DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1 and 3-10 are pending.
Claims 1 and 8 have been amended.
Claim 2 has been Canceled.
Response to Amendment
Claim Interpretation Under 35 U.S.C. 112(f): The Applicant’s amendments to the claims do not overcome the 112(f) interpretation of record. The 112(f) interpretation is maintained.
Claim Interpretation Under 35 U.S.C. 101: The Applicant’s amendments to the claims do not overcome the rejection of record. The 101 rejection is maintained.
Claim Interpretation Under 35 U.S.C. 103: The Applicant’s amendments to the claims do not overcome the rejection of record. The 103 rejection is maintained.
Response to Arguments
Claim Interpretation Under 35 U.S.C. 101: Applicant's arguments filed 12/05/2025 have been fully considered but they are not persuasive.
Applicant argues “The Office Action indicates that: Claims 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 2-7 and 9-10 are also rejected as they do not recite additional elements that integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
In response, Applicant respectfully submits that amended claim 1 is non-abstract for the reasons set below.
Applicant respectfully submits that amended claim 1 does not recite an abstract idea. Rather claim 1 addresses a technical challenge of how to effectively reduce the data volume during path search, and improve the efficiency of path planning. It is clear that this is a particular problem arising in the realm of computer (When the codes run on a computer device, the computer device will carry out various steps of the method described above).
For the purpose of addressing the above problem, the claimed solution recites a hoisting path planning model construction method, a hoisting path planning method and a crane, the hoisting path planning model construction method includes: building a crane model; constructing a hoisting system configuration space model based on a current operation scenario and the crane model, and the hoisting system configuration space model includes 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; and 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 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.
It is clear that the claimed invention is necessarily rooted in computer technology in order to overcome a problem specifically arising in the realm of the computer.
It is worth mentioning the case law "Enfish LLC Vs. Microsoft Corporation et al." (case no.
2015-1244), wherein the Federal Circuit Court (CAFC) observed that software can make non-abstract improvements to computer technology, just as hardware improvements can, and sometimes the improvements can be accomplished through either route. In other words, the court emphasized that such software resulting in the non-abstract improvements to the computer technology are not abstract in itself and therefore patent eligible under 35 U.S.C 101.
Applicant respectfully submits that since Applicant's claimed invention shows technical improvements pertaining to how to solve that problem that most hoisting path planning relies on a huge amount of hoisting system data, thus resulting in relatively low efficiency in path search, the claimed subject matter in claim 1 should hence be rendered as § 101 compliant. In view of the above, Applicant respectfully submits that claim 1 is non-abstract and therefore not related to judicial exception thereby satisfying requirements of 35 U.S.C 101.”
Examiner respectfully disagrees, that the claim 1 does not recite an abstract idea as the claim limitations “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”, 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” are recited at a high level of generality and under the broadest reasonable interpretation amount to a person or a person with the aid of pen and paper observing crane data, modelling a crane, and determining a hoisting path based on the modeled situation and if a claim limitation, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the ”Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Further in order show the claimed invention is necessarily rooted in computer technology in order to overcome a problem specifically arising in the realm of the computer and/or shows an improvement to the functioning of a computer or 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) and MPEP § 2106.05(f)). Specifically the claims as currently drafted, merely recite instructions to perform a hoisting path planning model construction method on generic component(s) or machinery (a hoisting path planning model construction device). Therefore due to the recitation of the a hoisting path planning model construction device, which in light of the specification paragraphs [0133]-[0135], [0138], and [0042] from Applicant’s specification as filed amounts to an electronic device, including a memory, a processor and a computer program stored in the memory and/or a computer which, being recited at a high generality and the mere recitation of instructions to perform the method on these generic components for example in the limitations of “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”, 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” the claim would not qualify as an improvement to the functioning of a computer.
Further with regard to “the case law "Enfish LLC Vs. Microsoft Corporation et al." (case no.
2015-1244), wherein the Federal Circuit Court (CAFC) observed that software can make non-abstract improvements to computer technology, just as hardware improvements can, and sometimes the improvements can be accomplished through either route”, the court emphasized the patent eligibility of the claims through the specific implementation of a of a solution to a problem in the software arts not the addition of general purpose computers added post-hoc to an abstract idea, that is the claims recited the specific data structure along with the specification’s discussion of the prior art and how the invention improved the way the computer stores and retrieves data in memory in order to demonstrate eligibility (see at least MPEP § 2106.05(a)).
Applicant argues “Without prejudice to the above, Applicant further submits that even though claim 1 is considered to be an abstract idea (which Applicant respectfully does not agree with), Applicant's claim 1 recites one or more elements/features that, either alone or in combination, are sufficient to amount to significantly more than the judicial exception (i.e., abstract idea) for the reasons set below.
Applicant respectfully submits that the claimed solution indeed recites a series of limitations that when considered individually and as an ordered combination, provide an inventive concept sufficient to confer eligibility. As claimed in claim 1, 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.
These are meaningful limitations that add more than generally linking the use of the abstract idea to the Internet, because they solve an Internet-centric problem with a claimed solution that is necessarily rooted in computer technology. These limitations, when taken as an ordered combination, provide unconventional steps that confine the abstract idea to a particular useful application. It is clear that the claimed solution purposefully arranges some particular steps in a particular order to achieve a technological solution to a technological problem specific to the computer.
It is worth mentioning case laws Alice Vs CLS Bank wherein the supreme court observed that if a claim contains one or more additional elements that show improvement in any technical field then such claim must be considered significantly more than the abstract idea itself thereby rendering the abstract idea patent eligible under 35 U.S.C 101. Since claim 1 provides an inventive solution possessing the technical advantages of effectively reducing the data volume during path search, and improving the efficiency of path planning, claim 1 shows improvement in the field of the computer and hence is patentable under 35 U.S.C 101.
In view of the above, claim 1 is patent eligible under 35 U.S.C 101 and should be allowable.
Applicant respectfully submits that independent claims 5 and 8-10 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, claims 1-4 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, that “Applicant's claim 1 recites one or more elements/features that, either alone or in combination, are sufficient to amount to significantly more than the judicial exception (i.e., abstract idea) for the reasons set below. Applicant respectfully submits that the claimed solution indeed recites a series of limitations that when considered individually and as an ordered combination, provide an inventive concept sufficient to confer eligibility… It is worth mentioning case laws Alice Vs CLS Bank wherein the supreme court observed that if a claim contains one or more additional elements that show improvement in any technical field then such claim must be considered significantly more than the abstract idea itself thereby rendering the abstract idea patent eligible under 35 U.S.C 101. Since claim 1 provides an inventive solution possessing the technical advantages of effectively reducing the data volume during path search, and improving the efficiency of path planning, claim 1 shows improvement in the field of the computer and hence is patentable under 35 U.S.C 101” consistent with reasons stated above as the claims amount to nothing more than an instruction to apply the abstract idea using a generic computer which the courts have found to not be enough to qualify as “significantly more” when recited in a claim with a judicial exception (see at least MPEP § 2106.05(f)). Therefore the 101 rejection is maintained.
Claim Interpretation Under 35 U.S.C. 103: Applicant's arguments filed 12/05/2025 have been fully considered but they are not persuasive.
Applicant argues “ The Office Action indicates that: Claims 1-2 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 1 recites:
"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
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;
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." (Emphasis added)
Applicant submits that all the above reference documents do not disclose the following features in the amended claim 1: 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.
The reasons are as follows.
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 technical features of the present application, which is "the upper vehicle body data comprises: a main arm luffing angle, an upper vehicle body rotation angle and a hook lifting length" and "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". In other words, Sasahara does not disclose the specific components of the upper vehicle body data and specific operating methods of 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". 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 feature of the present application is 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 upper vehicle body data comprises: a main arm luffing angle, an upper vehicle body rotation angle and a hook lifting length" and "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". In other words, Cai does not disclose the specific components of the upper vehicle body data and specific operating methods of 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". 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 feature of the present application is 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* 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. This invention optimizes the set data structure, significantly reduces computation time, and improves the efficiency of path planning. By generating an obstacle point set, it avoids the visual blind spot problem present in manual operation. The use of a specific heuristic function makes the planned hoisting path more consistent with the tower crane's working scenario and hoisting logic, thereby improving hoisting safety and efficiency.
However, referring to the full specification of Li, Li does not disclose the technical features of the present application, which is "the upper vehicle body data comprises: a main arm luffing angle, an upper vehicle body rotation angle and a hook lifting length" and "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". In other words, Li does not disclose the specific components of the upper vehicle body data and specific operating methods of 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". 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 feature of the present application is 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 upper vehicle body data comprises: a main arm luffing angle, an upper vehicle body rotation angle and a hook lifting length" and "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". In other words, Ueno does not disclose the specific components of the upper vehicle body data and specific operating methods of 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". 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 feature of the present application is 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.
Furthermore, 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.
Thus, 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.
Therefore, Applicant respectfully submits that independent claim 1 should be allowable.
By virtue of their respective direct or indirect dependencies on the amended inventive claim 1, claims 3-7 and 9 are also inventive.
The amended claim 8 has the above highlighted features as the amended claim 1, thus, the independent claim 8 should be allowable.
By virtue of their respective direct or indirect dependencies on the amended inventive claim 8, claim 10 is also inventive.”
Examiner respectfully 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). In particular Sasahara et al. (JP2019059593A), hereinafter Sasahara teaches 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 to construct a hoisting path planning model; 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, teaches utilizing crane information to generate candidate crane lifting paths and calculating upper vehicle body collision information. Li et al. (CN113901611A), hereinafter Li teaches, 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, and further Ueno was not utilized in order to teach any of the limitations of 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 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; 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. Therefore the teachings of Sasahara, Cai, and Li can be combine in order to have 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 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; 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 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.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: simulation module configured to build a crane model in claim 8, configuration space module configured to construct a hoisting system configuration space model based on a current operation scenario and the crane model in claim 8, grouping processing module configured to aim at the hoisting system configuration space model and the upper vehicle body data, generate upper vehicle body raster graphic data of the crane; and configured to aim at the hoisting system configuration space model and the lower vehicle body data, generate lower vehicle body raster graphic data of the crane in claim 8, construction module configured to use an A-star algorithm and combine the upper vehicle body raster graphic data and the lower vehicle body raster graphic data to construct a hoisting path planning model in claim 8, determination module configured to determine a starting point of a hoisting path and an end point of the hoisting path in claim 9, and planning module configured to input the starting point and the end point into a hoisting path planning model and output a hoisting planning path in claim 9.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. Specifically, the simulation module, configuration space module, grouping processing module, and construction module are a part of the hoisting path planning model construction device and the determination module, and planning module are a part of the hoisting path planning device and can be an electronic device, including a memory, a processor and a computer program stored in the memory and/or a computer (see at least [0133]-[0135], [0138], and [0042] from Applicant’s specification as filed).
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 and 3-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1 and 8 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite a hoisting path planning model construction method and a hoisting path planning model construction device respectively.
Claim 1 recites the limitations “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”, “ 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”, “determining the hook lifting length”, and “calculating upper vehicle body collision information” and Claim 8 recites the limitations “a simulation module configured to build a crane model”, “a configuration space module configured to construct a hoisting system configuration space model based on a current operation scenario and the crane model, and the hoisting system configuration space model comprises upper vehicle body data of a crane and lower vehicle body data of the crane”, “a construction module configured to use an A-star algorithm and combine the upper vehicle body raster graphic data and the lower vehicle body raster graphic data to construct a hoisting path planning model” , “determine the hook lifting length”, and “calculate upper vehicle body collision information”, as drafted, is a process that, under the broadest reasonable interpretation, covers performance of limitations in the mind, but for the recitation of generic computer components. That is other than reciting a computer nothing in the claims precludes the steps from being performed in the mind or by a human using pen and paper. For example, but for the recitation of a computer, these claims encompass a person observing crane data, modelling a crane, and determining a hoisting path based on the modeled situation. If a claim limitation, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the ”Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Claim 1 recites the additional element “ 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 Claim 8 recites additional element “ a grouping processing module configured to aim at the hoisting system configuration space model and the upper vehicle body data, generate upper vehicle body raster graphic data of the crane; and configured to aim at the hoisting system configuration space model and the lower vehicle body data, generate lower vehicle body raster graphic data of the crane”, which are recited at a high level of generality and amount to mere data gathering which is a form of insignificant extra-solution activity. Accordingly, the additional limitations do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claims 1 and 8 as a whole merely describes how to generally “apply” the concept of performing hoisting path planning. The claimed computer component is recited at a high generality and are merely invoked as a tool to perform an existing process. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Accordingly, even in combination, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea. As such the claims are ineligible.
Claims 3-7 and 9-10 are also rejected as they do not recite additional elements that integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Additionally, positively reciting a control step to control the crane to move in accordance to the hoisting planning path (see at least [0097] from Applicant’s specification as filed), may help to overcome the rejection.
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 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.
Regarding claim 1, (Currently Amended) Sasahara teaches a hoisting path planning model construction method, comprising: building 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 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 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 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 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; 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.
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.”) and 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).”).
Li teaches using an A-star algorithm to construct a hoisting path planning model (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;”).
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, 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 to construct a hoisting path planning model; 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 and calculating upper vehicle body collision information found in Cai and the teaching of 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 found in Li. One could combine the teachings in order to have 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 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; 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 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 and Li, Page 8, lines 17-24).
Regarding claim 4, (Original) the combination of Sasahara, Cai, and Li teaches the hoisting path planning model construction method according to claim 1 as detailed above.
Sasahara does not explicitly teach 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.
Sasahara teaches 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.” 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 lower vehicle body raster graphic data (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”).
Cai 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).”).
Li teaches using the A-star algorithm, performing a path planning to obtain path planning model (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 combine the teaching of Sasahara of upper vehicle body raster graphic data and lower vehicle body raster graphic data, 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 using the A-star algorithm, performing a path planning to obtain path planning model found in Li. One could combine the teachings in order to have a hoisting path planning model construction 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, (Original) 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 (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 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 8, (Currently Amended) Sasahara teaches a hoisting path planning model construction device, comprising: a simulation module configured to build 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”); a configuration space module configured to construct 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.”), and 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.”); a grouping processing module configured to aim at the hoisting system configuration space model and the upper vehicle body data, generate 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”); and configured to aim at the hoisting system configuration space model and the lower vehicle body data, generate 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 a construction module configured to combine 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 grouping processing module further configured to: determine 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 generate 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 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 use an A-star algorithm and combine the upper vehicle body raster graphic data and the lower vehicle body raster graphic data to construct a hoisting path planning model; divide the hook lifting length into a preset number of lifting intervals; and aim at an endpoint of each lifting intervals, perform a traversal search within the hoisting system configuration space model based on the main arm luffing angle and the upper vehicle body rotation angle, calculate upper vehicle body collision information.
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.”) and calculate 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).”).
Li teaches use an A-star algorithm and combine the upper vehicle body raster graphic data and the lower vehicle body raster graphic data to construct a hoisting path planning model (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;”); divide 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 aim at an endpoint of each lifting intervals, perform 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;”).
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 device, comprising: a simulation module configured to build a crane model; a configuration space module configured to construct a hoisting system configuration space model based on a current operation scenario and the crane model, and the hoisting system configuration space model comprises upper vehicle body data of a crane and lower vehicle body data of the crane; a grouping processing module configured to aim at the hoisting system configuration space model and the upper vehicle body data, generate upper vehicle body raster graphic data of the crane; and configured to aim at the hoisting system configuration space model and the lower vehicle body data, generate lower vehicle body raster graphic data of the crane; and a construction module configured to combine the upper vehicle body raster graphic data and the lower vehicle body raster graphic data to construct a hoisting path planning model; wherein the upper vehicle body data comprises: a main arm luffing angle, an upper vehicle body rotation angle and a hook lifting length; the grouping processing module further configured to: determine the hook lifting length; and generate the upper vehicle body raster graphic data of the crane with the teaching of utilizing crane information to generate candidate crane lifting paths and calculate upper vehicle body collision information found in Cai and the teaching of use an A-star algorithm and combine the upper vehicle body raster graphic data and the lower vehicle body raster graphic data to construct a hoisting path planning model; divide the hook lifting length into a preset number of lifting intervals; and aim at an endpoint of each lifting intervals, perform a traversal search within the hoisting system configuration space model based on the main arm luffing angle and the upper vehicle body rotation angle found in Li. One could combine the teachings in order to have a hoisting path planning model construction device, comprising: a simulation module configured to build a crane model; a configuration space module configured to construct a hoisting system configuration space model based on a current operation scenario and the crane model, and the hoisting system configuration space model comprises upper vehicle body data of a crane and lower vehicle body data of the crane; a grouping processing module configured to aim at the hoisting system configuration space model and the upper vehicle body data, generate upper vehicle body raster graphic data of the crane; and configured to aim at the hoisting system configuration space model and the lower vehicle body data, generate lower vehicle body raster graphic data of the crane; and a construction module configured to use an A-star algorithm and combine the upper vehicle body raster graphic data and the lower vehicle body raster graphic data to construct a hoisting path planning model; wherein the upper vehicle body data comprises: a main arm luffing angle, an upper vehicle body rotation angle and a hook lifting length; the grouping processing module further configured to: determine the hook lifting length; divide the hook lifting length into a preset number of lifting intervals; and aim at an endpoint of each lifting intervals, perform a traversal search within the hoisting system configuration space model based on the main arm luffing angle and the upper vehicle body rotation angle, calculate upper vehicle body collision information, and generate the upper vehicle body raster graphic data of the crane 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 and Li, Page 8, lines 17-24).
Regarding claim 9, (Original) Sasahara does not explicitly teach a hoisting path planning device, comprising: a determination module configured to determine a starting point of a hoisting path and an end point of the hoisting path; and a planning module configured to input the starting point and the end point into a hoisting path planning model and output a hoisting planning path, wherein 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 device, comprising: a determination module configured to determine 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 a planning module configured to input the starting point and 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 output a hoisting planning path, wherein 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 output a hoisting planning path, wherein 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 device, comprising: a determination module configured to determine a starting point of a hoisting path and an end point of the hoisting path; and a planning module configured to input the starting point and the end point into a hoisting path planning model found in Li, the suggested teaching of output a hoisting planning path, wherein 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 output a hoisting planning path, wherein 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 teaching in order to have a hoisting path planning device, comprising: a determination module configured to determine a starting point of a hoisting path and an end point of the hoisting path; and a planning module configured to input the starting point and the end point into a hoisting path planning model and output a hoisting planning path, wherein 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 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.
Discenzo et al. (US2007/0050115A1) Discloses facilitating crane control and anti-sway are utilizing a diagnostic component that includes a model component and a control component. The diagnostic component interfaces with an extrinsic data analysis component and a controller component. The diagnostic component receives operating condition information from the extrinsic data analysis component and performs predictive modeling, based on a current status and stored information. Further, the diagnostic component predicts the affect of the operating conditions on a crane and implements and/or recommends actions to mitigate the affect of the existing and/or predicted operating conditions. The diagnostic component further mitigates crane sway and/or induces crane sway to reduce container transit time. Intelligent agents are employed to provide trajectory planning and execution and/or to detect potential component failure.
Kang et al. (US2009/0182537A1) Discloses a simulation method and a simulation system for a construction crane. The simulation system includes an input device, a processing device, and a display device. The input device is used for inputting an instruction. Furthermore, the processing device includes a computation unit, a collision detection unit, a storage unit, and a graphic unit. The computation unit is used for computing the position and the direction of each part of the construction crane and the suspension parts. The collision detection model is used for detecting whether each part of the construction crane and the suspension parts will be in collision. Besides, the data obtained from the simulation method performed in the simulation system is saved in the storage unit. The graphic unit displays the 3D dynamics images of the construction crane and the suspension parts on the display device, corresponding to the data obtained from the computation unit.
Pivac (US2021/0370509A1) Discloses a system for performing interactions within a physical environment including: a robot base that undergoes movement relative to the environment; a robot arm mounted to the robot base, the robot arm including an end effector mounted thereon; a first tracking system that measures a robot base position; a second tracking system that measures movement of the robot base; and, a control system that uses a robot base position to at least partially control the robot arm to move the end effector along an end effector path, wherein the control system: determines the robot base position at least in part using signals from the first tracking system; and, in the event of failure of the first tracking system: determines a robot base position using signals from the second tracking system; and, controls the robot arm to move the end effector along the end effector path at a reduced end effector speed.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/A.R./Examiner, Art Unit 3662
/JELANI A SMITH/Supervisory Patent Examiner, Art Unit 3662