DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 9/14/2026 has been entered.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 6-8, 13-15, and 20-32 is/are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by Lund et al1 (“Lund”).
Regarding claim 1, Lund teaches a method comprising:
providing a first digital twin model of a first physical object and a second digital twin model of a second physical object (note that the claim does not limit or define what performs the providing or what the data is provided to; further note that the limitation encompasses providing in any manner first and second digital or virtual models of physical objects which correspond to digital twins where such models may be related or unrelated, and may be models of the same type of physical object or different types of physical objects; see Lund, paragraphs 0057-0058 teaching “digital wind farm 30 of FIG. 6 includes a plurality of wind turbines 202, a farm control system 57, a virtual control system 59, a machine-learning analytics engine 47, a model-based HMI 56, and a plurality of digital twin turbines 55” and “the digital twin turbines 55 are representations of the physical assets that include the physics-based models for the specific models of each asset, the unique operating characteristics and/or data that have been accumulated for each asset, current settings, and/or other information” and as in paragraph 0077, “generating, via the user interface, a digital representation of each of the wind turbines 202 in the wind farm 200 based on one or more user inputs” such that here the “digital twin turbines 55” are provided and may be generated and controlled according to user input);
establishing a virtual reality environment for simulating operation of the first digital twin model and the second digital twin model (note that “establishing a virtual reality environment” is given its broadest reasonable interpretation as any creating or configuring of some computer represented or virtual setting in which the models can be located and their operation simulated under some environmental conditions, but the form of the environment is not limited and does not require the virtual reality environment to be interactive, immersive or the like necessarily nor is the specific manner in which the virtual reality environment is to be implemented; see Lund, paragraphs 0058-0063 and figure 6 teaching “virtual control system 59 may include a systems model of how the integrated system of assets are likely to operate, and how changing the characteristics of a subset of the assets is likely to affect the other assets as well as farm performance in general. The analytics engine 47, such as the ABM associated with the farm 30, may support analytics for more effective modeling as it learns about the operation of the farm 200 over time and the interactions, as well as supporting new analytics services to improve the ability of the user to operate their farm optimally over time. In certain embodiments, the virtual control system 59 and the analytics engine 47 use or incorporate externally available data about weather patterns and other conditions to combine with the data coming from sensors and from the physical assets” and “dynamic digital representation of behaviors of the wind turbine(s) 202 may leverage two classes of constructs. For example, one class of constructs may emphasize physical models of turbine and geological characteristics including blade shapes of the turbines and local terrains (e.g., the geographical terrain where the wind farm is located). Such a construct may also include aerodynamic models for specific shapes of turbine blades and the local terrain where the turbine is located. Computational fluid dynamics (CFD) may be applied to the models to simulate interactions of airflows with surfaces defined by boundary conditions of the blade and terrain shapes. The geo-relationships among the turbines 202 in the wind farm 200 may be modeled” and “relationships among turbines 202 and their operational settings may be established based upon the overall asset hierarchy across turbines 202 and controls in the wind farm 200. In an example embodiment, the microclimate change and conditions for each turbine 202 are synthesized and interpreted from multiple sources. For example, the sources may include weather data and weather forecast of the area, sensors of surrounding turbines 202 and wind farm equipment, a level of turbulences attributed from neighboring turbines 202, or the like” and “model-based HMI 56 may provide one or more views of the virtual, digital twin farm and the relevant states of the assets and system performance, a virtual HMI, and so on” such that here modeled terrain and turbine locations and their operating parameters in the environment including environmental conditions establish a virtual reality environment for simulating operations of the digital twin models, where for example “views f the virtual, digital twin farm and the relevant states of the assents and system performance” correspond to the virtual environment provided; see also paragraphs 0079-0081 and figures 18-20 teaching “displaying, via the user interface, a digital equivalent of the wind farm 200 based on the topography and environmental information of the wind farm 200 and the digital representations of each of the wind turbines 202, e.g. as developed in FIGS. 12-17” such that here the digital twins are presented in a virtual environment such as the digital twin farm generated);
inserting the first digital twin model into the virtual reality environment at a first location and the second digital twin model into the virtual reality environment at a second location (see Lund, paragraphs 0077-0079 and figures 12-13 and 17-18 teaching “generating a digital twin or equivalent of a wind farm 200 having a plurality of wind turbines 202 is illustrated. As shown at 252, the method 250 includes receiving, via a user interface, topography and/or environmental information of the wind farm 200. As shown at 254, the method 250 includes generating, via the user interface, a digital representation of each of the wind turbines 202 in the wind farm 200 based on one or more user inputs” and “the representation 104 illustrates a layout of the wind farm 200 having one or more place holders for turbine information to be entered. Thus, as shown in FIG. 13, a user can select one of the place holders (as indicated by the shading) to enter relevant turbine information. Thus, the user interface is configured to receive wind turbine information for each of the wind turbines 200” and “interface 100 may provide additional information to the user when the turbine information is input, such as additional considerations and/or the location of the wind turbine in the farm 200” and for example figure 18 shows digital twins inserted at first and second locations);
simulating operation the first digital twin model in the virtual reality environment using one or more operational parameters received from the virtual reality environment to determine operational effectiveness of the first digital twin model (note that an operational parameter received from the virtual reality environment is extremely broad as the virtual reality environment includes each digital twin which make up the environment as well as parameters such as topography and variables like wind speed and temperature and the digital twins operate according to operating parameters that dictate how they are to respond to any inputs to the system such that these are all examples of operating parameters that can be received from the virtual reality environment and finally operational effectiveness of a digital twin model corresponds to any indication of the models performance; see Lund, paragraphs 0057-0058 teaching “the digital wind farm 30 of FIG. 6 includes a plurality of wind turbines 202, a farm control system 57, a virtual control system 59, a machine-learning analytics engine 47, a model-based HMI 56, and a plurality of digital twin turbines 55. In an example embodiment, the digital wind farm 30 of FIG. 6 assumes a set of turbines with SCADA or equivalent elements, associated with the digital wind farm 30 (e.g., also SCADA-based). Such a digital wind farm 30 may also include sensor inputs from throughout the farm 200 as well. The digital wind farm 30 in turn may be associated with the virtual control system that has a digital twin view of the farm 20” and “the digital twin turbines 55 are representations of the physical assets that include the physics-based models for the specific models of each asset, the unique operating characteristics and/or data that have been accumulated for each asset, current settings, and/or other information. Further, the virtual control system 59 may include a systems model of how the integrated system of assets are likely to operate, and how changing the characteristics of a subset of the assets is likely to affect the other assets as well as farm performance in general” such that the models and their operating characteristics and how they respond to the environment correspond to simulating operation of the digital twin models in the virtual reality environment and the operational effectiveness is the output of such simulations; see further paragraph 0063 teaching “model-based HMI 56 may provide one or more views of the virtual, digital twin farm and the relevant states of the assets and system performance, a virtual HMI, and so on. Virtual controls accessed through the model-based HMI 56 may be translated by the virtual control system and supporting analytics, as applied to the digital twin assets into a specific set of commands that would be executed by the physical farm control system and turbines across the farm 200. Accordingly, in an example embodiment, directions provided by an operator can be modeled before executing them on the physical equipment in order to find the right combination of physical control settings needed to achieve the operator's goals” such that these directions to model the digital twin farm correspond to simulating operation of the first digital twin in the environment using the operational parameters supplied such as the inputs to each digital twin as well as the operating characteristics that determine how inputs are processed in the simulation; see further paragraphs 0068-75 and figure 8 teaching “GUI 88 may also include one or more control features configured to optimize performance of the wind farm 200” and “control feature(s) may include at least one of a wind direction dial 99, a wind flow meter 92 illustrating the wind flow across the wind farm 200, a power output representation 85 of the wind farm 200, a turbine speed adjuster 94 configured to allow a user to modify a speed of a selected wind turbine 202, and/or a speed dial 95” and as in paragraphs 0080-0084 teaching “displaying the digital equivalent of the wind farm 200 may include displaying, via the user interface, one or more control icons 89, 90, 91 arranged with each of the digital representations of the wind turbines 202 as described herein. For example, as mentioned, the control icons 89, 90, 91 of each digital representation of the wind turbines 202 may include information regarding current and/or optimum operating conditions of the digital wind turbine” and “assessing current operating parameters of one or more of the wind turbines 202 in the wind farm 200 based on the digital twin representation. As shown at 310, the method 300 includes determining updated operating parameters for the wind farm 200 based on the assessment” such that this corresponds to simulating the operation of the first digital twin according to the current operating parameters);
determining, based on the simulated operation of the first digital twin model and a spatial relationship between the first location and the second location, a changed environmental operational parameter at the second location (see Lund, paragraph 0058 teaching simulated operation of a first digital twin model and determining its effect on other digital twins in the system based on the operational parameters supplied where “changing characteristics the physics-based models for the specific models of each asset, the unique operating characteristics and/or data that have been accumulated for each asset, current settings, and/or other information. Further, the virtual control system 59 may include a systems model of how the integrated system of assets are likely to operate, and how changing the characteristics of a subset of the assets is likely to affect the other assets as well as farm performance in general” and see further paragraphs 0061-0062 teaching “dynamic digital representation of behaviors of the wind turbine(s) 202 may leverage two classes of constructs. For example, one class of constructs may emphasize physical models of turbine and geological characteristics including blade shapes of the turbines and local terrains (e.g., the geographical terrain where the wind farm is located). Such a construct may also include aerodynamic models for specific shapes of turbine blades and the local terrain where the turbine is located. Computational fluid dynamics (CFD) may be applied to the models to simulate interactions of airflows with surfaces defined by boundary conditions of the blade and terrain shapes. The geo-relationships among the turbines 202 in the wind farm 200 may be modeled through stitching the local terrain around each turbine 202 and cross-referencing the global geographic data of the wind farm 200 to calibrate the overall precision” and “constructs may include measurements and operational settings from each turbine 202, as well as real-time and predicative data from multi-channels environmental conditions. The relationships among turbines 202 and their operational settings may be established based upon the overall asset hierarchy across turbines 202 and controls in the wind farm 200. In an example embodiment, the microclimate change and conditions for each turbine 202 are synthesized and interpreted from multiple sources. For example, the sources may include weather data and weather forecast of the area, sensors of surrounding turbines 202 and wind farm equipment, a level of turbulences attributed from neighboring turbines 202, or the like. More specifically, the level of turbulence may be derived from the CFD and geo-relationship described above, and dynamically updated upon the operational conditions and settings of their neighboring turbines” and see further paragraph 0084-0085 teaching “step of implementing at least one change to the current operating state of the selected wind turbine 202 may include implementing the at least one change to the current operating state of the selected wind turbine 202 via at least one control feature displayed on the user interface. For example, as mentioned in reference to FIG. 8, the control feature(s) may include at least one of a wind direction dial, a wind flow meter illustrating the wind flow across the wind farm, a power output of the wind farm 200, or a turbine speed adjuster configured to allow a user to modify a speed of a selected wind turbine 202” such that a user may change an operational parameter causing a change in the environment and then determine a changed environmental parameter at the second location based on simulating the first digital twin and based on their locations where for example “the step of observing the effect of the change to the current operating state of the selected wind turbine 202 may include observing how the wind farm 200 operates under at least one of one or more weather conditions, business scenarios, and/or maintenance scenarios. More specifically, the step of observing how the wind farm 200 operates under at least one of one or more weather conditions, business scenarios, and/or maintenance scenarios may include observing a wake effect on neighboring turbines after implementing the change” such that the first twin’s simulated airflow interaction supplies the simulated operation with their geo-relationship and neighboring locations being used to determine the resulted updated turbulence or airflow that will be affecting the downstream second digital twin turbine);
simulating operation of the second digital twin model using the changed environmental operational parameter to determine operational effectiveness of the second digital twin model (see Lund, paragraphs 0060-0062 as explained above teaching “multi-channel environmental conditions may include not only the dynamics of microclimate change of the turbine on the specific terrain, but also the expected turbulence from the neighboring turbine upon their operational conditions and settings” and treating “each turbine 202 as an active agent in an agent-based model (ABM). Its behaviors may be dynamically influenced by the environmental changes and the behaviors of other agents that are the neighboring turbines in the wind farm 200” and see further paragraphs 0080-0082 teaching determining operational effectiveness such as operating conditions of the digital twins when simulating where “as mentioned, the control icons 89, 90, 91 of each digital representation of the wind turbines 202 may include information regarding current and/or optimum operating conditions of the digital wind turbine” and “the method 300 includes displaying, via a user interface, the digital twin representation of the wind turbine 202. As shown at 308, the method 300 includes assessing current operating parameters of one or more of the wind turbines 202 in the wind farm 200 based on the digital twin representation. As shown at 310, the method 300 includes determining updated operating parameters for the wind farm 200 based on the assessment” and “the operating conditions of the wind turbine(s) 202 may include at least one of turbine speed, turbine loading, turbine location within the wind farm, maintenance schedules, power output, pitch angle, yaw angle, torque, tip speed ratio, individual turbine settings, or any other suitable operating parameter” such that here a neighboring second digital twin to the first digital twin is simulated by taking into account the operational parameters of the first digital twin which may affect an environmental parameter at a second location that causes the second location to experience reduced airflow or turbulence from the first simulated twin; see also paragraphs 0083-0085 discuss further changing various operating conditions and observing the results of the simulation and changed environmental parameters at a second digital twin in relation to a first digital twin where “the step of assessing current operating parameters of one or more of the wind turbines 202 in the wind farm 200 based on the digital twin representation may include selecting at least one wind turbine 202 to evaluate, implementing at least one change to a current operating state of the selected wind turbine 202, and observing an effect of the change to the current operating state of the selected wind turbine 202” and “the step of observing how the wind farm 200 operates under at least one of one or more weather conditions, business scenarios, and/or maintenance scenarios may include observing a wake effect on neighboring turbines after implementing the change” such that here a user may change the operational parameters simulated at first and then may observe changes to other digital twins operation based on such changes); and
enabling a user to visualize the simulated operation of the first digital twin model and the second digital twin model in the virtual reality environment (see Lund, paragraphs 0067-0074 and figures 18-19 teaching “the digital twin interface 86 includes a graphical user interface (GUI) 88 that displays a digital equivalent of the wind farm 200. In addition, as shown, the GUI 88 may be in communication with a wind turbine state controller 72 of each of the wind turbines 202 in accordance with the present disclosure. For example, as shown, the digital equivalent of the wind farm 200 may include environmental information as well as a digital representation of each of the wind turbines 202 arranged in the wind farm 200” and “the blades 22 of the turbine 202 corresponding to the controller may be static or animated to represent the speed of operation of the blades 22. Thus, a user can visualize wake effects between the wind turbines 202, e.g. during or after operating parameters of the wind farm 200 have been modified as shown in FIG. 19. For example, as shown, the GUI 88 (or GUI 100) may include a wake representation 80, 110 between each of the wind turbines 202 of the wind farm 200, e.g. produced by the speed of the blades 22 and/or the direction of the wind 109. More specifically, as shown, the wake representation 80 may be illustrated streaming from the blades 22 of the turbine state controller 72 (represented by the including a wake visualizer). The direction of the wake 80 can be identical for all the turbine images, or it can indicate the direction of the wind as it flows through the turbine 202” and “GUI 88 may include a representation that is a stylized version of the wind farm 200, or it may be a realistic representation of the farm 200. The wakes from each turbine 202 may be shown interacting to represent the physics of the wind dynamics across the farm as shown by resultant wake 80. The representation 80 may be a symbolic representation of the interaction to represent variables relevant to the efficiency or other properties of the farm, or may represent key properties of the physics of the interactions of the wind and wakes across the farm. Further, the shape and/or form of the wake 80 may change dependent upon the operating conditions of the turbines 202 and the environmental airflow caused by the turbines 202” such that here the user is able to visualize the simulated operation of the digital twins in the virtual reality environment).
Regarding claim 6, Lund teaches all that is required as applied to claim 1 above and further teaches wherein enabling a user to visualize comprises enabling a user to visualize on a virtual reality device (note that a virtual reality device as broadly recited corresponds to a device on which visualization of a virtual reality is enabled in any manner and as explained above a virtual reality corresponds to any computer or digital representation of any reality, physical or simulated, such that a device that enables visualization of a virtual reality is a virtual reality device and does not for example require a head-mounted or near-eye display, head tracking, or any otherwise immersive presentation; see Lund, paragraphs 0035-0038 teaching “any machine capable of executing the instructions 216, sequentially or otherwise, that specify actions to be taken by machine 200” and “the I/O components may include visual components (e.g., a display such as a plasma display panel (PDP), a light emitting diode (LED) display, a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)), acoustic components (e.g., speakers), haptic components (e.g., a vibratory motor, resistance mechanisms), other signal generators, and so forth” and paragraph 0063 teaching “model-based HMI 56 may provide one or more views of the virtual, digital twin farm and the relevant states of the assets and system performance, a virtual HMI, and so on. Virtual controls accessed through the model-based HMI 56 may be translated by the virtual control system and supporting analytics, as applied to the digital twin assets into a specific set of commands that would be executed by the physical farm control system and turbines across the farm 200. Accordingly, in an example embodiment, directions provided by an operator can be modeled before executing them on the physical equipment in order to find the right combination of physical control settings needed to achieve the operator's goals” such that these views provided through the display on the GUI enable a user to visualize on that display which functions as a virtual reality device presenting the virtual reality wind farm).
Regarding claim 7, Lund teaches all that is required as applied to claim 1 above and further teaches enabling a user to control the one or more operational parameters in the virtual reality environment (see Lund, paragraphs 0063-0066 teaching “operational settings” that can be controlled by a user and providing “virtual controls access through the model-based HMD…applied to the digital twin assets into a specific set of commands that would be executed by the physical farm control system and turbines across the farm 200. Accordingly, in an example embodiment, directions provided by an operator can be modeled before executing them on the physical equipment in order to find the right combination of physical control settings needed to achieve the operator's goals” and “interaction with a digital twin-based interface for controlling the wind farm 200 through the virtual control system 59 and intelligent analytics engine 47 (e.g., an ABM) may be implemented with a model that allows the user to interact with models of the assets themselves with controls that map to the user's model of the most critical elements of the asset operations” and “the user can model variations in settings to explore the pros and cons of multiple parameter settings. Accordingly, the user can determine whether a better performance and lifting balance can be achieved, given the owner's goals” such that here the user is able to control many operational parameters in the virtual reality environment).
Regarding claim 21, Lund teaches all that is required as applied to claim 1 above and further teaches wherein the changed environmental operational parameter characterizes a condition of the virtual reality environment external to the second digital twin model (see Lund, paragraphs 0060-0064 teaching modeling the digital twins themselves as well as environmental operational parameters characterizing a condition of the environment external to the second digital twin model such as effects on neighbors and the environment such that for example “multi-channel environmental conditions may include not only the dynamics of microclimate change of the turbine on the specific terrain, but also the expected turbulence from the neighboring turbine upon their operational conditions and settings. Through digitalizing wind turbine behaviors with multi-channels dynamic updates, some of the methods and systems described herein are configured to treat each turbine 202 as an active agent in an agent-based model (ABM). Its behaviors may be dynamically influenced by the environmental changes and the behaviors of other agents that are the neighboring turbines in the wind farm 200” and as in paragraph 0073 “GUI 88 may include a representation that is a stylized version of the wind farm 200, or it may be a realistic representation of the farm 200. The wakes from each turbine 202 may be shown interacting to represent the physics of the wind dynamics across the farm as shown by resultant wake 80. The representation 80 may be a symbolic representation of the interaction to represent variables relevant to the efficiency or other properties of the farm, or may represent key properties of the physics of the interactions of the wind and wakes across the farm. Further, the shape and/or form of the wake 80 may change dependent upon the operating conditions of the turbines 202 and the environmental airflow caused by the turbines 202. Contextual conditions that impact the turbines 202 within the farm 200 may be overlaid on the graphical representation of the farm 200. A representation of the turbines 202 that in balance are optimized (based on existing analytics) may also be provided” the “environmental airflow caused by the turbines” is a changed environmental operational characteristic characterizing an external condition of the second digital twin model).
Regarding claim 22, Lund teaches all that is required as applied to claim 1 above and further teaches wherein the changed environmental operational parameter is wind direction (see Lund, paragraphs 0083-0085 teaching “the step of assessing current operating parameters of one or more of the wind turbines 202 in the wind farm 200 based on the digital twin representation may include selecting at least one wind turbine 202 to evaluate, implementing at least one change to a current operating state of the selected wind turbine 202, and observing an effect of the change to the current operating state of the selected wind turbine 202” and “the step of implementing at least one change to the current operating state of the selected wind turbine 202 may include implementing the at least one change to the current operating state of the selected wind turbine 202 via at least one control feature displayed on the user interface. For example, as mentioned in reference to FIG. 8, the control feature(s) may include at least one of a wind direction dial, a wind flow meter illustrating the wind flow across the wind farm, a power output of the wind farm 200, or a turbine speed adjuster configured to allow a user to modify a speed of a selected wind turbine 202” and “observing the effect of the change to the current operating state of the selected wind turbine 202 may include observing how the wind farm 200 operates under at least one of one or more weather conditions, business scenarios, and/or maintenance scenarios. More specifically, the step of observing how the wind farm 200 operates under at least one of one or more weather conditions, business scenarios, and/or maintenance scenarios may include observing a wake effect on neighboring turbines after implementing the change” and as in paragraph 0073, “wakes from each turbine 202 may be shown interacting to represent the physics of the wind dynamics across the farm as shown by resultant wake 80” and “the shape and/or form of the wake 80 may change dependent upon the operating conditions of the turbines 202 and the environmental airflow caused by the turbines 202” where paragraphs 0060-0061 teach that the simulation takes into account effects from neighboring twins including how neighboring twins affect their environment where twin are “dynamically influenced by the environmental changes and the behaviors of other agents that are the neighboring turbines in the wind farm 200” and for example “Computational fluid dynamics (CFD) may be applied to the models to simulate interactions of airflows with surfaces defined by boundary conditions of the blade and terrain shapes. The geo-relationships among the turbines 202 in the wind farm 200 may be modeled through stitching the local terrain around each turbine 202 and cross-referencing the global geographic data of the wind farm 200 to calibrate the overall precision” such that according to the above the changed wind direction is simulated and changes the conditions such that a first turbine twin models its wake which then affects any downstream second digital twin participating in the simulation such that the effect is a changed environmental operational parameter of the changed wind direction of the wake caused by a first digital twin turbine such that this is then considered in the response and output of the second digital twin turbine).
Regarding claim 23, Lund teaches all that is required as applied to claim 22 above and further teaches wherein the first physical object and the second physical object are respective wind turbines, and wherein the first location is upstream of the second location based on the wind direction (see Lund, paragraphs 0083-0085 teaching “the step of assessing current operating parameters of one or more of the wind turbines 202 in the wind farm 200 based on the digital twin representation may include selecting at least one wind turbine 202 to evaluate, implementing at least one change to a current operating state of the selected wind turbine 202, and observing an effect of the change to the current operating state of the selected wind turbine 202” and “the step of implementing at least one change to the current operating state of the selected wind turbine 202 may include implementing the at least one change to the current operating state of the selected wind turbine 202 via at least one control feature displayed on the user interface. For example, as mentioned in reference to FIG. 8, the control feature(s) may include at least one of a wind direction dial, a wind flow meter illustrating the wind flow across the wind farm, a power output of the wind farm 200, or a turbine speed adjuster configured to allow a user to modify a speed of a selected wind turbine 202” and “observing the effect of the change to the current operating state of the selected wind turbine 202 may include observing how the wind farm 200 operates under at least one of one or more weather conditions, business scenarios, and/or maintenance scenarios. More specifically, the step of observing how the wind farm 200 operates under at least one of one or more weather conditions, business scenarios, and/or maintenance scenarios may include observing a wake effect on neighboring turbines after implementing the change” and as in paragraph 0073, “wakes from each turbine 202 may be shown interacting to represent the physics of the wind dynamics across the farm as shown by resultant wake 80” and “the shape and/or form of the wake 80 may change dependent upon the operating conditions of the turbines 202 and the environmental airflow caused by the turbines 202” where paragraphs 0060-0061 teach that the simulation takes into account effects from neighboring twins including how neighboring twins affect their environment where twin are “dynamically influenced by the environmental changes and the behaviors of other agents that are the neighboring turbines in the wind farm 200” and for example “Computational fluid dynamics (CFD) may be applied to the models to simulate interactions of airflows with surfaces defined by boundary conditions of the blade and terrain shapes. The geo-relationships among the turbines 202 in the wind farm 200 may be modeled through stitching the local terrain around each turbine 202 and cross-referencing the global geographic data of the wind farm 200 to calibrate the overall precision” such that according to the above the changed wind direction is simulated and changes the conditions such that a first turbine twin models its wake which then affects any downstream second digital twin participating in the simulation such that the effect is a changed environmental operational parameter of the changed wind direction of the wake caused by a first digital twin turbine such that this is then considered in the response and output of the second digital twin turbine which is downstream).
Regarding claim 24, Lund teaches all that is required as applied to claim 1 above and further teaches further comprising determining, based on the changed environmental operational parameter, a position of the second physical object relative to the first physical object that reduces an effect of the first physical object on the second physical object (see Lund, paragraphs 0060-0062 teaching consideration of “the expected turbulence from the neighboring turbine upon their operational conditions and settings” and deriving “the best combinations of operational settings” through the simulator and determining turbulence and effects on the environment and other turbines as where “the microclimate change and conditions for each turbine 202 are synthesized and interpreted from multiple sources. For example, the sources may include weather data and weather forecast of the area, sensors of surrounding turbines 202 and wind farm equipment, a level of turbulences attributed from neighboring turbines 202, or the like. More specifically, the level of turbulence may be derived from the CFD and geo-relationship described above, and dynamically updated upon the operational conditions and settings of their neighboring turbines” and see paragraphs 0065-0066 teaching “the user can compare the farm model performance against ideal targets, e.g. as defined by analytics within the cloud control system. More specifically, as shown at 154, the user can obtain recommendations about new parameter settings to improve performance and/or to push existing operating parameters closer to the targets” and “the user can model variations in settings to explore the pros and cons of multiple parameter settings. Accordingly, the user can determine whether a better performance and lifting balance can be achieved, given the owner's goals” such that through such changing of environmental parameters and operating parameters a user can determine a position of the second physical object relative to the first physical object that reduces an effect of the first physical object on the second physical object through such simulation which indicates how any change has an effect on other assets or aspects of the environment where this is further explained in paragraphs 0081-0082 teaching “assessing current operating parameters of one or more of the wind turbines 202 in the wind farm 200 based on the digital twin representation. As shown at 310, the method 300 includes determining updated operating parameters for the wind farm 200 based on the assessment” and the operating conditions and parameters may include locations of the first and second digital twins where “the operating conditions of the wind turbine(s) 202 may include at least one of turbine speed, turbine loading, turbine location within the wind farm, maintenance schedules, power output, pitch angle, yaw angle, torque, tip speed ratio, individual turbine settings, or any other suitable operating parameter” such that change of such location would be reflected in the simulation and selecting a location that lessens the neighboring turbines wake effects which could be seen by inspecting the operating conditions would thus also reduce the effect of the first physical object turbine on the second physical object turbine as they model the physical turbines).
Regarding claim 25, Lund teaches all that is required as applied to claim 1 above and further teaches wherein simulating operation of the second digital twin model comprises receiving the changed environmental operational parameter by the second digital twin model from the virtual reality environment as an input (see Lund, paragraphs 0060-0062 teaching “embodiments build on an integrated and dynamic digital representation of behaviors of at least one wind turbine 202 in the wind farm 200 that obtains real-time and predicative data from multi-channels environmental conditions, and generates recommendations of operational factors across-turbines in the wind farm in order to maximize overall productivity” and “multi-channel environmental conditions may include not only the dynamics of microclimate change of the turbine on the specific terrain, but also the expected turbulence from the neighboring turbine upon their operational conditions and settings” where as explained above each turbine is modeled along with other agents and their behavior is “dynamically influenced by the environmental changes and the behaviors of other agents that are the neighboring turbines in the wind farm 200” and environmental operational parameters such as turbulence is “derived from the CFD and geo-relationship described above, and dynamically updated upon the operational conditions and settings of their neighboring turbines” such that the dynamically updated turbulence from the first turbine is a changed environmental operational parameter and the second turbine’s twin obtains that parameter from the modeled environmental conditions as an input to influence its simulated behavior in the system).
Regarding claims 8, 13, 14, 26, 27, and 28, the instant claims recite an apparatus in the form of a “computer program product comprising a computer-readable storage medium having computer-usable program code embodied therein, the computer-usable program code configured to perform the following when executed by at least one processor” perform the same functions of the method as in claims 1, 6,7,21, 23, and 28, respectively. Note that Applicant’s Specification at paragraph 0017 explicitly disavows such a storage medium as any type of transitory signal as “computer readable storage medium, as that term is used in the present disclosure, is not to be construed as storage in the form of transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide, light pulses passing through a fiber optic cable, electrical signals communicated through a wire, and/or other transmission media” and only provides examples of non-transitory type storage media. Lund teaches such a computer program product and storage medium and processor (see Lund, paragraphs 0035-0036 and figure 2 teaching “the controller 26 may include one or more processor(s) 58 and associated memory device(s) 60 (and/or input/output (I/O) components, not shown) configured to perform a variety of computer-implemented functions (e.g., performing the methods, steps, calculations and the like disclosed herein)” and “memory device(s) 60 may generally include memory element(s) including, but are not limited to, computer readable medium (e.g., random access memory (RAM)), computer readable non-volatile medium (e.g., a flash memory), one or more hard disk drives, a floppy disk, a compact disc-read only memory (CD-ROM), compact disk-read/write (CD-R/W) drives, a magneto-optical disk (MOD), a digital versatile disc (DVD), flash drives, optical drives, solid-state storage devices, and/or other suitable memory elements”) which performs the method as explained above in the rejections of claims 1, 6,7,21, 23, and 28, respectively, respectively. In light of this, the limitations of claims 8, 13, 14, 26, 27, and 28 correspond to the limitations of claims 1, 6,7,21, 23, and 28, respectively; thus they are rejected on the same grounds as claims 1, 6,7,21, 23, and 28, respectively.
Regarding claims 15, 20, 29, 30, and 31 the instant claims recite an apparatus in the form of a “system comprising: at least one processor; at least one memory device operably coupled to the at least one processor and storing instructions for execution on the at least one processor, the instructions causing the processor to” perform the method as in claims 1-5 and 7, respectively. Lund teaches such an apparatus (see Lund, paragraphs 0035-0036 and figure 2 teaching “the controller 26 may include one or more processor(s) 58 and associated memory device(s) 60 (and/or input/output (I/O) components, not shown) configured to perform a variety of computer-implemented functions (e.g., performing the methods, steps, calculations and the like disclosed herein)” and “memory device(s) 60 may generally include memory element(s) including, but are not limited to, computer readable medium (e.g., random access memory (RAM)), computer readable non-volatile medium (e.g., a flash memory), one or more hard disk drives, a floppy disk, a compact disc-read only memory (CD-ROM), compact disk-read/write (CD-R/W) drives, a magneto-optical disk (MOD), a digital versatile disc (DVD), flash drives, optical drives, solid-state storage devices, and/or other suitable memory elements”) which performs the method where the functions of the method correspond to those as addressed in the rejections of claims 1, 7, 25, 23, and 31, respectively. In light of this, the limitations of claims 15, 20, 29, 30, and 31 correspond to the limitations of claims 1, 7, 25, 23, and 31, respectively; thus they are rejected on the same grounds as claims 1, 7, 25, 23, and 31, respectively.
Regarding claim 32, Lund teaches all that is required as applied to claim 15 above and further teaches wherein determining the changed environmental operational parameter comprises determining different values of the changed environmental operational parameter for different spatial relationships between the first location and the second location (see Lund, paragraph 0066 teaching “the user can model variations in settings to explore the pros and cons of multiple parameter settings. Accordingly, the user can determine whether a better performance and lifting balance can be achieved, given the owner's goals” and such variations that can be modeled include different spatial relationship between the first and second location and determining how these produce different values of the changed environmental parameter such as in paragraphs 0082-0085 teaching “the operating conditions of the wind turbine(s) 202 may include at least one of turbine speed, turbine loading, turbine location within the wind farm, maintenance schedules, power output, pitch angle, yaw angle, torque, tip speed ratio, individual turbine settings, or any other suitable operating parameter” and “implementing at least one change to the current operating state of the selected wind turbine 202 may include implementing the at least one change to the current operating state of the selected wind turbine 202 via at least one control feature displayed on the user interface. For example, as mentioned in reference to FIG. 8, the control feature(s) may include at least one of a wind direction dial, a wind flow meter illustrating the wind flow across the wind farm, a power output of the wind farm 200, or a turbine speed adjuster configured to allow a user to modify a speed of a selected wind turbine 202” and “the step of observing how the wind farm 200 operates under at least one of one or more weather conditions, business scenarios, and/or maintenance scenarios may include observing a wake effect on neighboring turbines after implementing the change” and as in paragraphs 0061-0062 as explained above the spatial relationships set by the user are simulated according to neighboring locations where “the level of turbulence may be derived from the CFD and geo-relationship described above, and dynamically updated upon the operational conditions and settings of their neighboring turbines” such that here a user can change turbine locations and simulate the effects of such placement relative to another turbine where such different placement affects the simulated outputs and inputs of all of the assets being modeled in the system).
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 of the claims as previously presented and as previously rejected, have been 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. As can be seen in the above rejections, Lund anticipates each of the pending claims as fully explained above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
See Hazra et al (US PGPUB No. 20140039843) – Hazra teaches 3D modeling of wind turbines in relation to each other and the effect of a first digital twin of a turbine on a second digital twin of a turbine causing a changed environmental parameter at the second turbine location based on the location of both turbines (see Hazra, paragraphs 0018-0054 and figures 2 and 4).
See Machefaux et al (US PGPBUB No. 20230086699) – Machefaux teaches modeling of wind turbines and their effects on each other which cause changed environmental operational parameters at a second turbine based on a first turbine location so that operating conditions can be determined in a simulation (see Machefaux, paragraphs 0028-0098 and figures 1-5).
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/SCOTT E SONNERS/Examiner, Art Unit 2613
/XIAO M WU/Supervisory Patent Examiner, Art Unit 2613
1 US PGPUB No. 20160333854
