DETAILED ACTION
Notice of 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 .
Claim(s) 16-31 are pending and are allowed.
Response to Amendment
The current office action is responsive to the amendment filled on 02/26/2026.
Amended claim 28 is being fully considered by the examiner.
In response to applicant’s amendments to claim 28, all the 35 U.S.C. 112 rejections of claims 28 as set forth in the previous office action has been withdrawn.
THIS ACTION IS MADE FINAL.
Claim Rejections - 35 USC § 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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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 filling date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 16-19, 21, 25, 27-29 and 31 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kreidler (US20170308057A1) [hereinafter Kreidler] in view of Hase et al. (JP2001225243A) [hereinafter Hase], and further in view of ZHOU (CN110962124A) [hereinafter ZHOU].
Regarding claim 16:
Kreidler discloses, A computer-implemented method for instantaneous performance management of a machine tool, the method comprising: [¶11: “provide a method for” “analyzing the quality of a workpiece machined by at least one CNC machine yielding information about the quality of parts/workpiece immediately or shortly after the machining process.” “to allow a systematic error analysis and possibly error elimination over the full process chain from engineering over machining to the finished workpiece.”… ¶17: “The actual machining process using the real physical machine is started and during the machining process under consideration realtime” “process data of the at least one CNC machine,” “are continuously recorded over the machining process of the workpiece.” “provided with realtime” “process data recorded over the full machining process of the workpiece for quality, machine and process”];
receiving, by a processing unit, real-time condition data associated with one or more components of the machine tool from one or more sources, wherein the condition data is indicative of one or more operating conditions of the machine tool in real-time; [¶17: “during the machining process under consideration realtime” “process data of the at least one CNC machine,” “are continuously recorded over the machining process of the workpiece” “these recorded realtime” “data of the real process are used as input data to the digital machine model” “in general to find a wide spectrum of problems on the parts and to relate them at least to typical problems with regard to the machine and the machining process”… ¶20: “the recorded realtime process data may primarily include” “actual position,” “actual speed,” “actual acceleration,” “actual jerk,” “actual torque,” “actual drive force and/or” “actual drive current with regard to at least one linear or rotary drive axis. In addition or alternatively, the recorded realtime process data may include a process-related force, torque, pressure, torsion, deflection, strain, vibration, temperature, energy distribution and/or energy consumption of at least one part of the CNC machine.”];
configuring a digital twin of the machine tool… [¶14: “providing a digital machine model of the CNC machine with realtime and non-realtime process data of the at least one CNC machine, the realtime”… ¶17: “data of the real process are used as input data to the digital machine model for simulating the machining process under consideration,”
Examiner notes that, in broadest reasonable interpretation in light of applicant’s specification, digital twin is interpreted as dynamic virtual model based on computer models as described in applicant’s specification ¶8: “configuring a digital twin of the machine tool based on the parameter value. In one implementation, the digital twin is a dynamic virtual replica based on one or more of physics-based models, Computer-Aided Design (CAD) models, Computer-Aided Engineering (CAE) models, one-dimensional (1D) models, two-dimensional (2D) models, three-dimensional (3D) models, finite-element (FE) models, descriptive models, metamodels, stochastic models, parametric models, reduced-order models, statistical models, heuristic models, prediction models, ageing models, machine learning models, Artificial Intelligence models, deep learning models, system models, knowledge graphs and so on. In one embodiment, configuring the digital twin of the machine tool comprises updating the digital twin of the machine tool based on the condition data and the computed parameter value. The digital twin of the machine tool is updated to replicate, through simulations, a response substantially similar to a response of the machine tool in real-time.”
As such Kreidler discloses, digital twin as virtual model of CNC machine];
simulating a behavior of the one or more critical components based on the configured digital twin in a simulation environment; [¶15: “simulating the machining process under consideration by means of the digital machine model based at least partially on the recorded realtime” “process data.”… ¶17: “these recorded realtime” “data of the real process are used as input data to the digital machine model for simulating the machining process under consideration,”… ¶41: “Considering for example a milling CNC machine, milling forces, machine vibrations, tool vibrations, positioning accuracies, spindle speed, axes speeds, accelerations, jerks, bending moments of the mechanical components and error characteristics of the mechanical components as well as the state of the tools may be used as characterizing process parameter.”… ¶19: “the machine model may be a kinematic model, a multibody-simulation model or a finite-element-method (FEM) model of the CNC machine.” “multibody-simulation models are able to model the interaction of a plurality of mechanical moveable bodies represented by single movable masses which are connected by elastic joints.” “FEM models are similar to multibody-simulation models but more detailed and sophisticated. This is due to the fact that they are built up by a large number of small volumes with suitable masses, wherein adjacent volumes are connected to each elastically.”
Kreidler discloses, operating behaviors of the mechanical components are simulated based on digital machine model/twin of the machine tool];
predicting an impact on the performance of the machine tool based on the behavior of the one or more critical components in the simulation environment; and [¶15: “simulating the machining process under consideration by means of the digital machine model based at least partially on the recorded realtime” “process data.”… ¶17: “these recorded realtime” “data of the real process are used as input data to the digital machine model for simulating the machining process under consideration, thereby enabling for analyzing the quality of the machined workpiece and, moreover, in general to find a wide spectrum of problems on the parts and to relate them at least to typical problems with regard to the machine and the machining process.”… ¶41: “Considering for example a milling CNC machine, milling forces, machine vibrations, tool vibrations, positioning accuracies, spindle speed, axes speeds, accelerations, jerks, bending moments of the mechanical components and error characteristics of the mechanical components as well as the state of the tools may be used as characterizing process parameter.”… ¶64: “identifying” “in the CNC machine,” “possible reasons related to the identified deviations between the virtually re-engineered workpiece and the CAD model of the workpiece;”];
optimizing an operation of the machine tool during run-time based on the impact on the performance of the machine tool and the real-time condition data. [¶17: “these recorded realtime” “data of the real process are used as input data to the digital machine model for simulating the machining process under consideration, thereby enabling for analyzing the quality of the machined workpiece and, moreover, in general to find a wide spectrum of problems on the parts and to relate them at least to typical problems with regard to the machine and the machining process.”… ¶79: “FIG. 3a shows a simulated tool path of a machining process derived by simulating the machining process under consideration by means of a digital machine model provided with realtime and non-realtime process data recorded during the machining process;”… ¶40: “simulation may be continuously provided with the recorded realtime and non-realtime data during the ongoing machining process,”… ¶41: “The respective process parameters data have to be recorded in real time over the full process under consideration, e.g., machine vibrations, milling forces or tool vibration.”… ¶63: “the method may thus allow for identifying” “in the CNC machine, in particular in the controller, in electrical drives, the actuators and the mechanical system of the CNC machine, reasons related to identified quality and process issues.” “may provide a solution for a systematic error analysis and error elimination over the full process chain from the engineering via machining to the finished workpiece.”… ¶66-¶74 “Accordingly, on a third level, the method may further comprise remedying the identified reasons, wherein remedying the identified problems may include” “adapting the geometry of the part program by changes of the CAM strategy in order to avoid critical machine vibrations or critical movements and to improve the overall dynamic behavior of the machine; and/or regarding a cutting machining process, changing the CAM strategy with regard to the relation between cutting depth, spindle speed and feed rate or other methods to improve cutting volume and/or quality; and/or adapting an error compensation table of the CNC machine or activating an error compensation functionality of the controller of the CNC machine; and/or adapting at least one drive parameter in order to change the motion characteristics of the CNC machine;”],
but Kreidler doesn’t explicitly disclose, and
Hase discloses, computing a dynamic stiffness value associated with one or more critical components which is likely to affect a performance of the machine tool based on the condition data using physics-based models; [page2, ¶9: “provided an input means for inputting data, a cut amount predicting means for calculating a change amount of a cut amount caused by deformation of a work surface of a workpiece,” “Machining force predicting means for calculating the machining force for the tool,” “calculating the response displacement waveform when a machining force is applied to the dynamic characteristics of the tool and jig and the dynamic characteristic model of the workpiece,”…
page 3, ¶25: “the dynamic stiffness data constructing means 7 is accessed to calculate the dynamic stiffness data in the cutting process at the respective initial values (step 103).”…
page 3, ¶27: “Next, based on the dynamic stiffness data of the cutting process stored in the dynamic stiffness data storage means 12 in the processing force predicting means 2 and the amount of change in the cut amount due to the deformation of the processing surface previously obtained, a minute time step is performed. The machining force for the cutting amount is calculated (step 105).”
Hase discloses, calculating the dynamic stiffness data (based on applied force and displacement in response to the force) associated with components that affects performance using dynamic model and simulation, based on the input data that are condition data such as amount of change],
Therefore, it would have been obvious to one of ordinary skill in the art before the filling date of the claimed invention to have combined the capability of determining dynamic stiffness associated with components of the machine tool based on the operating condition of the machine tool using a physics based model, where dynamic stiffness may affect performance to efficiently optimizing cnc machining taught by Hase with the method taught by Kreidler as discussed above in order to have reasonable expectation of success such as to efficiently optimizing cnc machining [Hase: (page 4, ¶40): “the cutting conditions can be efficiently optimized”],
but Hase doesn’t explicitly disclose, and
ZHOU discloses, configuring a digital twin of the machine tool based on the dynamic stiffness value; [page5, ¶6-¶7: “firstly, performing kinematic calibration on a robot according to a description file of robot body parameters, identifying robot structure parameters, compensating static errors, and simultaneously recording measurement and calculation data in a calibration process; establishing a dynamic stiffness model of the whole robot, identifying modal parameters through a modal analysis experiment and recording modal experiment data; step three, detecting whether the pose precision and the dynamic stiffness model of the robot meet the positioning precision requirement of the tail end of the robot or not:”…
page 5, ¶11: “according to the structural parameters of the robot, the sensitivity of the dynamic characteristics of the robot is analyzed, the structural parameters comprise three types of functional components, joint connection modes and structural components, wherein the functional components comprise a driving unit, a connecting rod unit and a speed reducer unit, the joint connection modes comprise integrated connection and coupling connection,”…
page 6, ¶4-¶5: “establishing a robot simulation model according to the robot structure parameters;” “establishing a simulation dynamic stiffness model according to the robot simulation model;”].
Therefore, it would have been obvious to one of ordinary skill in the art before the filling date of the claimed invention to have combined the capability of configuring a digital twin of the machine tool based on the dynamic stiffness value to have better machining accuracy by improving error compensation and dynamic stiffness model taught by ZHOU with the method taught by Kreidler and Hase as discussed above in order to have reasonable expectation of success such as to have better machining accuracy by improving error compensation and dynamic stiffness model [ZHOU: page 9, ¶2): “so that the accuracy of the static error compensation and the dynamic stiffness model is improved, and the problem of poor machining accuracy of the robot is solved”].
Regarding claim 17:
Kreidler, Hase and ZHOU disclose all the elements of claim(s) 16, and
ZHOU further discloses, wherein the configuring the digital twin of the machine tool based on the dynamic stiffness value comprises:
updating the digital twin of the machine tool based on the condition data and the computed dynamic stiffness value. [page 9, ¶2: “analyzing the stress of the robot in different poses and combining the parameters of the robot body and a dynamic stiffness model, realizing static error compensation in the calibration process, then” “correcting” “the dynamic stiffness model of the whole robot obtained by kinematics calibration, so that the accuracy of the static error compensation and the dynamic stiffness model is improved, and the problem of poor machining accuracy of the robot is solved;”].
Regarding claim 18:
Kreidler, Hase and ZHOU disclose all the elements of claim(s) 16,
Kreidler further discloses, the simulating the behavior of the one or more critical components based on the configured digital twin in the simulation environment comprises:
generating a simulation instance based on the configured digital twin; and [¶15: “simulating the machining process under consideration by means of the digital machine model based at least partially on the recorded realtime” “process data.”];
executing the simulation instance in a simulation environment using a simulation model for generating simulation results indicative of a behavior of the one or more critical components. [¶15: “simulating the machining process under consideration by means of the digital machine model based at least partially on the recorded realtime” “process data.”… ¶40: “simulation may be continuously provided with the recorded realtime and non-realtime data during the ongoing machining process, the result of the above described quality analysis application may be available”… ¶46: “visualizing the simulated machining process, in particular visualizing”… ¶58: “tool data, operation sequences for tool compensation, settings of smoothing functions,”… ¶60: “the CNC data, including settings with respect to machine error compensation methods and to the adaption of parameters, e.g. tolerances, jerk limits, for smoothing the tool paths, parameter settings for damping functions, data with respect to feed forward or momentum control.”].
Regarding claim 19:
Kreidler, Hase and ZHOU disclose all the elements of claim(s) 16,
Kreidler further discloses, wherein the behavior of the one or more critical components is associated with a stability of the machine tool. [¶64: “identifying” “in the CNC machine, in particular in the controller, in electrical drives, the actuators and the mechanical system of the CNC machine, possible reasons related to the identified deviations between the virtually re-engineered workpiece and the CAD model of the workpiece; and/or reasons related to the identified deviations between the calculated tool path and the ideal tool path; and/or reasons related to the identified deviations of the one or the plurality of process parameters along the calculated and/or ideal tool path.” (¶64)].
Regarding claim 21:
Kreidler, Hase and ZHOU disclose all the elements of claim(s) 19 and 16,
Kreidler further discloses, wherein predicting the impact on the performance of the machine tool based on the behavior of the one or more critical components comprises:
predicting the impact on accuracy of machining associated with the machine tool based on the stability of the machine tool. [¶41: “Considering for example a milling CNC machine, milling forces, machine vibrations, tool vibrations, positioning accuracies, ”… ¶64: “identifying” “in the CNC machine, in particular in the controller, in electrical drives, the actuators and the mechanical system of the CNC machine, possible reasons related to the identified deviations between the virtually re-engineered workpiece and the CAD model of the workpiece; and/or reasons related to the identified deviations between the calculated tool path and the ideal tool path; and/or reasons related to the identified deviations of the one or the plurality of process parameters along the calculated and/or ideal tool path.”].
Regarding claim 25:
Kreidler, Hase and ZHOU disclose all the elements of claim(s) 16,
Kreidler further discloses, generating one or more recommendations for improving a design of the one or more critical components based on the impact on the performance of the machine tool. [¶94: “visualize/display/provide the recorded realtime milling force data with regard to the corresponding point on the simulated or ideal tool path or the superposition of both tool paths. The same date may also be visualized/displayed/provided with regard to the corresponding point on the re-engineered or ideal surface of the workpiece or the superposition of both.” “identify possible defects on the workpiece surface and to relate these defects to specific process issues, e.g. an overload of the milling tool. Analogously, providing the same method with NC program code recorded during the machining process and properly mapped to the recorded tool path parameters allows e.g. to allocate a possibly erroneous NC program line to a possible defect on the workpiece surface. Hence, the method according to the present invention does not only allow for an “on-line” quality analysis, but also for an “on-line” process analysis of the machining process.”].
Regarding claim 27:
Kreidler, Hase and ZHOU disclose all the elements of claim(s) 16,
Kreidler further discloses, An apparatus for instantaneous performance management of the machine tool, the apparatus comprising: [¶77: “FIG. 1 illustrates an example of a system architecture for recording realtime and non-realtime process data of a CNC machine and for transferring said data a cloud-platform for data analysis using the method for part analytics according the present invention;”… ¶94: “FIG. 1, realtime data from the external force-sensor 30 may be mapped to realtime data on the actual position of the drive axes.” “visualize/display/provide the recorded realtime milling force data with regard to the corresponding point on the simulated or ideal tool path or the superposition of both tool paths.”].
one or more processing units; and a memory unit communicatively coupled to the one or more processing units, [¶34: “FIG. 1 illustrates an example of a system architecture for recording realtime and non-realtime process data of a CNC machine and for transferring said data a cloud-platform for data analysis using the method for part analytics according the present invention;” (¶77)… “a stand-alone computer separate from the CNC controller and the cloud-based server as a separate computer provides more processor-performance and more memory. Alternatively, in case of small data volumes, the client device may be a so-called thin client, that may be part of the CNC controller or may be installed locally.”].
wherein the memory unit comprises a condition management module stored in a form of machine-readable instructions executable by the one or more processing units, wherein the condition management module is configured to perform method steps according to claim 16. [¶34: “a stand-alone computer separate from the CNC controller and the cloud-based server as a separate computer provides more processor-performance and more memory. Alternatively, in case of small data volumes, the client device may be a so-called thin client, that may be part of the CNC controller or may be installed locally.”].
Regarding claim 28 (amended):
Kreidler, Hase and ZHOU disclose all the elements of claim(s) 16,
Kreidler further discloses, A system for instantaneous performance management of the machine tool, the system comprising: the one or more sources configured for providing the real-time condition data associated with the machine tool; [¶77 “FIG. 1 illustrates an example of a system architecture for recording realtime” “process data of a CNC machine and for transferring said data a cloud-platform for data analysis using the method for part analytics according the present invention;”… ¶17: “during the machining process under consideration realtime” “process data of the at least one CNC machine, such as the actual geometrical tool path parameters, e.g. actual axis positions, speeds, accelerations and jerks, are continuously recorded over the machining process of the workpiece. ”… ¶83: “The machine-embedded measuring devices/sensors 15.1-15.5 used for measuring the actual positions of each axis may also be connected to the fieldbus 12.”
Examiner notes the claim interpretation of the limitation “one or more sources” as described in the Claim Interpretation section of the current office action.];
an apparatus according to claim 16, communicatively coupled to the one or more sources, wherein the apparatus is configured for instantaneous performance management of the machine tool based on the real-time condition data. [¶77: “process data of a CNC machine and for transferring said data a cloud-platform for data analysis using the method for part analytics according the present invention;”… ¶17: “during the machining process under consideration realtime” “process data of the at least one CNC machine,” “are continuously recorded over the machining process of the workpiece. ”… ¶83: “the machine-embedded measuring devices 15.1-15.5, e.g. high-resolution linear scales, are continuously measuring the actual position for feedback via the fieldbus 12 to the CNC controller 11.”… ¶15: “simulating the machining process under consideration by means of the digital machine model based at least partially on the recorded realtime” “process data.”… ¶17: “these recorded realtime” “data of the real process are used as input data to the digital machine model for simulating the machining process under consideration, thereby enabling for analyzing the quality of the machined workpiece and, moreover, in general to find a wide spectrum of problems on the parts and to relate them at least to typical problems with regard to the machine and the machining process.”
Examiner notes the 35 U.S.C. 112(d) rejection as described in the current office action. Claim 28 is independent claim but has been treated as dependent claim, because it depends from method of claim 16. Claim 16 is a method and not as recited “apparatus according to claim 16.” Therefore claim 28 is in improper dependent form as described in the 35 U.S.C. 112 section of the current office action.
Kreidler teaches, receiving condition data from the sources in order to perform instantaneous/real time performance management such as using the real time data to perform real time performance monitoring and optimization/solution];
Regarding claim 29:
Kreidler, Hase and ZHOU disclose all the elements of claim(s) 16,
Kreidler further discloses, A computer program product, comprising a non-transitory computer readable hardware storage device having computer readable program code stored therein, said program code executable by a processor of a computer system to implement the method according to claim 16. [¶34: “a stand-alone computer separate from the CNC controller and the cloud-based server as a separate computer provides more processor-performance and more memory.”… ¶9: “Most of the CNC controllers provide realtime data recording software. The recording software is switched on at the NC start of machining by manually set triggers and the behavior of controllers, drives and sensors is recorded”… ¶22: “The recorded non-realtime process data may include a NC (Numerical Control) program code and/or NC program configuration data, in particular a respective active NC program line or NC block.”].
Regarding claim 31:
Kreidler, Hase and ZHOU disclose all the elements of claim(s) 16, and
ZHOU further discloses, the digital twin is continuously calibrated to replicate substantially similar responses of the machine tool in real-time. [page 9, ¶2: “realizing static error compensation in the calibration process, then iteratively optimizing the analysis process, and correcting the structural parameters of the robot and the dynamic stiffness model of the whole robot obtained by kinematics calibration, so that the accuracy of the static error compensation and the dynamic stiffness model is improved, and the problem of poor machining accuracy of the robot is solved;”
page 12, ¶11: “performing kinematic calibration on the stress conditions of the robot at different poses, calculating an elastic deformation error caused by the self weight of the robot in a calibration process, correcting static error compensation in the calibration process, compensating based on the corrected static error, returning to the step two, and correcting the built complete machine dynamic stiffness model of the robot;”].
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kreidler, Hase and ZHOU, and further in view of Matsumura (US20190025794A1) [hereinafter Matsumura].
Regarding claim 20:
Kreidler, Hase and ZHOU disclose all the elements of claim(s) 19, but they do not explicitly disclose, predicting the impact on a cycle time associated with the machine tool based on the stability of the machine tool.
However, Matsumura discloses, predicting the impact on a cycle time associated with the machine tool based on the stability of the machine tool. [¶13: “reward output unit (for example, a reward output unit 3021 described later) configured to output a reward value in reinforcement learning on the basis of the cycle time and the machining accuracy included in the state information;”… ¶16: “the numerical control device causing the tool machine to perform cutting by executing the machining program, and the cycle time and the machining accuracy being acquired by the numerical control device performing the machining program.”].
Therefore, it would have been obvious to one of ordinary skill in the art before the filling date of the claimed invention to have combined the capability of predicting the impact on a cycle time associated with the machine tool based on the stability of the machine tool to reduce machining time while maintaining machining accuracy taught by Matsumura with the method taught by Kreidler, Hase and ZHOU as discussed above. A person of ordinary skill in the machine tool optimization field would have been motivated to make such combination in order to have reasonable expectation of success such as to reduce machining time while maintaining machining accuracy [Matsumura: ¶10: “capable of reducing machining time while maintaining machining accuracy”].
Claim(s) 22-23 and 26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kreidler, Hase and ZHOU, and further in view of Lin (US20200341459A1) [hereinafter Lin].
Regarding claim 22:
Kreidler, Hase and ZHOU disclose all the elements of claim(s) 16, but they do not explicitly disclose, wherein the behavior of the one or more critical components is associated with one or more defects in the one or more critical components.
However, Lin discloses, wherein the behavior of the one or more critical components is associated with one or more defects in the one or more critical components. [¶10: “predictive maintenance method for a component (TD) of a production tool and a computer program product thereof for performing maintenance immediately when the component is very likely to enter a dead state, and quantitatively showing the possibility of the component entering the dead state, by using a pre-alarm scheme and a death related indicator (DCI).”… ¶11: “plural event indicative values are obtained according to if an abnormal event occurs when the component is processing each of the workpieces, in which the event indicative values are one-to-one corresponding to the sets of process data.” “Thereafter, a first determination operation is performed to determine if the component is in a sick state according to the value of the aging feature (yT) corresponding to each of the workpieces”].
Therefore, it would have been obvious to one of ordinary skill in the art before the filling date of the claimed invention to have combined the capability of detecting behavior of the component where the behavior of the component is associated with defect in the component to perform maintenance on the component of the production tool on time taught by Lin with the method taught by Kreidler, Hase and ZHOU as discussed above in order to have reasonable expectation of success such as to be able to perform maintenance on the component of the production tool on time [Lin: ¶9: “performing maintenance on the component of the production tool in time”].
Regarding claim 23:
Kreidler, Hase, ZHOU and Lin disclose all the elements of claim(s) 16 and 22,
Lin further discloses, computing a remaining useful life of the one or more critical components based on the one or more defects. [¶11: “a first determination operation is performed to determine if the component is in a sick state according to the value of the aging feature (yT) corresponding to each of the workpieces” “n operation is performed to compute differences between the death time point and the respective time points at which the workpieces are processed, thereby obtaining plural predicted remaining useful life values (RULt), where t stands for the tth workpieces and t is an integer.”].
Regarding claim 26:
Kreidler, Hase and ZHOU disclose all the elements of claim(s) 16,
Lin further discloses, scheduling a maintenance activity for the machine tool based on the impact on the performance. [¶10 “predictive maintenance method for a component (TD) of a production tool and a computer program product thereof for performing maintenance immediately when the component is very likely to enter a dead state, and quantitatively showing the possibility of the component entering the dead state, by using a pre-alarm scheme and a death related indicator (DCI).”… ¶26: “accurately predict the RUL of the component of the production tool in real time, so as to perform maintenance on the component of the production tool in time; and can perform maintenance immediately when the component is very likely to enter a dead state, and can quantitatively show the possibility of the component entering the dead state.”].
Claim(s) 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kreidler, Hase and ZHOU, and further in view of Wang (US20170227945A1) [hereinafter Wang].
Regarding claim 24:
Kreidler, Hase and ZHOU disclose all the elements of claim(s) 16,
Kreidler further discloses, wherein optimizing the operation of the machine tool based on the impact on the performance of the machine tool comprises:
identifying at least one control parameter for improving the performance of the machine tool based on the impact; [¶66-¶74: “remedying the identified reasons, wherein remedying the identified problems may include” “adapting the geometry of the part program by changes of the CAM strategy in order to avoid critical machine vibrations or critical movements and to improve the overall dynamic behavior of the machine; and/or regarding a cutting machining process, changing the CAM strategy with regard to the relation between cutting depth, spindle speed and feed rate or other methods to improve cutting volume and/or quality; and/or adapting an error compensation table of the CNC machine or activating an error compensation functionality of the controller of the CNC machine; and/or adapting at least one drive parameter in order to change the motion characteristics of the CNC machine; and/or activating a filter functionality, smoothing functions and/or other motion optimization functions of the controller of the CNC machine;”];
computing a value of the control parameter based on the impact on the performance of the machine tool; [¶66-¶74 “Accordingly, on a third level, the method may further comprise remedying the identified reasons, wherein remedying the identified problems may include” “adapting the geometry of the part program by changes of the CAM strategy in order to avoid critical machine vibrations or critical movements and to improve the overall dynamic behavior of the machine; and/or regarding a cutting machining process, changing the CAM strategy with regard to the relation between cutting depth, spindle speed and feed rate or other methods to improve cutting volume and/or quality; and/or adapting an error compensation table of the CNC machine or activating an error compensation functionality of the controller of the CNC machine;” (¶66-¶74)],
but Kreidler, Hase and ZHOU do not explicitly disclose, and
Wang discloses, simulating an operation of the machine tool based on the computed value of the control parameter; [¶20: “at step 108, may calculate values of one or more operating conditions ” “at step 108, may calculate values of one or more operating conditions which could lead to undesirable results, such as result in damage to cutting tools, the machine tool or the workpiece if the machine tool were to continue to operate at or above that value. Such operating conditions include tool operating conditions and/or machine tool operating conditions, which may include but are not limited to any of cutting forces, spindle power, radial load at the spindle (e.g., at the spindle bearings), tool deflection, bending moment on the tool, bending moment on the spindle or at the spindle interface, cutting torque at the tool holder/spindle interface, temperature of the tool, load on one or more of the machine tool axes servos. Such operating conditions may be based on one or more machining conditions of the current motion step simulated at step 106 as described above.” “information relevant to the subsequent calculation of operating conditions may be simulated at step 106 by an existing program and relevant data extracted to form the basis for the calculation at step 108. Each of the calculations at step 108 may be considered a predicted value of each such respective operating condition of the specific motion step which is being simulated, and is also referred to herein as predicted value.”
Wang fig. 2, simulation, step 108; simulating with calculated control parameter such as cutting load, temperature, bending moment etc.];
comparing a simulated performance of the machine tool from the simulated operation to a threshold value; and [¶20: “at step 108, may calculate values of one or more operating conditions ”… ¶21: “At step 110, simulation 100 determines, for the current motion step, whether any predicted value calculated at step 108 exceeds a limit, which may be a predetermined limit, which is relevant to that operating condition.” “Such an assessment may, for example, be a comparison of the predicted values to machine tool specifications (e.g., power and torque limits), thrust force limit for one or more drive axis and cutting tool limits, such as but not limited to cutting tool's characteristic temperature below which the cutting tool material can maintain its mechanical strength, and workpiece attributes. Such assessment may include whether the respective predicted values are outside of respective predetermined tolerances of the limit.”…
Wang fig. 2, step 110; compare simulated performance with threshold/limit values to check if it exceeds the threshold/limit];
if the simulated performance of the machine tool is greater than the threshold value, applying the computed value of the control parameter to operate the machine tool. [¶21: “At step 110, simulation 100 determines, for the current motion step, whether any predicted value calculated at step 108 exceeds a limit, which may be a predetermined limit, which is relevant to that operating condition.” “a comparison of the predicted values to machine tool specifications (e.g., power and torque limits), thrust force limit for one or more drive axis and cutting tool limits,” “whether the respective predicted values are outside of respective predetermined tolerances of the limit.”… ¶24: “at step 116, proceeding from step 110 to step 116 as indicated by the dashed line. For example, simulation 100 may reduce the feed rate. Simulation 100 may then return to an appropriate step of simulation 100.” (¶24)
Wang fig. 2, step 110 to step 116; compare simulated performance with threshold/limit values to check if it exceeds the threshold/limit; and then if it exceeds the limit, then apply computed operating parameter such as feed rate adjustment):].
Therefore, it would have been obvious to one of ordinary skill in the art before the filling date of the claimed invention to have combined the capability of simulating an operation of the machine tool based on the computed value of the control parameter; comparing a simulated performance of the machine tool from the simulated operation to a threshold value; if the simulated performance of the machine tool is greater than the threshold value, applying the computed value of the control parameter to operate the machine tool to reduce or eliminate the potential for undesirable results/performance taught by Wang with the method taught by Kreidler, Hase and ZHOU as discussed above in order to have reasonable expectation of success such as to reduce or eliminate the potential for undesirable results/performance [Wang: ¶17: “simulation 100 may identify simulated operating conditions which may lead to undesirable results so that the NC program is or may be revised to reduce or eliminate the potential for such undesirable results,”].
Claim(s) 30 is rejected under 35 U.S.C. 103 as being unpatentable over Kreidler, Hase and ZHOU, and further in view of Sonoda et al. (US20130026963A1) [hereinafter Sonoda].
Regarding claim 30:
Kreidler, Hase and ZHOU disclose all the elements of claim(s) 16, but they do not explicitly disclose, the dynamic stiffness value is computed over predefined intervals of time during operation of the machine tool.
However, Sonoda discloses, the dynamic stiffness value is computed over predefined intervals of time during operation of the machine tool. [¶10: “a correcting unit that corrects” “the estimated spring constant for each sampling period so that the estimated torque error calculated by the estimated torque error calculating unit is minimum.”].
Therefore, it would have been obvious to one of ordinary skill in the art before the filling date of the claimed invention to have combined the capability of computing dynamic stiffness value over predefined intervals of time during operation of the machine tool to minimize the error resulting in better optimization of the machine taught by Sonoda with the method taught by Kreidler, Hase and ZHOU as discussed above in order to have reasonable expectation of success such as to minimize the error resulting in better optimization of the machine [Sonoda: ¶10: “a correcting unit that corrects” “the estimated spring constant for each sampling period so that the estimated torque error calculated by the estimated torque error calculating unit is minimum.”].
Response to Arguments
Applicant's arguments filed 02/26/2026 have been fully considered but they are not persuasive.
Applicant responds
(a) Rejections under 35 U.S.C. § 103
Applicant contends that claim 16 is not obvious and unpatentable over Kreidler in view of Hase, and further in view of Zhou…
the combination of cited references does not teach or render obvious, "configuring a digital twin of the machine tool based on the dynamic stiffness value."
Zhou teaches first establishing a simulation model based on structural parameters, and then establishing a dynamic stiffness model based on that simulation model. The dynamic stiffness model is the output of the simulation model in Zhou, and not an input used to configure a digital twin…. Claim 16 requires the opposite sequence. The claimed method first computes a dynamic stiffness value based on condition data, and then configures a digital twin based on that computed dynamic stiffness value…
Rather, Zhou uses a simulation model as an input to build a stiffness model.
combining Zhou with Kreidler would only yield Kreidler's digital machine model with a stiffness model derived from it. This combination would not yield a digital twin that is configured based on a computed dynamic stiffness value computed with real-time condition data, as required by claim 16. Zhou provides no teaching or suggestion of using a dynamic stiffness value as an input to configure a digital twin. One having ordinary skill in the art looking at Zhou would learn to derive stiffness information from a simulation model but not to use stiffness information to configure a digital twin.
(Page(s): 9-10)
With respect to (a) above, Examiner appreciates the interpretative description given by Applicant in response.
In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007).
Overall, the claim is broad, and broadly recites, calculating/determining a dynamic stiffness value using the received operating conditions of the machine tool and using this dynamic stiffness value, configuring/modifying a generic digital twin of the machine tool and simulation of the digital twin, prediction of performance and optimization based on simulation. Hase, teaches the computation of dynamic stiffness value, and Kreidler discloses, digital twin based simulation of machine component behavior and prediction of performance and optimization based on simulation as described in the previous office action.
Regarding the limitation, configuring a digital twin of the machine tool based on the dynamic stiffness value, in broadest reasonable interpretation, this limitation describes, a digital twin of the machine tool is configured based on dynamic stiffness value, where configuring means any configuration or modification to the digital twin based on dynamic stiffness value, and where the value can be any value of the dynamic stiffness. As described in the previous office action, ZHOU discloses, configuring/modifying a digital twin of the machine/robot based on establishing/computing a dynamic stiffness model/values of the machine/robot. One of ordinary skill in the art before the filling date of the claimed invention to have combined the above described teachings of ZHOU with the method taught by Kreidler and Hase in order to have reasonable expectation of success such as to have better machining accuracy by improving error compensation and dynamic stiffness model [ZHOU: page 9, ¶2): “so that the accuracy of the static error compensation and the dynamic stiffness model is improved, and the problem of poor machining accuracy of the robot is solved”].
Applicant’s arguments are fully considered, but for the above described reasons, they are not persuasive; therefore, 35 USC § 103 rejections of claim 16 as set forth in the previous office action are maintained. 35 USC § 103 rejections of claims 17-31 as set forth in the previous office action are also maintained for the same reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is listed in the PTO-892 Notice of Reference Cited.
Thomsen et al. (US 20200265329 A1) - AI EXTENSIONS AND INTELLIGENT MODEL VALIDATION FOR AN INDUSTRIAL DIGITAL TWIN:
¶170: the digital twin 2306 can be used to drive a virtual simulation of the asset in connection with testing of control software to be deployed in the industrial environment,…to predict future asset behavior or play back past asset behaviors based on an analysis of historical automation data, to perform live or historical operational analytics, or other such applications. Depending on the type of application performed by the digital twin 2306, the digital twin 2306 can be fed with live (real-time) data from the BIDTs during operation of the industrial asset or with historical time-series BIDT data generated and stored during prior operation of the asset.
Yang et al. (US20180272491A1) - Tool wear monitoring and predicting method:
¶6: Provide a tool wear monitoring and predicting method, thereby simultaneously monitoring and predicting cutting tool wear values and cutting tool life values of cutting tools respectively mounted on several tool machines by using a tool cyber-physical prediction (TCPP) scheme.
Huang (US6785641B1) - Simulating the dynamic response of a drilling tool assembly and its application to drilling tool assembly design optimization and drilling performance optimization:
col 4, lines 25-30: Methods for simulating the dynamic response of drilling tool assemblies may be used to generate a visual representation of drilling, to design drilling tool assemblies, and to optimize the drilling performance of a drilling tool assembly.
Guerra, RH et al. (Guerra, RH et al. Digital Twin-Based Optimization for Ultraprecision Motion Systems With Backlash and Friction. IEEE Access, vol. 7, pp. 93462-93472, July 2019, [online], [retrieved on 05 May 2025]. Retrieved from the Internet) - Digital Twin-Based Optimization for Ultraprecision Motion Systems With Backlash and Friction:
page 93464, section: B. DIGITAL TWIN DESCRIPTION AND IMPLEMENTATION: A digital twin-based optimization procedure is presented for an ultraprecision motion system with a flexible shaft connecting the motor to the (elastic) load, which is subject to both backlash and friction…The procedure includes the virtual representation of mechanical and electrical components, non-linearities (backlash and friction), and the corresponding control system. A procedure for digital twin-based optimization is also presented, in which the maximum absolute position error is minimized while maintaining accuracy with no significant increase in the control effort. The optimal settings for the controller parameters and for the backlash peak amplitude, the backlash peak time, and the hysteresis amplitude are then determined, in order to guarantee an appropriate dynamic response in the presence of backlash and friction (page 93462, abstract). A DT was implemented, in order to analyze the behavior of the system. It is composed of two main components: the electromechanical model of the system and the model of the P-PI cascade controller (Fig. 2). The representation of the mechanical part is inspired in a system with two masses that has a spring, in order to represent three clearly differentiated elements: the motor, the shaft, and the load. The parameters of this model are: the axis torsional stiffness, K.
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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/M.S./
Patent Examiner,
Art Unit 2116
/KENNETH M LO/Supervisory Patent Examiner, Art Unit 2116
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