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
Priority
Acknowledgment is made of applicant's claim for domestic benefit based on provisional application 63/404,313 filed on September 7, 2022.
DETAILED ACTION
Claims 1 - 15 are pending in the application.
Claims 1 and 9 are independent.
This action is Final based on a new 35 U.S.C. §103 prior art reference(s) that was necessitated by the applicant’s amendment; see MPEP §706.07(a).
Given the amended specification, the objection is rescinded.
Given the amended claims, the previous 35 U.S.C. 112(b) rejections and the 35 U.S.C. 112(f) claim interpretation(s) are rescinded. However, new 35 U.S.C. 112(b) rejections are stated below given the amended claims.
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.
Claims 1 – 15 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, regards as the invention. Claims 1 and 9 recites the limitation "movable elements.” However, the second reference to this element in claims 1 and 9 and the specification refer to this element as a “moving” element. The term “movable” element has a different meaning from “moving” element and may be new matter. Dependent claims 2 – 8 and 10 – 15 depend from claims 1 and 9 and are also rejected under 35 U.S.C. 112(b). Appropriate action is required.
Claims 1 – 15 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, regards as the invention. Claims 1 and 9 recites the limitation "tooling.” However, this element is not defined by the claim or the specification, and one having ordinary skill may not understand the meaning of this term as stated in the claim. Dependent claims 2 – 8 and 10 – 15 depend from claims 1 and 9 and are also rejected under 35 U.S.C. 112(b). Appropriate action is required.
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.
Claim 1, 2, 3, 5, 6, 8 – 10, 12, 13, and 15 are is rejected under 35 U.S.C. 103 as being unpatentable over Matl et al. (PG Pub. No. 20220048707), herein “Matl,” in view of Artigas et al. (PG Pub. No. 20200368861), herein “Artigas.”
Regarding claim 1,
Matl teaches a method for dynamic nest and routing adjustment in an automation system, the method comprising: (Abstract: “A system and method for a dynamic robot kitting line that can include: processing a set of order requests and setting a packing fulfillment plan process for a robotic kitting line, wherein robotic kitting line comprises a conveyor system…” Par. 0002.)
operating the automation system; (Par. 0023: “In one variation, the system and method may additionally or alternatively involve the design and operation of an automated robotic kitting system, such as described herein, that is customized for streamlined fulfilling packing orders that can include various quantities of a variety of item types.”)
receiving operational data related to the automation system from a conveyor system and at least one automation station; (Par. 0102: “The packing control system can additionally be in communication with a sensing system which may be monitoring item bins, tote position, item placement in totes, item position in item bins, and/or any suitable aspect. The system may include a sensing system which can include one or more types of sensors such as camera/imaging devices, proximity sensors, contact sensors, and/or other suitable types of sensors.” Par. 0114: “The digital orders will generally convey what items are to be collected together for an order. The digital orders may additionally specify timing or priority properties for a particular order. The digital orders may additionally have item arrangement details, and/or other constraints or details related to packing of items for an order.” Par. 0115: “Configuration of the packing control system for processing the set of order requests and setting the packing fulfillment plan process for the robotic kitting line may include a variety of computer-implemented processes that determine properties of the packing fulfillment plan. The set of computer-implemented processes used and the objectives of those processes may use application dependent optimization processes.” See also Par. 0092.
analyzing the operational data to determine if adjustment of nest offsets or routing of parts is required; determining an adjusted nest offset or part routing based on the operational data; (Par. 0116: “In one example, a kitting usage scenario may have the configuration of the packing control system 130 evaluate conditions of the robotic kitting line and the order requests to minimize swaps of item bins and to enhance throughput in processing the kits. The packing control system 130 may implement one or more analysis processes to output order-to-tote assignments and/or item bin positioning to enhance balance of work performed by the robotic workcells (e.g., reducing instances when one robotic workcell is holding up work by other robotic workcells) and to reduce changes to item bin positioning (e.g., reducing time and labor involved in updating item bins).” Par. 0187: “Managing operation of the robotic kitting line may additionally include various processes related to the sensing the state of the robotic kitting line such as monitoring item bin status and monitoring item placement success (e.g., detecting grasping errors). Sensing the state of the robotic kitting line may be performed through various sensing approaches. In one preferred approach, sensing state includes collecting image data and processing the image data to determine status information. This may include performing computer vision on visual image data. This may alternatively include collecting a depth image (or other 3D or other forms of depth information) and performing some analysis on this multi-dimensional image information. For example, the quantity of items in an item bin may be estimated based on analysis of the depth map of the contents in the item bin.” See also Par. 0026, 0103, 0164, and 0174. Examiner Note – Specification paragraph 0010 and 0011 define nest offset as by controlling an accessory on a pallet of the conveyor system or by controlling a moving element of the conveyor system.)
implementing the adjusted nest offset or part routing via a configuration module of the automation system; and return to operating the automation system. (Par. 0096: “In one variation, the conveyor system 120 may include a tote hold system, which functions to divert a tote to a holding station as shown in FIG. 5. A robotic workcell 110 may each have a tote hold system with a plurality of holding stations. As with the item bins and the totes, the totes in the holding station are preferably within a reachable area of the robotic system 111. A tote diverter mechanism could be a directionally controlled conveyor system, a piston, or other mechanism to push or redirect an item tote 121 elsewhere or any suitable mechanism to move a tote between a position on a conveyor system and a holding station. In some cases, the tote hold system may be used to temporarily hold a tote. The tote hold system could also be used to reorganize or adjust the order of totes. Alternatively, the robotic system 111 may be configured to selectively manipulate an item tote 121 and divert the tote between the conveyor system 120 and a hold station.” See also Par. 0129, 0131, 0165, 0170, and 0171.)
Matl may implicitly teach but does not explicitly teach the amended portions of individual mov[ing] elements and a nest offset that is a position correction of the moving element. However, Artigas does teach the automation system (Par. 0009: “higher degree of robot automation in assembly lines, and particularly automotive final assembly lines…”) comprising a conveyor system having a plurality of individually movable (moving) elements (moving robots on a track and assisting a conveyor) configured to carry parts between automation stations, (Par. 0003: “An assembly robot for such an assembly line may include an end effector carrying a tool or carrying an assembly element to be attached or mounted to the vehicle body, and the end effector of the robot can be controlled to perform a certain operation in a target position of the vehicle body. In some cases the robot may be driven on tracks parallel to the assembly line.” Par. 0178: “…methods and assembly units as disclosed herein, one industrial robot or several industrial robots may be employed, either working jointly, synchronised, or independently from each other. When several robots are employed, they may each have an associated vision system, or they may all be controlled using data from the same vision system.” Par. 0182: “The robots may be directly fixed to a floor through the robot base or it may be mounted on a supporting structure, roof mounted, mounted on a track, etc.” Par. 0055.)
wherein a nest offset is a positional correction amount for a moving element or an accessory on the moving element to compensate for tolerance differences among parts, tooling, or the accessory. (Par. 0039: “As mentioned above, the assembly methods of the present disclosure are particularly efficient with parts that move continuously, because the visual servoing and compliant behaviour provide the ability to at least partly compensate in real time positioning errors, arising from multiple sources, between the end effector of the robot (or an assembly element carried by the end effector) and the part, and therefore the continuous advance of the part is not a major hurdle for the accuracy of the assembly operation.” Par. 0034: “In methods for assembling parts disclosed herein, the movement of the assembly robot and its end effector, which may e.g. carry an element to be assembled on the part at the target area thereof, is done by performing a visual servoing process in real time, using data from a vision system that monitors, directly or indirectly, the actual position of the target area of the vehicle body or other part.” Par. 0035: “Such real time visual servoing based on the monitoring of the target area allows at least partly compensating errors due to tolerances in tracking the advance of the part (e.g. due to the resolution of an encoder tracking the conveyor motion), as well as errors with respect to the intended trajectory of the part due to vibrations or other uneven movement of the conveyor or other transport system, as well as errors due to differences between the CAD design of the part and the physical shape/features of the part, and positioning errors of the part on the conveyor. Furthermore, this may be achieved without stopping the advance of the part.” See also Par. 0007, 0009, 0021, 0060, and 0073. See also Par. 0013, 0024, 0107 and Clause 7 – processing of data for compensation or synchronization.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have combined the method and system of dynamic adjustment of a control element or a robot of a conveyor system wherein the system uses an analysis process to determine bin positioning as in Matl with a system and method that incorporates a moving element such as multiple robots on a track used in a conveyor system wherein the robot that holds the parts is positionally corrected based on the tolerances of the system and parts as in Artigas in order to provide a higher degree of automation in assembly lines by overcoming errors, tolerances, and inaccuracies arising from the continuous motion of parts. (Par. 0004 and 0009
Regarding claim 2,
The previously cited reference(s) teach the limitations of claim 1 which claim 2 depends. Matl also teaches that wherein the analyzing and determining are performed by a machine learning (ML) module. (Par. 0026: “Related to the variety of items, the planned fulfillment of the orders implemented through the system and method can be based at least in part on the predictive data modeling for robotic pick-and-place handling. For example, planned packing of items can differ depending on if the machine learning models indicate higher confidence or lower confidence in handling the item. For example, grasp planning data modeling (based on computer vision-based analysis of items) may have higher confidence for items with packaging that visually are classified to be easier to grasp compared to an item with visual characteristics where grasping is challenging or where it is unfamiliar. The state of AI or machine learning models as it relates to items in the orders can alter how the system and method plan out distributing items across the various bins.” See also paragraphs 0033, 0106, 0143, 0166, 0172, and 0200. Examiner’s Note – Artigas may also teach a learning element in Par. 0105 and 0106.)
Regarding claim 3,
The previously cited reference(s) teach the limitations of claim 2 which claim 3 depends. Matl also teaches that further comprising providing feedback to update the ML module based on operational data received after the implementing. (Par. 0143: “The method is preferably implemented in combination with a robotic kitting system such as the one described above but may alternatively be used in combination with any suitable robotic kitting system that includes a plurality of robotic picker systems arranged along the length of a conveyor system, and where each robotic picker system has a plurality of item bins within grasping range. Preferably, there is a redundancy of at least a subset of item bins for one type of item. The method can adapt to different and new packing order objectives. The method may additionally or alternatively adapt operation of the robotic kitting line to the real-time conditions such as: updates to training of machine learning models used in grasp planning of a robotic system, success rates of packing a type of item, configuration of a robot (e.g., type of end effector), and/or other conditions.”)
Regarding claim 5,
The previously cited reference(s) teach the limitations of claim 1 which claim 5 depends. Matl also teaches that the automation system includes a plurality of automation stations and the receiving operational data comprises receiving data relating to the failure of at least one of the plurality of automation stations, indicating a need for an adjustment in routing of parts. (Par. 0112: “Another potential packing fulfillment objective could be to reduce dependence on post-processing correction. This objective may optimize for higher probabilities of the system successfully automatically resolving packing mistakes. For example, this objective could result in more redundant item bins being placed downstream so that placement errors can be automatically corrected if an upstream robotic workcell 100 fails to properly place an item.” See paragraphs 0125, 0129 – 0131, 0164, 0165, 0171, 0187, 0193 – 0195.)
Regarding claim 6,
The previously cited reference(s) teach the limitations of claim 1 which claim 6 depends. Matl also teaches that the receiving operational data comprises receiving vision data of the at least one automation station, indicating a need for an adjustment of nest offsets. (Par. 0026: “Related to the variety of items, the planned fulfillment of the orders implemented through the system and method can be based at least in part on the predictive data modeling for robotic pick-and-place handling. For example, planned packing of items can differ depending on if the machine learning models indicate higher confidence or lower confidence in handling the item. For example, grasp planning data modeling (based on computer vision-based analysis of items) may have higher confidence for items with packaging that visually are classified to be easier to grasp compared to an item with visual characteristics where grasping is challenging or where it is unfamiliar. The state of AI or machine learning models as it relates to items in the orders can alter how the system and method plan out distributing items across the various bins.” Par. 0132: “The sensing system functions to collect data of the objects and the environment. The sensing system preferably includes an imaging system, which functions to collect image data. The imaging system preferably includes at least one imaging device with a field of view of a region of interest within the robotic kitting line such as the item bins and/or the totes on the conveyor system 120. The imaging system may additionally include multiple imaging devices used to collect image data from multiple perspectives of a distinct region, overlapping regions, and/or distinct non-overlapping regions. The set of imaging devices (e.g., one imaging device or a plurality of imaging devices) may include a visual imaging device (e.g., a camera). The set of imaging devices may additionally or alternatively include other types of imaging devices such as a depth camera. Other suitable types of imaging devices may additionally or alternatively be used.” Par. 0187.)
Regarding claim 8,
The previously cited reference(s) teach the limitations of claim 1 which claim 8 depends. Matl also teaches that the adjustment of nest offset is performed by controlling a moving element of the conveyor system. (Par. 0101: “The packing control system 130 as discussed can be communicatively coupled to the robotic system in of each robotic workcell 110 and the conveyor system 120. In one variation, the packing control system 130 can be fully control in control of both the robotic workcell no and the conveyor system 120. In another variation, the packing control system 130 can be in control of the robotic workcell no and an observer of a conveyor system that is controlled external to the system. For example, the system may operate around a conveyor line that is continuously operated, pre-configured to move in a certain way, or controlled by another control system. The packing control system 130 may alternatively be interested with the robotic workcells 110 and/or the conveyor system 120 in any suitable configuration.” Par. 0123: “Configuration to direct loading of the set of item bins may include configuration to direct an automated item delivery robot to transport an item bin to a specified location of a workcell within the set of workcells 110. This can include communicating to an item delivery robot. This may alternatively include actively controlling an item delivery robot.” Par. 0038, 0084, 0088, 0098, 0099, and 0101.)
Regarding claims 9 and 10, they are directed to a system or apparatuses to implement the method of steps set forth in claims 1 - 3. Matl and Artigas teach the claimed method of steps in claims 1 – 3. Claim 9 teaches a machine learning module which is embodied in claim 2. Therefore, Matl and Artigas teach the system or apparatuses to implement the claimed method of steps in claims 9 and 10.
Regarding claims 12, 13, and 15, they are directed to a system or apparatuses to implement the method of steps set forth in claims 5, 6, and 8. Matl and Artigas teach the claimed method of steps in claims 5, 6, and 8. Therefore, Matl and Artigas teach the system or apparatuses, to implement the claimed method of steps, in claims 12, 13, and 15.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Matl in view of Artigas in further view of Park (PG Pub. No. 20230205147), herein “Park.”
Regarding claim 7,
The previously cited reference(s) teach the limitations of claim 1 which claim 7 depends. Matl does not teach (however, Artigas may implicitly teach) controlling an accessory (robot and robotic arm) on a conveyor or rail. However, Park does teach that the adjustment of nest offset is performed by controlling an accessory on a pallet of the conveyor system. (Par. 0020: “…the controller may control a movement of the transfer robot such that the transfer robot performs a loading or unloading operation of the substrate in/from the slots of the transfer target object along the position coordinates for each slot.” Park shows in figure 5 an accessory on a pallet (base item 810) that moves on a conveyor (rail 143). See also claim 8 or Park.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have combined the method and system of dynamic adjustment of a control element of a conveyor system wherein the system uses an analysis process to determine bin positioning as in Matl with control the robotic device that is positioned on a pallet (body or base) that moves along a conveyor system (rail) as in Park in order to have a method of have transfer equipment be capable of precise teaching through vision processing by a rail system (Par. 0005)
Allowable Subject Matter
Claims 11 and 4 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims pending resolving all intervening issues such as the 35 U.S.C. §112(b) rejections above. Reasons for allowance will be held in abeyance pending final recitation of the claims. The prior art does not disclose: the elements of the base claim(s) and wherein the wherein the ML processor comprises a current ML model and the ML processor module is configured to: analyze input using the current ML model to determine whether the current ML model can provide an output result based on the input; if the current ML model cannot provide an output result, request further input, determine if the input is sufficient, and when the input is sufficient, update the ML model; and if the current ML model can provide an output result, output the result.
Response to Arguments
Applicant’s arguments with respect to all claims have been considered but may be moot because the arguments do not apply in light of the new reference being used in the current rejection necessitated by amendment. Specifically, the new reference, Artigas teaches overlapping elements of Matl of independent claims 1 and 9 and teaches the amended elements as rejected. Artigas teaches a robot on a moving track (conveyor) wherein the robot compensates for tolerances and errors in parts and/or gripper. The amendments contain a few unclear or indefinite elements as explained above. Given the new reference and the new 35 USC 112(b) rejections, the application is not in condition for allowance at this time.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Stubbs et al. (US PG Pub. No. 20170080566) is related to the instant application and teaches robotic manipulators that are coupled with a convey system/belt (item 130). Paragraph 0074 also teaches analyzing manipulation data of the robot stations and uses machine learning algorithm to collected data used to effect the use of a robot arm (item 110) on the conveyor.
Nguyen et al. (PG Pub. No. 20210064937), herein “Nguyen” is pertinent to the claim invention and teaches only some elements of amended claims 4 and 11.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHAD G ERDMAN whose telephone number is (571)270-0177. The examiner can normally be reached Mon - Fri 7am - 3pm or 4pm EST..
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kenneth Lo can be reached at (571) 272-9774. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHAD G ERDMAN/Primary Examiner, Art Unit 2116
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