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 .
Status of Claims
Claims 1-19 have been currently amended. Claim 20 has been newly added. Claims 1-20 are currently pending and presented for examination below.
Response to Arguments
Applicant's amendments filed September 16th, 2025 with respect to the Drawing Objection have been fully considered and are therefore withdrawn.
Applicant's amendments filed September 16th, 2025 with respect to the Objection have been fully considered and are therefore withdrawn.
Applicant's amendments filed September 16th, 2025 with respect to the 35 U.S.C. 112 have been fully considered and are therefore withdrawn.
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 (i.e., changing from AIA to pre-AIA ) 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.
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.
Claims 1-2, 4, 6, 8-9, 11, 15-16 and 18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Pappas et al., Pub. No. US2020/0319613 A1, hereinafter referred to as Pappas.
As per Claim 1, 8 and 15, Pappas discloses a method and system comprising:
a forklift having a load handling system, the load handling system including a mast and a plurality of forks (see at least Figure 5., 0002, 0030);
a camera, coupled to the load handling system, for obtaining visual input data of an environment (see at least 0047, 0059, “machine vision”, “cameras”);
a plurality of non-camera sensors coupled to the forklift for obtaining sensor data (see at least 0059, “sensors”); and
a control system configured to process the visual input data and the sensor data, wherein the control system comprises functionality for (see at least 0059):
determining a location of an entrance door to a pallet storage area using a machine learning model utilizing the visual input data and the sensor data (see at least 0044, 0091, 0093, “pickup location”, “the system 1200 includes an artificial intelligence component 1202 that utilizes machine learning to facilitate the analyzing of information by the analysis component 116 from the plurality of sensors 112 and the context component 114” and “the integration component 1402 can enable the forklift to integrate with and communicate with other forklifts, trucks, smart pallets, vans, trucks and other delivery vehicles, carts, robots, drones, warehouse loading docks, garage doors, loading docks of businesses…[examiner notes that warehouse loading docks store pallets of goods]);
determining a position of a plurality of pallet’s face-side pockets using the machine learning model utilizing the visual input data and the sensor data (see at least 0070, “the forklift is equipped with machine vision to allow for self-navigation and engagement or avoidance of objects. For example, the machine vision can facilitate the forklift self-navigating as well as identifying a load to engage with. The machine vision can facilitate the forklift self-orienting to position forks to insert into pallets, or beneath or around a load and lift, lower and position a palletized or un-palletized load.”[examiner notes while Pappas does not use the word pallet face-side pockets Pappas discloses inserting into pallets]);
determining a position of the plurality of forks using the machine learning model utilizing the visual input data and the sensor data (see at least 0045, 0093, “forklift location”, “can enable the forklift to integrate with and communicate with other forklifts”);
adjusting the plurality of forks based on a difference between the position of the plurality of forks and the position of the plurality of pallet’s face-side pockets (see at least 0067, 0097, “each forklift's sensors can determine weight distribution and adjust accordingly as the load is transported. “and “tilting forks”); and
determining whether a pallet is unsafe to extract due to a presence of load restraints (see at least Figure 17, 0105, “At 1706, it is determined if the weight and size of the load are suitable for the forklift and the delivery vehicle. For example, the weight of the load may exceed the safety limit of the forklift, or the size of the load may be too large for the delivery vehicle. If either the weight or size of the load is not suitable for the forklift or the delivery vehicle 1708, then remediation is required. For example, the load on the pallet can be separated and placed on two pallets.”).
As per Claims 2, 9 and 16, Pappas discloses wherein the machine learning model utilizes a continuous stream of visual input data captured by the camera to determine the difference between the position of the plurality of forks and the position of the plurality of pallet’ dace-side pockets (see at least 0070, 0089, “machine vision” and “machine learning to facilitate the analyzing…”).
As per Claims 4, 11, and 18, Pappas discloses wherein the control system is configured to adjust the plurality of forks vertically and horizontally, and is further configured to adjust a tilt angle of the plurality of the forks (see at least 0067, “the forklift can pick up or drop off loads on an incline, e.g. tilting forks…”).
As per Claims 6, Pappas discloses wherein if the control system determines whether the pallet is unsafe to extract due to the presence of load restraints, the control system prompts for operator assistance (see at least Figure 17, “Once action is taken 1710 to remediate, step 1706 is repeated…”).
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.
Claims 3, 10 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Pappas in view of Tracy et al., Pun. US2024/0308827 A1, hereinafter referred to as Tracy.
As per Claims 3, 10 and 17, Pappas fails to explicitly disclose wherein the machine learning model recognizes a let fork and a right fork from the plurality of forks based on the visual input data and the sensor data. Pappas does disclose the forklift can pick up or drop off loads on an incline, e.g., tilting forks (forward or backwards) (see at least 0067). Further, Tracy teaches the above limitation (see at least Abstract, “The vehicle may further include a fork position sensor to determine whether one or more forks of the material handling vehicle are extended and/or retracted.”).
Pappas discloses a forklift system determining position of forks while Tracy teaches specifically a system determining individual fork position.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Pappas and include determining individual fork position as taught by Tracy with a reasonable expectation of success because it allows the forklift to make proper adjustments when engaging with a pallet.
Claims 5, 12 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Pappas in view of Lacher, Pub. No. US2018/0037448 A1, hereinafter referred to as Lacher.
As per Claims 5, 12 and 19, Pappas discloses wherein the control system is configured to adjust the plurality of forks vertically and is further configured to adjust a tilt angle of the plurality of the forks (see at least 0067, 0070, the forklift can pick up or drop off loads on an incline, e.g., tilting forks (forward or backwards) …”).
Pappas fails to explicitly disclose adjusting horizontally. However, Lacher teaches adjusting horizontally (see at least 0027, “Some other forklifts may have an actuator for adjusting its tines vertically, horizontally, rotationally, or by extending them in response to power signals received from the power machine 10 at power port 114.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Pappas and include adjusting a fork from a forklift horizontally as taught by Lacher with a reasonable expectation of success because it provides the forklift with more flexibility when engaging with pallets and delivery vehicles.
Claims 7, 13-14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Pappas in view of Feng et al., Pub. No. US2023/0341847 A1, hereinafter referred to as Feng.
As per Claims 7, 13 and 20, Pappas fails to explicitly disclose wherein the control system trains the machine learning model using a training technique selected from a group consisting of early stopping, adaptive learning rates, and cross-validation. Pappas discloses he artificial intelligence component 1202 can employ any suitable machine-learning based techniques, statistical-based techniques and/or probabilistic-based techniques. For example, the artificial intelligence component 1202 can employ expert systems, fuzzy logic, SVMs, Hidden Markov Models (HMMs), greedy search algorithms, rule-based systems, Bayesian models (e.g., Bayesian networks), neural networks, other non-linear training techniques, data fusion, utility-based analytical systems, systems employing Bayesian models, etc. In another aspect, the artificial intelligence component 1202 can perform a set of machine learning computations. For example, the artificial intelligence component 1202 can perform a set of clustering machine learning computations, a set of logistic regression machine learning computations, a set of decision tree machine learning computations, a set of random forest machine learning computations, a set of regression tree machine learning computations, a set of least square machine learning computations, a set of instance-based machine learning computations, a set of regression machine learning computations, a set of support vector regression machine learning computations, a set of k-means machine learning computations, a set of spectral clustering machine learning computations, a set of rule learning machine learning computations, a set of Bayesian machine learning computations, a set of deep Boltzmann machine computations, a set of deep belief network computations, and/or a set of different machine learning computations (0091). [Examiner notes it would have been obvious to use any well-known machine learning training technique].
Further, Feng teaches this limitation (see at least 0047, “In 450, analysis module 112 optimizes, improves, and/or cross-validates trained machine learning models. For example, data for training datasets and/or testing datasets may be updated and/or revised to include more labeled data indicating different resource types, pallet packing scenarios, and/or the like.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Pappas and include using various machine learning techniques as taught by Feng with a reasonable expectation of success because it provides well known machine learning techniques to improve the model (Feng 0034).
As per Claim 14, Pappas fails to disclose wherein the control system estimates a generalization error by evaluating a performance of the machine learning model as compared to a machine learning model set. Pappas discloses he artificial intelligence component 1202 can employ any suitable machine-learning based techniques, statistical-based techniques and/or probabilistic-based techniques. For example, the artificial intelligence component 1202 can employ expert systems, fuzzy logic, SVMs, Hidden Markov Models (HMMs), greedy search algorithms, rule-based systems, Bayesian models (e.g., Bayesian networks), neural networks, other non-linear training techniques, data fusion, utility-based analytical systems, systems employing Bayesian models, etc. In another aspect, the artificial intelligence component 1202 can perform a set of machine learning computations. For example, the artificial intelligence component 1202 can perform a set of clustering machine learning computations, a set of logistic regression machine learning computations, a set of decision tree machine learning computations, a set of random forest machine learning computations, a set of regression tree machine learning computations, a set of least square machine learning computations, a set of instance-based machine learning computations, a set of regression machine learning computations, a set of support vector regression machine learning computations, a set of k-means machine learning computations, a set of spectral clustering machine learning computations, a set of rule learning machine learning computations, a set of Bayesian machine learning computations, a set of deep Boltzmann machine computations, a set of deep belief network computations, and/or a set of different machine learning computations (0091). [Examiner notes it would have been obvious to use any well-known machine learning training technique].
Further, Feng teaches this limitation (see at least 0030, 0034, “In this case, one or more criteria may be used during the assignment, such as ensuring that similar resource types, similar pallet packing scenarios, dissimilar resource types, dissimilar pallet packing scenarios, and/or the like may be used in each of the training and testing datasets. In general, any suitable method may be used to assign the data to the training or testing datasets.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Pappas and include using various machine learning techniques as taught by Feng with a reasonable expectation of success because it provides well known machine learning techniques to improve the model (Feng 0034).
Conclusion
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 Fadey S Jabr whose telephone number is (571)272-1516. The examiner can normally be reached Monday-Friday 8:30am-4:30pm.
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FADEY S. JABR
Supervisory Patent Examiner
Art Unit 3668
/Fadey S. Jabr/Supervisory Patent Examiner, Art Unit 3668
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