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 .
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 06/25/2026 has been entered.
Status of the Claims
Claims 1, 3, 5-7, and 10 are pending.
Claims 1 and 7 have been amended.
Claims 2, 4, and 8-9 have been canceled.
Response to Amendment
Claim interpretation Under 35 U.S.C. §112(f): Applicant’s amendments to the claims overcome the claim interpretation under 112f. The 112f claim interpretation is withdrawn.
Rejections Under 35 U.S.C. §101: Applicant’s amendments to the claims do not overcome the rejection of record. The 101 rejections are maintained.
Rejections Under 35 U.S.C. §103: Applicant’s amendments to the claims do not overcome the rejection of record. The 103 rejections are maintained.
Response to Arguments
Rejections Under 35 U.S.C. §101: Applicant's arguments filed 06/25/2026 have been fully considered but they are not persuasive.
Applicant argues “Claim 1 of the present application is not directed merely to an abstract idea or a generic implementation of mathematical calculations on a computer. Rather, claim 1 recites a specific technical solution for constructing a hoisting path planning model for a crane operating in a real- world physical environment.
Specifically, claim 1 recites constructing a specialized hoisting system configuration space model including upper vehicle body data and lower vehicle body data of the crane. The claim further requires separately generating upper vehicle body raster graphic data and lower vehicle body raster graphic data based on different motion characteristics of the crane. In particular, claim 1 recites: determining a hook lifting length; dividing the hook lifting length into a preset number of lifting intervals; performing traversal searches within the hoisting system configuration space model based on the main arm luffing angle and upper vehicle body rotation angle; calculating upper vehicle body collision information; and separately performing A-star path planning on upper vehicle body raster graphic data and lower vehicle body raster graphic data to respectively obtain an upper vehicle body path planning model and a lower vehicle body path planning model. These limitations are not directed to merely performing mathematical operations in the abstract. Instead, the claim recites a particular manner of configuring and processing crane motion data to address technical problems arising in crane path planning systems, including excessive configuration-space dimensionality, motion coupling complexity, and collision-aware path searching.
The claim 1 of the present application specifically improves crane path planning by decoupling upper vehicle body actions and lower vehicle body actions into separate raster graphic processing paths and separately performing traversal and path planning operations thereon. Such grouped and hierarchical processing reduces computational complexity and improves path planning efficiency for crane operations in physical operating environments.”
Examiner respectfully disagrees, as in order to show improvement to any other technology or technical field, the claims must recite the details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient. Thus, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology (see at least MPEP 2106.05(a)(II)). Specifically, the claims as currently drafted merely recites instructions to perform a hoisting path planning model construction method on a generic component of machinery. Therefore, due to the recitation of the processor at a high level of generality and the mere recitation of instructions to perform the method on this component, the claim would not qualify as an improvement to an existing technology.
Applicant argues “Further, claim 1 of the present application is meaningfully tied to a physical machine, namely a crane, and to physical movement constraints associated with crane operations. The claim explicitly requires generating collision information associated with crane movement states and constructing a hoisting path planning model based on the physical operating scenario of the crane. Accordingly, the claim is directed to improving the technological field of crane motion planning and control rather than merely organizing or analyzing information.”
Examiner respectfully disagrees, as the recitation of “applied in a crane” serves as a means to generally link the judicial exception (abstract idea) to a particular technological environment and/or field of use. MPEP 2106.05(h) states “Another consideration when determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.” Because this claim limitation does no more than to limit the method to be performed in the field of crane motion planning, this limitation is considered a field of use limitation and does not integrate the judicial exception into a practical application.
Applicant argues “Moreover, the recited A-star algorithm is not claimed in isolation. Rather, the A-star algorithm is applied within a specifically configured crane configuration-space framework that includes: separate upper vehicle body raster graphic data and lower vehicle body raster graphic data; hook lifting interval segmentation; traversal searches based on crane motion parameters; and collision-information-based path planning.
Therefore, the claim does not merely recite an abstract mathematical concept performed on a generic computer. Instead, the claim integrates any alleged abstract idea into a practical application that improves crane path planning technology and computational processing associated with real-world crane operations. At a minimum, the claims improve another technology or technical field and therefore satisfy the considerations set forth in MPEP §2106.05(a). The claims additionally impose meaningful limitations that are not merely insignificant extra-solution activity or generic computer implementation.
Accordingly, Applicant respectfully submits that claim 1 is patent-eligible under 35 U.S.C. § 101, and withdrawal of the § 101 rejection is respectfully requested.
Applicant respectfully submits that independent claims 5 and 10 are allowable at least for reasons similar to those discussed with respect to claim 1.
By virtue of their respective direct or indirect dependencies on independent claim 1, claim 3 should also be allowable.
By virtue of their respective direct or indirect dependencies on independent claim 5, claims 6-7 should also be allowable.
In view of the above, Applicant respectfully requests the 101 rejections be withdrawn.”
Examiner respectfully disagrees, as the claim as drafted merely recites “wherein the using the A-star algorithm and combining the upper vehicle body raster graphic data and the lower vehicle body raster graphic data to construct the hoisting path planning model comprises: using the A-star algorithm, performing a path planning on the upper vehicle body raster graphic data and the lower vehicle body raster graphic data respectively to obtain an upper vehicle body path planning model and a lower vehicle body path planning model; and combining the upper vehicle body path planning model and the lower vehicle body path planning model, and constructing the hoisting path planning model”, which utilizes the processor as a tool to perform the existing process of path planning using an A-star algorithm at a high level of generality and amounts to the mere recitation of instructions to perform the method and/or the limitation(s) on the generic component (processor). Therefore, the claim would not qualify as an improvement to an existing technology. Therefore the 101 rejections are maintained.
Rejections Under 35 U.S.C. §103: Applicant's arguments filed 06/25/2026 have been fully considered but they are not persuasive.
Applicant argues “Claims 1 and 4-10 are rejected under 35 U.S.C. 103 as being unpatentable over Sasahara et al. (JP2019059593A) in view of Cai et al. (US2016/0247067A1) in further view of Li et al. (CN113901611A), hereinafter Sasahara, Cai, and Li respectively.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Sasahara et al. (JP2019059593A) in view of Cai et al. (US2016/0247067A1) view of Li et al. (CN113901611A) in further view of Ueno (US2023/0063720A1), hereinafter Sasahara, Cai, Li, and Ueno respectively.
In response for independent claim 1:
Claim t recites:
"A hoisting path planning model construction method, applied in a A crane, the method comprising:
building, by a processor, a crane model;
constructing, by the processor, a hoisting system configuration space model based on a current operation scenario and the crane model, wherein the hoisting system configuration space model comprises upper vehicle body data of a crane and lower vehicle body data of the crane;
aiming at the hoisting system configuration space model and the upper vehicle body data, generating, by the processor, upper vehicle body raster graphic data of the crane; aiming at the hoisting system configuration space model and the lower vehicle body data, generating, by the processor, lower vehicle body raster graphic data of the crane; and
using an A-star algorithm and combining the upper vehicle body raster graphic data and the lower vehicle body raster graphic data to construct a hoisting path planning model for controlling movement of the crane;
wherein the upper vehicle body data comprises: a main arm luffing angle, an upper vehicle body rotation angle and a hook lifting length;
the aiming at the hoisting system configuration space model and the upper vehicle body data, generating the upper vehicle body raster graphic data of the crane comprises:
determining the hook lifting length;
dividing the hook lifting length into a preset number of lifting intervals; and
aiming at an endpoint of each lifting intervals, performing a traversal search within the hoisting system configuration space model based on the main arm luffing angle and the upper vehicle body rotation angle, calculating upper vehicle body collision information, and generating the upper vehicle body raster graphic data of the crane;
wherein the using the A-star algorithm and combining the upper vehicle body raster graphic data and the lower vehicle body raster graphic data to construct the hoisting path planning model comprises:
using the A-star algorithm, performing a path planning on the upper vehicle body raster graphic data and the lower vehicle body raster graphic data respectively to obtain an upper vehicle body path planning model and a lower vehicle body path planning model; and
combining the upper vehicle body path planning model and the lower vehicle body path planning model, and constructing the hoisting path planning model." (Emphasis added)
Applicant submits that all the above reference documents do not disclose the following features in the amended claim 1: the using the A-star algorithm and combining the upper vehicle body raster graphic data and the lower vehicle body raster graphic data to construct the hoisting path planning model comprises: using the A-star algorithm, performing a path planning on the upper vehicle body raster graphic data and the lower vehicle body raster graphic data respectively to obtain an upper vehicle body path planning model and a lower vehicle body path planning model; and combining the upper vehicle body path planning model and the lower vehicle body path planning model, and constructing the hoisting path planning model. The reasons are as follows.
Specifically, claim 1 requires a decoupled and hierarchical path planning architecture in which: upper vehicle body raster graphic data and lower vehicle body raster graphic data are separately processed; separate A-star path planning operations are independently performed on the respective raster graphic datasets; separate upper vehicle body and lower vehicle body path planning models are generated; and the separately generated path planning models are subsequently combined to construct the final hoisting path planning model.
Referring to the specification of Sasahara, Sasahara discloses a crane operation support device that supports the operation of a crane using three-dimensional computer graphics includes measurement units that measure the attitudes of movable parts, a height measurement unit that measures the height of a hook block, crane model placement means that places movable part models in a virtual three-dimensional space based on the measurement results of the measurement units and places a hook block model in the three-dimensional space based on the measurement results of the measurement units, load model placement means that places a suspended load model in the three-dimensional space based on the measurement results of the measurement units, and drawing means that displays the movable part models, the hook block model, and the suspended load model on a display device by drawing processing.
However, referring to the full specification of Sasahara, Sasahara does not disclose the distinguishing technical features of the present application, which is "the using the A-star algorithm and combining the upper vehicle body raster graphic data and the lower vehicle body raster graphic data to construct the hoisting path planning model comprises: using the A-star algorithm, performing a path planning on the upper vehicle body raster graphic data and the lower vehicle body raster graphic data respectively to obtain an upper vehicle body path planning model and a lower vehicle body path planning model; and combining the upper vehicle body path planning model and the lower vehicle body path planning model, and constructing the hoisting path planning model". In other words, Sasahara does not disclose or suggest independently performing A-star path planning on respective raster graphic datasets to generate separate path planning models. Moreover, Sasahara does not have the technical effect of the highlight features, that is, "since the hoisting path planning model constructed is based on the upper vehicle body raster graphic data and the lower vehicle body raster graphic data, the entire path is divided into two groups: upper vehicle body and lower vehicle body, which effectively reducing the data volume during path search, and improving the efficiency of path planning". Thus, the above highlight features of the present application are not disclosed in Sasahara.
Therefore, Sasahara clearly fails to disclose such an arrangement providing the technical advantages of how to effectively reduce the data volume during path search and improve the efficiency of path planning. In other words, Sasahara fails to teach or suggest the above highlighted features and the solution of the above highlighted features cannot be obtained and/or realized by the person skilled in the art in any reference to Sasahara and common general knowledge in the art. Therefore, for the reasons mentioned above, the above highlighted features are non-obvious in light of Sasahara.
Referring to the specification of Cai, a method is proposed for automatically generating a crane lifting path describing the motion of a crane. The method includes: laser scanning a plant to generate one or more point clouds; using the point clouds to identify objects to be lifted by the crane; rasterizing the laser scanned point clouds to generate digital data describing the plant and in a format for input to a Graphics Processing Unit (GPU); and iteratively optimizing a crane lifting path, including using the GPU and the digital data to detect collisions between one or more cranes and the plant if the crane follows the crane lifting path.
However, referring to the full specification of Cai, Cai does not disclose the technical features of the present application, which is "the using the A-star algorithm and combining the upper vehicle body raster graphic data and the lower vehicle body raster graphic data to construct the hoisting path planning model comprises: using the A-star algorithm, performing a path planning on the upper vehicle body raster graphic data and the lower vehicle body raster graphic data respectively to obtain an upper vehicle body path planning model and a lower vehicle body path planning model; and combining the upper vehicle body path planning model and the lower vehicle body path planning model, and constructing the hoisting path planning model". In other words, the portions cited by the Examiner merely disclose that rasterized results may be used for different functional processes, such as driving trajectory planning, lifting path optimization, and collision detection. However, Cai does not disclose: separately performing path planning on upper vehicle body raster graphic data and lower vehicle body raster graphic data; generating an upper vehicle body path planning model and a lower vehicle body path planning model independently; or combining separately generated path planning models as required by claim 1. Instead, Cai merely describes different application functions performed using rasterized data and does not disclose the claimed decoupled dual-model planning structure. Moreover, Cai does not have the technical effect of the highlight features, that is, "since the hoisting path planning model constructed is based on the upper vehicle body raster graphic data and the lower vehicle body raster graphic data, the entire path is divided into two groups: upper vehicle body and lower vehicle body, which effectively reducing the data volume during path search, and improving the efficiency of path planning". Thus, the above highlight features of the present application arc not disclosed in Cai.
Therefore, Cai clearly fails to disclose such an arrangement providing the technical advantages of how to effectively reduce the data volume during path search and improve the efficiency of path planning. In other words, Cai fails to teach or suggest the above highlighted features and the solution of the above highlighted features cannot be obtained and/or realized by the person skilled in the art in any reference to Cai and common general knowledge in the art. Therefore, for the reasons mentioned above, the above highlighted features are non-obvious in light of Cai.
Referring to the specification of Li, Li discloses a method and apparatus for tower crane hoisting path planning based on an improved A-star algorithm are disclosed. The method
includes: establishing a 3D mesh model of the construction site in cylindrical coordinates and generating a set of mesh nodes; generating an obstacle point set based on obstacles in the construction site and the set of mesh nodes; obtaining the coordinates of the hoisting start point and the hoisting end point in cylindrical coordinates; and planning the hoisting path from the hoisting start point to the hoisting end point based on the hoisting start point, the hoisting end point, the set of mesh nodes, the obstacle point set, and a preset hoisting path planning strategy.
However, referring to the full specification of Li, Li does not disclose the technical features of the present application, which is "the using the A-star algorithm and combining the upper vehicle body raster graphic data and the lower vehicle body raster graphic data to construct the hoisting path planning model comprises: using the A-star algorithm, performing a path planning on the upper vehicle body raster graphic data and the lower vehicle body raster graphic data respectively to obtain an upper vehicle body path planning model and a lower vehicle body path planning model; and combining the upper vehicle body path planning model and the lower vehicle body path planning model, and constructing the hoisting path planning model". In other words, Li merely teaches using an A-star algorithm for crane path planning in a general sense. Li does not disclose or suggest dividing crane configuration space into upper vehicle body raster graphic data and lower vehicle body raster graphic data, nor independently performing separate A-star path planning operations thereon to generate separate planning models. Moreover, Li does not have the technical effect of the highlight features, that is, "since the hoisting path planning model constructed is based on the upper vehicle body raster graphic data and the lower vehicle body raster graphic data, the entire path is divided into two groups: upper vehicle body and lower vehicle body, which effectively reducing the data volume during path search, and improving the efficiency of path planning". Thus, the above highlight features of the present application are not disclosed in Li.
Therefore, Li clearly fails to disclose such an arrangement providing the technical advantages of how to effectively reduce the data volume during path search and improve the efficiency of path planning. In other words, Li fails to teach or suggest the above highlighted features and the solution of the above highlighted features cannot be obtained and/or realized by the person skilled in the art in any reference to Li and common general knowledge in the art. Therefore, for the reasons mentioned above, the above highlighted features are non-obvious in light of Li.
Referring to the specification of Ueno, Ueno discloses a performance information server, including: a request acquisition unit that acquires, from a work machine display operation application operating on a terminal capable of displaying an image including a work machine, a request including a conveyance condition under which the work machine conveys a load; a storage unit that stores specification data of the work machine in mapping with model information of a work machine; a control unit that acquires model information of a work machine having a capability to convey the load under the conveyance condition by referring to the specification data; and a response presentation unit that presents a response including the model information acquired by the control unit to the work machine display operation application.
However, referring to the full specification of Ueno, Ueno does not disclose the technical features of the present application, which is "the using the A-star algorithm and combining the upper vehicle body raster graphic data and the lower vehicle body raster graphic data to construct the hoisting path planning model comprises: using the A-star algorithm, performing a path planning on the upper vehicle body raster graphic data and the lower vehicle body raster graphic data respectively to obtain an upper vehicle body path planning model and a lower vehicle body path planning model; and combining the upper vehicle body path planning model and the lower vehicle body path planning model, and constructing the hoisting path planning model". In other words, Ueno does not disclose specific operating methods of the "constructing the hoisting path planning model". Moreover, Ueno does not have the technical effect of the highlight features, that is, "since the hoisting path planning model constructed is based on the upper vehicle body raster graphic data and the lower vehicle body raster graphic data, the entire path is divided into two groups: upper vehicle body and lower vehicle body, which effectively reducing the data volume during path search, and improving the efficiency of path planning". Thus, the above highlight features of the present application are not disclosed in Ueno.
Therefore, Ueno clearly fails to disclose such an arrangement providing the technical advantages of how to effectively reduce the data volume during path search and improve the efficiency of path planning. In other words, Ueno fails to teach or suggest the above highlighted features and the solution of the above highlighted features cannot be obtained and/or realized by the person skilled in the art in any reference to Ueno and common general knowledge in the art.
Therefore, for the reasons mentioned above, the above highlighted features are non-obvious in light of Ueno.
Thus, at least by the highlight features, the amended claim 1 has at least the following significance: the hoisting path planning model constructed is based on the upper vehicle body raster graphic data and the lower vehicle body raster graphic data, the entire path is divided into two groups: upper vehicle body and lower vehicle body, which effectively reducing the data volume during path search, and improving the efficiency of path planning.”
Examiner respectively disagrees, in response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Specifically, Sasahara et al. (JP2019059593A), hereinafter Sasahara, is utilized to teach a hoisting path planning model construction method, comprising: building a crane model; constructing a hoisting system configuration space model based on a current operation scenario and the crane model, wherein the hoisting system configuration space model comprises upper vehicle body data of a crane and lower vehicle body data of the crane; aiming at the hoisting system configuration space model and the upper vehicle body data, generating upper vehicle body raster graphic data of the crane; aiming at the hoisting system configuration space model and the lower vehicle body data, generating lower vehicle body raster graphic data of the crane; and combining the upper vehicle body raster graphic data and the lower vehicle body raster graphic data; wherein the upper vehicle body data comprises: a main arm luffing angle, an upper vehicle body rotation angle and a hook lifting length; the aiming at the hoisting system configuration space model and the upper vehicle body data, generating the upper vehicle body raster graphic data of the crane comprises: determining the hook lifting length; and generating the upper vehicle body raster graphic data of the crane. Cai et al. (US2016/0247067A1), hereinafter Cai, is utilized to teach utilizing crane information to generate candidate crane lifting paths and calculating upper vehicle body collision information. Li et al. (CN113901611A), hereinafter Li, is utilized to teach using an A-star algorithm to construct a hoisting path planning model, dividing the hook lifting length into a preset number of lifting intervals; and aiming at an endpoint of each lifting intervals, performing a traversal search within the hoisting system configuration space model based on the main arm luffing angle and the upper vehicle body rotation angle. Ueno (US2023/0063720A1), hereinafter Ueno, was not utilized in order to teach any of the limitations of the amended claim 1. Therefore the teachings of Sasahara, Cai, and Li would have been able to be combined in order to teach a hoisting path planning model construction method, applied in a crane, the method comprising: building, by a processor, a crane model; constructing, by the processor, a hoisting system configuration space model based on a current operation scenario and the crane model, wherein the hoisting system configuration space model comprises upper vehicle body data of a crane and lower vehicle body data of the crane; aiming at the hoisting system configuration space model and the upper vehicle body data, generating, by the processor, upper vehicle body raster graphic data of the crane; aiming at the hoisting system configuration space model and the lower vehicle body data, generating, by the processor, lower vehicle body raster graphic data of the crane; and using an A-star algorithm and combining the upper vehicle body raster graphic data and the lower vehicle body raster graphic data to construct a hoisting path planning model for controlling movement of the crane; wherein the upper vehicle body data comprises: a main arm luffing angle, an upper vehicle body rotation angle and a hook lifting length; the aiming at the hoisting system configuration space model and the upper vehicle body data, generating the upper vehicle body raster graphic data of the crane comprises: determining the hook lifting length; dividing the hook lifting length into a preset number of lifting intervals; and aiming at an endpoint of each lifting intervals, performing a traversal search within the hoisting system configuration space model based on the main arm luffing angle and the upper vehicle body rotation angle, calculating upper vehicle body collision information, and generating the upper vehicle body raster graphic data of the crane; wherein the using the A-star algorithm and combining the upper vehicle body raster graphic data and the lower vehicle body raster graphic data to construct the hoisting path planning model comprises: using the A-star algorithm, performing a path planning on the upper vehicle body raster graphic data and the lower vehicle body raster graphic data respectively to obtain an upper vehicle body path planning model and a lower vehicle body path planning model; and combining the upper vehicle body path planning model and the lower vehicle body path planning model, and constructing the hoisting path planning model with a reasonable expectation of success as one would have been motivated to do so in order to improve an operator’s ability to grasp the condition of the crane and further support the operator in maneuvering the crane (see at least Sasahara, Page 3, lines 40-42 and Li, Page 8, lines 17-24). Therefore the 103 rejection is maintained.
Applicant argues “The Examiner's rejection can only be achieved through impermissible hindsight reconstruction using Applicant's disclosure as a roadmap. Importantly, the claimed architecture achieves specific technical advantages that are neither taught nor suggested by the cited references. As explained in the present specification, separately performing path planning on upper vehicle body raster graphic data and lower vehicle body raster graphic data reduces configuration-space dimensionality, reduces coupling among crane motion parameters, and reduces path-search computational complexity. None of the cited references recognizes or suggests solving crane path planning complexity problems through the claimed decoupled dual- layer A-star planning architecture. Therefore, one of ordinary skill in the art would not have been motivated to modify the cited references in the particular manner required by claim 1, nor would such modification have been obvious absent improper hindsight. Therefore, Sasahara, Cai, Li and Ueno, taken alone or in combination, fail to disclose, suggest or teach the above highlighted features as recited in amended claim 1. Applicant respectfully submits that independent claim 1 should be allowable.
Applicant respectfully submits that independent claims 5 and 10 are allowable at least for reasons similar to those discussed with respect to claim 1.
By virtue of their respective direct or indirect dependencies on independent claim 1, claim 3 should also be allowable.
By virtue of their respective direct or indirect dependencies on independent claim 5, claims 6-7 should also be allowable.
Withdrawal of the rejections and allowance of those claims are respectfully requested ”
Examiner respectively disagrees, in response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3, 5-7, and 10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1. A hoisting path planning model construction method, applied in a crane, the method comprising:
building, by a processor, a crane model;
constructing, by the processor, a hoisting system configuration space model based on a current operation scenario and the crane model, wherein the hoisting system configuration space model comprises upper vehicle body data of a crane and lower vehicle body data of the crane;
aiming at the hoisting system configuration space model and the upper vehicle body data, generating, by the processor, upper vehicle body raster graphic data of the crane; aiming at the hoisting system configuration space model and the lower vehicle body data, generating, by the processor, lower vehicle body raster graphic data of the crane; and
using an A-star algorithm and combining the upper vehicle body raster graphic data and the lower vehicle body raster graphic data to construct a hoisting path planning model for controlling movement of the crane;
wherein the upper vehicle body data comprises: a main arm luffing angle, an upper vehicle body rotation angle and a hook lifting length;
the aiming at the hoisting system configuration space model and the upper vehicle body data, generating the upper vehicle body raster graphic data of the crane comprises:
determining the hook lifting length;
dividing the hook lifting length into a preset number of lifting intervals; and
aiming at an endpoint of each lifting intervals, performing a traversal search within the hoisting system configuration space model based on the main arm luffing angle and the upper vehicle body rotation angle, calculating upper vehicle body collision information, and generating the upper vehicle body raster graphic data of the crane;
wherein the using the A-star algorithm and combining the upper vehicle body raster graphic data and the lower vehicle body raster graphic data to construct the hoisting path planning model comprises:
using the A-star algorithm, performing a path planning on the upper vehicle body raster graphic data and the lower vehicle body raster graphic data respectively to obtain an upper vehicle body path planning model and a lower vehicle body path planning model; and
combining the upper vehicle body path planning model and the lower vehicle body path planning model, and constructing the hoisting path planning model.
101 Analysis - Step 1: Statutory category – Yes
The claim recites a method including at least one step. The claim falls within one of the four statutory categories (see at least MPEP 2106.03).
101 Analysis - Step 2A Prong one evaluation: Judicial Exception – Yes – Mental processes and/or mathematical concepts.
In Step 2A, Prong one of the 2019 Patent Eligibility Guidance (PEG), a claim is to be analyzed to determine whether it recites subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) mental processes, and/or c) certain methods of organizing human activity.
The Office submits that the foregoing bolded limitation(s) constitutes judicial exceptions in terms of “mental processes” and/or mathematical concept because under its broadest reasonable interpretation, the limitations can be “performed in the human mind, or by a human using a pen and paper” (see at least MPEP 2106.04(a)(2)(III)) and/or recites a mathematical concept (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations) (see at least MPEP2106.04 (a)(2)(I)).
The claim recites the limitations of “building, by a processor, a crane model; constructing, by the processor, a hoisting system configuration space model based on a current operation scenario and the crane model, wherein the hoisting system configuration space model comprises upper vehicle body data of a crane and lower vehicle body data of the crane; using an A-star algorithm and combining the upper vehicle body raster graphic data and the lower vehicle body raster graphic data to construct a hoisting path planning model for controlling movement of the crane; determining the hook lifting length; calculating upper vehicle body collision information; using the A-star algorithm, performing a path planning on the upper vehicle body raster graphic data and the lower vehicle body raster graphic data respectively to obtain an upper vehicle body path planning model and a lower vehicle body path planning model; and combining the upper vehicle body path planning model and the lower vehicle body path planning model, and constructing the hoisting path planning model”. This limitation, as drafted, is a simple process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of “by the processor”. That is, other than reciting “by a processor” nothing in the claim elements precludes the step from practically being performed in the mind or by a human with the aid of a pen and paper. For example, but for the “by a processor” language, the claims encompass a person observing crane data, modelling a crane, and determining a hoisting path based on the modeled situation. The mere nominal recitation of “by a processor” does not take the claim limitations out of the mental process grouping.
Thus, the claim recites a mental process.
The claim recites “building, by a processor, a crane model; constructing, by the processor, a hoisting system configuration space model based on a current operation scenario and the crane model, wherein the hoisting system configuration space model comprises upper vehicle body data of a crane and lower vehicle body data of the crane; using an A-star algorithm and combining the upper vehicle body raster graphic data and the lower vehicle body raster graphic data to construct a hoisting path planning model for controlling movement of the crane; determining the hook lifting length; calculating upper vehicle body collision information; and using the A-star algorithm, performing a path planning on the upper vehicle body raster graphic data and the lower vehicle body raster graphic data respectively to obtain an upper vehicle body path planning model and a lower vehicle body path planning model and combining the upper vehicle body path planning model and the lower vehicle body path planning model, and constructing the hoisting path planning model” as drafted, is a process that, under the broadest reasonable interpretation covers a mathematical concept (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations) and/or mathematical manipulation of a data by a general purpose computer (processor). If a claim limitation, under its broadest reasonable interpretation covers mathematical relationships, mathematical formulas or equations, and mathematical calculations but for reh recitation of generic computer components, then it falls within the “mathematical concepts” grouping of abstract ideas.
Thus, the claim recites an abstract idea.
101 Analysis - Step 2A Prong two evaluation: Practical Application – No
In Step 2A, Prong two of the 2019 PEG, a claim is to be evaluated whether, as a whole, it integrates the recited judicial exception into a practical application. As noted in MPEP 2106.04(d), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. The courts have indicated that additional elements such as: merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The Office submits that the foregoing underlined limitation(s) recite additional elements that do not integrate the recited judicial exception into a practical application.
The claim recites additional elements or steps of generating, by the processor, upper vehicle body raster graphic data of the crane; and generating, by the processor, lower vehicle body raster graphic data of the crane. The generating steps by the processor are recited at a high level of generality (i.e. as a general means of gathering crane data for use in the combining and/or constructing steps), and amount to mere data gathering, which is a form of insignificant extra-solution activity. The “hoisting path planning model construction method” merely describes how to generally “apply” the otherwise mental judgements and/or mathematical concepts using a generic or general-purpose computer, i.e. a processor. The method is recited at a high level of generality and is merely automates the building, constructing, and combining steps.
Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
101 Analysis - Step 2B evaluation: Inventive concept - No
In Step 2B of the 2019 PEG, a claim is to be evaluated as to whether the claim, as a whole, amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim (see at least MPEP 2106.05).
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B. Here, the receiving steps and the displaying step were considered to be insignificant extra-solution activity in Step 2A, and thus they are re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The specification does not provide any indication that the processor is anything other than a conventional computer within a crane. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Accordingly, a conclusion that the collecting step is well-understood, routine, conventional activity is supported under Berkheimer.
Thus, the claim is ineligible.
Additionally, positively reciting a control step (i.e., “controlling the crane…”) to control the crane to move in accordance to the constructed hoisting planning path (see at least [0097] from Applicant’s specification as filed), may help to overcome the rejection.
Dependent Claims
Dependent claims 3, 5-7, and 10 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of the dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application consistent with that mentioned above with respect to independent claim 1. Therefore, dependent claims 3, 5-7, and 10 are not patent eligible under the same rationale as provided for in the rejection of independent claim 1.
Therefore, claims 3, 5-7, and 10 are ineligible under 35 USC §101.
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 1, 5-7, and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Sasahara et al. (JP2019059593A) in view of Cai et al. (US2016/0247067A1) in further view of Li et al. (CN113901611A), hereinafter Sasahara, Cai, and Li respectively.
Regarding claim 1, (Currently Amended) Sasahara teaches a hoisting path planning model construction method, applied in a crane, the method comprising: building, by a processor, a crane model (see at least Page 4, line 48 “crane 10 are modeled in a virtual three-dimensional space by the crane operation support device 30”); constructing, by the processor, a hoisting system configuration space model based on a current operation scenario and the crane model (see at least Page 12, lines 25-28 “(2) In addition to the crane model 110 and the suspended load model 102, the finished model 101 is also displayed on the display device 32. The finished model 101 is modeled according to design data, ie, building model data 70. The relative positional relationship between the finished model 101 and the crane model 110 is the same as the relative positional relationship between the actual finished model 1 and the crane 10.”), wherein the hoisting system configuration space model comprises upper vehicle body data of a crane and lower vehicle body data of the crane (see at least Page 8, lines 40-41 “As shown in FIG. 7, the crane model data 60 is a collection of lower model data 61, upper model data 62, boom model data 64, jib model data 65 and hook block model data 67.”); aiming at the hoisting system configuration space model and the upper vehicle body data, generating, by the processor, upper vehicle body raster graphic data of the crane (see at least Page 8, line 45-50 “The upper model data 62 is composed of three-dimensional shape information 62a and node position information 62b. The three-dimensional shape information 62a defines the position of each point of the upper model 112 by a local coordinate system in order to model the three-dimensional shape of the upper model 112 in a three-dimensional space. The nodal point position information 62 b defines the position of the nodal point connecting the lower model 111 and the upper model 112 by the local coordinate system of the lower model 111.” also see at least Page 11, lines 15-16 “Then, the computer 31 rasterizes the finished model 101, the suspended load model 102, and the crane model 110 on the screen”); aiming at the hoisting system configuration space model and the lower vehicle body data, generating, by the processor, lower vehicle body raster graphic data of the crane (see at least Page 8, lines 42-44 “The lower model data 61 is composed of three-dimensional shape information 61a. The three-dimensional shape information 61a defines the position of each point of the lower model 111 by a local coordinate system in order to model the three-dimensional shape of the lower model 111.” also see at least Page 11, lines 15-16 “Then, the computer 31 rasterizes the finished model 101, the suspended load model 102, and the crane model 110 on the screen”); and combining the upper vehicle body raster graphic data and the lower vehicle body raster graphic data (see at least Page 9, lines 48-52 “Next, the computer 31 coordinates-converts the lower model 111, the upper model 112, the boom model 114, and the jib model 115 from the local coordinate system of the lower model 111 to the world coordinate system based on the detection values of the position/orientation measurement unit 45. . Thereby, the lower model 111, the upper model 112, the boom model 114, and the jib model 115 are arranged in the three-dimensional space of the world coordinate system.” also see at least Page 5, lines 14-15 “The crane model 110 comprises a lower model 111, an upper model 112, a boom model 114, a jib model 115 and a hook block model 117.”); wherein the upper vehicle body data comprises: a main arm luffing angle (see at least Page 5, line 51 – Page 6, line 1 “a boom ups and downs angle measurement unit 42, a jib ups and downs angle measurement unit 43” also see at least Page 7, lines 46-54 and Page 8, lines 1-9), an upper vehicle body rotation angle (see at least Page 6, lines 38-39 “The swing angle measurement unit 41 is a posture measurement unit for measuring the posture of the upper swing body 12, that is, the swing angle of the upper swing body 12.”) and a hook lifting length (see at least Page 8, lines 10-11 “3.9. Height Measurement Unit The height measurement unit 44 is a sensor that measures the height of the hook block 17.”); the aiming at the hoisting system configuration space model and the upper vehicle body data, generating the upper vehicle body raster graphic data of the crane comprises: determining the hook lifting length (see at least Page 10, lines 1-3 “Next, the computer 31 coordinate-converts the hook block model 117 from the local coordinate system to the world coordinate system based on the detection value of the height measurement unit 44 and the detection value of the position / direction measurement unit 45.” also see at least Page 8, lines 10-16); and generating the upper vehicle body raster graphic data of the crane (see at least Page 8, line 45-50 “The upper model data 62 is composed of three-dimensional shape information 62a and node position information 62b. The three-dimensional shape information 62a defines the position of each point of the upper model 112 by a local coordinate system in order to model the three-dimensional shape of the upper model 112 in a three-dimensional space. The nodal point position information 62 b defines the position of the nodal point connecting the lower model 111 and the upper model 112 by the local coordinate system of the lower model 111.”).
Examiner interprets that processor is encompassed at least by crane operation support device 30 and/or computer 31, a main arm luffing angle is encompassed at least by the hoisting angle of the boom 14 and/or the elevation angle of the jib 15, upper vehicle body rotation angle is encompassed at least by the swing angle of the upper swing body 12, and a hook lifting length is encompassed at least by height of the hook block 17.
Sasahara does not explicitly teach using an A-star algorithm and combining the upper vehicle body raster graphic data and the lower vehicle body raster graphic data to construct a hoisting path planning model for controlling movement of the crane; dividing the hook lifting length into a preset number of lifting intervals; and aiming at an endpoint of each lifting intervals, performing a traversal search within the hoisting system configuration space model based on the main arm luffing angle and the upper vehicle body rotation angle, calculating upper vehicle body collision information, and wherein the using the A-star algorithm and combining the upper vehicle body raster graphic data and the lower vehicle body raster graphic data to construct the hoisting path planning model comprises: using the A-star algorithm, performing a path planning on the upper vehicle body raster graphic data and the lower vehicle body raster graphic data respectively to obtain an upper vehicle body path planning model and a lower vehicle body path planning model; and combining the upper vehicle body path planning model and the lower vehicle body path planning model, and constructing the hoisting path planning model.
Cai teaches utilizing crane information to generate candidate crane lifting paths (see at least [0059] “Referring to FIG. 7, inputs to the process include crane information 601 (e.g. the boom length, operation limits and OBB). These are used to generate 602 an initial population of candidate crane lifting paths. A GPU 608 then performs fitness evaluation 603 using an objective function. The candidate crane lifting paths which are found to have high fitness are used by a GPU 609 to provide selection and crossover 604. There is then a mutation step 605, also done by the GPU 609. In step 606 it is determined whether a termination criterion is met. If so, the candidate fitness algorithm with the highest fitness score is labeled as a "pass" result, and is the final result 607 output from the embodiment. If not, the optimization process reverts to step 603, using the new candidate crane lifting paths generated in steps 604 and 605.”), calculating upper vehicle body collision information (see at least [0021] “5) iteratively optimizing the a crane lifting path, including using the GPU to detect collisions between one or more cranes, and the rasterized plant.” also see at least [0041] “lifting path optimisation (step 6) including using a GPU to detect candidate lifting paths which cause a collision; and real time collision detection as the object is moved (step 7).”), and suggests combining the upper vehicle body path planning model and the lower vehicle body path planning model, and constructing the hoisting path planning model (see at least [0040]-[0041] “In step 4, the results are rasterized (this process is also referred to here as "digitization") to produce a well-formatted multi-layer depth map. The result is used for three processes: driving trajectory planning optimization (step 5) in which the motion of the crane as it is driven along pre-defined road regions within the plant is planned; lifting path optimisation (step 6) including using a GPU to detect candidate lifting paths which cause a collision; and real time collision detection as the object is moved (step 7).” also see at least [0016]-[0021]).
Li teaches a hoisting path planning model construction method, applied in a crane (see at least Page 7, lines 17-20 “The invention also includes an improved tower crane hoisting path planning device based on the A* algorithm. The tower crane hoisting path planning device includes a three dimensional grid model establishment module, an obstacle point set generation module, a hoisting starting and ending point coordinate acquisition module, Hoisting path planning module and hoisting control module;”), dividing the hook lifting length into a preset number of lifting intervals (see at least Page 6 lines 33-36 “Convert the angle coordinates θ of all nodes to integers between 0 and a-1; The angle coordinate of the node is used as the index of the first-level list, and the radius coordinate r and height coordinate h of the node are used as the second-level list. Further, the step size is set to 1°, and the angle coordinate θ is divided into 360 equal parts.”); and aiming at an endpoint of each lifting intervals, performing a traversal search within the hoisting system configuration space model based on the main arm luffing angle and the upper vehicle body rotation angle (see at least Page 9 lines 3-5 “As a heuristic path search algorithm, the core of the A* algorithm is to introduce a heuristic function. Each time the bootstrap program selects a node, it tends to select a node that is closer to the end point,” also see at least Page 5 line 35 – Page 6 line 2 “According to the hoisting starting point S, the hoisting end point G, the grid node set, the obstacle point set U0 and the preset hoisting path planning strategy, plan the hoisting path from the hoisting starting point S to the hoisting end point G; Among them, the hoisting path planning strategy includes: S101: Establish a set of reachable points open list and a set of non concerned points close list;”), using the A-star algorithm, performing a path planning on to obtain (see at least Page 7, lines 17-20 “The invention also includes an improved tower crane hoisting path planning device based on the A* algorithm. The tower crane hoisting path planning device includes a three dimensional grid model establishment module, an obstacle point set generation module, a hoisting starting and ending point coordinate acquisition module, Hoisting path planning module and hoisting control module; of which”).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of Sasahara of a hoisting path planning model construction method, applied in a crane, the method comprising: building, by a processor, a crane model; constructing, by the processor, a hoisting system configuration space model based on a current operation scenario and the crane model, wherein the hoisting system configuration space model comprises upper vehicle body data of a crane and lower vehicle body data of the crane; aiming at the hoisting system configuration space model and the upper vehicle body data, generating, by the processor, upper vehicle body raster graphic data of the crane; aiming at the hoisting system configuration space model and the lower vehicle body data, generating, by the processor, lower vehicle body raster graphic data of the crane; and combining the upper vehicle body raster graphic data and the lower vehicle body raster graphic data; wherein the upper vehicle body data comprises: a main arm luffing angle, an upper vehicle body rotation angle and a hook lifting length; the aiming at the hoisting system configuration space model and the upper vehicle body data, generating the upper vehicle body raster graphic data of the crane comprises: determining the hook lifting length; and generating the upper vehicle body raster graphic data of the crane with the teaching of utilizing crane information to generate candidate crane lifting paths, calculating upper vehicle body collision information found in Cai, the suggested teaching of combining the upper vehicle body path planning model and the lower vehicle body path planning model, and constructing the hoisting path planning model found in Cai, and the teachings of a hoisting path planning model construction method, applied in a crane, dividing the hook lifting length into a preset number of lifting intervals; and aiming at an endpoint of each lifting intervals, performing a traversal search within the hoisting system configuration space model based on the main arm luffing angle and the upper vehicle body rotation angle, using the A-star algorithm, performing a path planning on to obtain found in Li. One would have been able to combine the teachings in order to have a hoisting path planning model construction method, applied in a crane, the method comprising: building, by a processor, a crane model; constructing, by the processor, a hoisting system configuration space model based on a current operation scenario and the crane model, wherein the hoisting system configuration space model comprises upper vehicle body data of a crane and lower vehicle body data of the crane; aiming at the hoisting system configuration space model and the upper vehicle body data, generating, by the processor, upper vehicle body raster graphic data of the crane; aiming at the hoisting system configuration space model and the lower vehicle body data, generating, by the processor, lower vehicle body raster graphic data of the crane; and using an A-star algorithm and combining the upper vehicle body raster graphic data and the lower vehicle body raster graphic data to construct a hoisting path planning model for controlling movement of the crane; wherein the upper vehicle body data comprises: a main arm luffing angle, an upper vehicle body rotation angle and a hook lifting length; the aiming at the hoisting system configuration space model and the upper vehicle body data, generating the upper vehicle body raster graphic data of the crane comprises: determining the hook lifting length; dividing the hook lifting length into a preset number of lifting intervals; and aiming at an endpoint of each lifting intervals, performing a traversal search within the hoisting system configuration space model based on the main arm luffing angle and the upper vehicle body rotation angle, calculating upper vehicle body collision information, and generating the upper vehicle body raster graphic data of the crane; wherein the using the A-star algorithm and combining the upper vehicle body raster graphic data and the lower vehicle body raster graphic data to construct the hoisting path planning model comprises: using the A-star algorithm, performing a path planning on the upper vehicle body raster graphic data and the lower vehicle body raster graphic data respectively to obtain an upper vehicle body path planning model and a lower vehicle body path planning model; and combining the upper vehicle body path planning model and the lower vehicle body path planning model, and constructing the hoisting path planning model with a reasonable expectation of success. One would have been motivated to do so in order to improve an operator’s ability to grasp the condition of the crane, support the operator in maneuvering the crane (see at least Sasahara, Page 3, lines 40-42 and Li, Page 8, lines 17-24), and further to improve worksite safety (see at least Cai, [0026] and Li, Page 8, lines 17-24).
Regarding claim 5, (Original) Sasahara does not explicitly teach a hoisting path planning method, comprising: determining a starting point of a hoisting path and an end point of the hoisting path; and inputting coordinates of the starting point and coordinates of the end point into a hoisting path planning model, outputting a hoisting planning path as an optimal hoisting path, and the hoisting path planning model is obtained according to the hoisting path planning model construction method according to claim 1.
Li teaches a hoisting path planning method, comprising: determining a starting point of a hoisting path and an end point of the hoisting path (see at least Page 5 lines 33-34 “Obtain the coordinates of the hoisting starting point S and the coordinates of the hoisting end point G in cylindrical coordinates, which are (rS, θS, hS) and (rG, θG, hG) respectively”); and inputting coordinates of the starting point and coordinates of the end point into a hoisting path planning model (see at least Page 5 line 35 – Page 6 line 2 “According to the hoisting starting point S, the hoisting end point G, the grid node set, the obstacle point set U0 and the preset hoisting path planning strategy, plan the hoisting path from the hoisting starting point S to the hoisting end point G;” also see at least Page 7, lines 17-20 “The invention also includes an improved tower crane hoisting path planning device based on the A* algorithm. The tower crane hoisting path planning device includes a three dimensional grid model establishment module, an obstacle point set generation module, a hoisting starting and ending point coordinate acquisition module, Hoisting path planning module and hoisting control module; of which”). Li suggests outputting a hoisting planning path as an optimal hoisting path, and the hoisting path planning model is obtained according to the hoisting path planning model construction method according to claim 1 (see at least Page, 6 lines 27-28 “S109: Starting from the hoisting end point G, trace back along the parent node of each node to the hoisting start point S, and connect the backtracking nodes in sequence, which is the hoisting path.”).
Cai more explicitly teaches outputting a hoisting planning path as an optimal hoisting path, and the hoisting path planning model is obtained according to the hoisting path planning model construction method according to claim 1 (see at least [0028] “The invention may alternatively be expressed as a computer (such as a general purpose computer) comprising a data storage device storing program instructions for implementation by the processor to cause the processor to carry out the method, and thereby output an optimized the crane lifting path. The system may further include the crane itself, and the output crane lifting path may be transmitted to the crane, for performance by the crane.” also see at least [0059]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Sasahara with the teaching of a hoisting path planning method, comprising: determining a starting point of a hoisting path and an end point of the hoisting path; and inputting coordinates of the starting point and coordinates of the end point into a hoisting path planning model found in Li, the suggested teaching of outputting a hoisting planning path as an optimal hoisting path, and the hoisting path planning model is obtained according to the hoisting path planning model construction method according to claim 1 found in Li, and the teaching of outputting a hoisting planning path as an optimal hoisting path, and the hoisting path planning model is obtained according to the hoisting path planning model construction method according to claim 1 found in Cai. One could combine the teachings in order to have a hoisting path planning method, comprising: determining a starting point of a hoisting path and an end point of the hoisting path; and inputting coordinates of the starting point and coordinates of the end point into a hoisting path planning model, outputting a hoisting planning path as an optimal hoisting path, and the hoisting path planning model is obtained according to the hoisting path planning model construction method according to claim 1 with a reasonable expectation of success. One would have been motivated to do so in order to improve an operator’s ability to grasp the condition of the crane, support the operator in maneuvering the crane (see at least Sasahara, Page 3, lines 40-42 and Li, Page 8, lines 17-24), and further to improve worksite safety (see at least Cai, [0026] and Li, Page 8, lines 17-24).
Regarding claim 6, (Original) the combination of Sasahara, Cai, and Li teaches the hoisting path planning method according to claim 5 as detailed above.
Sasahara teaches aiming at the upper vehicle body raster graphic data (see at least Page 8 line 45-50 “The upper model data 62 is composed of three-dimensional shape information 62a and node position information 62b. The three-dimensional shape information 62a defines the position of each point of the upper model 112 by a local coordinate system in order to model the three-dimensional shape of the upper model 112 in a three-dimensional space. The nodal point position information 62 b defines the position of the nodal point connecting the lower model 111 and the upper model 112 by the local coordinate system of the lower model 111.”) and the lower vehicle body raster graphic data respectively (see at least Page 8 lines 42-44 “The lower model data 61 is composed of three-dimensional shape information 61a. The three-dimensional shape information 61a defines the position of each point of the lower model 111 by a local coordinate system in order to model the three-dimensional shape of the lower model 111.”).
Sasahara does not explicitly teach after the outputting the hoisting planning path, further comprising: aiming at the upper vehicle body raster graphic data and the lower vehicle body raster graphic data respectively, and starting to search for an upper vehicle body raster graphic data node and a lower vehicle body raster graphic data node from the starting point; aiming at each of the upper vehicle body raster graphic data node and the lower vehicle body raster graphic data node, and determining a departed cost and a predicted cost; and marking the departed cost and the predicted cost in an open list, searching for a node with a smallest total cost in the open list, and using the node with the smallest total cost as a new starting point to start search until the end point is reached.
Li more explicitly teaches after the outputting the hoisting planning path, further comprising: starting to search for a node from the starting point (see at least Page 6 line 3 “Put the hoisting starting point S into the open list of the reachable point set”); determining a departed cost and a predicted cost (see at least Page, 6 lines 15-17 “calculate the total cost f(m) of the node m =g(m)+h(m), where g(m) represents the actual cost of moving from the hoisting origin S to node m via its parent node n, g(m)=g(n)+|rm-rn |+rn·|θm-θn|+|hm-hn|, h(m) is the heuristic function of node m”); and marking the departed cost and the predicted cost in an open list (see at least Page, 6 line 14-15 “S106: If the node m is not in the reachable point set open list, add the node m to the reachable point set open list, set the node n as the parent node of the node m, and calculate the total cost f(m) of the node m =g(m)+h(m)”), searching for a node with a smallest total cost in the open list (see at least Page 6 line 5 “S103: In the open list of the set of reachable points, select the node n with the smallest total cost f(n)”), and using the node with the smallest total cost as a new starting point to start search until the end point is reached (see at least Page 6, lines 25-26 “Repeat steps S103 to S108 until the node selected from the open list of the reachable point set is the hoisting destination G”).
Examiner interprets that departed cost is encompassed at least by actual cost, a predicted cost is encompassed at least by heuristic function of node m, and marking the departed cost and the predicted cost in an open list is encompassed at least by add the node m to the reachable point set open list.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of Sasahara of aiming at the upper vehicle body raster graphic data and the lower vehicle body raster graphic data respectively with the teachings of after the outputting the hoisting planning path, further comprising: starting to search for a node from the starting point; determining a departed cost and a predicted cost; and marking the departed cost and the predicted cost in an open list, searching for a node with a smallest total cost in the open list, and using the node with the smallest total cost as a new starting point to start search until the end point is reached found in Li. One could combine the teachings in order to have a hoisting path planning method after the outputting the hoisting planning path, further comprising: aiming at the upper vehicle body raster graphic data and the lower vehicle body raster graphic data respectively, and starting to search for an upper vehicle body raster graphic data node and a lower vehicle body raster graphic data node from the starting point; aiming at each of the upper vehicle body raster graphic data node and the lower vehicle body raster graphic data node, and determining a departed cost and a predicted cost; and marking the departed cost and the predicted cost in an open list, searching for a node with a smallest total cost in the open list, and using the node with the smallest total cost as a new starting point to start search until the end point is reached with a reasonable expectation of success. One would have been motivated to do so in order to improve an operator’s ability to grasp the condition of the crane, support the operator in maneuvering the crane (see at least Sasahara, Page 3, lines 40-42 and Li, Page 8, lines 17-24), and further to improve worksite safety (see at least Cai, [0026] and Li, Page 8, lines 17-24).
Regarding claim 7, (Currently amended) the combination of Sasahara, Cai, and Li teaches the hoisting path planning method according to claim 5 as detailed above.
Sasahara does not explicitly teach after the outputting the hoisting planning path, further comprising: converting the hoisting planning path into an action sequence of the crane based on the hoisting system configuration space model; and generating a crane control instruction based on the action sequence.
Li more explicitly teaches after the outputting the hoisting planning path, further comprising: converting the hoisting planning path into an action sequence of the crane based on the hoisting system configuration space model (see at least Page 8 lines 15-16 “The hoisting control module is connected with the hoisting path planning module, and the hoisting control module is used for generating tower crane operation instructions according to the hoisting path.”); and generating a crane control instruction based on the action sequence for controlling movement of the crane (see at least Page 13 lines 5-13 “The present invention also provides an improved tower crane hoisting path planning system based on the A* algorithm. As shown in FIG. 4, the system includes a tower crane, a tower crane hoisting path planning device 10, a driving device 20 and a braking device 30. The device 20 and the braking device 30 are connected in communication with the tower crane hoisting path planning device 10, wherein: the tower crane hoisting path planning device 10 is used to plan the hoisting path from the hoisting starting point S to the hoisting end G, and according to the hoisting path planning device 10 The path generates tower crane operation instructions; the driving device 20 is installed on the tower crane, and is used to drive the tower crane to carry out hoisting operations according to the tower crane operation instructions; the braking device 30 is installed on the tower crane, and is used for the operation according to the tower crane operation instruction. The tower crane brakes.”).
Examiner interprets that action sequence of the crane is encompassed at least by crane operation instructions and generating a crane control instruction based on the action sequence is encompassed at least by drive the tower crane to carry out hoisting operations according to the tower crane operation instructions.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Sasahara with the teaching of after the outputting the hoisting planning path, further comprising: converting the hoisting planning path into an action sequence of the crane based on the hoisting system configuration space model; and generating a crane control instruction based on the action sequence for controlling movement of the crane found in Li. One could combine the teachings in order to have a hoisting path planning method after the outputting the hoisting planning path, further comprising: converting the hoisting planning path into an action sequence of the crane based on the hoisting system configuration space model; and generating a crane control instruction based on the action sequence with a reasonable expectation of success. One would have been motivated to do so in order to improve an operator’s ability to grasp the condition of the crane, support the operator in maneuvering the crane (see at least Sasahara, Page 3, lines 40-42 and Li, Page 8, lines 17-24), and further to improve worksite safety (see at least Cai, [0026] and Li, Page 8, lines 17-24).
Regarding claim 10, (Original) Sasahara teaches a crane (see at least Page 5, lines 19-20 “When the operator operates the crane 10, the crane model 110 is simultaneously displayed to operate in the same manner as the operation of the crane 10.”).
Sasahara does not explicitly teach wherein the crane is configured to execute the hoisting path planning method according to claim 5.
Li more explicitly teaches a crane, wherein the crane is configured to execute the hoisting path planning method according to claim 5 (see at least Page 13 lines 5-7 “The present invention also provides an improved tower crane hoisting path planning system based on the A* algorithm. As shown in FIG. 4, the system includes a tower crane, a tower crane hoisting path planning device 10, a driving device 20 and a braking device 30.” also see at least Page 13 lines 14-17 “The improved tower crane hoisting path planning method and device based on the A* algorithm of the present invention replaces the method of simply relying on manual driving, and the three-dimensional grid model of the construction site established by cylindrical coordinates is used as the basis for hoisting path planning, and the collection is optimized.”).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of Sasahara of a crane with the more explicit teaching of a crane, wherein the crane is configured to execute the hoisting path planning method according to claim 5 taught by Li. One could combine the teachings in order to have a crane, wherein the crane is configured to execute the hoisting path planning method according to claim 5 with a reasonable expectation of success. One would have been motivated to do so in order to improve an operator’s ability to grasp the condition of the crane, support the operator in maneuvering the crane (see at least Sasahara, Page 3, lines 40-42 and Li, Page 8, lines 17-24), and further to improve worksite safety (see at least Cai, [0026] and Li, Page 8, lines 17-24).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Sasahara et al. (JP2019059593A) in view of Cai et al. (US2016/0247067A1) view of Li et al. (CN113901611A) in further view of Ueno (US2023/0063720A1), hereinafter Sasahara, Cai, Li, and Ueno respectively.
Regarding claim 3, (Original) the combination of Sasahara, Cai, and Li teaches the hoisting path planning model construction method according to claim 1 as detailed above.
Sasahara teaches wherein the lower vehicle body data comprises walking parameters (see at least Page 8 lines 17-28 “3.10. Position/Direction Measurement Unit The position/orientation measurement unit 45 is a sensor that measures the position and orientation of the lower traveling body 11. More specifically, the position/orientation measurement unit 45 horizontally irradiates the laser light toward the target 9 installed on the ground, and receives the reflected light of the laser light from the target 9 to obtain the lower part. It is a laser measuring instrument which detects the distance from the traveling body 11 to the position/direction measuring unit 45 and the irradiation angle of the laser light. The distance and irradiation angle detected by the position/orientation measurement unit 45 can be converted to the position and orientation of the lower traveling body 11. When the output signal of the position/orientation measurement unit 45 is processed by the microcomputer 45 a, the output signal is converted into the position and direction of the lower traveling body 11. A detection value representing the position and the orientation is output from the microcomputer 45 a to the wireless module 40, and is transmitted to the wireless module 35 by the wireless module 40. The computer 31 acquires the detection value from the wireless module 35 as the position and direction of the lower traveling body 11.”).
Examiner interprets that walking parameters is encompassed at least by detection value representing the position and the orientation.
Sasahara suggests the aiming at the hoisting system configuration space model and the lower vehicle body data, generating the lower vehicle body raster graphic data of the crane comprises: scanning and traversing within the hoisting system configuration space model based on the walking parameters and the steering parameters to obtain lower vehicle body collision information (see at least Page 13, lines 17-24 “(3) In the above embodiment, one crane 10 is steered, the model 110 of the crane 10 is modeled and arranged in the virtual three-dimensional space by the computer 31, and the model 110 is displayed on the display device 32 by rendering processing. It is done. On the other hand, a plurality of cranes 10 may be steered, the models 110 of the cranes 10 may be modeled and arranged in the virtual three-dimensional space by the computer 31, and the models 110 may be displayed on the display device 32 by the rendering process… In this case, units 51 to 55 as described above are provided for any of the cranes 10. When a plurality of models 110 are displayed, they contribute to the collision prevention between the cranes 10. That is, the operator who maneuvers one crane 10 can maneuver so that the load 2 or the crane 10 does not collide with the other cranes 10.”); and generating the lower vehicle body raster graphic data of the crane according to the lower vehicle body collision information (see at least Page 8, lines 42-44 “The lower model data 61 is composed of three-dimensional shape information 61a. The three-dimensional shape information 61a defines the position of each point of the lower model 111 by a local coordinate system in order to model the three-dimensional shape of the lower model 111.”).
Ueno more explicitly teaches wherein the lower vehicle body data comprises walking parameters and steering parameters (see at least [0156] “The input unit 11 receives a movement route information request. The operator inputs the movement route information request from the input screen displayed on the display unit 12. The input unit 11 receives input of the movement conditions of the work machine regarding the BIM application A. The movement conditions include, for example, information on the movement distance of the work machine, information on the movement direction of the work machine, and information on the steering amount of the steering wheel. The movement conditions may include information on the steering mode of the work machine.”).
Examiner interprets that walking parameters is encompassed at least by information on the movement distance of the work machine and information on the movement direction of the work machine and steering parameters is encompassed at least by information on the steering amount.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of Sasahara of wherein the lower vehicle body data comprises walking parameters and the suggested teaching of the aiming at the hoisting system configuration space model and the lower vehicle body data, generating the lower vehicle body raster graphic data of the crane comprises: scanning and traversing within the hoisting system configuration space model based on the walking parameters and the steering parameters to obtain lower vehicle body collision information; and generating the lower vehicle body raster graphic data of the crane according to the lower vehicle body collision information with the more explicit teaching of wherein the lower vehicle body data comprises walking parameters and steering parameters found in Ueno. One could combine the teachings in order to have a hoisting path planning model construction method wherein the lower vehicle body data comprises walking parameters and steering parameters; the aiming at the hoisting system configuration space model and the lower vehicle body data, generating the lower vehicle body raster graphic data of the crane comprises: scanning and traversing within the hoisting system configuration space model based on the walking parameters and the steering parameters to obtain lower vehicle body collision information; and generating the lower vehicle body raster graphic data of the crane according to the lower vehicle body collision information with a reasonable expectation of success. One would have been motivated to do so in order to improve an operator’s ability to grasp the condition of the crane and further support the operator in maneuvering the crane (see at least Sasahara, Page 3, lines 40-42).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chen et al. (US2023/0159308A1) Discloses an automatic hoisting and transporting method for a tower crane, the method including: building a three-dimensional grid model for a construction site, and generating a grid node set; generating an obstacle node set and a feasible area node set; obtaining coordinates of an initial node and an end node; planning a hoisting and transporting path from the initial node to the end node, and generating corresponding operating parameters; controlling the tower crane to transport an object according to the operating parameters, and calculating a swing range of the hoisted and transported object and a swing arm at a current position; determining whether a collision may occur; and predicting whether the tower crane may be overturned.
Peng et al. (Design and Implementation of Visual Simulation System for Shipborne Crane control) Discloses a visual simulation system for a shipborne crane with virtual reality is designed and realized by OpenGL on VC++ (Visual C++) platform. The present system can realize the visual simulation for trajectory-planning and joint control along track at the certain states. The main characteristics of structure and function of the visual simulation system are described. The software scheme is brought out. It's essential for virtual simulation software to apply in the studies of kinematics and dynamics simulation for this kind of shipborne crane.
An et al. (Three dimensional hoisting simulation system based on virtual reality for truck crane) Discloses a system of three-dimensional hoisting simulation based on virtual reality technology for truck crane. Firstly, a development framework of MFC is used to enhance the interactive performance of the hoisting simulation system. With the help of underlying graphics libraries of OpenGL and Intra3D which improve the three-dimensional display effect, a visual scene of hoisting simulation is obtained. Then a hoisting simulation system with complete functions for truck crane is designed. Meanwhile, several key technologies such as collision detecting, deflection calculating and hoisting path planning, are applied to strengthen the performance of the hoisting plan. Finally, the effectiveness of the system is proved in the simulation.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALYSSA N RORIE whose telephone number is (571)272-6962. The examiner can normally be reached Monday - Friday (out of office every other Friday) 7:30 am - 5:00 pm.
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/A.R./Examiner, Art Unit 3662
/JELANI A SMITH/Supervisory Patent Examiner, Art Unit 3662
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