DETAILED ACTION
Receipt of applicant’s amendment the claims filed 2/2/2026 is acknowledge. Claims 1 and 13 have been amended to incorporate the limitation previously recited in claim 11. Claim 11 has been canceled. Claims 1-10 and 12-17 are discussed on the merits below. Claims 18 - 20 are withdrawn.
Response to Arguments
Applicant argues “Kanunikov does not disclose a plurality of distinct grippers wherein the orientation of the grippers is adjustable to accommodate an irregular surface” (see Remarks page 8 of 9).
This argument is not persuasive. Examiner maintains that Kanunikov discloses a plurality of grippers because Kanunikov explicitly discloses “it is understood that the end-effector 304 can have a different configuration. For example, the end-effector 304 can have a suction pad with integrated suction channels, a pincher type gripping device, or any other type of gripping system for grabbing objects” ([0051], emphasis added). A “pincher type gripping device” is understood to include at least two fingers, each of which may be considered one gripper in the same manner as applicant’s end-effector 212 (i.e. Fig. 3, page 8, lines 11-17: “a mechanical gripper…in the form of fingers gripper which comes with a plurality of fingers 214”), or in a similar manner as applicant’s gripper subassembly 400 (i.e. Fig. 4A described on page 14, lines 1-3: “the needle base 422 can be mounted onto a respective mounting plate 412 and the mounting plate can be pivotable about a pivoting axis 414”. Note that “needles” are not recited in claim 1 and the mounting plates 420 are analogous to fingers 214 of a gripper). Kanunikov’s disclosure of a “pincher type gripping device” also addresses “the orientation [of the grippers] being adjustable” since the fingers of a pincher must pivot, rotate, or otherwise reorient themselves relative to each other in order to achieve the pinching functionality. Finally, “to accommodate an irregular surface” is not only an inherent characteristic of a pincher type gripping device but explicitly addressed by Kanunikov in, for example, [0077] which discloses accounting for “unexpected real-world conditions (e.g., partially-opened containers and/or warped container walls)”. Hence, Examiner maintains that Kanunikov reads on claims 1 and 13.
Applicant argues “Werni does not teach a plurality of such grippers mounted together, nor does it teach that their orientation is adjustable to accommodate irregular surfaces” (see Remarks page 8 of 9).
Since Werni is not relied upon to reject claim 1 or 13, the relevance of this argument is uncertain. However, to the extent that Werni is relied upon for dependent claim 12, Examiner maintains that Kanunikov discloses “a plurality of grippers” which are adjustably oriented to accommodate irregular surfaces as explained above. The claims do not recite “mounted together”. Werni is cited merely to teach a “needle” gripper as further defined in claim 12.
Applicant argues “there is no motivation in the prior art to modify Kanunikov's box-handling robot with a complex, multi-gripper, adjustable-orientation end-effector. ... Introducing an adjustable-orientation multi-gripper (useful for soft, irregular lumps like laundry bags) would likely render Kanunikov's system unsuitable for its intended purpose” (see Remarks page 8 of 9).
This argument is not persuasive. As noted above, no modification is needed to reject claims 1 and 13 because Kanunikov explicitly discloses that the end-effector may be a pincher type gripping device. However, to the extent that this argument is relevant to claim 12, it is unpersuasive. The proposal to modify Kanunikov to utilize a needle griper (i.e. to include needles in the gripper, or to replace the generic gripper with a needle gripper) does not render Kanunikov inoperable for its intended purpose; rather it would simply allow the end-effector to handle softer/penetrable materials (e.g. cardboard boxes, wooden crates, etc.).
Therefore, applicant’s arguments are not persuasive and the prior art rejections have been maintained.
Claim Objections
Claim 12 objected to because of the following informalities: Claim 12 recites “the gripper” where it should state “the gripper module”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 5 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
With regard to claim 5, the claim recites “A learning assisted robotic system in accordance with claim 1, wherein the end-effector is arranged to pick and release the target object respectively.” However, claim 1 has been amended to recite “wherein the robotic module includes an end-effector comprising a gripper module configured to pick and release the target object” (lines 5-6). The addition of the word “respectively” in claim 5 does not perceptibly narrow the scope of the invention already claimed in claim 1. Therefore, claim 5 is an improper dependent claim because it does not further limit claim 1. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim 1 – 8, 13 – 14, and 16 – 17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 20210129334 Kanunikov et al.
With regard to claim 1, Kanunikov discloses: .
A learning assisted robotic system for transferring one or more target objects (see fig. 3), comprising:
a robotic module arranged to transfer a target object from a starting position to a destination (see fig. 1 and 3 showing a transport robot), at least one of the starting position and the destination being a three-dimensional environment at least partially enclosed (see fig. 1 showing robot 102 retrieving an item (starting position) and robot 104 placing an item in a bin/box/case; see also [0030] The tasks can be combined in sequence to perform an operation that achieves a goal, such as to unload objects from a truck or a van and store them in a warehouse or to unload objects from storage locations and prepare them for shipping. For another example, the task can include placing the objects on a target location (e.g., on top of a pallet and/or inside a bin/cage/box/case)) and having an entrance through which being accessible by the robotic module (see fig. 1 robot 102 entering the rear of the truck; see fig. 3 item 308 showing a placement cart/cage in which robot 302 places object), wherein the robotic module includes an end-effector comprising a gripper module configured to pick and release the target object, the gripper module comprising a plurality of grippers with the orientation being adjustable ([0051]: the end-effector 304 can have … a pincher type gripping device … for grabbing objects, where a “pincher type” gripper is understood to have a plurality of fingers/grippers which reorient relative to each other to achieve the pinching functionality) to accommodate the target object with irregular surface ([0077]: account for unexpected real-world conditions (e.g., partially-opened containers and/or warped container walls)); and
a learning module arranged to learn the three-dimensional positions of the entrance and the target object based on one or more training data sets (See Fig. 4A-4D; fig. 6, Fig. 7C, and figs. 9A and 9B; see also [0036] discussing acquiring sensor reading to detect the drop location and to identify the target object; [0047]. [0054] “the robotic system 100 can obtain real-time images of the actual placement platforms (e.g., carts and/or cages) as they are placed during operation of the robotic system 100. The robotic system 100 can analyze the real-time images to detect abnormalities in the placement platforms, such as reduction in a placement zone (e.g., in comparison to a predetermined or an expected space) caused by partial-opening, misalignment, and/or warpage in the vertical walls”, [0064] – [0065] describing a model of the placement platform 308 being updated dynamically; [0067], [0071], [0131] discussing recognizing the support walls as potential obstacles; [0143] discussing dynamically generating a models when the contain/bin is recognized to not be as expected),
wherein the learning module is further arranged to derive a navigational path based on the learnt three-dimensional positions whereby the robotic module is operable to navigate through the derived navigational path to transfer the target object from the starting position to the destination (see fig. 9A and 9B illustrating planning of path 901 to avoid the container walls and place the object; [0022] “The motion plan can correspond to operations of the robotic units to approach an object at its starting location, grip the object with the end-effector, lift and transfer the object to its placement location, and release/place the object at the placement location.”; [0125] – [0128] describing overlying the models to prevent interference with, inter alia, the walls of the cart/bin; see also [0131], [0134] –[ 0136] discussing using the model of the cart/bin to ensure an interference free path of the manipulator and end effector.).
With regard to claim 2, Kanunikov discloses:
further comprising a sensing unit arranged to capture the data associated with the position and orientation of the object proximate to the robotic module and the training data set includes the captured data by the sensing unit (see fig. 3 items 310, 312, 314; [0036], [0054]).
With regard to claim 3, wherein the robotic module is arranged to navigate to an intermediate position from an initial position and the sensing unit is arranged to capture the data associated with the position and orientation of the object proximate to the robotic module whereby the learning module is arranged to derive the further movement of the robotic module forming part of the navigational path based on the captured data by the sensing unit at the initial position and the intermediate position respectively (see fig. 11 step 1104 and fig. 12; [0150] discussing generating the model prior to the real-time operation; [0210] explaining the additional adjustment to the models being during the real-time operation; [0202] “In other words, the robotic system 100 may obtain the images depicting the container before any objects are placed therein or after placing one or more objects.”)
With regard to claim 4, wherein the learning module is further configured to estimate the depth of the partially enclosed three-dimensional environment beyond the entrance based on the learnt three-dimensional positions and the robotic module is arranged to navigate into the partially enclosed three-dimensional environment based on the estimated depth (see fig. 9A and 9B; see fig. 11 especially steps 1142, 1144; [0191], [0131], [0134], [0135]).
With regard to claim 5, Kanunikov discloses:
wherein the end-effector is arranged to pick and release the target object respectively (fig. 1 and 3).
With regard to claim 6, Kanunikov discloses:
wherein the learning module is further configured to determine the maneuverable space within the partially enclosed three-dimensional environment and to determine a pick area beyond the entrance and proximate to the target object based on the learnt three-dimensional positions and the robotic module is arranged to estimate the pose for placing the end-effector and navigate the end-effector to the determined pick area. (fig. 1, [0022], [0035], [0036])
With regard to claim 7, Kanunikov discloses:
wherein the learning module is further configured to estimate the pose of the target object and the robotic module is arranged to position the end-effector proximate to the target object and pick up the target object (see [0035]).
With regard to claim 8, Kanunikov discloses:
wherein the learning module is further configured to determine the maneuverable space between the entrance and a further three-dimensional environment and the robotic module is arranged to navigate the end-effector to release a picked target object to the further three-dimensional environment (fig. 3, 9A, and 9B; [0022]).
With regard to claim 11, Kanunikov discloses:
wherein the end-effector further includes a gripper module configured to pick and release the target object, the gripper module comprising a plurality of grippers with the orientation being adjustable to accommodate target object with irregular surface (see fig. 3).
With regard to claim 13, Kanunikov discloses:
A learning assisted method of transferring one or more target objects, comprising the steps of:
learning the three-dimensional positions of the entrance of an at least partially enclosed three-dimensional environment and a target object based on one or more training data sets sets (See Fig. 4A-4D; fig. 6, Fig. 7C, and figs. 9A and 9B; see also [0036] discussing acquiring sensor reading to detect the drop location and to identify the target object; [0047]. [0054] “the robotic system 100 can obtain real-time images of the actual placement platforms (e.g., carts and/or cages) as they are placed during operation of the robotic system 100. The robotic system 100 can analyze the real-time images to detect abnormalities in the placement platforms, such as reduction in a placement zone (e.g., in comparison to a predetermined or an expected space) caused by partial-opening, misalignment, and/or warpage in the vertical walls”, [0064] – [0065] describing a model of the placement platform 308 being updated dynamically; [0067], [0071], [0131] discussing recognizing the support walls as potential obstacles; [0143] discussing dynamically generating a models when the contain/bin is recognized to not be as expected);
deriving a navigation path for a robotic module based on the learnt three-dimensional positions; and navigating the robotic module through the derived navigational path to transfer the target object from the starting position to the destination (see fig. 9A and 9B illustrating planning of path 901 to avoid the container walls and place the object; [0022] “The motion plan can correspond to operations of the robotic units to approach an object at its starting location, grip the object with the end-effector, lift and transfer the object to its placement location, and release/place the object at the placement location.”; [0125] – [0128] describing overlying the models to prevent interference with, inter alia, the walls of the cart/bin; see also [0131], [0134] –[ 0136] discussing using the model of the cart/bin to ensure an interference free path of the manipulator and end effector.) using an end-effector comprising a gripper module configured to pick and release the target object, the gripper module comprising a plurality of grippers with the orientation being adjustable ([0051]: the end-effector 304 can have … a pincher type gripping device … for grabbing objects, where a “pincher type” gripper is understood to have a plurality of fingers/grippers which reorient relative to each other to achieve the pinching functionality) to accommodate the target object with irregular surface ([0077]: account for unexpected real-world conditions (e.g., partially-opened containers and/or warped container walls)).
With regard to claim 14, Kanunikov discloses:
A learning assisted method in accordance with claim 13, further comprising the steps of:
capturing the data associated with the position and orientation of the object proximate to the robotic module; retrieving at least part of the training data set from the captured data; and learning the three-dimensional positions of the entrance of the partially enclosed three-dimensional environment and the target object based on one or more training data sets (See Fig. 4A-4D; fig. 6, Fig. 7C, and figs. 9A and 9B; see also [0036] discussing acquiring sensor reading to detect the drop location and to identify the target object; [0047]. [0054] “the robotic system 100 can obtain real-time images of the actual placement platforms (e.g., carts and/or cages) as they are placed during operation of the robotic system 100. The robotic system 100 can analyze the real-time images to detect abnormalities in the placement platforms, such as reduction in a placement zone (e.g., in comparison to a predetermined or an expected space) caused by partial-opening, misalignment, and/or warpage in the vertical walls”, [0064] – [0065] describing a model of the placement platform 308 being updated dynamically; [0067], [0071], [0131] discussing recognizing the support walls as potential obstacles; [0143] discussing dynamically generating a models when the contain/bin is recognized to not be as expected; see fig. 9A and 9B illustrating planning of path 901 to avoid the container walls and place the object; [0022] “The motion plan can correspond to operations of the robotic units to approach an object at its starting location, grip the object with the end-effector, lift and transfer the object to its placement location, and release/place the object at the placement location.”; [0125] – [0128] describing overlying the models to prevent interference with, inter alia, the walls of the cart/bin; see also [0131], [0134] –[ 0136] discussing using the model of the cart/bin to ensure an interference free path of the manipulator and end effector).
With regard to claim 16, Kanunikov disclose:
the steps of:
learning the three-dimensional positions of the entrance of an at least partially enclosed initial three-dimensional environment and a target object positioned within the initial three-dimensional environment based on one or more training data sets (fig. 1; [0035] , [0036]);
deriving a first navigation path for a robotic module based on the learnt three-dimensional positions; navigating the robotic module into the initial three-dimensional environment from an initial position through the derived first navigational path to pick the target object ([0022]);
learning the three-dimensional position of a destinated three-dimensional environment based on one or more training data sets; deriving a second navigation path for the robotic module based on the learnt three-dimensional positions (See Fig. 4A-4D; fig. 6, Fig. 7C, and figs. 9A and 9B; see also [0036] discussing acquiring sensor reading to detect the drop location and to identify the target object; [0047]. [0054] “the robotic system 100 can obtain real-time images of the actual placement platforms (e.g., carts and/or cages) as they are placed during operation of the robotic system 100. The robotic system 100 can analyze the real-time images to detect abnormalities in the placement platforms, such as reduction in a placement zone (e.g., in comparison to a predetermined or an expected space) caused by partial-opening, misalignment, and/or warpage in the vertical walls”, [0064] – [0065] describing a model of the placement platform 308 being updated dynamically; [0067], [0071], [0131] discussing recognizing the support walls as potential obstacles; [0143] discussing dynamically generating a models when the contain/bin is recognized to not be as expected),; and
navigating the robotic module to the destinated three-dimensional environment through the derived second navigational path to release the picked target object (see fig. 9A and 9B illustrating planning of path 901 to avoid the container walls and place the object; [0022] “The motion plan can correspond to operations of the robotic units to approach an object at its starting location, grip the object with the end-effector, lift and transfer the object to its placement location, and release/place the object at the placement location.”; [0125] – [0128] describing overlying the models to prevent interference with, inter alia, the walls of the cart/bin; see also [0131], [0134] –[ 0136] discussing using the model of the cart/bin to ensure an interference free path of the manipulator and end effector.).
With regard to claim 17, Kanunikov discloses:
A learning assisted method in accordance with claim 14, further comprising the steps of:
learning the three-dimensional positions of the entrance of an initial three-dimensional environment and a target object positioned within the first three-dimensional environment based on one or more training data sets (fig. 1; [0035] , [0036]);;
deriving a first navigation path for a robotic module based on the learnt three-dimensional positions, navigating the robotic module into the initial three-dimensional environment through the derived first navigational path to pick the target object ([0022]);
learning the three-dimensional position of the entrance of an at least partially enclosed destinated three-dimensional environment based on one or more training data sets; deriving a second navigation path for the robotic module based on the learnt three-dimensional position; (See Fig. 4A-4D; fig. 6, Fig. 7C, and figs. 9A and 9B; see also [0036] discussing acquiring sensor reading to detect the drop location and to identify the target object; [0047]. [0054] “the robotic system 100 can obtain real-time images of the actual placement platforms (e.g., carts and/or cages) as they are placed during operation of the robotic system 100. The robotic system 100 can analyze the real-time images to detect abnormalities in the placement platforms, such as reduction in a placement zone (e.g., in comparison to a predetermined or an expected space) caused by partial-opening, misalignment, and/or warpage in the vertical walls”, [0064] – [0065] describing a model of the placement platform 308 being updated dynamically; [0067], [0071], [0131] discussing recognizing the support walls as potential obstacles; [0143] discussing dynamically generating a models when the contain/bin is recognized to not be as expected);
and
navigating the robotic module into the destinated three-dimensional environment through the derived second navigational path to release the picked target object (see fig. 9A and 9B illustrating planning of path 901 to avoid the container walls and place the object; [0022] “The motion plan can correspond to operations of the robotic units to approach an object at its starting location, grip the object with the end-effector, lift and transfer the object to its placement location, and release/place the object at the placement location.”; [0125] – [0128] describing overlying the models to prevent interference with, inter alia, the walls of the cart/bin; see also [0131], [0134] –[ 0136] discussing using the model of the cart/bin to ensure an interference free path of the manipulator and end effector.).
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.
Claims 9 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over US 20210129334 Kanunikov et al. in view of US 2021/0178593 Ye et al.
With regard to claim 9, Kanunikov discloses:
wherein the sensing unit further includes a depth camera unit arranged to capture one or more images associated with the three-dimensional environment (see [0035], [0036])
Kanunikov does not disclose that “the depth camera unit being movable with respect to a base carrying the robotic module.
However, Ye discloses a camera mounted on a robot which is movable with respect to the base of the robot (see fig. 3A [0004]; fig. 5A and [0050] discussing the camera mounted on the robot arm)
Therefore, prior to the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to modify Kanunikov to have a camera on the robot arm. Such a modification would provide a camera the moves with the robot, observes the end effector and can be moved close to objects to increase detail (fig. 8A, [0076], [0077]).
With regard to claim 15, Kanunikov discloses:
the steps of:
capturing the data associated with the position and orientation of the object proximate to the robotic module at an initial position of the robotic module (see [0035], [0036])
Kanunikov does not disclose:
navigating the robotic module to an intermediate position from the initial position;
capturing the data associated with the position and orientation of the object proximate to the robotic module at the intermediate position of the robotic module; and
deriving a further movement of the robotic module based on the captured data at the initial position and the intermediate position respectively.
However, Ye discloses a camera mounted on a robot (see fig. 5A item 3200); Additionally, Ye discloses taking an image at an initial position to perform initial processing of the image (see fig. 5A, 5B, 6 and 7) and the moving the robot to a second position, acquiring additional image information with more detail and process the second image to add more detail to the environmental data and to acquire details from different perspective. (see fig. 8A, 8B, 8C, 9; [0076] – [0077], [0081]).
Additionally, Kanunikov discloses, initially acquiring environmental data, including an image, and processing the data to determine a task plan for picking and placing and object (see fig. 11 and associated text as cited in the claims above). Kanunikov further discloses, one the task has begun, acquiring additional image information to adjust the task plan based on unexpected details (see fig. 12 and associated text as cited in the claims above)
Therefore, prior to the effective filing date of the invention, it would have been obvious to one of ordinary skill in the to modify Kanunikov by adding a camera to the robot arm near the end effector. And to move the robot arm with the camera to acquire images from additional positions in the environment. This modification would have allowed for acquiring image data from different perspectives as well as more detailed data from closer to objects thus allowing for a more detailed and accurate model of the environment.
Claims 10 is rejected under 35 U.S.C. 103 as being unpatentable over US 20210129334 Kanunikov et al. in view of US 2012/0283875 Klumpp et al.
With regard to claim 10, Kanunikov discloses all of the elements of claim 2 as discussed above.
Kanunikov does not disclose but Klumpp does disclose: wherein the sensing unit further includes a compliant end-effector arranged to contact a target object and the data associated with the contact force of the data forms at least part of the training data set (see at least abstract, claim 1; fig. 5, [0048], [[0050])
Therefore, prior to the effective filing data of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify Kanunikov to detect contact with an object and contours of the work piece based on monitoring a contact force (as suggested by Klumpp). Such a modification would verify contact with an objected as well as allow switching to a compliant control mode to prevent damage to the object at contact ([0017]).
Claims 12 is rejected under 35 U.S.C. 103 as being unpatentable over US 20210129334 Kanunikov et al. in view of US 2016/0257509 Werni et al
With regard to claim 12, Kanunikov discloses all of the elements of claim 1 as discussed above.
Kanunikov does not teach but Werni does teach:
where the gripper includes a needle gripper (see [0005])
Therefore, prior to the effective filing data of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify Kanunikov to utilize a needle gripper. Such a modification would allow for handling of softer material such as fabrics, fiber or other penetrable materials.
Conclusion
THIS ACTION IS MADE FINAL. 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 ADAM R MOTT whose telephone number is (571)270-5376. The examiner can normally be reached M-F 9 - 5:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, James Trammell can be reached at (571) 272-6712. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ADAM R MOTT/Supervisory Patent Examiner, Art Unit 3657
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