DETAILED ACTION
1. Claims 1-3, 5-10, 12-17, and 19-20 have been presented for examination.
Claims 4, 11, and 18 have been cancelled.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
3. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 5/14/26 has been entered.
i) Following Applicants arguments and amendments the previously presented 101 rejection is WITHDRAWN.
ii) In view of Applicants amendments and arguments an additional prior art rejection has been presented below.
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) 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.
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.
4. Claims 1-3, 5-10, 12-17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Agbas, Erdem, and Ali Osman Kusakci. "A simulation approach for aircraft cargo loading considering weight and balance constraints." International Journal of Business Ecosystem & Strategy (2687-2293) 3.1 (2021): 21-31, hereafter Agbas in view of Gong et al. U.S. Patent Publication No. 2020/0164510, hereafter Gong further in view of U.S. Patent No. 11370545, hereafter Karni.
Regarding Claim 1: The reference discloses A method comprising:
capturing a plurality of property data related to a transport; (Agbas. Bottom of page 23, “The model was performed for the Airbus 330 freighter. A total of 50 sets of real-world data were used. These data were: registration of the aircraft (e.g.TC-JDO), the weight variant (range, payload and dynamic modes for A330), crew number (cockpit, crew), pantry code, water amount (%), crew baggage (amount), fuel on board (kg), trip fuel (kg), taxi fuel (kg), planned payload (kg), in addition to list of ULDs.”)
capturing a plurality of property data related to a plurality of cargo items; (Agbas. Page 26, Figure 3a)
generating a digital twin simulation of the transport and each cargo item; and (Agbas. Page 27, Figure 4a)
generating a loading plan of each cargo item onto the transport based on the generated digital twin simulation, (Agbas. Page 28, Figure 4)
Agbas does not explicitly recite wherein the loading plan is generated in executable computer code ingestible by a robotic loading device and transmitting the loading plan to the robotic loading device for execution of the loading plan;
wherein the plurality of property data related to each cargo item in the plurality of cargo items is captured by one or more sensors affixed to or embedded within a loading device loading cargo onto the transport.
However Gong discloses wherein the loading plan is generated in executable computer code ingestible by a robotic loading device and transmitting the loading plan to the robotic loading device for execution of the loading plan; (Gong. [0019] Meanwhile, one or more autonomous loading robots 150 assigned to the order can retrieve the arrival information and determine a cargo placement plan. In one embodiment, a processor 160 can utilize the arrival information (e.g. vehicle 100 make and model) and run a loading algorithm to determine the placement plan based on expected dimensions in an expected cargo holding area of the vehicle 100. The processor 160 can be an internal device within the loading robot 150 or external. In an alternative embodiment, the loading algorithm is run on the server 130 and the predetermined cargo placement plan is communicated to the autonomous loading robot 150 thereafter.)
wherein the plurality of property data related to each cargo item in the plurality of cargo items is captured by one or more sensors affixed to or embedded within a loading device, loading cargo onto the transport (Gong. [0027] In one embodiment, an imaging sensor scans the cargo before loading. Each piece of cargo comprises one or more fiducial marks that, when scanned, convey distinguishing features of the respective piece of cargo to the loading robot. One such feature can be the manipulation point(s) of the cargo, which can detail how and where the cargo should be picked up by the manipulator for loading. For example, as depicted in FIG. 2 where the cargo comprises a handle 260, a scan of the fiducial marks may determine that the robot arm manipulator 230 should grasp the cargo 250 by the handle 260 for transport. Alternatively, if the cargo is irregularly shaped or must maintain a certain orientation when transported, a scan of the fiducial marks will convey specialized manipulation points to the loading robot that meet such loading requirements and are compatible with the type of manipulator employed. As discussed further below in reference to FIG. 5, the fiducial marks can convey other types of cargo features, and can be QR-codes, barcodes, or any other tag configured to convey information)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize the sensor mechanism of Gong for the simulation in Agbas since this would allow for automated loading of cargo since as per Gong, [0003] “Loading cargo manually can decrease speed and efficiency of operation, particularly in automated factories where the production and transportation of goods relies heavily on AGVs (e.g., automated guided vehicles; robots) and fully autonomous machinery.”
Agbas and Gong do not explicitly recite executing, by the robotic loading device, the loading plan wherein executing comprises supplementing the transport with additional weight, separate from the cargo items, placed at one or more locations of a storage area of the transport to lower a resultant center of gravity below a center-of-gravity threshold.
However Karni discloses executing, by the robotic loading device, the loading plan wherein executing comprises supplementing the transport with additional weight, separate from the cargo items, placed at one or more locations of a storage area of the transport to lower a resultant center of gravity below a center-of-gravity threshold. (Karni. Column 2, Line 58- Column 3, Line 4, “Presently processes for loading aircrafts include taking measurements and inputting relevant loading data can calculating on a per-flight basis relevant parameters that must be acceptable to allow for safe flight. Loads can be shifted to properly balance and weight the aircraft prior to takeoff. On passenger planes, one will recognize this may involve moving passengers to different locations on the plane prior to takeoff; on cargo aircrafts, this may involve moving the cargo to different locations and/or adding weight at locations within the cargo bay to achieve acceptable CG placement. In small aircraft, this may involve moving ballast such as packs of bottled water near a forward or aft portion of the aircraft to adjust the CG; for larger aircraft, other forms of ballasting may be used.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize the placing of ballast as per Karni for the cargo loading in Agbas and Gong in order to “allow for safe flight.” (Karni. Column 2, Line 58- Column 3, Line 4) The feature of using ballast as a means of balance and desired center of gravity is also noted in the background of the instant application in at least [0002].
Regarding Claim 2: The reference discloses The method of claim 1, wherein the generated loading plan is a series of steps to load the plurality of cargo items onto the transport with a lowest resultant center of gravity. (Agbas. Page 29, top, “iv. The optimal or near optimal CG (considering the fuel consumption) is achieved.”)
Regarding Claim 3: The reference discloses The method of claim 1, wherein the generated loading plan is a series of steps to load the plurality of cargo items onto the transport with an optimized distribution of weight throughout a storage area of the transport. (Agbas. Page 30, middle, “In our study we have shown that automatic air cargo loading by a simulation model is possible. The main aim was loading of all the given set of ULDs considering the weight and balance constraints.”)
Regarding Claim 5: The reference discloses The method of claim 1, further comprising: transmitting the generated loading plan to a user. (Agbas. Page 28, Figure 5. Page 29, “According to the results of conducted experiments, all ULDs were successfully loaded with the simulation model and also with the semi-manual automatic cargo loading (ACL) method by the loadmaster.”)
Regarding Claim 6: The reference discloses The method of claim 1, wherein the plurality of property data related to the transport is selected from a group consisting of transport length, transport width, transport height, transport storage area length, transport storage area width, transport storage area height, center of gravity while fully unloaded, transport carry capacity, number of wheels, individual wheel carry capacity, number of axles, position of each wheel, age of each tire, tread depth of each tire, pressure value of each tire, oil life status, vehicle mileage value, suspension age, suspension type, transport self-weight, (Agbas. Page 24, middle, “By entering the data regarding registration of the aircraft (e.g.TC-JDO), the weight variant (range, payload and dynamic modes for A330), crew number (cockpit, crew), panitry code, water amount (%), crew baggage (amount), fuel on board (kg), trip fuel (kg), taxi fuel (kg), planned payload (kg), the simulation model calculates the dry operation weight (DOW), zero fuel weight (ZFW), take-off weight (TOW), landing weight (LW).”) individual component age, and individual component weight limit. (Page 30, 2nd paragraph, “The assignment of the ULDs to a specific position is performed by considering several constraints. Weight and balance limits are the main constraints. There are many weight limits per each defined section of the aircraft.”)
Regarding Claim 7: Agbas does not explicitly disclose however Gong discloses The method of claim 1, wherein the plurality of property data related to the plurality of cargo items is selected from a group consisting of stacked weight limit, fragility, and orientation of each surface of each cargo item. (Gong. [0031] As mentioned above, in certain embodiments each piece of cargo comprises one or more fiducial marks. Fiducial marks can be QR-codes, barcodes, or any other tag configured to convey information. FIG. 5 depicts a single fiducial mark 510 on the handle 260 of a piece of cargo 250. When scanned, a fiducial mark conveys distinguishing features of the respective piece of cargo to the loading robot, including manipulation points as detailed above. In embodiments, fiducial marks can be configured to convey the weight of a piece of cargo, its contents, its orientation, whether or not the cargo is fragile, or any other desired feature.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize the sensor information of Gong for the simulation in Agbas since this would allow for automated loading of cargo since as per Gong, [0003] “Loading cargo manually can decrease speed and efficiency of operation, particularly in automated factories where the production and transportation of goods relies heavily on AGVs (e.g., automated guided vehicles; robots) and fully autonomous machinery.”
Regarding Claim 8: See rejection for claim 1
Regarding Claim 9: See rejection for claim 2
Regarding Claim 10: See rejection for claim 3
Regarding Claim 12: See rejection for claim 5
Regarding Claim 13: See rejection for claim 6
Regarding Claim 14: See rejection for claim 7
Regarding Claim 15: See rejection for claim 1
Regarding Claim 16: See rejection for claim 2
Regarding Claim 17: See rejection for claim 3
Regarding Claim 19: See rejection for claim 5
Regarding Claim 20: See rejection for claim 6
Conclusion
5. All Claims are rejected.
6. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
i) U.S. Patent Publication No. 20090304482
ii) U.S. Patent Publication No. 20070067141
iii) Zhao, Xiangling, et al. "Optimization approach to the aircraft weight and balance problem with the centre of gravity envelope constraints." IET Intelligent Transport Systems 15.10 (2021): 1269-1286.
7. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Saif A. Alhija whose telephone number is (571) 272-8635. The examiner can normally be reached on M-F, 10:00-6:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Renee Chavez, can be reached at (571) 270-1104. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300. Informal or draft communication, please label PROPOSED or DRAFT, can be additionally sent to the Examiners fax phone number, (571) 273-8635.
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SAA
/SAIF A ALHIJA/Primary Examiner, Art Unit 2186
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