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
This action is in response to the Applicant’s arguments and amendments filed on 11/19/2025. Applicant amended claims 1, 2, 9; and added claim 10. Claims 1-10 are pending and are examined below.
RESPONSE TO REMARKS AND ARGUMENTS
In regards to the claim rejections under § 101, Applicant’s amendments and arguments filed on 11/19/2025 have been fully considered but are unpersuasive.
As to claim 1, Applicant argues that the claimed invention provides details regarding a specific improvement to a learning model itself. Applicant that the claims now recite not a generic learning model that is being applied to an environment of a mobile crane, but a specific improved learning model that is implemented with specific additional evaluation functions that are related to a mobile crane.
Examiner respectfully disagrees. The claimed invention merely directs a generic application of machine learning technology in a technological environment. To aid in the analysis of the instant application, Examiner refers to Recentive Analytics, Inc. v. Fox Corp.1 (hereinafter Recentive) wherein the court held that “patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.” (Recentive, p. 18.) In Recentive, the claimed invention — in a similar fashion as Applicant’s instant claimed invention — utilized machine learning (ML) features such as “event parameters,” “neural network ML model,” “training the ML model to identify relationships between different event parameters and the one or more event target features using historical data,” “providing the one or more user-specific event parameters and the one or more user-specific event weights to the trained ML model,” “generating, via the trained ML model, a schedule for the future series of live events that is optimized relative to the one or more prioritized event target features” and the like. Summarizing, Applicant’s claimed invention is similar to Recentive’s in the notion that both inventions are reciting generic features of machine learning models to arrive at an optimized result (i.e., crane position placement and live event schedule, respectively).
In Recentive, the court reasoned that rather than providing a specific implementation of a solution to a problem in the software arts or a specific means or method that solves a problem in an existing technological process, the claims merely used machine learning in a new environment. (See Recentive, p. 13.) The court rationalized, “We have long recognized that ‘[a]n abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment,’” and “the application of existing technology to a novel database does not create patent eligibility.” (See Recentive, p. 14.) The court concluded that the claims are ineligible because “patents may be directed to abstract ideas where they disclose the use of an ‘already available [technology], with [its] already available basic functions, to use as [a] tool[] in executing the claimed process.’” (See Recentive, p. 15.)
Now, Examiner respectfully submits that the foregoing analysis applies to Applicant’s claimed invention as Applicant is merely applying a generic machine learning model to the technological environment of crane positioning. Specifically as to the amended language, the mere addition of further evaluation functions does not go beyond a generic use of machine learning technology as it is well-known that machine learning models use evaluation functions to arrive at an optimal result. In fact, the functions explicitly recite performing the mental process of performing evaluations. Overall, said features do not lay out improvements to the machine learning model itself. In contrast, the claim merely limits the recited abstract ideas to a technological environment, which is insufficient for patent eligibility.
Accordingly, the rejections under § 101 are maintained.
In regards to the claim rejections under 103, Applicant’s arguments and amendments filed on 11/19/2025 have been fully considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
CLAIM REJECTIONS—35 U.S.C. § 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.
The patent eligibility test is performed below for independent claims 1 and 9.
Step 1—Does the claim fall within a statutory category?
Claim 1: Yes, the claim recites a machine or manufacture.
Claim 9: Yes, the claim recites a process.
Step 2A, Prong One—Is a judicial exception recited?
Claim 1 is provided below with the abstract idea indicated in bold and additional elements without bold. Examiner notes that claim 9 recite similar subject matter but for minor differences; hence, the analysis of claim 1 will pertain to claim 9 as well.
1. A crane position determination device mounted on a mobile crane including a travelling body and outriggers, the crane position determination device comprising:
an input unit that receives an input of a working condition of crane work;
a determination unit that has a learning model and that determines a placement position of the mobile crane based on an output of the learning model; and
a notification unit that provides notification of the placement position of the mobile crane, wherein
the learning model is a model that learns to output information pertaining to an optimum placement position of the mobile crane with respect to the working condition based on a reward function including at least one evaluation function pertaining to the crane work,
wherein the learning model is a neural network executing a neural network program and including a plurality of nodes forming an input layer, a hidden layer, and an output layer, a number of nodes of the output layer is a number obtained by adding a number of candidates for a stop position at which the traveling body is stopped to a number of candidates for an extension position at which each of the outriggers extend;
wherein a coupling strength between the nodes of the output layer is weighted by θ assigned by the neural network program reading a parameter database;
wherein the evaluation function includes
an operation efficiency evaluation function of evaluating operation efficiency of the mobile crane,
a path evaluation function of evaluating a carrying path of a load, and a region evaluation function of evaluating appropriateness of a placement position of the mobile crane with respect to a work region, and
a region evaluation function of evaluating appropriateness of a placement position of the mobile crane with respect to a work region,
wherein the input unit, the determination unit, and the notification unit are each implemented via at least one processor.
The above shows: yes, a judicial exception is recited. But for the additional elements, the claim limitations pertaining to determining a placement position of a mobile crane; calculating a reward value; determining a target value; and performing evaluations are processes which can practically be performed in the human mind with or without the use of a physical aid. Specifically, the broadest reasonable interpretation (BRI) of the claim encompasses performing evaluations and judgments over received data to arrive at an optimum value. The courts have held such forms of observation, evaluation, judgment, or opinion to represent the abstract idea of a mental process. As a result, the bolded limitations represent a mental process. Accordingly, the claim recites an abstract idea. (See MPEP 2106.04(a)(2)(C)(III).)
Step 2A, Prong Two—Is the abstract idea integrated into a practical application?
No. The claims as a whole merely use generic computer components—i.e., a determination unit and a processor—that are recited at a high level of generality such that they cannot be considered more than mere instructions to apply the judicial exception using generic computer components. Therefore, the abstract idea is not integrated into a practical application.
Furthermore, the claimed learning model and associated limitations do not integrate the abstract idea into a practical application because the claim is merely using the learning model in a generic fashion to apply the abstract idea in a certain technological environment (i.e., crane position determination) without putting forth an improvement in how the learning model functions. Outside of nominal mentions of ordinary features and functions of generic neural networks, the claimed invention fails to set forth sufficient detail as to how the neural network accomplishes such in a way that differs from performing ordinary features of generic neural networks. Hence, the claimed learning model merely indicates a field of use or technological environment in which the judicial exception is performed. That is, the claim merely confines the use of the abstract idea to a particular technological environment (neural networks) and thus fails to add an inventive concept to the claims. (See MPEP 2106.05(h))
Step 2B—Does the claim provide an inventive concept?
No. The additional elements of the claims amount to:
Insignificant pre-solution activity in the form of mere data gathering:
receives an input of a working condition of crane work
output information pertaining to an optimum placement position of the mobile crane with respect to the working condition
Insignificant post-solution activity:
provides notification of the placement position of the mobile crane
Generic computer components which carry out insignificant extra-solution activity:
input unit
notification unit
Additionally, the claimed learning model does not provide an inventive concept because, as explained above, the claim is merely using the learning model in a generic fashion to apply the abstract idea without placing any limits on how the learning model functions, and the claim merely confines the use of the abstract idea to a particular technological environment (neural networks) and thus fails to add an inventive concept to the claims.
Claims 2–8 and 10 from claim 1 but do not render the claimed invention patent eligible because they are directed to:
Additional mental steps:
evaluating appropriateness of a placement position with respect to an extension state of an outrigger
calculating operation efficiency,
determining a placement position of the outrigger,
determining an orientation of the mobile crane and/or an extension width of the outrigger,
calculating J(Work) through an equation,
calculating J(Path) through an equation,
indicating appropriateness of a stop position with respect to a work region, and
calculating environmental coefficients based on the environmental data;
or insignificant extra-solution activity (e.g., gathering data):
outputting information pertaining to an optimum placement position of an outrigger,
providing notification of the placement position of the outrigger,
outputting the orientation of the mobile crane and/or the extension width of the outrigger, and
receiving environmental data measured at a work site
Claims 1–10 do not pass the patent eligibility test. Accordingly, claims 1–10 are rejected under § 101.
CLAIM REJECTIONS—35 U.S.C. § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. §§ 102 and 103 (or as subject to pre-AIA 35 U.S.C. §§ 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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, 2 and 6–9 are rejected under § 103 as being unpatentable over Rösth et al. (US20210347288A1; “Rösth”) in view of Tsuneki et al. (US20200257252A1; “Tsuneki”), in view of Vogt et al. (US9302890B1; “Vogt”), in view of Quraishi (US5548512A; “Quraishi”), in view of Stake (US20130079974A1; “Stake”), in view of Rotem (US20210206605A1; “Rotem”) and in view of Nakamura et al. (US20190003152A1; “Nakamura”)
As to claim 1, Rösth discloses a crane position determination device mounted on a mobile crane including a travelling body and outriggers, the crane position determination device comprising:
an input unit that receives an input of a working condition of crane work (“Input unit 20” – see at least ¶ 49. Continuing, the input unit 20 may receive a “trajectory instruction 18”—i.e., a working condition. See at least ¶ 36.);
a determination unit that has a learning model and that determines a placement position of the mobile crane based on an output of the learning model (“[C]rane controller 22 comprises a machine learning algorithm including a neural network trained to calculate the movements of the crane components based on the received trajectory instruction 18 in relation to the current position of the crane boom system 6” – see at least ¶ 39. See also ¶ 37, which ties a trajectory instruction 18 to a “future wanted position” of the crane. Hence, calculating movements of crane components meets the broadest reasonable interpretation (BRI) of determining a placement position of a mobile crane because the calculated movements yield a desired final placement position.);
wherein the determination unit is further configured to sample a plurality of data sets (“Generally, machine learning algorithms, herein implemented by a neural network, build a mathematical model based on sample data, known as ‘training data’, in order to make predictions or decisions without being explicitly programmed to perform the task.” ¶ 50. )
the learning model is a model that learns to output information pertaining to an optimum placement position of the mobile crane with respect to the working condition pertaining to the crane work (“The neural network may be further trained to calculate the movements of the crane components to take into account the lifting capacity, the energy efficiency, and/or the speed of the movement if there are several degrees of freedom (i.e. if the movements could be realized by various alternatives)” – see at least ¶ 39.); and
wherein the input unit, the determination unit, and the notification unit are each implemented via at least one processor (Crane controller 22 – see at least ¶ 39 and FIG. 2.),
wherein the evaluation function includes an operation efficiency evaluation function of evaluating operation efficiency of the mobile crane (“The neural network may be further trained to calculate the movements of the crane components to take into account … the energy efficiency … of the movement” – see at least ¶ 39.).
Rösth fails to explicitly disclose:
the learning model is a model that learns to output information pertaining to an optimum placement position of the mobile crane with respect to the working condition based on a reward function including at least one evaluation function pertaining to the crane work;
calculate, based on the reward function, a reward value for each data set of the plurality of data sets; and
determine, based on the reward value calculated for the plurality of data sets, a target value output from the output layer for a corresponding data set.
Nevertheless, Tsuneki teaches:
a learning model is a model that learns to output information pertaining to an optimum placement position of a mobile vehicle with respect to a working condition based on a reward function including at least one evaluation function pertaining to the vehicle’s work (A “reward output unit 2021 calculates an evaluation function value” based on a position error, and the “reward output unit 2021 outputs a reward by comparing the evaluation function value f(PD(S)) and the evaluation function value f(PD(S′))” such that “the machine learning device 200 can select an optimal action … with respect to the state S including the position error information and the position commands” – see at least ¶ 102. Here, the reward output unit meets the BRI of a reward function because it takes in an input and outputs a reward based on at least one evaluation function.);
calculate, based on the reward function, a reward value for each data set of the plurality of data sets (A “reward output unit 2021 calculates an evaluation function value” based on a position error, and the “reward output unit 2021 outputs a reward by comparing the evaluation function value f(PD(S)) and the evaluation function value f(PD(S′))” such that “the machine learning device 200 can select an optimal action … with respect to the state S including the position error information and the position commands” – see at least ¶ 102.); and
determine, based on the reward value calculated for the plurality of data sets, a target value output from the output layer for a corresponding data set (A “reward output unit 2021 calculates an evaluation function value” based on a position error, and the “reward output unit 2021 outputs a reward by comparing the evaluation function value f(PD(S)) and the evaluation function value f(PD(S′))” such that “the machine learning device 200 can select an optimal action … with respect to the state S including the position error information and the position commands” – see at least ¶ 102.).
Rösth discloses: a crane position determination device, comprising a determination unit that has a learning model and that determines a placement position of the mobile crane based on an output of the learning model. Tsuneki teaches: a learning model utilizing a reward function including at least one evaluation function to arrive at an optimum placement position of a mobile vehicle.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Rösth to include the feature of: a learning model is a model that learns to output information pertaining to an optimum placement position of a mobile vehicle with respect to a working condition based on a reward function including at least one evaluation function pertaining to the vehicle’s work; calculate, based on the reward function, a reward value for each data set of the plurality of data sets; and determine, based on the reward value calculated for the plurality of data sets, a target value output from the output layer for a corresponding data set, as taught by Tsuneki, with a reasonable expectation of success because one of ordinary skill in the art would have readily recognized that a reward function including at least an evaluation function is part and parcel of typical neural network applications in the vehicle control arts, as such is useful for training an agent (e.g., mobile crane) via reinforcement to perform desired actions based on evaluating the agent’s action in a given environment.
The combination of Rösth and Tsuneki fails to explicitly disclose: a notification unit that provides notification of the placement position of the mobile crane.
Nevertheless, Vogt teaches: a notification unit that provides notification of the placement position of the mobile crane (A “display interface 32” is configured to display the “real-time position of the crane” – see at least col. 5, ll. 39–58 and FIG. 2.)
Rösth discloses: a crane position determination device, comprising a determination unit that has a learning model and that determines a placement position of the mobile crane based on an output of the learning model. Tsuneki teaches: a learning model utilizing a reward function including at least one evaluation function to arrive at an optimum placement position of a mobile vehicle. Vogt teaches: providing notification of the placement position of the mobile crane.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Rösth and Tsuneki to include the feature of: a notification unit that provides notification of the placement position of the mobile crane, as taught by Vogt, with a reasonable expectation of success because this feature is useful “to provide information to the crane operator.” (Vogt, col. 6, ll. 17–25.)
The combination of Rösth, Tsuneki and Vogt fails to explicitly disclose:
wherein the learning model is a neural network executing a neural network program and including a plurality of nodes forming an input layer, a hidden layer, and an output layer, a number of nodes of the output layer is a number obtained by a number of candidates for a stop position at which the traveling body is stopped; and
input the plurality of data sets to the input layer.
Nevertheless, Quraishi teaches:
wherein a learning model is a neural network executing a neural network program and including a plurality of nodes forming an input layer, a hidden layer, and an output layer, a number of nodes of the output layer is a number obtained by a number of candidates for a position pertaining to the traveling body (A navigation system for a traveling body comprises “neural networks,” wherein “[e]ach network was fully interconnected with five input layer nodes, forty-five hidden layer nodes, and a single output layer node.” See at least col. 6, ll., 16–22 and FIGS. 3A–3C, 4, 5. Additionally, the output layer produces an “output representing an identification of the spacial position of [the] vehicle.” See at least claim 1.); and
input the plurality of data sets to the input layer (input data sets – see at least col. 3, ll. 45–54.)
Rösth discloses: a crane position determination device, comprising a determination unit that has a learning model and that determines a placement position of the mobile crane based on an output of the learning model. Tsuneki teaches: a learning model utilizing a reward function including at least one evaluation function to arrive at an optimum placement position of a mobile vehicle. Vogt teaches: providing notification of the placement position of the mobile crane. Quraishi teaches: a neural network program including a plurality of nodes forming an input layer, a hidden layer, and an output layer, wherein a number of nodes of the output layer is a number obtained by a number of candidates for a position pertaining to the traveling body; and inputting data sets to an input layer.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Rösth, Tsuneki and Vogt to include the feature of: wherein a learning model is a neural network executing a neural network program and including a plurality of nodes forming an input layer, a hidden layer, and an output layer, a number of nodes of the output layer is a number obtained by a number of candidates for a position pertaining to the traveling body; and input the plurality of data sets to the input layer, as taught by Quraishi, to yield the claim limitation at issue with a reasonable expectation of success because (1) it is well-known in the art that a neural network, by definition, comprises an input layer, hidden layer and output layer with a respective number of nodes; and (2) this feature is useful for providing “useful outputs identifying the position and orientation of [a] vehicle.” (Quraishi, col. 5, ll. 43–48.) Furthermore, it is well-known in the machine learning arts that input data are provided to an input layer.
Additionally, one of ordinary skill in the art would have found it obvious in light of the combination of Rösth, Tsuneki, Vogt and Quraishi to arrive at a number of nodes of the output layer is a number obtained by a number of candidates for a stop position at which the traveling body is stopped. Recall that Rösth discloses a learning model which determines a placement position of the mobile crane (i.e., a stop position of a traveling body) based on an output of the learning model. One of ordinary skill in the art would have recognized from the ordinary understanding of neural networks that Rösth’s neural network, comprising an output layer as taught by Quraishi, would necessarily reflect candidates for a stop position of the traveling body (crane) in order to successfully operate. Otherwise, Rösth’s neural network would be unable to output a placement position of a crane.
The combination of Rösth, Tsuneki, Vogt and Quraishi fails to explicitly disclose a number of nodes of the output layer is a number obtained by a number of candidates for an extension position at which each of the outriggers extend.
Nevertheless, Stake teaches: outputting information pertaining to an optimum placement position of an outrigger with respect to a working condition (“A valid operation position” of an outrigger of a crane may be determined and outputted – see at least ¶ 62 and FIGS. 6–13. The foregoing is necessarily performed in respect to the crane’s working environment (i.e., working condition).).
Rösth discloses: a crane position determination device, comprising a determination unit that has a learning model and that determines a placement position of the mobile crane based on an output of the learning model. Tsuneki teaches: a learning model utilizing a reward function including at least one evaluation function to arrive at an optimum placement position of a mobile vehicle. Vogt teaches: providing notification of the placement position of the mobile crane. Quraishi teaches: a neural network program including a plurality of nodes forming an input layer, a hidden layer, and an output layer, wherein a number of nodes of the output layer is a number obtained by a number of candidates for a position pertaining to the traveling body; and inputting data sets to an input layer. Stake teaches: determining an optimum placement position of an outrigger with respect to a working condition.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Rösth, Tsuneki, Vogt and Quraishi with the feature of: outputting information pertaining to an optimum placement position of an outrigger with respect to a working condition, as taught by Stake, with a reasonable expectation of success because these features are useful for “monitoring the status of the crane outriggers.” (Stake, ¶ 2.)
Now, recall that Rösth discloses utilizing a learning model to determine a placement position of a mobile crane. One of ordinary skill in the art would have recognized that, with a reasonable expectation of success, Rösth’s learning model can yield a placement position of an outrigger as an outrigger is a typical mechanical component of a crane, and can thus function as a simple substitution for Rösth’s “crane tip” in Rösth’s processing. The foregoing is useful for exploiting the advantages of a learning model for yielding an optimum position of an outrigger. And building upon that, one of ordinary skill in the art would have recognized from the ordinary understanding of neural networks that Rösth’s neural network, comprising an output layer as taught by Quraishi and adapted to output an extension of an outrigger as taught by Stake, would necessarily reflect candidates for an extension position of an outrigger in order to successfully operate.
The combination of Rösth, Tsuneki, Vogt, Quraishi and Stake fails to explicitly disclose:
wherein a coupling strength between the nodes of the output layer is weighted by θ assigned by the neural network program reading a parameter database; and
Nevertheless, Mita teaches:
wherein a coupling strength between the nodes of the output layer is weighted by θ assigned by the neural network program reading a parameter database (“Learning of a neural network entails deciding …coupling strength Wkj between the intermediate layer and the output layer.” See at least col. 10, ll. 66–67 to col. 11, ll. 1–21 and FIG. 2. Also, “at step S402, the initial values of weighting coefficients (coupling strengths) Wji, Wkj are applied.” See at least col. 11, ll. 30–34. NOTE: An initial value of Wkj is necessarily acquired from a parameter database given the ordinary understanding of computer processing, which entails that data is typically acquired from a database.).
Rösth discloses: a crane position determination device, comprising a determination unit that has a learning model and that determines a placement position of the mobile crane based on an output of the learning model. Tsuneki teaches: a learning model utilizing a reward function including at least one evaluation function to arrive at an optimum placement position of a mobile vehicle. Vogt teaches: providing notification of the placement position of the mobile crane. Quraishi teaches: a neural network program including a plurality of nodes forming an input layer, a hidden layer, and an output layer, wherein a number of nodes of the output layer is a number obtained by a number of candidates for a position pertaining to the traveling body; and inputting data sets to an input layer. Stake teaches: determining an optimum placement position of an outrigger with respect to a working condition. Mita teaches: obtaining a coupling strength between nodes of an output layer, wherein the weight θ of the coupling strength is updated based on a calculated error between an output of the output layer and the target value determined for each data set of the plurality of data sets.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Rösth, Tsuneki, Vogt, Quraishi and Stake with the features of: wherein a coupling strength between the nodes of the output layer is weighted by θ assigned by the neural network program reading a parameter database; and wherein the weight θ of the coupling strength is updated based on a calculated error between an output of the output layer and the target value determined for each data set of the plurality of data sets, as taught by Mita, with a reasonable expectation of success because (1) it is well-known in the art that output nodes of a typical neural network are associated with a coupling strength; and (2) this feature is useful for providing “a reduction in the scale of the neural network and greater efficiency.” (Mita, col. 5, ll. 15–26.)
The combination of Rösth, Tsuneki, Vogt, Quraishi, Stake and Mita fails to explicitly disclose: wherein the evaluation function includes a path evaluation function of evaluating a carrying path of a load.
Nevertheless, Rotem teaches: a path evaluation function of evaluating a carrying path of a load (“The calculated route may be calculated based on load's loading point and destination point, …, the load data (dimensions, weight, shape, contents, etc.)” ¶ 120.)
Rösth discloses: a crane position determination device, comprising a determination unit that has a learning model and that determines a placement position of the mobile crane based on an output of the learning model. Tsuneki teaches: a learning model utilizing a reward function including at least one evaluation function to arrive at an optimum placement position of a mobile vehicle. Vogt teaches: providing notification of the placement position of the mobile crane. Quraishi teaches: a neural network program including a plurality of nodes forming an input layer, a hidden layer, and an output layer, wherein a number of nodes of the output layer is a number obtained by a number of candidates for a position pertaining to the traveling body; and inputting data sets to an input layer. Stake teaches: determining an optimum placement position of an outrigger with respect to a working condition. Mita teaches: obtaining a coupling strength between nodes of an output layer, wherein the weight θ of the coupling strength is updated based on a calculated error between an output of the output layer and the target value determined for each data set of the plurality of data sets. Rotem teaches: a path evaluation function of evaluating a carrying path of a load.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Rösth, Tsuneki, Vogt and Quraishi to include the feature of: a path evaluation function of evaluating a carrying path of a load, as taught by Rotem, with a reasonable expectation of success because this feature is useful “for selecting the optimal route among the routes, timewise, energy-wise, and/or minimal crane-wear-wise.” (Rotem, ¶ 74.)
The combination of Rösth, Tsuneki, Vogt, Quraishi, Stake, Mita and Rotem fails to explicitly disclose: wherein the evaluation function includes a region evaluation function of evaluating appropriateness of a placement position of the mobile crane with respect to a work region.
Nevertheless, Nakamura teaches: a region evaluation function of evaluating appropriateness of a placement position of a mobile work machine with respect to a work region (“The work assist system for the work machine … is configured to determine the region to be excavated S [i.e., work region] …, and to calculate the position of the excavator 1 suited for excavation in the next excavation operation as the work position Pw …. In addition, the work assist system is configured to calculate the distance Lw from the frontmost end Cf of the lower travel structure 10 of the hydraulic excavator 1 to the work position Pw …. It is thereby possible to guide the hydraulic excavator 1 to the position suited for keeping the excavation amount even when the bench height changes, and it is, therefore, possible to maintain the work efficiency high.” ¶ 63. NOTE: That is, the work assist system provides the function of evaluating the appropriateness of a placement of the position of the mobile work machine with respect to a work region as to maximize work efficiency.).
Rösth discloses: a crane position determination device, comprising a determination unit that has a learning model and that determines a placement position of the mobile crane based on an output of the learning model. Tsuneki teaches: a learning model utilizing a reward function including at least one evaluation function to arrive at an optimum placement position of a mobile vehicle. Vogt teaches: providing notification of the placement position of the mobile crane. Quraishi teaches: a neural network program including a plurality of nodes forming an input layer, a hidden layer, and an output layer, wherein a number of nodes of the output layer is a number obtained by a number of candidates for a position pertaining to the traveling body; and inputting data sets to an input layer. Stake teaches: determining an optimum placement position of an outrigger with respect to a working condition. Mita teaches: obtaining a coupling strength between nodes of an output layer, wherein the weight θ of the coupling strength is updated based on a calculated error between an output of the output layer and the target value determined for each data set of the plurality of data sets. Rotem teaches: a path evaluation function of evaluating a carrying path of a load. Nakamura teaches: a region evaluation function of evaluating appropriateness of a placement position of a mobile work machine with respect to a work region
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Rösth, Tsuneki, Vogt, Quraishi, Stake, Mita and Rotem to include the feature of: a region evaluation function of evaluating appropriateness of a placement position of a mobile work machine with respect to a work region, as taught by Nakamura, with a reasonable expectation of success because this feature is useful for guiding a work machine toa position suited for performing its work task. (See Nakamura, ¶ 7.)
Independent claim 9 is rejected for at least the same reasons as claim 1 as the claims recite similar subject matter but for minor differences.
As to claim 2, the combination of Rösth, Tsuneki, Vogt and Quraishi fails to explicitly disclose: wherein the evaluation function further includes a stability evaluation function of evaluating appropriateness of a placement position of the mobile crane in consideration of an extension state of an outrigger.
Nevertheless, Stake teaches: evaluating appropriateness of a placement position of the mobile crane in consideration of an extension state of an outrigger (“A valid operation position” of an outrigger of a crane may be determined and outputted – see at least ¶ 62 and FIGS. 6–13. The foregoing is necessarily performed in respect to the crane’s working environment (i.e., working condition).).
Rösth discloses: a crane position determination device, comprising a determination unit that has a learning model and that determines a placement position of the mobile crane based on an output of the learning model. Tsuneki teaches: a learning model utilizing a reward function including at least one evaluation function to arrive at an optimum placement position of a mobile vehicle. Vogt teaches: providing notification of the placement position of the mobile crane. Quraishi teaches: a neural network program including a plurality of nodes forming an input layer, a hidden layer, and an output layer, wherein a number of nodes of the output layer is a number obtained by a number of candidates for a position pertaining to the traveling body; and inputting data sets to an input layer. Stake teaches: determining an optimum placement position of an outrigger with respect to a working condition; and evaluating appropriateness of a placement position of the mobile crane in consideration of an extension state of an outrigger.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Rösth, Tsuneki, Vogt and Quraishi with the feature of: evaluating appropriateness of a placement position of the mobile crane in consideration of an extension state of an outrigger, as taught by Stake, with a reasonable expectation of success because these features are useful for “monitoring the status of the crane outriggers.” (Stake, ¶ 2.)
As to claim 6, the combination of Rösth, Tsuneki, Vogt and Quraishi fails to explicitly disclose:
the learning model outputs information pertaining to an optimum placement position of an outrigger with respect to the working condition, wherein
the determination unit determines a placement position of the outrigger based on an output of the learning model, and wherein
the notification unit provides notification of the placement position of the outrigger together with a placement position of the mobile crane.
Nevertheless, Stake teaches:
outputting information pertaining to an optimum placement position of an outrigger with respect to a working condition (“A valid operation position” of an outrigger of a crane may be determined and outputted – see at least ¶ 62 and FIGS. 6–13. The foregoing is necessarily performed in respect to the crane’s working environment (i.e., working condition)), wherein
a determination unit determines a placement position of the outrigger (“A valid operation position” of an outrigger of a crane may be determined and outputted – see at least ¶ 62 and FIGS. 6–13.), and wherein
a notification unit provides notification of the placement position of the outrigger together with a placement position of the mobile crane (“When an outrigger 16 is in a valid operating position, a shaded hexagon 602 indicates that the operating position is valid” – see at least ¶ 62 and FIGS. 6–13.).
Rösth discloses: a crane position determination device, comprising a determination unit that has a learning model and that determines a placement position of the mobile crane based on an output of the learning model; wherein the working condition includes information pertaining to a position of a carrying source and information pertaining to a position of a carrying destination. Tsuneki teaches: a learning model utilizing a reward function including at least one evaluation function to arrive at an optimum placement position of a mobile vehicle. Vogt teaches: providing notification of the placement position of the mobile crane. Quraishi teaches: a neural network program including a plurality of nodes forming an input layer, a hidden layer, and an output layer, wherein a number of nodes of the output layer is a number obtained by a number of candidates for a position pertaining to the traveling body. Stake teaches: determining an optimum placement position of an outrigger with respect to a working condition, and notifying the placement position of the outrigger with respect to the placement position of the mobile crane.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Rösth, Tsuneki, Vogt and Quraishi with the features of: outputting information pertaining to an optimum placement position of an outrigger with respect to a working condition, a determination unit determines a placement position of the outrigger, and a notification unit provides notification of the placement position of the outrigger together with a placement position of the mobile crane, as taught by Stake, with a reasonable expectation of success because these features are useful for “monitoring the status of the crane outriggers.” (Stake, ¶ 2.)
While Stake does not explicitly disclose: the learning model outputs information pertaining to an optimum placement position of an outrigger with respect to the working condition, wherein the determination unit determines a placement position of the outrigger based on an output of the learning model, the foregoing limitations would have been obvious to one of ordinary skill in the art. Recall that Rösth discloses utilizing a learning model to determine a placement position of a mobile crane. One of ordinary skill in the art would have recognized that, with a reasonable expectation of success, that Rösth’s learning model can yield a placement position of an outrigger as an outrigger is a typical mechanical component of a crane, and can thus function as a simple substitution for Rösth’s “crane tip” in Rösth’s processing. The foregoing is useful for exploiting the advantages of a learning model for yielding an optimum position of an outrigger.
As to claim 7, the combination of Rösth, Tsuneki, Vogt and Quraishi fails to explicitly disclose:
the determination unit determines an orientation of the mobile crane and/or an extension width of the outrigger based on the determined placement position of the outrigger, and wherein
the notification unit outputs the orientation of the mobile crane and/or the extension width of the outrigger.
Nevertheless, Stake teaches:
the determination unit determines an extension width of the outrigger based on the determined placement position of the outrigger (“[T]he outrigger positioning object 318 will display a linear representation of the length of the outrigger” – see at least ¶ 63.), and wherein
the notification unit outputs the extension width of the outrigger (“[T]he outrigger positioning object 318 will display a linear representation of the length of the outrigger” – see at least ¶ 63.).
Rösth discloses: a crane position determination device, comprising a determination unit that has a learning model and that determines a placement position of the mobile crane based on an output of the learning model; wherein the working condition includes information pertaining to a position of a carrying source and information pertaining to a position of a carrying destination. Tsuneki teaches: a learning model utilizing a reward function including at least one evaluation function to arrive at an optimum placement position of a mobile vehicle. Vogt teaches: providing notification of the placement position of the mobile crane. Quraishi teaches: a neural network program including a plurality of nodes forming an input layer, a hidden layer, and an output layer, wherein a number of nodes of the output layer is a number obtained by a number of candidates for a position pertaining to the traveling body. Stake teaches: determining an optimum placement position of an outrigger with respect to a working condition, and notifying the placement position of the outrigger with respect to the placement position of the mobile crane; the determination unit determines an extension width of the outrigger based on the determined placement position of the outrigger, and the notification unit outputs the extension width of the outrigger.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Rösth, Tsuneki, Vogt and Quraishi with the features of: the determination unit determines an extension width of the outrigger based on the determined placement position of the outrigger, and the notification unit outputs the extension width of the outrigger, as taught by Stake, with a reasonable expectation of success because these features are useful for “monitoring the status of the crane outriggers.” (Stake, ¶ 2.)
As to claim 8, Rösth discloses: a mobile crane comprising the crane position determination device according to claim 1 (“Vehicle 4 comprising a crane 2: - see at least ¶ 52. See also rejection of claim 1 above.).
Claims 3 and 5 are rejected under § 103 as being unpatentable over Rösth in view of Tsuneki, in view of Vogt, in view of Quraishi, in view of Stake, in view of Mita, in view of Rotem and in view of Nakamura as applied to claim 1 — further in view of Tanizumi et al. (US20160119589A1; “Tanizumi”) and in view of Kawai et al. (US20190300339A1; “Kawai”).
As to claim 3, Rösth discloses: the working condition includes information pertaining to a position of a carrying source and information pertaining to a position of a carrying destination (The input unit 20 may receive a “trajectory instruction 18”—i.e., a working condition. See at least ¶ 36. Continuing, the trajectory instruction 18 may comprise a “wanted position of the crane tip 18” - see at least ¶ 37. The trajectory instruction 18 meets the BRI of information pertaining to a position of a carrying source and information pertaining to a position of a carrying destination because the trajectory instruction represents a desired change in position from a source to a destination of the crane’s tip, which is associated with carrying a load.).
The combination of Rösth, Tsuneki, Vogt, Quraishi, Stake, Mita, Rotem and Nakamura fails to explicitly disclose: wherein the working condition includes information pertaining to a load of a suspended load and information pertaining to a work region.
Nevertheless, Tanizumi teaches: wherein a working condition includes information pertaining to a load of a suspended load and information pertaining to a work region (When “an operator inputs the load weight,” a “work region line” may concurrently be determined. See at least ¶ 114. That is, the inputted load weight analogizes to information pertaining to a load of a suspended load, and the work region line analogizes to information pertaining to a work region.).
Rösth discloses: a crane position determination device, comprising a determination unit that has a learning model and that determines a placement position of the mobile crane based on an output of the learning model. Tsuneki teaches: a learning model utilizing a reward function including at least one evaluation function to arrive at an optimum placement position of a mobile vehicle. Vogt teaches: providing notification of the placement position of the mobile crane. Quraishi teaches: a neural network program including a plurality of nodes forming an input layer, a hidden layer, and an output layer, wherein a number of nodes of the output layer is a number obtained by a number of candidates for a position pertaining to the traveling body; and inputting data sets to an input layer. Stake teaches: determining an optimum placement position of an outrigger with respect to a working condition. Mita teaches: obtaining a coupling strength between nodes of an output layer, wherein the weight θ of the coupling strength is updated based on a calculated error between an output of the output layer and the target value determined for each data set of the plurality of data sets. Rotem teaches: a path evaluation function of evaluating a carrying path of a load. Nakamura teaches: a region evaluation function of evaluating appropriateness of a placement position of a mobile work machine with respect to a work region Tanizumi teaches: wherein a working condition includes information pertaining to a load of a suspended load and information pertaining to a work region.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Rösth, Tsuneki, Vogt, Quraishi, Stake, Mita, Rotem and Nakamura to include the feature of: wherein a working condition includes information pertaining to a load of a suspended load and information pertaining to a work region, as taught by Tanizumi, with a reasonable expectation of success because it is well-known in the art that a working condition of a mobile crane may include both a load of a suspended load and a work region of the mobile crane as the foregoing elements are indicative of what work the crane will perform. Hence, one of ordinary skill in the art would have found it obvious to include Tanizumi’s working conditions into Rösth’s working conditions as to provide a more holistic illustration of a mobile crane’s working environment and capability, thereby yielding a more robust learning model for determining an optimum position of a mobile crane.
The combination of Rösth, Tsuneki, Vogt, Quraishi, Stake, Mita, Rotem, Nakamura and Tanizumi fails to explicitly disclose: wherein the working condition includes information pertaining to operation efficiency of the mobile crane.
Nevertheless, Kawai teaches: wherein a working condition includes information pertaining to operation efficiency of a mobile crane (For performing work, “an efficiency of the motor [of a crane] for at least one of the obtained rotation speed and a value of a current is determined using the efficiency map” – see at least ¶ 221; see also ¶¶ 190–199.).
Rösth discloses: a crane position determination device, comprising a determination unit that has a learning model and that determines a placement position of the mobile crane based on an output of the learning model. Tsuneki teaches: a learning model utilizing a reward function including at least one evaluation function to arrive at an optimum placement position of a mobile vehicle. Vogt teaches: providing notification of the placement position of the mobile crane. Quraishi teaches: a neural network program including a plurality of nodes forming an input layer, a hidden layer, and an output layer, wherein a number of nodes of the output layer is a number obtained by a number of candidates for a position pertaining to the traveling body; and inputting data sets to an input layer. Stake teaches: determining an optimum placement position of an outrigger with respect to a working condition. Mita teaches: obtaining a coupling strength between nodes of an output layer, wherein the weight θ of the coupling strength is updated based on a calculated error between an output of the output layer and the target value determined for each data set of the plurality of data sets. Rotem teaches: a path evaluation function of evaluating a carrying path of a load. Nakamura teaches: a region evaluation function of evaluating appropriateness of a placement position of a mobile work machine with respect to a work region Tanizumi teaches: wherein a working condition includes information pertaining to a load of a suspended load and information pertaining to a work region. Kawai teaches: wherein a working condition includes information pertaining to operation efficiency of a mobile crane.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Rösth, Tsuneki, Vogt, Quraishi, Stake, Mita, Rotem, Nakamura and Tanizumi to include the feature of: wherein a working condition includes information pertaining to operation efficiency of a mobile crane, as taught by Kawai, with a reasonable expectation of success because it is well-known in the art that a working condition of a mobile crane may include a mobile crane’s operation efficiency as the foregoing element is indicative of what work the crane will perform. Hence, one of ordinary skill in the art would have found it obvious to include Kawai’s working condition into Rösth’s working conditions as to provide a more holistic illustration of a mobile crane’s working environment and capability, thereby yielding a more robust learning model for determining an optimum position of a mobile crane.
As to claim 5, the combination of Rösth, Tsuneki, Vogt, Quraishi, Stake, Mita, Rotem and Nakamura fails to explicitly disclose: the working condition further includes information pertaining to an environment of the work region.
Nevertheless, Tanizumi teaches: the working condition includes information pertaining to an environment of the work region (When “an operator inputs the load weight,” a “work region line” may concurrently be determined. See at least ¶ 114. Here, the work region line analogizes to information pertaining to an environment of the work region as the work region line defines what environment the crane will work in.).
Rösth discloses: a crane position determination device, comprising a determination unit that has a learning model and that determines a placement position of the mobile crane based on an output of the learning model. Tsuneki teaches: a learning model utilizing a reward function including at least one evaluation function to arrive at an optimum placement position of a mobile vehicle. Vogt teaches: providing notification of the placement position of the mobile crane. Quraishi teaches: a neural network program including a plurality of nodes forming an input layer, a hidden layer, and an output layer, wherein a number of nodes of the output layer is a number obtained by a number of candidates for a position pertaining to the traveling body; and inputting data sets to an input layer. Stake teaches: determining an optimum placement position of an outrigger with respect to a working condition. Mita teaches: obtaining a coupling strength between nodes of an output layer, wherein the weight θ of the coupling strength is updated based on a calculated error between an output of the output layer and the target value determined for each data set of the plurality of data sets. Rotem teaches: a path evaluation function of evaluating a carrying path of a load. Nakamura teaches: a region evaluation function of evaluating appropriateness of a placement position of a mobile work machine with respect to a work region Tanizumi teaches: wherein a working condition includes information pertaining to a load of a suspended load and information pertaining to a work region. Kawai teaches: wherein a working condition includes information pertaining to operation efficiency of a mobile crane. Tanizumi teaches: wherein a working condition includes information pertaining to a load of a suspended load, information pertaining to a work region, and information pertaining to an environment of the work region.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Rösth, Tsuneki, Vogt, Quraishi, Stake, Mita, Rotem and Nakamura to include the feature of: the working condition includes information pertaining to an environment of the work region, as taught by Tanizumi, with a reasonable expectation of success because it is well-known in the art that a working condition of a mobile crane may include a work environment of the mobile crane as the foregoing element is indicative of what work the crane will perform. Hence, one of ordinary skill in the art would have found it obvious to include Tanizumi’s working condition into Rösth’s working conditions as to provide a more holistic illustration of a mobile crane’s working environment and capability, thereby yielding a more robust learning model for determining an optimum position of a mobile crane.
Claim 4 is rejected under § 103 as being unpatentable over Rösth in view of Tsuneki, in view of Vogt, in view of Quraishi, in view of Stake, in view of Mita, in view of Rotem, in view of Nakamura, in view of Tanizumi and in view of Kawai as applied to claim 3 — further in view of Imura et al. (US20160003266A1; “Imura”) and in view of Faloney, JR. (US20210354960A1; “Faloney”).
As to claim 4, the combination of Rösth, Tsuneki, Vogt, Quraishi, Stake, Mita, Rotem, Nakamura and Tanizumi fails to explicitly disclose: the operation efficiency is calculated based on winch efficiency of a winch.
Nevertheless, Kawai teaches: the operation efficiency is calculated based on winch efficiency of a winch (“The winch drum 11 around which the wire rope 16 is wound, is connected to a rotation shaft 13a of the motor 13 with the speed reducer 12 being interposed.” See at least ¶ 33 and FIG. 1. Then, for performing work, “an efficiency of the motor [of a crane] for at least one of the obtained rotation speed and a value of a current is determined using the efficiency map” – see at least ¶ 221; see also ¶¶ 190–199. The foregoing analogizes to winch efficiency because the motor controls the winch; hence, the efficiency of the motor acts as the efficiency of the winch.).
Rösth discloses: a crane position determination device, comprising a determination unit that has a learning model and that determines a placement position of the mobile crane based on an output of the learning model. Tsuneki teaches: a learning model utilizing a reward function including at least one evaluation function to arrive at an optimum placement position of a mobile vehicle. Vogt teaches: providing notification of the placement position of the mobile crane. Quraishi teaches: a neural network program including a plurality of nodes forming an input layer, a hidden layer, and an output layer, wherein a number of nodes of the output layer is a number obtained by a number of candidates for a position pertaining to the traveling body; and inputting data sets to an input layer. Stake teaches: determining an optimum placement position of an outrigger with respect to a working condition. Mita teaches: obtaining a coupling strength between nodes of an output layer, wherein the weight θ of the coupling strength is updated based on a calculated error between an output of the output layer and the target value determined for each data set of the plurality of data sets. Rotem teaches: a path evaluation function of evaluating a carrying path of a load. Nakamura teaches: a region evaluation function of evaluating appropriateness of a placement position of a mobile work machine with respect to a work region Tanizumi teaches: wherein a working condition includes information pertaining to a load of a suspended load and information pertaining to a work region. Kawai teaches: wherein a working condition includes information pertaining to operation efficiency of a mobile crane. Tanizumi teaches: wherein a working condition includes information pertaining to a load of a suspended load, information pertaining to a work region, and information pertaining to an environment of the work region.
Tanizumi teaches: wherein a working condition includes information pertaining to a load of a suspended load, information pertaining to a work region, and information pertaining to an environment of the work region. Kawai teaches: wherein a working condition includes information pertaining to operation efficiency of a mobile crane; wherein the operation efficiency is calculated based on winch efficiency of a winch.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Rösth, Tsuneki, Vogt, Quraishi, Stake, Mita, Rotem, Nakamura and Tanizumi to include the feature of: the operation efficiency is calculated based on winch efficiency of a winch, as taught by Kawai, with a reasonable expectation of success because it is well-known in the art that the efficiency of a winch can have a significant impact on crane work; hence, it is useful to consider winch efficiency as a form of operation efficiency.
The combination of Rösth, Tsuneki, Vogt, Quraishi, Stake, Mita, Rotem, Nakamura, Tanizumi and Kawai fails to explicitly disclose: the operation efficiency is calculated based on turning efficiency of a turning body.
Nevertheless, Imura teaches: the operation efficiency is calculated based on turning efficiency of a turning body (“hydraulic swinging efficiency” may be obtained – see at least ¶ 38.).
Rösth discloses: a crane position determination device, comprising a determination unit that has a learning model and that determines a placement position of the mobile crane based on an output of the learning model. Tsuneki teaches: a learning model utilizing a reward function including at least one evaluation function to arrive at an optimum placement position of a mobile vehicle. Vogt teaches: providing notification of the placement position of the mobile crane. Quraishi teaches: a neural network program including a plurality of nodes forming an input layer, a hidden layer, and an output layer, wherein a number of nodes of the output layer is a number obtained by a number of candidates for a position pertaining to the traveling body; and inputting data sets to an input layer. Stake teaches: determining an optimum placement position of an outrigger with respect to a working condition. Mita teaches: obtaining a coupling strength between nodes of an output layer, wherein the weight θ of the coupling strength is updated based on a calculated error between an output of the output layer and the target value determined for each data set of the plurality of data sets. Rotem teaches: a path evaluation function of evaluating a carrying path of a load. Nakamura teaches: a region evaluation function of evaluating appropriateness of a placement position of a mobile work machine with respect to a work region Tanizumi teaches: wherein a working condition includes information pertaining to a load of a suspended load and information pertaining to a work region. Kawai teaches: wherein a working condition includes information pertaining to operation efficiency of a mobile crane. Tanizumi teaches: wherein a working condition includes information pertaining to a load of a suspended load, information pertaining to a work region, and information pertaining to an environment of the work region. Tanizumi teaches: wherein a working condition includes information pertaining to a load of a suspended load, information pertaining to a work region, and information pertaining to an environment of the work region. Kawai teaches: wherein a working condition includes information pertaining to operation efficiency of a mobile crane; wherein the operation efficiency is calculated based on winch efficiency of a winch. Imura teaches: the operation efficiency is calculated based on turning efficiency of a turning body.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Rösth, Tsuneki, Vogt, Quraishi, Stake, Mita, Rotem, Nakamura, Tanizumi and Kawai to include the feature of: the operation efficiency is calculated based on turning efficiency of a turning body, as taught by Imura, with a reasonable expectation of success because it is well-known in the art that turning efficiency can have a significant impact on crane work; hence, it is useful to consider turning efficiency as a form of operation efficiency.
The combination of Rösth, Tsuneki, Vogt, Quraishi, Stake, Mita, Rotem, Nakamura, Tanizumi, Kawai and Imura fails to explicitly disclose: the operation efficiency is calculated based on derricking efficiency of a boom and telescopic efficiency of a boom.
Nevertheless, Rodney teaches: a crane configured to perform derricking and telescoping of a boom (“The controllers 94 can include … a jib luffing [i.e., derricking] controller 94B for controlling the flow of hydraulic oil to the jib cylinder 40 for moving the jib portion 32 in the up and down direction, and a jib telescoping controller 94C for controlling the telescoping or translational movement of the jib extension components 130 relative to each other and which compose the jib portion 32.” Emphasis added; see at least ¶ 26 and FIG. 6. Examiner notes that luffing is known in the art as a synonym for derricking.).
Rösth discloses: a crane position determination device, comprising a determination unit that has a learning model and that determines a placement position of the mobile crane based on an output of the learning model. Tsuneki teaches: a learning model utilizing a reward function including at least one evaluation function to arrive at an optimum placement position of a mobile vehicle. Vogt teaches: providing notification of the placement position of the mobile crane. Quraishi teaches: a neural network program including a plurality of nodes forming an input layer, a hidden layer, and an output layer, wherein a number of nodes of the output layer is a number obtained by a number of candidates for a position pertaining to the traveling body; and inputting data sets to an input layer. Stake teaches: determining an optimum placement position of an outrigger with respect to a working condition. Mita teaches: obtaining a coupling strength between nodes of an output layer, wherein the weight θ of the coupling strength is updated based on a calculated error between an output of the output layer and the target value determined for each data set of the plurality of data sets. Rotem teaches: a path evaluation function of evaluating a carrying path of a load. Nakamura teaches: a region evaluation function of evaluating appropriateness of a placement position of a mobile work machine with respect to a work region Tanizumi teaches: wherein a working condition includes information pertaining to a load of a suspended load and information pertaining to a work region. Kawai teaches: wherein a working condition includes information pertaining to operation efficiency of a mobile crane. Tanizumi teaches: wherein a working condition includes information pertaining to a load of a suspended load, information pertaining to a work region, and information pertaining to an environment of the work region. Tanizumi teaches: wherein a working condition includes information pertaining to a load of a suspended load, information pertaining to a work region, and information pertaining to an environment of the work region. Kawai teaches: wherein a working condition includes information pertaining to operation efficiency of a mobile crane; wherein the operation efficiency is calculated based on winch efficiency of a winch. Imura teaches: the operation efficiency is calculated based on turning efficiency of a turning body. Rodney teaches: a crane configured to perform derricking and telescoping of a boom.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Rösth, Tsuneki, Vogt, Quraishi, Stake, Mita, Rotem, Nakamura, Tanizumi, Kawai and Imura to include the feature of: a crane configured to perform derricking and telescoping of a boom, as taught by Rodney, with a reasonable expectation of success because it is well-known in the art that cranes may be configured to perform derricking and telescoping.
While Rodney does not explicitly disclose obtaining the efficiencies of derricking and telescoping, calculating operation efficiency based on derricking efficiency and telescoping efficiency would have been obvious in light of the combination of cited prior art. Rösth, Kawai, and Imura illustrate that it is known in the art that certain mechanical processes of a mobile crane have associated efficiencies which can be calculated and optimized. One of ordinary skill in the art would have recognized that in a similar manner, efficiencies can be calculated and subsequently optimized for Rodney’s derricking and telescoping as these processes are also well-known mechanical processes associated with crane work. In this fashion, the determination of a placement position of a mobile crane may be enhanced by considering as many efficiencies as possible.
ALLOWABLE SUBJECT MATTER
Claim 10 is objected to as being dependent upon a rejected base claim, but would be allowable (1) if rewritten in independent form including all of the limitations of the base claim and intervening claims; and (2) if the 101 rejections are adequately addressed.
The following is an Examiner’s statement of reasons for allowance:
Marinelli et al. (WO2019073456A9; “Marinelli”) discloses: providing an environmental coefficient, wherein the environmental coefficient is calculated based on environmental data to account for environmental effects of operation of a crane (“The invention provides, furthermore, to set values of more conditions processable for a correction of the safe load radius by the predictive stability control method and system of the invention. As a few examples the following conditions can be used: Wind speed- and direction of the same (load swing/side load).” Page 10, ll. 20-25.)
Before and at the time of the effective filing date of the invention, the prior art of record considered calculating operation efficiency of a crane by considering turning efficiency, derricking efficiency and winch efficiency. (See § 103 rejections supra.) Nakamura disclosed the claimed region evaluation function indicate appropriateness of a stop position with respect to a work condition. (See id.) Finally, Marinelli provides the further teaching that environmental effects may be accounted for in regards to operation of a crane.
However, the prior art of record failed to explicitly disclose at least: the path evaluation function J(Path) calculated as J(Path) = a/b where a and b are chord lengths of an arc passing through a carrying source position and a carrying destination position with the stop position as a center point.
For at least the foregoing reason, claim 10 distinguishes from the prior art.
CONCLUSION
Applicant’s amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, this action is final. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire three months from the mailing date of this action. In the event a first reply is filed within two months of the mailing date of this final action and the advisory action is not mailed until after the end of the three-month shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to Mario C. Gonzalez whose telephone number is (571) 272-5633. The Examiner can normally be reached M–F, 10:00–6:00 ET.
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If attempts to reach the Examiner by telephone are unsuccessful, the examiner’s supervisor, Fadey S. Jabr, can be reached on (571) 272-1516. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/M.C.G./Examiner, Art Unit 3668
/Fadey S. Jabr/Supervisory Patent Examiner, Art Unit 3668
1 Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025)