at least one end effector to grasp the object based on the subset of touch regions.
the processor-executable instructions or data which cause the at least one processor to control the at least one end effector to grasp the object cause the at least one processor to control the at least one end effector to grasp the object based on the subset of touch regions.
Claim 6. The method of claim 5, wherein:
Claim 6. The robot control module of claim 5, wherein:
the method further comprises accessing, by the robot controller, a work objective of the robot system; and
the processor-executable instructions or data further cause the at least one processor to access a work objective of the processor-based system; and
the processor-executable instructions which cause the robot controller to select the subset of touch regions cause the robot controller to select the subset of touch regions based on the work objective of the robot system.
the processor-executable instructions or data which cause the at least one processor to select the subset of touch regions cause the at least one processor to select the subset of touch regions based on the work objective of the processor-based system.
Claim 7. The method of claim 5, wherein selecting the subset of touch regions comprises selecting the subset of touch regions based on proximity of the subset of touch regions at the object to the robot body or to the at least one end effector.
Claim 7. The robot control module of claim 5, wherein the processor-executable instructions or data which cause the at least one processor to select the subset of touch regions cause the at least one processor to select the subset of touch regions based on proximity of the subset of touch regions at the object to the robot body or to the at least one end effector.
Claim 8. The method of claim 5, wherein:
Claim 8. The robot control module of claim 5, wherein:
the touch heatmap is indicative of frequency of touch at each touch region in the plurality of touch regions; and
the touch heatmap is indicative of frequency of touch at each touch region in the plurality of touch regions; and
selecting the subset of touch regions comprises selecting the subset of touch regions based on frequency of touch as indicated in the touch heatmap.
the processor-executable instructions or data which cause the at least one processor to select the subset of touch regions cause the at least one processor to select the subset of touch regions based on frequency of touch as indicated in the touch heatmap.
Claim 9. The method of claim 5, wherein:
Claim 9. The robot control module of claim 5, wherein:
the at least one non-transitory processor-readable storage medium further stores a library of grasp primitives usable by the at least one end effector; and
the at least one non-transitory processor-readable storage medium further stores a library of grasp primitives usable by the at least one end effector; and
selecting the subset of touch regions comprises selecting the subset of touch regions based on the subset of touch regions being graspable in accordance with at least one grasp primitive in the library of grasp primitives.
the processor-executable instructions or data which cause the at least one processor to select the subset of touch regions cause the at least one processor to select the subset of touch regions based on the subset of touch regions being graspable in accordance with at least one grasp primitive in the library of grasp primitives.
Claim 10. The method of claim 5, wherein:
Claim 10. The robot control module of claim 5, wherein:
the at least one non-transitory processor-readable storage medium further stores a library of grasp primitives usable by the at least one end effector;
the at least one non-transitory processor-readable storage medium further stores a library of grasp primitives usable by the at least one end effector;
the method further comprises selecting, by the robot controller, a first grasp primitive of the library of grasp primitives capable of grasping the object in accordance with the subset of touch regions; and
the processor-executable instructions or data further cause the at least one processor to select a first grasp primitive of the library of grasp primitives capable of grasping the object in accordance with the subset of touch regions; and
controlling the at least one end effector to grasp the object based on the subset of touch regions comprises controlling the at least one end effector to grasp the object the object in accordance with the first grasp primitive.
the processor-executable instructions or data which cause the at least one processor to control the at least one end effector to grasp the object based on the subset of touch regions further cause the at least one processor to grasp the object in accordance with the first grasp primitive.
Claim 11. The method of claim 1, wherein:
Claim 11. The robot control module of claim 1, wherein:
the at least one end effector includes a first end effector and a second end effector;
the at least one end effector includes a first end effector and a second end effector;
the touch heatmap is indicative of a plurality of touch regions of the object model;
the touch heatmap is indicative of a plurality of touch regions of the object model;
the method further comprises selecting, by the robot controller, a first subset of touch regions and a second subset of touch regions from the plurality of touch regions; and
the processor-executable instructions or data further cause the at least one processor to select a first subset of touch regions and a second subset of touch regions from the plurality of touch regions; and
controlling the at least one end effector to grasp the object comprises controlling the first end effector to grasp the object in accordance with the first subset of touch regions and to control the second end effector to grasp the object in accordance with the second subset of touch regions.
the processor-executable instructions or data which cause the at least one processor to control the at least one end effector to grasp the object cause the at least one processor to control the first end effector to grasp the object in accordance with the first subset of touch regions and to control the second end effector to grasp the object in accordance with the second subset of touch regions.
Claim 12. The method of claim 1, wherein accessing the touch heatmap comprises accessing metadata associated with the object model which represents the touch heatmap.
Claim 12. The robot control module of claim 1, wherein the touch heatmap is stored as metadata associated with the object model.
Claim 13. The method of claim 1, wherein:
Claim 14. The robot control module of claim 1, wherein:
the robot system further includes a remote device remote from the robot body, and a communication interface which communicatively couples the remote device and the robot body;
the robot body carries the at least one sensor;
the remote device includes the robot controller;
the robot body carries the at least one sensor;
a remote device remote from the robot body includes the at least one processor;
capturing the sensor data is performed at the robot body;
the method further comprises transmitting, by the communication interface, the sensor data from the robot body to the remote device;
accessing the object model, accessing the touch heatmap, and controlling the at least one end effector are performed at the remote device; and
controlling the at least one end effector comprises the robot controller preparing and sending control instructions to the robot body via the communication interface.
the processor-executable instructions or data further cause the processor-based system to transmit, by a communication interface between the robot body and the remote device, the sensor data from the robot body to the remote device; and
the processor-executable instructions or data which cause the at least one processor to control the at least one end effector cause the at least one processor to prepare and send control instructions to the robot body via the communication interface.
Claim 14. The method of claim 1, wherein:
Claim 15. The robot control module of claim 1, wherein:
the robot system further comprises a remote device remote from the robot body, and a communication interface which communicatively couples the remote device and the robot body;
the robot body carries the at least one sensor, a first processor of the at least one processor, and a first non-transitory processor-readable storage medium of the at least one non-transitory processor-readable storage medium;
the robot body carries the at least one sensor, a first processor of the at least one processor, and a first non-transitory processor-readable storage medium of the at least one non-transitory processor-readable storage medium;
the remote device includes a second processor of the at least one processor, and a second non-transitory processor-readable storage medium of the at least one non-transitory processor-readable storage medium;
the remote device includes a second processor of the at least one processor, and a second non-transitory processor-readable storage medium of the at least one non-transitory processor-readable storage medium;
capturing the sensor data and controlling the at least one end effector are performed at the robot body;
accessing the object model and accessing the touch heatmap are performed at the remote device;
the method further comprises transmitting, by the communication interface, the sensor data from the robot body to the remote device; and
the method further comprises transmitting, by the communication interface, data indicating the object model and the at least one touch region from the remote device to the robot body.
the processor-executable instructions or data include first processor-executable instructions or data stored at the first non-transitory processor-readable storage medium that when executed cause the robot system to:
capture the sensor data by the at least one sensor;
transmit, via a communication interface between the robot body and the remote device, the sensor data from the robot body to the remote device; and
control, by the first at least one processor, the at least one end effector to grasp the object; and
the processor-executable instructions or data include second processor-executable instructions or data stored at the second non-transitory processor-readable storage medium that when executed cause the robot system to:
access, from the second non-transitory processor-readable storage medium, the object model representation;
access, from the second non-transitory processor-readable storage medium, the touch heatmap; and
transmit, via the communication interface, data indicating the object model and the at least one touch region to the robot body.
Table 2: Comparison of claims in Instant Application 18799726 vs. Application 18799719
Claims of Instant Application 18799726
Claims of Application 18799719
Claim 1. A method for operating a robot system a robot controller including at least one processor and at least one non-transitory processor-readable storage medium storing a library of object models and a library of associated touch heatmaps, the method comprising:
Claim 1. A robot system comprising:
including a robot body,
a robot body having at least one end effector;
at least one sensor, and
at least one sensor;
a robot controller including at least one processor and at least one non-transitory processor-readable storage medium storing a library of object models and a library of associated touch heatmaps, the method comprising:
a robot controller including at least one processor and at least one non-transitory processor-readable storage medium storing processor-executable instructions which, when executed by the at least one processor, cause the robot system to:
capturing, by the at least one sensor, sensor data including a representation of an object;
capture, by the at least one sensor carried by a robot body of the processor-based system, sensor data including a representation of an object;
accessing, by the robot controller in the library of object models, an object model representation of the object based on the sensor data;
access, in the library of object models, an object model representation of the object based on the sensor data;
accessing, by the robot controller in the library of touch heatmaps, a touch heatmap associated with the object model, the touch heatmap indictive of at least one touch region of the object model; and
access, in the library of touch heatmaps, a touch heatmap associated with the object model, the touch heatmap indictive of a plurality of touch regions of the object model;
access a work objective of the robot system;
select a subset of touch regions from the plurality of touch regions based at least in part on the work objective of the robot system; and
controlling, by the robot controller, the at least one end effector to grasp the object based on the at least one touch region of the object model.
control, by the robot controller, the at least one end effector to grasp the object based at least in part on the subset of touch regions of the object model.
Claim 2. The method of claim 1, wherein:
Claim 2. The robot system of claim 1, wherein:
the at least one non-transitory processor-readable storage medium further stores a library of grasp primitives;
the at least one non-transitory processor-readable storage medium further stores a library of grasp primitives;
the method further comprises accessing, by the robot controller, a first grasp primitive from the library of grasp primitives; and
the processor-executable instructions further cause the at least one processor to access a first grasp primitive from the library of grasp primitives; and
controlling the end effector to grasp the object further comprises controlling the at least one end effector to grasp the object in accordance with the first grasp primitive.
the processor-executable instructions which cause the robot controller to control the at least one end effector to grasp the object further cause the robot controller to control the at least one end effector to grasp the object in accordance with the first grasp primitive.
Claim 3. The method of claim 2, wherein the touch heatmap is further indicative of at least one grasp primitive including the first grasp primitive for grasping the object model at the at least one touch region.
Claim 3. The robot system of claim 2, wherein the touch heatmap is further indicative of at least one grasp primitive including the first grasp primitive for grasping the object model at the at least one touch region.
Claim 4. The method of claim 2, further comprising selecting, by the robot controller, the first grasp primitive from the library of grasp primitives based on the touch heatmap.
Claim 4. The robot system of claim 2, wherein the processor-executable instructions further cause the robot controller to select the first grasp primitive from the library of grasp primitives based at least in part on the touch heatmap.
Claim 5. The method of claim 1, wherein:
Claim 1. A robot system comprising:
a robot body having at least one end effector;
at least one sensor;
a robot controller including at least one processor and at least one non-transitory processor-readable storage medium storing processor-executable instructions which, when executed by the at least one processor, cause the robot system to:
capture, by the at least one sensor, sensor data including a representation of an object;
access, in a library of object models, an object model representation of the object based on the sensor data;
access a touch heatmap associated with the object model…
access a work objective of the robot system;
the touch heatmap is indicative of a plurality of touch regions of the object model;
the touch heatmap indictive of a plurality of touch regions of the object model;
the method further comprises selecting, by the robot controller, a subset of touch regions from the plurality of touch regions; and
select a subset of touch regions from the plurality of touch regions based at least in part on the work objective of the robot system; and
controlling the at least one end effector to grasp the object comprises controlling the at least one end effector to grasp the object based on the subset of touch regions.
control, by the robot controller, the at least one end effector to grasp the object based at least in part on the subset of touch regions of the object model.
Claim 6. The method of claim 5, wherein:
Claim 1. A robot system comprising:
a robot body having at least one end effector;
at least one sensor;
a robot controller including at least one processor and at least one non-transitory processor-readable storage medium storing processor-executable instructions which, when executed by the at least one processor, cause the robot system to:
capture, by the at least one sensor, sensor data including a representation of an object;
access, in a library of object models, an object model representation of the object based on the sensor data;
access a touch heatmap associated with the object model, the touch heatmap indictive of a plurality of touch regions of the object model…
control, by the robot controller, the at least one end effector to grasp the object based at least in part on the subset of touch regions of the object model.
the method further comprises accessing, by the robot controller, a work objective of the robot system; and
access a work objective of the robot system;
the processor-executable instructions which cause the robot controller to select the subset of touch regions cause the robot controller to select the subset of touch regions based on the work objective of the robot system.
select a subset of touch regions from the plurality of touch regions based at least in part on the work objective of the robot system; and
Claim 7. The method of claim 5, wherein selecting the subset of touch regions comprises selecting the subset of touch regions based on proximity of the subset of touch regions at the object to the robot body or to the at least one end effector.
Claim 7. The robot system of claim 1, wherein the processor-executable instructions which cause the robot controller to select the subset of touch regions cause the at least one processor to select the subset of touch regions based at least in part on proximity of the subset of touch regions at the object to the robot body or to the at least one end effector.
Claim 8. The method of claim 5, wherein:
Claim 16. A robot system comprising:
a robot body having at least one end effector;
at least one sensor;
a robot controller including at least one processor and at least one non-transitory processor-readable storage medium storing processor-executable instructions which, when executed by the at least one processor, cause the robot system to:
capture, by the at least one sensor, sensor data including a representation of an object;
access, in a library of object models, an object model representation of the object based on the sensor data;
access a touch heatmap associated with the object model,
the touch heatmap is indicative of frequency of touch at each touch region in the plurality of touch regions; and
the touch heatmap indictive of a plurality of touch regions of the object model and the touch heatmap indicative of frequency of touch at each touch region in the plurality of touch regions;
selecting the subset of touch regions comprises selecting the subset of touch regions based on frequency of touch as indicated in the touch heatmap.
select a subset of touch regions from the plurality of touch regions based at least in part on frequency of touch as indicated in the touch heatmap; and
control, by the robot controller, the at least one end effector to grasp the object based at least in part on the subset of touch regions of the object model.
Claim 9. The method of claim 5, wherein:
Claim 9. The robot system of claim 1, wherein:
the at least one non-transitory processor-readable storage medium further stores a library of grasp primitives usable by the at least one end effector; and
the at least one non-transitory processor-readable storage medium further stores a library of grasp primitives usable by the at least one end effector; and
selecting the subset of touch regions comprises selecting the subset of touch regions based on the subset of touch regions being graspable in accordance with at least one grasp primitive in the library of grasp primitives.
the processor-executable instructions which cause the robot controller to select the subset of touch regions cause the robot controller to select the subset of touch regions based at least in part on the subset of touch regions being graspable in accordance with at least one grasp primitive in the library of grasp primitives.
Claim 10. The method of claim 5, wherein:
Claim 10. The robot system of claim 1, wherein:
the at least one non-transitory processor-readable storage medium further stores a library of grasp primitives usable by the at least one end effector;
the at least one non-transitory processor-readable storage medium further stores a library of grasp primitives usable by the at least one end effector;
the method further comprises selecting, by the robot controller, a first grasp primitive of the library of grasp primitives capable of grasping the object in accordance with the subset of touch regions; and
the processor-executable instructions further cause the robot controller to select a first grasp primitive of the library of grasp primitives capable of grasping the object in accordance with the subset of touch regions; and
controlling the at least one end effector to grasp the object based on the subset of touch regions comprises controlling the at least one end effector to grasp the object the object in accordance with the first grasp primitive.
the processor-executable instructions which cause the robot controller to control the at least one end effector to grasp the object based at least in part on the subset of touch regions further cause the robot controller to grasp the object in accordance with the first grasp primitive.
Claim 11. The method of claim 1, wherein:
Claim 11. The robot system of claim 1, wherein:
the at least one end effector includes a first end effector and a second end effector;
the at least one end effector includes a first end effector and a second end effector;
the touch heatmap is indicative of a plurality of touch regions of the object model;
the processor-executable instructions that cause the robot controller to select a subset of touch regions from the plurality of touch regions,
the method further comprises selecting, by the robot controller, a first subset of touch regions and a second subset of touch regions from the plurality of touch regions; and
further cause the robot controller to select a first subset of touch regions and a second subset of touch regions from the plurality of touch regions; and
controlling the at least one end effector to grasp the object comprises controlling the first end effector to grasp the object in accordance with the first subset of touch regions and to control the second end effector to grasp the object in accordance with the second subset of touch regions.
the processor-executable instructions which cause the robot controller to control the at least one end effector to grasp the object cause the at least one processor to control the first end effector to grasp the object in accordance with the first subset of touch regions and to control the second end effector to grasp the object in accordance with the second subset of touch regions.
Claim 12. The method of claim 1, wherein accessing the touch heatmap comprises accessing metadata associated with the object model which represents the touch heatmap.
Claim 12. The robot system of claim 1, wherein the touch heatmap is stored as metadata associated with the object model.
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.
Claim(s) 1-7 and 9-14 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Sugahara et al. (US 20190087976 A1, hereinafter Sugahara).
Regarding claim 1, Sugahara teaches:
A method for operating a robot system including a robot body, at least one sensor, and a robot controller including at least one processor and at least one non-transitory processor-readable storage medium storing a library of object models and a library of associated touch heatmaps (at least as in paragraph 0128, “The robot 40 includes a robotic hand 41, a camera 42, a model storage 43 and an image recognition unit 44”; at least as in paragraph 0080-0081, “The images which indicate specific regions of the object, such as the gripping location are called the extracted images in the following… The extracted images generated by the gripping location specifying unit 22a can be combined with the distance images, forming teacher data (ground truth) for learning the image recognition models”; at least as in paragraph 0099-0100, “The CAD model storage 23 saves the three-dimensional model of the whole object. The CAD model storage 23 also saves the extracted images…The image storage 13 could also be nonvolatile memory such as NAND, MRAM, FRAM or the like. Storage devices such as optical discs, hard discs, SSDs or the like may be used”), the method comprising:
capturing, by the at least one sensor, sensor data including a representation of an object (at least as in paragraph 0159, “the robot 40 takes an image of the object using the camera 42. (Step S203) The image taken by the camera 42 could be an arbitrary image from an arbitrary angle”);
accessing, by the robot controller in the library of object models, an object model representation of the object based on the sensor data (at least as in paragraph 0160, “When the image is obtained, the robot 40 refers to the distance image to confirm whether it is possible to detect the target object by using the object detection model. If the robot 40 supports multiple object detection models, the robot confirms the object detection model that can be used for detecting the target object”);
accessing, by the robot controller in the library of touch heatmaps, a touch heatmap associated with the object model, the touch heatmap indictive of at least one touch region of the object model (at least as in paragraph 0161, “If the target object is detected, the robot 40 specifies the gripping location (specific region) by using the semantic segmentation model for the corresponding shape of object”; at least as in paragraph 0091, “It is possible to select multiple locations as the gripping locations”; at least as in paragraph 0082, “A plurality of training images can be saved in the CAD model storage 23. The feature of the gripping location specifying unit 22a could be implemented using three-dimensional CAD software running on the image processing device 20”); and
controlling, by the robot controller, the at least one end effector to grasp the object based on the at least one touch region of the object model (at least as in paragraph 0164, “(Step S206) The robotic hand 41 is manipulated based on the locational information for the gripping location, obtained in step S205”).
Regarding claim 2, Sugahara further teaches:
The method of claim 1, wherein:
the at least one non-transitory processor-readable storage medium further stores a library of grasp primitives (at least as in paragraph 0162, “by transformation of coordinates, the three-dimensional locational information of the gripping location relative to the point of reference within the coordinate system of the distance image could be transformed to coordinates used in the coordinate system of the robotic hand 41. The transformed locational information could be transmitted by using control signals”; at least as in paragraph 0180, “The three-dimensional models are generated by using the 3D point cloud data in the images of FIG. 17. Since only the coordinates corresponding to the gripping locations are labeled in the images for the three-dimensional models, FIG. 18 has only black-and-white images”; at least as in paragraph 0137, “Working data includes images and coordinate information used in object detection and detections of regions within the objects”);
the method further comprises accessing, by the robot controller, a first grasp primitive from the library of grasp primitives (at least as in paragraph 0161, “(Step S205) The robot 40 transforms the information on the location within the distance image to locational information used for controlling the robotic hand 41”); and
controlling the end effector to grasp the object further comprises controlling the at least one end effector to grasp the object in accordance with the first grasp primitive (at least as in paragraph 0164, “(Step S206) The robotic hand 41 is manipulated based on the locational information for the gripping location, obtained in step S205. The detailed instructions of the robotic hand 41 are sent by the image recognition unit 44”).
Regarding claim 3, Sugahara further teaches:
The method of claim 2, wherein the touch heatmap is further indicative of at least one grasp primitive including the first grasp primitive for grasping the object model at the at least one touch region (at least as in paragraph 0161, “(Step S205) The robot 40 transforms the information on the location within the distance image to locational information used for controlling the robotic hand 41”; at least as in paragraph 0162, “by transformation of coordinates, the three-dimensional locational information of the gripping location relative to the point of reference within the coordinate system of the distance image could be transformed to coordinates used in the coordinate system of the robotic hand 41. The transformed locational information could be transmitted by using control signals”; at least as in paragraph 0180, “The three-dimensional models are generated by using the 3D point cloud data in the images of FIG. 17. Since only the coordinates corresponding to the gripping locations are labeled in the images for the three-dimensional models, FIG. 18 has only black-and-white images”).
Regarding claim 4, Sugahara further teaches:
The method of claim 2, further comprising selecting, by the robot controller, the first grasp primitive from the library of grasp primitives based on the touch heatmap (at least as in paragraph 0164, “(Step S206) The robotic hand 41 is manipulated based on the locational information for the gripping location, obtained in step S205. The detailed instructions of the robotic hand 41 are sent by the image recognition unit 44”).
Regarding claim 5, Sugahara further teaches:
The method of claim 1, wherein:
the touch heatmap is indicative of a plurality of touch regions of the object model (at least as in paragraph 0161, “If the target object is detected, the robot 40 specifies the gripping location (specific region) by using the semantic segmentation model for the corresponding shape of object”; at least as in paragraph 0091, “It is possible to select multiple locations as the gripping locations”; at least as in paragraph 0082, “A plurality of training images can be saved in the CAD model storage 23. The feature of the gripping location specifying unit 22a could be implemented using three-dimensional CAD software running on the image processing device 20”);
the method further comprises selecting, by the robot controller, a subset of touch regions from the plurality of touch regions (at least as in paragraph 0089, wherein multiple areas may be identified to be used as the gripping location of the object; at least as in paragraph 0091, “It is possible to select multiple locations as the gripping locations”; at least as in paragraph 0093, “locations with insufficient mechanical strength could be excluded from the gripping locations, regardless of the shapes of the regions”; at least as in paragraph 0180-0181, wherein multiple areas may be identified as the gripping locations of the coffee cups and may be chosen from the angle viewed); and
controlling the at least one end effector to grasp the object comprises controlling the at least one end effector to grasp the object based on the subset of touch regions (at least as in paragraph 0164, “(Step S206) The robotic hand 41 is manipulated based on the locational information for the gripping location, obtained in step S205”).
Regarding claim 6, Sugahara further teaches:
The method of claim 5, wherein:
the method further comprises accessing, by the robot controller, a work objective of the robot system (at least as in paragraph 0130, “The example of tasks performed by the robotic hand 41 includes, removing objects from the belt conveyors or baskets, transportation of objects and assortment of objects. However, other tasks could be handled, as well. Examples of objects that are gripped by the robotic hand include industrial products, packaged goods, food, and material such as metal or woods. However, organisms such as crops or fishery can be handled, as well. Thus, the objects that are handled are not limited to objects of a specific category”); and
the processor-executable instructions which cause the robot controller to select the subset of touch regions cause the robot controller to select the subset of touch regions based on the work objective of the robot system (at least as in paragraph 0089, wherein the gripping location identified may be based on the robotic hand being used and “If multiple areas have been identified, the region which is closer to the center of the object is chosen”; at least as in paragraph 0091, “If multiple locations are identified, the location with the greatest mechanical strength is selected to reduce the possibility of damage…Depending on the position and the location of the objects, there are locations that cannot be gripped”; at least as in paragraph 0188-0193, wherein the parts of the bodies of human-beings or animals may be treated as the specific regions for semantic segmentation and image recognition such as for detecting personnel involved in suspicious activities or analyzing movement of sport athletes).
Regarding claim 7, Sugahara further teaches:
The method of claim 5, wherein selecting the subset of touch regions comprises selecting the subset of touch regions based on proximity of the subset of touch regions at the object to the robot body or to the at least one end effector (at least as in paragraph 0161, wherein the camera captures a distance image, identifies the gripping location within the image, and transforms the information on the location based on the distance image; at least as in paragraph 0089, “If multiple areas have been identified, the region which is closer to the center of the object is chosen”).
Regarding claim 9, Sugahara further teaches:
The method of claim 5, wherein:
the at least one non-transitory processor-readable storage medium further stores a library of grasp primitives usable by the at least one end effector (at least as in paragraph 0162, “by transformation of coordinates, the three-dimensional locational information of the gripping location relative to the point of reference within the coordinate system of the distance image could be transformed to coordinates used in the coordinate system of the robotic hand 41. The transformed locational information could be transmitted by using control signals”; at least as in paragraph 0180, “The three-dimensional models are generated by using the 3D point cloud data in the images of FIG. 17. Since only the coordinates corresponding to the gripping locations are labeled in the images for the three-dimensional models, FIG. 18 has only black-and-white images”; at least as in paragraph 0137, “Working data includes images and coordinate information used in object detection and detections of regions within the objects”); and
selecting the subset of touch regions comprises selecting the subset of touch regions based on the subset of touch regions being graspable in accordance with at least one grasp primitive in the library of grasp primitives (at least as in paragraph 0164, “(Step S206) The robotic hand 41 is manipulated based on the locational information for the gripping location, obtained in step S205. The detailed instructions of the robotic hand 41 are sent by the image recognition unit 44”; at least as in paragraph 0089, wherein multiple areas may be identified to be used as the gripping location of the object; at least as in paragraph 0091, “It is possible to select multiple locations as the gripping locations”; at least as in paragraph 0093, “locations with insufficient mechanical strength could be excluded from the gripping locations, regardless of the shapes of the regions”; at least as in paragraph 0180-0181, wherein multiple areas may be identified as the gripping locations of the coffee cups and may be chosen from the angle viewed).
Regarding claim 10, Sugahara further teaches:
The method of claim 5, wherein:
the at least one non-transitory processor-readable storage medium further stores a library of grasp primitives usable by the at least one end effector (at least as in paragraph 0162, “by transformation of coordinates, the three-dimensional locational information of the gripping location relative to the point of reference within the coordinate system of the distance image could be transformed to coordinates used in the coordinate system of the robotic hand 41. The transformed locational information could be transmitted by using control signals”; at least as in paragraph 0180, “The three-dimensional models are generated by using the 3D point cloud data in the images of FIG. 17. Since only the coordinates corresponding to the gripping locations are labeled in the images for the three-dimensional models, FIG. 18 has only black-and-white images”; at least as in paragraph 0137, “Working data includes images and coordinate information used in object detection and detections of regions within the objects”);
the method further comprises selecting, by the robot controller, a first grasp primitive of the library of grasp primitives capable of grasping the object in accordance with the subset of touch regions (at least as in paragraph 0161, “(Step S205) The robot 40 transforms the information on the location within the distance image to locational information used for controlling the robotic hand 41”); and
controlling the at least one end effector to grasp the object based on the subset of touch regions comprises controlling the at least one end effector to grasp the object the object in accordance with the first grasp primitive (at least as in paragraph 0164, “(Step S206) The robotic hand 41 is manipulated based on the locational information for the gripping location, obtained in step S205. The detailed instructions of the robotic hand 41 are sent by the image recognition unit 44”).
Regarding claim 11, Sugahara further teaches:
The method of claim 1, wherein:
the at least one end effector includes a first end effector and a second end effector (at least as in paragraph 0030, “the system may include multiple robots”);
the touch heatmap is indicative of a plurality of touch regions of the object model (at least as in paragraph 0161, “If the target object is detected, the robot 40 specifies the gripping location (specific region) by using the semantic segmentation model for the corresponding shape of object”; at least as in paragraph 0091, “It is possible to select multiple locations as the gripping locations”; at least as in paragraph 0082, “A plurality of training images can be saved in the CAD model storage 23. The feature of the gripping location specifying unit 22a could be implemented using three-dimensional CAD software running on the image processing device 20”);
the method further comprises selecting, by the robot controller, a first subset of touch regions and a second subset of touch regions from the plurality of touch regions (at least as in paragraph 0089, wherein multiple areas may be identified to be used as the gripping location of the object; at least as in paragraph 0091, “It is possible to select multiple locations as the gripping locations”; at least as in paragraph 0093, “locations with insufficient mechanical strength could be excluded from the gripping locations, regardless of the shapes of the regions”; at least as in paragraph 0180-0181, wherein multiple areas may be identified as the gripping locations of the coffee cups and may be chosen from the angle viewed); and
controlling the at least one end effector to grasp the object comprises controlling the first end effector to grasp the object in accordance with the first subset of touch regions and to control the second end effector to grasp the object in accordance with the second subset of touch regions (at least as in paragraph 0089-0090, wherein “If the gripping location of the objects are specified, not only the shape of the object but also the robotic hand which is used need to be taken into consideration” such that various gripping locations may be indicated for different robotic hands).
Regarding claim 12, Sugahara further teaches:
The method of claim 1, wherein accessing the touch heatmap comprises accessing metadata associated with the object model which represents the touch heatmap (at least as in paragraph 0081-0082, “The teacher data could be a pair of an extracted image and the corresponding distance image. The teacher data could be distance images that are marked making the regions specified in the extracted images distinguishable from other regions…the marking of the regions could be done by setting attribute information to the corresponding pixels in the image”).
Regarding claim 13, Sugahara further teaches:
The method of claim 1, wherein:
the robot system further includes a remote device remote from the robot body, and a communication interface which communicatively couples the remote device and the robot body (at least as in paragraph 0128, “The robot 40 includes a robotic hand 41, a camera 42, a model storage 43 and an image recognition unit 44”; at least as in paragraph 0137, “The image recognition unit 44 could be also located in an external computer located outside of the body of the robot 40”; at least as in paragraph 0058, “The characteristic learning device 30 and the robot 40 can send or receive data via electrical connections or wireless communication”);
the robot body carries the at least one sensor (at least as in paragraph 0133, “the camera 42 mounted on the robot 40 is used for taking images of objects”);
the remote device includes the robot controller (at least as in paragraph 0162, “The transformed locational information could be transmitted by using control signals. The control signals are sent from the image recognition unit 44 to the robotic hand 41”);
capturing the sensor data is performed at the robot body (at least as in paragraph 0133, “the camera 42 mounted on the robot 40 is used for taking images of objects”);
the method further comprises transmitting, by the communication interface, the sensor data from the robot body to the remote device (at least as in paragraph 0159, “the robot 40 takes an image of the object using the camera 42”; at least as in paragraph 0135 and Fig. 1, wherein “The image recognition unit 44 manipulates the camera 42”);
accessing the object model, accessing the touch heatmap, and controlling the at least one end effector are performed at the remote device (at least as in paragraph 0135, “By using the learned models, the image recognition unit 44 can detect objects and specific regions within the objects. Also, based on the result of the detection of regions within the object, the robotic hand 41 is controlled, enabling the gripping and transportation of objects. The image recognition unit 44 can use the detection models and the semantic segmentation models saved in the model storage 43. The image recognition unit 44 can also use models saved in storage space outside of the robot 40”; at least as in paragraph 0136, “the image recognition unit 44 manages the detection models and the semantic segmentation models saved in the model storage 43”; at least as in paragraph 0137, “The image recognition unit 44 may have storage space such as memory, buffer, cache or the like to save working data temporary for controlling the robotic hand 41. Working data includes images and coordinate information used in object detection and detections of regions within the objects”); and
controlling the at least one end effector comprises the robot controller preparing and sending control instructions to the robot body via the communication interface (at least as in paragraph 0162, “The control signals are sent from the image recognition unit 44 to the robotic hand 41. Thus, the robot 40 moves the robotic hand 41 to the specified gripping location”).
Regarding claim 14, Sugahara further teaches:
The method of claim 1, wherein:
the robot system further comprises a remote device remote from the robot body, and a communication interface which communicatively couples the remote device and the robot body (at least as in paragraph 0128, “The robot 40 includes a robotic hand 41, a camera 42, a model storage 43 and an image recognition unit 44”; at least as in paragraph 0134, “the model storage 43 can be located in outside of the robot 40”; at least as in paragraph 0058, “The characteristic learning device 30 and the robot 40 can send or receive data via electrical connections or wireless communication”);
the robot body carries the at least one sensor, a first processor of the at least one processor, and a first non-transitory processor-readable storage medium of the at least one non-transitory processor-readable storage medium (at least as in paragraph 0133, “the camera 42 mounted on the robot 40 is used for taking images of objects”; at least as in paragraph 0138, “The features of the imaging controller 16 in the three-dimensional imaging device 10, the components in the image processing device 20, the components in the characteristic learning device 30 and the image recognition unit 44 in the robot 40 could be implemented with processing circuits. Processors such as the CPU (Central Processing Unit) are examples of the processing circuit. On the CPU, software (programs) can operate, realizing the aforementioned features”);
the remote device includes a second processor of the at least one processor, and a second non-transitory processor-readable storage medium of the at least one non-transitory processor-readable storage medium (at least as in paragraph 0134, “the model storage 43 can be located in outside of the robot 40… the distance image could be saved in an external storage device, an external server or cloud storage”);
capturing the sensor data and controlling the at least one end effector are performed at the robot body (at least as in paragraph 0133, “the camera 42 mounted on the robot 40 is used for taking images of objects”; at least as in paragraph 0138, “The image recognition unit 44 could be located within the body of the robot 40”; at least as in paragraph 0159, “the robot 40 takes an image of the object using the camera 42”; at least as in paragraph 0162, “The transformed locational information could be transmitted by using control signals. The control signals are sent from the image recognition unit 44 to the robotic hand 41”);
accessing the object model and accessing the touch heatmap are performed at the remote device (at least as in paragraph 0134, “The model storage 43 saves the detection models and the semantic segmentation models learned at the learning unit 31 of the characteristic learning device 30… the model storage 43 can be located in outside of the robot 40”);
the method further comprises transmitting, by the communication interface, the sensor data from the robot body to the remote device (at least as in paragraph 0133, “the camera 42 mounted on the robot 40 is used for taking images of objects”; at least as in paragraph 0138, “The image recognition unit 44 could be located within the body of the robot 40”; at least as in paragraph 0159, “the robot 40 takes an image of the object using the camera 42”; at least as in paragraph 0162, “The transformed locational information could be transmitted by using control signals. The control signals are sent from the image recognition unit 44 to the robotic hand 41”); and
the method further comprises transmitting, by the communication interface, data indicating the object model and the at least one touch region from the remote device to the robot body (at least as in paragraph 0134, “The model storage 43 saves the detection models and the semantic segmentation models learned at the learning unit 31 of the characteristic learning device 30… the model storage 43 can be located in outside of the robot 40”).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sugahara et al. (US 20190087976 A1, hereinafter Sugahara) in view of Lian et al. (US 20220402128 A1, hereinafter Lian).
Regarding claim 8, Sugahara teaches the method of claim 5 but does not explicitly teach wherein:
the touch heatmap is indicative of frequency of touch at each touch region in the plurality of touch regions; and
selecting the subset of touch regions comprises selecting the subset of touch regions based on frequency of touch as indicated in the touch heatmap.
However, Lian, in the same field of endeavor of robot manipulation and controlling the grasp based on an obtained object model and grasping area heatmaps, specifically teaches:
the touch heatmap is indicative of frequency of touch at each touch region in the plurality of touch regions (at least as in paragraph 0016, “To achieve these results, a dense 3D point-wise grasping area heatmap can be modeled. For each grasp in the codebook, a grasping process can first be simulated. The hand-object contact points can be identified by computing their signed distance with respect to the gripper mesh. If the grasp is stable, for example the object is lifted successfully against gravity, the count n(G) for all contacted points on the object can be increased by 1. Otherwise, the grasp can be skipped”); and
selecting the subset of touch regions comprises selecting the subset of touch regions based on frequency of touch as indicated in the touch heatmap (at least as in paragraph 0016, “If the manipulator does not obstruct the placement, and if the object can steadily rest in the receptacle, the count of joint grasp and task success n(G,T) on the contact points can be increased by 1. After all grasps are verified, for each point on the object point cloud, its task relevance can be computed according to: P(T|G)=n(G,T)/n(G) … The highest task-relevance score indicates the best grasp for the object for the given task”).
Therefore, it would have been obvious to one of the ordinary skill in the art at the effective filing date of the instant invention to modify the teachings of Sugahara, to include Lian's teaching of generating a heatmap and selecting a grasp based on the most stable grasp indicated by the frequency of success, since Lian teaches wherein the heatmap control method allows a robot control system to learn grasping areas for various objects without human intervention thus improving safety and optimizes performance for particular tasks or objects.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RICARDO ICHIKAWA VISCARRA whose telephone number is (571)270-0154. The examiner can normally be reached M-F 9-12 & 2-4 PST.
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, Adam Mott can be reached on (571) 270-5376. 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.
/RICARDO I VISCARRA/Examiner, Art Unit 3657
/ADAM R MOTT/Supervisory Patent Examiner, Art Unit 3657
Prosecution Insights