Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Claims *** are pending. Claims *** are rejected.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-17 recites the limitation of “the upright container”. There is insufficient antecedent basis for this limitation in the claim.
Claims 8-12 recited “a third 3D camera” but “a first” and “second camera” has not been recited. rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being incomplete for omitting essential elements, such omission amounting to a gap between the elements. See MPEP § 2172.01. The omitted elements are: a first and a second camera.
Also; “a second set of robotic arms” there is no mention of a first set of robotic arms.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claim *** is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Jinandar US 20250123623.
1. A method for feeding a plurality of containers into a filling line, comprising:
identifying a container from said containers, 118; the artificial intelligence system 145 is trained to perform a variety of computer vision tasks, such as identifying an object (container is an object such as a bottle, can, cap, top or container recited in 121). [different objects, items can be introduced for machine learning]
said identified container located proximal to a top of a heap of said containers within a containers bin using a first 3D camera of a 3D vision inspection system; 118; artificial intelligence system 145 is trained to perform a variety of computer vision tasks, such as identifying an object, tracking the position of an object, and/or determining the shape of an object. (objects will be on the top of a heap as no indication of why objects at top would be bypassed or how objects within the heap would be visible); the artificial intelligence system 145 analyzes image data that is captured by the camera 110;
116; The camera 110 may include a variety of camera types, such as an RGB, infrared, and/or 3D camera.
picking the identified container from the containers bin using a first set of robotic arms; 5; The robotic arm uses a vision system with artificial intelligence (AI) feedback to plan paths for picking and/or placing items until the particular task is complete.
placing the picked container onto
one of a (select one of)
conveyor input 128; conveyor 245 may carry a tote 215 for the robot arm 130 to pick items 210 and another conveyor 245 may carry a tote 215 for the robot arm 130 to place items 210.
and (Not selected options but still addressed)
an accumulation table with the container in an upright orientation; (orientation is with accumulation table) 128; another conveyor 245 may carry a tote [accumulation table 184; a conveyor or layer (accumulation) table] 215 for the robot arm 130 to place items 210. 194; determines how the items are placed on the pallet, the orientation [upright orientation] of the items,
identifying a 3D position of an input of a conveyor for transporting the upright container to a filling station using a second 3D camera of the 3D vision system Fig. 2; 230; and (to a filling station, intended us); 194. [items are placed based on orientation i.e. upright] 123; the robot camera 225 may include multiple cameras [1st, 2nd, …nth cameras] to provide multiple viewpoints on the robot
placing the upright container into the conveyor input while maintaining the upright orientation of the container. 128; another conveyor 245 may carry a tote [accumulation table] 215 for the robot arm 130 to place items 210. 194. [items are placed based on orientation i.e. upright]
2. The method of claim 1, wherein the container is one of a bottle and a vial. 115; bags, boxes, parcels, cartons, packages, bottles, cans, and/or other objects.
3. The method of claim 1, wherein the robotic arms comprise six-axis articulated robotic arms. 191; the robot arm can be a 4, 5, 6, or 7 axis robot arm.
4. The method of claim 1, wherein the 3D vision system comprises structured light cameras 116 RGB or infrared, stereo vision cameras 3D camera, or time-of-flight cameras.
5. The method of claim 1, wherein the conveyor input comprises an accumulation table.124,164, 184; 128; another conveyor 245 may carry a tote [accumulation table 184; a conveyor or layer (accumulation) table] 215 for the robot arm 130 to place items 210.
6. The method of claim 1, further comprising rotating the container to correct misalignment.194; the stacking pattern determines how the items are placed on the pallet, the orientation of the items [orientation i.e. alignment], and the number of layers of the items in which the items are stacked. 118; changing the orientation of placing an object.
7. The method of claim 1, wherein machine learning algorithms are used to prioritize the selection of easily accessible container to optimize pick efficiency. 194; a stacking pattern in robotic palletization can be designed to optimize the use of space on the pallet and ensure that the items are stable and secure during transportation. 118; picking an object, and/or placing an object, the artificial intelligence system 145 may utilize a machine learning algorithm to perform such tasks.
8. is rejected using the same rejections as made to claim 1.
9. The method of claim 8, wherein the alignment station is configured to orient the caps in an upward direction. Same as upright, and misalignment of claims 1 and 6.
10. The method of claim 8, wherein the placement of the cap into the capping line is based on positional data from the 3D vision system. 5; the robotic system checks for position/orientation or otherwise monitors items along the travel path.
11. The method of claim 8, wherein the robotic arms are synchronized to minimize processing time between cap pickup and placement.185; “Pickable” generally refers to the quality of ease and/or efficiency [time and fewer movements which is also time] with which an item can be retrieved from a storage location.
12. The method of claim 8, wherein the alignment of caps includes a flipping mechanism controlled by vision-guided robotics to ensure proper orientation. 155; the management system 115 may determine the shape, position, orientation, and/or other characteristics of the end effector 205 and/or the items 210. 118; changing the orientation of placing an object. [flipping mechanism]
13 is rejected using the same rejections as made to claim 1.
13. A system for feeding a plurality of containers and a plurality of caps into a filling line and a capping line, comprising:
a containers bin configured to hold a heap of containers; 128, tote of items. 118; the artificial intelligence system 145 is trained to perform a variety of computer vision tasks, such as identifying an object (container is an object such as a bottle, can, cap, top or container recited in 121). [different objects, items can be introduced for machine learning] Also, 4; The robotic system plans the motions of a robotic arm on-the-fly. In other words, the movement of the robotic arm is not pre-programmed or hard-coded. In this way, the robotic system is adaptable to handle many different types and arrangements of items.
a caps bin configured to hold a heap of caps; 128, tote of items. 118; the artificial intelligence system 145 is trained to perform a variety of computer vision tasks, such as identifying an object (container is an object such as a bottle, can, cap, top or container recited in 121). [different objects, items can be introduced for machine learning]
an alignment station configured to align the cap; 194; the stacking pattern determines how the items are placed on the pallet, the orientation of the items [orientation i.e. alignment], and the number of layers of the items in which the items are stacked. 118; changing the orientation of placing an object.
a 3D vision inspection system comprising at least three 3D cameras; 194. [items are placed based on orientation i.e. upright] 123; the robot camera 225 may include multiple cameras [1st, 2nd, …nth cameras] to provide multiple viewpoints on the robot
a first set of robotic arms configured to pick and align the containers in an upright orientation; 128; another conveyor 245 may carry a tote [accumulation table] 215 for the robot arm 130 to place items 210. 194. [items are placed based on orientation i.e. upright]
and a second set of robotic arms configured to pick and align the caps. 128; another conveyor 245 may carry a tote [accumulation table] 215 for the robot arm 130 to place items 210. 194. [items are placed based on orientation i.e. upright], Fig.1 130 and 135 first and second set of robotic arms.
14. The system of claim 13, wherein the 3D vision inspection system is communicatively coupled to a central computer system configured to control the robotic arms and perform vision-based alignment. Fig. 1 and 2.
15. The system of claim 13, wherein the 3D vision inspection system is trained using artificial intelligence models to detect and classify one of the containers and the caps. 4; The robotic system plans the motions of a robotic arm on-the-fly. In other words, the movement of the robotic arm is not pre-programmed or hard-coded. In this way, the robotic system is adaptable to handle many different types [containers and caps] and arrangements of items.
16. The system of claim 13, further comprising one of a conveyor and an accumulation table positioned downstream of an alignment station. 128; another conveyor 245 may carry a tote [accumulation table] 215 for the robot arm 130 to place items 210. [downstream is after the robot places items]
17. The system of claim 13, wherein the first and second robotic arms operate simultaneously to feed the containers and the caps independently into the filling line and the capping line. Caps are items and can be used to train the robots as recited above. Fig.1 shows 130 and 135 first and second robotic arms operating simultaneously.
Conclusion
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/SIHAR A KARWAN/Examiner, Art Unit 3664
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