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 . Claims 1-20 as originally filed are pending and have been considered as follows.
Priority
1. Acknowledgement is made that this application is a continuation of U.S. Patent Application No. 17/019,546 filed on 09/14/2020.
Information Disclosure Statement
2. The information disclosure statements (IDS) filed on 08/05/2024, 04/14/2025, 09/15/2025, 10/14/2025, and 10/31/2025 are being considered by the examiner.
Claim Interpretation
3. The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
4. The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
5. This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“processing unit” configured to in claims 11, 13, and 16.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
A review of the specification (citation to US pub. No. 20240408750) shows the following appears to be the corresponding structure described in the specification for the 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph limitations:
“processing unit” corresponds to processing unit 1104 and can be a processor [0128].
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Double Patenting
6. 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 USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The 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/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-4, 7-15, and 18-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 7, 11, 14, and 19-20 of U.S. Patent No. 12070860. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims cover substantially the same scope.
Refer to the table below to see claim mapping for double patenting:
Instant Application -18/784,401
U.S. Patent No. 12070860
1. A method for controlling a robotic item handler, the method comprising:
1. (Currently Amended) A method for controlling a robotic item handler comprising, the method comprising:
2. The method of claim 1, comprising:
obtaining first point cloud data related to a first three-dimensional image captured by a first sensor device of the robotic item handler;
obtaining second point cloud data related to a second three-dimensional image captured by a second sensor device of the robotic item handler; and
transforming the first point cloud data and the second point cloud data to generate the combined point cloud data.
obtaining first point cloud data related to a first three-dimensional image captured by a first sensor device of the robotic item handler;
obtaining second point cloud data related to a second three-dimensional image captured by a second sensor device of the robotic item handler;
transforming the first point cloud data and the second point cloud data to generate combined point cloud data;
constructing a machine learning model based on a combined point cloud data as an input to a convolution neural network;
constructing a machine learning model based on the combined point cloud data as an input to a convolution neural network;
outputting, via the machine learning model, a decision classification indicative of a first probability associated with a first operating mode and a second probability associated with a second operating mode;
outputting, via the machine learning model, a decision classification indicative of a first probability associated with a first operating mode and a second probability associated with a second operating mode, wherein:
4. The method of claim 1, wherein:
(i) the first operating mode is associated with picking an item by grasping the item using an end effector of a robotic arm of the robotic item handler, and
(ii) the second operating mode is associated with sweeping a pile of items from an item docking station with a platform of the robotic item handler.
(i) the first operating mode is associated with picking an item by grasping the item using an end effector of a robotic arm of the robotic item handler, and
(ii) the second operating mode is associated with sweeping a pile of items from an item docking station with a platform of the robotic item handler;
selecting one of the first operating mode and the second operating mode of the robotic item handler based on the decision classification outputted by the machine learning model;
selecting one of the first operating mode and the second operating mode of the robotic item handler based on the decision classification outputted by the machine learning model, wherein:
3. The method of claim 1, wherein:
(i) the first operating mode is selected in response to the first probability being higher than the second probability, and
(ii) the second operating mode is selected in response to the second probability being higher than the first probability.
(i) the first operating mode is selected in response to the first probability being higher than the second probability, and
(ii) the second operating mode is selected in response to the second probability being higher than the first probability;
operating the robotic item handler according to the selection of the one of the first operating mode and the second operating mode;
operating the robotic item handler according to the selection of the one of the first operating mode and the second operating mode;
evaluating the selection of the one of: the first operating mode and the second operating mode of the robotic item handler based on a pre-defined heuristic associated with past operations of the robotic item handler;
adjusting a first weight associated with the decision classification and a second weight associated with a pre-defined heuristic associated with past operations of the robotic item handler, based on a performance associated with the output of the machine learning model over a period of time, wherein during initial stages of learning by the machine learning model, the first weight is less than the second weight and after a substantial period of time is utilized to train the machine learning model, the second weight is less than the first weight; and
adjusting a first weight associated with the decision classification and a second weight associated with the pre-defined heuristic used for the evaluation of the selection, based on a performance associated with the output of the machine learning model over a period of time, wherein during initial stages of learning by the machine learning model, the first weight is less than the second weight and after a substantial period of time is utilized to train the machine learning model, the second weight is less than the first weight; and
controlling the robotic item handler based on an evaluation of the selection using the pre-defined heuristic.
controlling the robotic item handler based on the evaluation of the selection.
2. (Previously Presented) The method of claim 1, wherein the first sensor device is coupled to the robotic arm of the robotic item handler and the second sensor device is coupled to the platform of the robotic item handler.
3. (Original) The method of claim 1, wherein the robotic item handler is a robotic carton unloader configured for performing at least one of:
loading and unloading of items.
5. The method of claim 2, further comprising:
operating the robotic item handler according to the second operating mode upon determining that height of an item is below a pre-defined height.
6. The method of claim 1, wherein the pre-defined heuristic defines coordinates an item in a three-dimensional space.
7. The method of claim 1, comprising:
obtaining first point cloud data related to a first three-dimensional image captured by a first sensor device of the robotic item handler, wherein the first sensor device is a vision system configured to provide at least one of a depth perception, an edge recognition and a 3D image of at least a wall of the item in a three-dimensional space.
8. The method of claim 7, wherein the vision system is further configured to recognize at least one of edges, shape, and distance of the item in front of the robotic item handler.
19. (Previously Presented) The method of claim 1, wherein the first probability is a probability of a first type of stacking of items and the second probability is a probability of a second type of stacking of items.
20. (Previously Presented) The method of claim 19, wherein the first type of stacking of items corresponds to stacking of items in a pile or wall, and wherein the second type of stacking of items corresponds to scattered items over an area.
9. The method of claim 1, wherein the combined point cloud data comprises a first portion representing a Red-Green-Blue (RGB) image data and a second portion representing a depth image data.
11. (Original) The robotic item unloader of claim 7, wherein the first sensor device and the second sensor device comprises at least one depth camera and a color camera, and wherein the first sensor device is coupled to the robotic arm of the robotic item unloader and the second sensor device is coupled to the platform of the robotic item unloader.
10. The method of claim 9, further comprising:
processing the RGB image data through a first set of layers of the convolutional neural network and processing the depth image data through a second set of layers of the convolutional neural network.
11. (Original) The robotic item unloader of claim 7, wherein the first sensor device and the second sensor device comprises at least one depth camera and a color camera, and wherein the first sensor device is coupled to the robotic arm of the robotic item unloader and the second sensor device is coupled to the platform of the robotic item unloader.
11. A robotic item handler comprising:
7. (Currently Amended) A robotic item unloader comprising:
12. The robotic item handler of claim 11, comprising a vision system comprising:
a first sensor device positioned at a first location on the robotic item handler;
a second sensor device positioned at a second location on the robotic item handler;
a robotic arm comprising an end effector configured to operate in a first operating mode; and
a platform comprising a conveyor configured to operate in a second operating mode.
a vision system comprising:
a first sensor device positioned at a first location on the robotic item unloader; and
a second sensor device positioned at a second location on the robotic item unloader;
a robotic arm comprising an end effector configured to operate in a first operating mode;
a platform comprising a conveyor configured to operate in a second operating mode; and
a processing unit configured to:
a processing unit comprising one or more processors, the processing unit communicatively coupled to at least one of the vision system, the platform, and the robotic arm, the processing unit configured to:
13. The robotic item handler of claim 11, wherein the processing unit is configured to:
obtain first point cloud data related to a first three-dimensional image captured by a first sensor device;
obtain second point cloud data related to a second three-dimensional image captured by the second sensor device; and
transform the first point cloud data and the second point cloud data to generate a combined point cloud data.
obtain first point cloud data related to a first three-dimensional image captured by the first sensor device;
obtain second point cloud data related to a second three-dimensional image captured by the second sensor device;
transform the first point cloud data and the second point cloud data to generate a combined point cloud data;
construct a machine learning model by using a combined point cloud data as an input to a convolution neural network;
construct a machine learning model by using the combined point cloud data as an input to a convolution neural network;
output, by the machine learning model, a decision classification indicative of a first probability associated with a first operating mode and a second probability associated with the second operating mode;
output, by the machine learning model, a decision classification indicative of a first probability associated with the first operating mode and a second probability associated with the second operating mode, wherein:
15. The robotic item handler of claim 14, wherein:
(i) the first operating mode is associated with picking an item by grasping the item using an end effector of a robotic arm of the robotic item handler, and
(ii) the second operating mode is associated with sweeping a pile of items from an item docking station with a platform of the robotic item handler.
(i) the first operating mode is associated with picking an item by grasping the item using an end effector of a robotic arm of the robotic item unloader, and
(ii) the second operating mode is associated with sweeping a pile of items from an item docking station with a platform of the robotic item unloader;
select one of the first operating mode and the second operating mode of the robotic item handler based on the decision classification outputted by the machine learning model;
select one of the first operating mode and the second operating mode of the robotic item unloader based on the decision classification outputted by the machine learning model, wherein:
14. The robotic item handler of claim 13, wherein:
(i) the first operating mode is selected in response to the first probability being higher than the second probability, and
(ii) the second operating mode is selected in response to the second probability being higher than the first probability.
(i) the first operating mode is selected in response to the first probability being higher than the second probability, and
(ii) the second operating mode is selected in response to the second probability being higher than the first probability;
control the robotic item handler by:
control the robotic item unloader by:
operating a robotic arm of the robotic item handler according to the selection of the one of the first operating mode and the second operating mode;
operating the robotic arm of the robotic item unloader according to the selection of the one of the first operating mode and the second operating mode;
evaluate the selection of the first operating mode and the second operating mode, based on a pre-defined heuristic associated with past operations of the robotic item unloader;
adjust a first weight associated with the decision classification and a second weight associated with a pre-defined heuristic associated with past operations of the robotic item handler, based on a performance associated with the output of the machine learning model over a period of time, wherein during initial stages of learning by the machine learning model, the first weight is less than the second weight and after a substantial period of time utilized to train the machine learning model, the second weight is less than the first weight; and
adjust a first weight associated with the decision classification and a second weight associated with the pre-defined heuristic used for the evaluation of the selection, based on a performance associated with the output of the machine learning model over a period of time, wherein during initial stages of learning by the machine learning model, the first weight is less than the second weight and after a substantial period of time utilized to train the machine learning model, the second weight is less than the first weight: and
control the robotic item handler based on an evaluation of the selection using the pre-defined heuristic.
control the robotic item unloader based on the evaluation of the selection.
8. (Previously Presented) The robotic item unloader of claim 7, wherein in the second operating mode, a section of the platform is configured to sweep the pile of items, thereby, guiding one or more items of the pile of items onto the conveyor.
9. (Previously Presented) The robotic item unloader of claim 7, wherein in the first operating mode, a section of the robotic arm is configured to pick the item by grasping a portion of the item using the end effector of the robotic arm.
11. (Original) The robotic item unloader of claim 7, wherein the first sensor device and the second sensor device comprises at least one depth camera and a color camera, and wherein the first sensor device is coupled to the robotic arm of the robotic item unloader and the second sensor device is coupled to the platform of the robotic item unloader.
12. (Original) The robotic item unloader of claim 7, wherein to perform at least one of:
loading and unloading of a plurality of items from an item docking station, the processing unit is configured to:
generate a first command to operate the robotic arm according to the first operating mode; and
generate a second command to operate the platform according to the second operating mode.
16. The robotic item handler of claim 15, wherein the processing unit is further configured to:
operate the robotic item handler according to the second operating mode upon determining that height of the item is below a pre-defined height.
17. The robotic item handler of claim 16, wherein the pre-defined heuristic defines co-ordinates of the item in a three-dimensional space.
18. The robotic item handler of claim 12, wherein the vision system is configured to:
provide at least one of a depth perception, an edge recognition and a 3D image of at least a wall of the item in a three-dimensional space; and
recognize at least one of edges, shapes, and distance the items in front of the robotic item handler.
19. (Previously Presented) The method of claim 1, wherein the first probability is a probability of a first type of stacking of items and the second probability is a probability of a second type of stacking of items.
20. (Previously Presented) The method of claim 19, wherein the first type of stacking of items corresponds to stacking of items in a pile or wall, and wherein the second type of stacking of items corresponds to scattered items over an area.
19. A non-transitory computer readable medium that stores thereon computer-executable instructions that in response to execution by a processor, perform operations comprising:
14. (Currently Amended) A non-transitory computer readable medium that stores thereon computer-executable instructions that in response to execution by a processor, perform operations comprising:
obtaining first point cloud data related to a first three-dimensional image captured by a first sensor device of a robotic item handler;
obtaining second point cloud data related to a second three-dimensional image captured by a second sensor device of the robotic item handler;
transforming the first point cloud data and the second point cloud data to generate a combined point cloud data;
constructing a machine learning model by using a combined point cloud data as an input to a convolution neural network;
constructing a machine learning model by using the combined point cloud data as an input to a convolution neural network;
outputting, by the machine learning model, a decision classification indicative of a first probability associated with a first operating mode of a robotic item handler and a second probability associated with a second operating mode;
outputting, by the machine learning model, a decision classification indicative of a first probability associated with a first operating mode of the robotic item handler and a second probability associated with a second operating mode, wherein:
(i) the first operating mode is associated with picking an item by grasping the item using an end effector of a robotic arm of the robotic item handler, and
(ii) the second operating mode is associated with sweeping a pile of items from an item docking station with a platform of the robotic item handler;
generating a first command to operate the robotic item handler based on the decision classification;
generating a first command to operate the robotic item handler based on the decision classification, wherein the first command is to:
20. The non-transitory computer readable medium of claim 19, wherein the first command is to:
(i) operate the robotic item handler according to the first operating mode in response to the first probability being higher than the second probability; and
(ii) operate the robotic item handler according to the second operating mode in response to the second probability being higher than the first probability.
(i) operate the robotic item handler according to the first operating mode in response to the first probability being higher than the second probability; and
(ii) operate the robotic item handler according to the second operating mode in response to the second probability being higher than the first probability;
evaluating a selection of the operating mode of the robotic item handler based on a predefined heuristic associated with past operations of the robotic item handler;
adjusting a first weight associated with the decision classification and a second weight associated with a pre-defined heuristic associated with past operations of the robotic item handler, based on a performance associated with the output of the machine learning model over a period of time, wherein during initial stages of learning by the machine learning model, the first weight is less than the second weight and after a substantial period of time utilized to train the machine learning model, the second weight is less than the first weight; and
adjusting a first weight associated with the decision classification and a second weight associated with the pre-defined heuristic used for the evaluation of the selection, based on a performance associated with the output of the machine learning model over a period of time, wherein during initial stages of learning by the machine learning model, the first weight is less than the second weight and after a substantial period of time utilized to train the machine learning model, the second weight is less than the first weight; and
generating a second command to operate the robotic item handler based on an evaluation of a selection of one of the first operating mode and the second operating mode using the pre-defined heuristic.
generating a second command to operate the robotic item handler based on the evaluation of the selection.
Allowable Subject Matter
7. Claims 1-20 would be allowable upon filing of a terminal disclaimer to overcome the nonstatutory double patenting rejection set forth in this Office action.
8. The following is a statement of reasons for the indication of allowable subject matter:
The available prior art fails to teach or suggest adjusting a first weight associated with the decision classification and a second weight associated with a pre-defined heuristic associated with past operations of the robotic item handler, based on a performance associated with the output of the machine learning model over a period of time, wherein during initial stages of learning by the machine learning model, the first weight is less than the second weight and after a substantial period of time utilized to train the machine learning model, the second weight is less than the first weight, in combination with the further limitations of the independent claims.
Islam et al. (“Planning, Learning and Reasoning Framework for Robot Truck Unloading”) teaches a method and system for autonomous robot truck unloading that comprises constructing a machine learning model based on point cloud data, outputting, via the machine learning model, a decision classification associated with a first operating mode and a second operating mode, selecting one of the first operating mode and the second operating mode of the robotic item handler based on the decision classification outputted by the machine learning model, operating the robotic item handler according to the selection of the one of the first operating mode and the second operating mode, and controlling the robotic item handler based on an evaluation of the selection. Wicks (US 20150352721) teaches a method, device, and system for a robotic carton unloader that comprises obtaining a second point cloud data to generate combined point cloud data. Mousavian (US 20200361083) teaches a machine learning system that generates grasp poses that can be used by a robot to manipulate an object and comprises constructing a machine learning model based on combined point cloud data as an input to a convolution neural network and applying/adjusting weights to control and refine the output from the machine learning model to attain an accurate grasp.
The combination of Islam, Wicks, and Mousavian fails to teach adjusting a first weight associated with the decision classification and a second weight associated with a pre-defined heuristic associated with past operations of the robotic item handler, based on a performance associated with the output of the machine learning model over a period of time, wherein during initial stages of learning by the machine learning model, the first weight is less than the second weight and after a substantial period of time utilized to train the machine learning model, the second weight is less than the first weight.
Therefore, the combination of features is considered to be allowable.
Conclusion
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/T.M.L./Examiner, Art Unit 3656
/KHOI H TRAN/Supervisory Patent Examiner, Art Unit 3656