DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Responsive to the communication dated 6/11/2026.
Claims 1 – 10 are presented for examination.
Priority
ADS dated 4/28/2023 claims domestic priority to 63336597 dated 4/29/2022.
Information Disclosure Statement
IDS dated 4/28/2023, 9/07/2023, 6/11/2026 have been reviewed. See attached.
Drawings
The drawing, Fig 1, and 2 dated 4/28/2023 are disclosed as illustrating manufacturing systems. The claims, however, are directed towards a method of controlling operation of machines through categorical labels of structured data. This method is not illustrated in the drawing. The drawings are objected to under 37 CFR 1.83(a). The drawings must show every feature of the invention specified in the claims and the drawing do not show every claimed feature.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The abstract dated 4/28/2023 has 54 words, 4 lines, and no legal phraseology. The abstract is accepted.
Claim Interpretation
Paragraphs 10 and 11 describe a standardized structural data model. Such a model is illustrated in Table 1, (copied from the specification) is shown below.
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Paragraph 11 indicates that components belong to an “abstraction” in a hierarchical parent-child relationship and that an “abstraction” is a subsystem.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1 - 10 are rejected under 35 U.S.C. 103 as being unpatentable over Wiegman_2021 (US 11,592,791 B1) in view of Bhat_2021 (Real-Time Flight Simulation of Hydrobatic AUVs Over the Full 0 - 360 Envelope, IEEE Journal of Oceanic Engineering, VOL. 46, No. 4, October 2021) in view of Vosgien_2015 (Model-based system engineering enabling design-analysis data integration in digital design environments: application to collaborative aerodynamics simulation-based design process and turbojet integration studies, HAL open science, Sep 11 2015) in view of Sato_2020 (US 2020/0097663 A1).
Claim 1. Weigman_2021 makes obvious “A method comprising: (COL 1 lines 5 – 10: “… the present invention is directed to systems and methods for flight control system using simulator data…”; FIG. 6) following activation of a first machine, instantiating in a controller of the first machine a [lookup table] describing the first machine COL 3 lines 11 – 24: “… Aspects of the present disclosure can also be used to provide a lookup table used by the flight control system to determine the effectiveness of actuators under given operating conditions and operational states… in some embodiments, the flight control system uses the lookup table to determine an optimal set of actuators and associated parameters (e.g., RPM, angle, etc., as applicable) to achieve a set of forces and moments determined to achieve an objective e.g., to respond to inputs received via one or more manual flight control devices (inceptors), or other flight directives, such as inputs received…” EXAMINER NOTE: an actuator is a machine.) “Instantiating in the controller a version of a machine learning model trained on the second machine and in communication with the [lookup table]; and controlling operation of the first machine according to output of the machine learning model” (COL 1 lines 26 – 29: “… FIG. 10 is a block diagram illustrating an exemplary embodiment of a machine-learning process…”; COL 15 lines 54 – 67: “… in some cases, flight simulator 116 may include one or more physics models which represent analytically or through data-based, such as without limitation machine-learning processes…”; COL 21 lines 23 – 45: “… with continued reference to FIG. 1 generate a machine-learning model, wherein the machine-learning model is configured to receive the plurality of measured flight data as an input and output an aerodynamic model output as a function of a training data. In a non-limiting embodiment, the machine-learning model may include a trained machine-learning model (e.g., a classifier) trained by the training data and configured to receive the plurality of measured flight data and output the aerodynamic model output… a non-limiting embodiment, the training data may be received from a database… the machine-learning algorithm may include any machine-learning algorithm to train the machine-learning model using the training data…”; COL 25 lines 52 – 58: “… in a non-limiting embodiment, computing device 112 may generate allocation command datum 156 as a function of a machine-learning model. In a non-limiting embodiment, the machine-learning model may include inputs such as a moment datum, a vehicle torque limit, any prioritization data, and a plurality of measured flight data and output allocation command datum 156 and/or at least modified commands 152…”; COL 50 lines 24 – 27: “… in embodiments, flight controller 904 may be installed in an aircraft, may control the aircraft remotely, and/or may include an element installed in the aircraft and a remote element in communication therewith…”; COL 55 lines 4 – 12: “… still referring to FIG. 9, flight controller 904 may receive autonomous machine-learning model from a remote device and/or FPGA that utilizes one or more autonomous machine learning processes…”;
COL 55 lines 32 – 35: “… the updated machine-learning model may be transmitted by the remote device and/or FPGA and received by flight controller 904 as a software update, firmware update, or corrected habit machine-learning model…”;
EXAMINER NOTE: updating a flight controller 904 with an updated machine-learning model is an instantiation of a newly trained version of a machine learning model that is trained to control all the various component-machines that comprise the aircraft (i.e., assembly of component-machines)
COL 6 lines 27 – 45: “… with continued reference of FIG. 1, plurality of measured flight data 108 may include a flight component state data… the flight component state data of a plurality of flight components. “flight components”, for this purpose of this disclosure, includes components related to and mechanically connected to an aircraft that manipulates a fluid medium in order to propel a maneuver the aircraft through the fluid medium…”; COL 1 lines 13 – 20: “… flight control systems typically use simulator models and/or data derived from such models to control aircraft actuators, such as propellers, lift fans and other vertical flight rotors, ailerons, elevators, flaps, rudders, and the like during flight… aerodynamic surfaces such as wings, tails, stabilizers, etc.; and the aerodynamic effect of other structures, such as the fuselage, pylons, etc….”;
EXAMINER NOTE: the machine-learning model is trained to measure and control the states of all the machines that control the flight of the aircraft. Accordingly, the machine-learning model is trained on, at least, a first and second machine because, for example, a first machine may be a lift fan while a second machine may be an aileron or flap, or rudder.
COL 3 lines 11 – 24: “… Aspects of the present disclosure can also be used to provide a lookup table used by the flight control system to determine the effectiveness of actuators under given operating conditions and operational states… in some embodiments, the flight control system uses the lookup table to determine an optimal set of actuators and associated parameters (e.g., RPM, angle, etc., as applicable) to achieve a set of forces and moments determined to achieve an objective e.g., to respond to inputs received via one or more manual flight control devices (inceptors), or other flight directives, such as inputs received…”
EXAMINER NOTE: This teaches a flight controller in communication with a lookup table (e.g., standardized structured data model) to control state parameters such as, for example, RPM, angle, etc. “as applicable”.).
Weigman_2021 does not explicitly recite: “standardized structured data model” nor “according to a predefined categories populated with predefined labels that are indicative of measured parameters of the first machine, components of the first machine, and subsystems of the first machine, wherein the predefined labels have a parent-child relationship defined by the predefined categories and in which the predefined labels indicative of the measured parameters are categorized by the predefined labels indicative of the components, and the predefined labels indicative of the components are categorized by the predefined labels indicative of the subsystems, and wherein the predefined categories and predefined labels correspond to categories and labels describing a second machine such that the parent-child relationship correlates to a parent-child relationship of the labels describing the second machine” nor “standard structural model”
Bhat_2021, however, makes obvious “standardized structured data model” and “according to a categories populated with labels that are indicative of measured parameters of the first machine, components of the first machine (Page 1117 section III Multifidelity Hydrodynamic Database: “… the key idea is to populate the lookup table… the database may be built up with high fidelity… such a multifidelity database provides a straightforward way of modeling advanced maneuvers… the same approach can also be used for the added mass terms… in this article, the proposed approach is based on two categories of hydrodynamic data sets – one for the mail hull bodies and one for control surfaces (see Fig. 5). More advanced components such as nozzles can be assembled from a number of smaller airfoil profiles while nonslender bodies can also be included…”; page 1120: “… considering the aerospace domain… aerodynamic lookup tables for flight simulation… multifidelity methods has also been proposed for aerodynamic data over the full envelope…”; page 1120: “… for all subcomponents in an assembly, full envelope hydrodynamic databases are synthesized as lookup tables… computer hydrodynamic forces based on database lookup tables…”; page 1121: “… a hydrodynamic database can be assembled for each component… algorithms 1 and 2 provide example procedures for populating such a database… similarly, consider wings and control surfaces…” Page 1121: Algorithm 1, Algorithm 2; page 1124 TABLE IV; page 1124: “… the hydrodynamic database for the control surface comprising the SAM thrust vectoring nozzle is similarly synthesized…”; page 1129: “… the generalized method described in this article offers a relatively easy and straightforward solution to synthesize a hydrodynamic model over the full envelope… with a realistic set of coefficients, it is shown that the simulator can offer good performance… databases can be populated with data from different flow scenarios and based on different methods. Some examples are provided in the case study but there can be many other ways of adding data, which then depend on operational scenarios… wile the case study focuses on slender torpedo-shaped vehicles, a similar approach can be used to build up data sets for other shapes…”; page 1130: “… this article presents a strategy for assembling a… database… this enables direct use of the simulations as efficient tools in controller design… minimize the need for large-scale tuning of controllers once implemented… this framework also provides a useful tool in future research on reinforcement learning and system identification strategies to achieve even more accurate dynamics models and controller designs…”)
,,
Wiegman_2021 and Bhat_2021 are analogous art because they are from the same field of endeavor called machine controllers. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Wiegman_2021 and Bhat_2021. The rationale for doing so would have been that Wiegman_2021 teaches to have a machine controller (e.g., a flight controller) that uses a lookup table to determine the effectiveness of actuators under given operating conditions and uses the lookup table to determine next state optimal parameters used to control the machine to achieve a set of forces and moments determined to achieve control objectives (See COL 3 lines 12 – 24). Bhat_2021 teaches to build a database and to populate the database with high fidelity data that fully covers the entire control envelope in order to compute forces based on the database lookup tables in order to achieve target control envelope forces. Therefore, it would have been obvious to combine the database tables for individual components as taught by Bhat_2021 with the flight control system as taught by Wiegman_2021 for the benefit of having the lookup tables used by the flight control system to obtain the invention as specified in the claims.
Additionally, Weigman_2021 teaches combine a simulation that incorporates a machine-learning model with a controller in order to have a flight controller (FIG. 1, FIG. 10; COL 15 lines 65 – 67: “… in some cases, flight simulator 116 may include one or more physical models, which represent analytically or through data-based, such as without limitation machine-learning processes…”) and Bhat_2021 teaches a simulator the uses a lookup tables from a structured database to have high fidelity information that covers the entire control envelope for the purpose of improving the simulation result, and further teaches that the improves simulation results and that that “this framework also provides a useful tool in future research on reinforcement learning and system identification strategies to achieve even more accurate dynamics models and controller design…” (page 1130 section VII CONCLUSION). Therefore, it would have been obvious to combine the database lookup tables as taught by Bhat_2021 with the flight controller of Weigman_2021 for the benefit of having “even more accurate dynamics models” and therefore an improved controller.
Bhat_2021 illustrates that the AUV (Autonomous Underwater Vehicle) in Fig. 3 as having a Hull/body, Wings, Control Surfaces, and a Nozzle, and further illustrates in Fig. 8 “hardware subsystems on SAM” include a battery pack, a LCB trim, variable buoyance system (VBS), TCG trim, and thrust vectoring and propulsion system. Further, Bhat_2021 illustrates to build a structured database where the rows contain components, such has body and control surfaces, in TABLE IV on page 1124 and also state that the database for the control surfaces comprising the SAM thrust vectoring nozzle is similarly synthesized using Algorithm 2 (see page 1124). On page 1121 Algorithm 1 generates the database for the body while Algorithm 2 generates the database for the wings. Therefore, Bhat_2021 clearly teaches to build at least a table/database that include the components of body, wings, control surfaces, and thrust vectoring nozzle.
While these teaching may properly be found to imply to those of ordinary skill in the art that the database tables may be, for example, a table with hierarchical rows of Assembly and/or subassembly, as this is the logical tree structure of the illustrated UAV, Bhat_2021 does not EXPLICITLY illustrate a table with, for example, a column labeled assembly and a column labeled subassembly/subsystems.
Therefore, Wiegman_2021 and Bhat_2021 does not explicitly illustrate a database/table according to “predefined” categories and “predefined” labels containing columns labeled components “and subsystems of the first machine, wherein the predefined labels have a parent-child relationship defined by the predefined categories and in which the predefined labels indicative of the measured parameters are categorized by the predefined labels indicative of the components, and the predefined labels indicative of the components are categorized by the predefined labels indicative of the subsystems, and wherein the predefined categories and predefined labels correspond to categories and labels describing a second machine such that the parent-child relationship correlates to a parent-child relationship of the labels describing the second machine.”
Vosgien_2015, however, makes obvious “predefined” categories and “predefined” labels containing columns labeled components “and subsystems of the first machine, wherein the predefined labels have a parent-child relationship defined by the predefined categories and in which the predefined labels indicative of the measured parameters are categorized by the predefined labels indicative of the components, and the predefined labels indicative of the components are categorized by the predefined labels indicative of the subsystems, and wherein the predefined categories and predefined labels correspond to categories and labels describing a second machine such that the parent-child relationship correlates to a parent-child relationship of the labels describing the second machine” (Page 112 – 113: “… the PDM Schema… allows specifying properties associated product data and parts definitions by linking a representation of the property values to the object which the property is associated. A property is the definition of a special quantity and may reflect physics or arbitrary, user defined measurements. A number of pre-defined property type names are also proposed… an item property is a characteristic of an item. Each item property is either a general item property… a mass, a material property…”; Page 113 Figure 68 illustrates that item properties can have upper and lower limits and they can be general_item_properties. The figure provides examples of properties such as center_of_mass, Moment_of_inertia, mass, etc. EXAMINER NOTE: Table 1 of the instant specification discloses “features” which are illustrated as having upper and lower limits. Accordingly, the properties of the components/sub-assemblies/assemblies in the schema are interpreted as the disclosed “features.”; Page 117 – 118: “… structural assemblies – whose structure involve hierarchical relationships between product_components… define the product breakdown nodes relating a parent assembly to its related constituents: parent-children relationships are made between product_definition entitles representing a view definition of the part master through the use of the sub-types of the assembly_component_usage entity. In that case, the relationship itself represents the usage occurrence of a constituent definition within the immediate parent assembly definition… parent-child relationships are made between the… parent assembly and the… used parts or components of this assembly through the use of sub-types… the item instance represents the usage occurrence of a design item definition within the immediate parent assembly definition…”; Page 119 Figure 74 illustrates a schema that includes parent-child assembly/sub-assembly relationships and illustrates a class hierarchy for components. EXAMINER NOTE: Table 1 of the instant specification discloses the hierarchical structure as “Abstractions”. See Column 1 title: “Abstractions”. Figure 74 also refers to the “Abstract product structure” and links the components to the hierarchical product structure relationships.)
Wiegman_2021 and Bhat_2021 and Vosgien_2015 are analogous art because they are from the same field of endeavor called tables storing machine information. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Wiegman_2021 and Bhat_2021 and Vosgien_2015. The rationale for doing so would have been that Wiegman_2021 teaches to look up data in tables to determine parameters for controlling, for example, actuators under given operating conditions in order to control a machine (e.g., aircraft). Vosgien_2015 teaches an engineering data management schema that has parent-child hierarchical tables that may be queried (e.g., a database) and contain property information for that components, subassemblies, assemblies used in the machine (e.g., aircraft) and is based on the ISO 10303 Standard for the Exchange of Product model data (STEP). Therefore, it would have been obvious to combine Wiegman_2021 and Vosgien_2015for the benefit of having tables that organize parameters required for optimizing control of a machine (e.g., aircraft) in an industry standard format. to obtain the invention as specified in the claims.
While Weigman_2021 and Bhat_2021 and Vosgien_2015 all teach to have database tables that contain component specific parameters/features and all clearly teach hierarchical parent-child relationships between assemblies, sub-assemblies, and components none of these references individually and explicitly illustrate to jointly display, in a common table, the data objects (e.g., parameters/features) for the hierarchical assembly levels row-by-row similarly as illustrated in the instant specification at Table 1. Although the independent claim does not necessarily require such a table, Sato_2020, however, does make obvious “instantiating in a controller of a first machine a standardized structured data model” in a common table (FIG. 5, FIG. 6, FIG. 7, FIG. 10, FIG. 11, FIG. 15;
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Par 7: “… the storage unit includes: a configuration information holding unit that stores information about components of the product… information holding unit that stores product IDs of respective products associated with asset values of the respective components of the products…”; Par 10: “… the storage unit includes: the program; a configuration information holding unit that stores information about components of the product…”; P{ar 28: “… FIG. 15 is a block diagram showing the internal configuration of an in-vehicle device…”; par 73: “… product ID, and acquires information from the component hierarchy column 53 and the component column 54…”; Par 75: “… the component hierarchy column 54 and the component column 54 in the configuration information table 5…”; Par 115 – 116: “… FIG. 15 is a block diagram showing the internal configuration of an in-vehicle device 4… the storage unit 95 includes a terminal configuration information holding unit 96 that stores configuration information about the in-vehicle device…”; Par 117: “… processing unit 92 performs a process of updating the terminal configuration information holding unit 96, when updating the version of various kinds of software implemented in the in-vehicle device 4…”; Par 136: “… table 11 (FIG. 11) holds information in the formats designed on the basis of hierarchy. Therefore, it is possible to output… [information]… in an easy-to-understand manner…”).
Weigman_2021 and Sato_2020 are analogous art because they are from the same field of endeavor called database tables. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Weigman_2021 and Sato_2020. The rationale for doing so would have been that Weigman_2021 teaches that the flight controller may be installed onto the aircraft and a remote element my be in communication with the aircraft (COL 50 lines 24 – 27). Weigman_2021 also teaches that controller updates may be transmitted to the aircraft by remote devices (COL 55 lines 9 = 35). Sato_2020, however, teaches that “in recent years, technologies for acquiring various kinds of information by communicating with external information communication devices are realizing safe driving support and automatic driving of a vehicle have started spreading for in-vehicle communication systems” and “in such an in-vehicle communication system, the risk of receiving a cyber attack from the outside is increasing” (par 2). To counteract such increased dangers Sato_2020 teaches to have a storage unit that stores a common table that organizes component parameters/features hierarchically row-by-row and to query such a lookup table to asses and counteract component vulnerabilities. Therefore, it would have been obvious to combine the lookup table of Weigman_2021 used to provide flight control parameters/features and with the table of Sato_2020 which organizes parameters and features hectically row-by-row for the benefit of having both flight control and vulnerability parameters/features available to help protect the sensitive flight control system from cyber attach risks to obtain the invention as specified in the claims.
Claim 2. Weigman_2021 makes obvious further comprising receiving data from the second machine defining settings for the second machine and updating the version of the machine learning model with the data such that the controller implements the settings” (COL 55: “… an updated machine-learning model may be comprised of a firmware update, a software update, an autonomous machine-learning process correction, and the like thereof. As a non-limiting example a software update may incorporate a new simulation data that relates to a modified flight element…” )
Claim 3. Vosgien_2015 makes obvious “wherein some of the predefined labels include information defining a range of target values for a corresponding one or more of the measured parameters” (page 113 Figure 68: +upper_limit, +lower_limit)
Claim 4. Vosgien_2015 makes obvious “wherein some of the predefined labels include information indicating corresponding signals are correlated” (page 129: “… in AP233, an interface connector is the term for the part of a system that interacts with other systems or the environment and the interrace connection is the link between connectors. The connectors correspond to the concept of “ports” as defined in object-oriented modelling approaches. This is illustrated in Figure 86:
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Figure 87, shown below illustrates the hierarchical interface connection for the components in the machine.
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Claim 5. Vosgien_2015 makes obvious “wherein some of the predefined labels include information indicating corresponding signal values are affected by operation of at least one of the components” (page 129: “… an interface_connector is a specialization of Product that identifies a part of a product with which one or more other products or the environment interacts… each interface_connector_occurence represents the place where the product used in an assembly can interact with other products in the assembly. The interaction is represented by an interface_connection that relates a connected pair of interface_connector_occurences. An interface_specification is a specification of Product that provides a definition of necessary attributes for one or more items that participate in an interface… the relationship is captured through the use of the interface_definition_for entity. A Hierarchical_interface_connection is a specialization of interfaceconnection that provides an interconnection between components at different levels in an assembly. Each connection point in the assembly is represented by an interfaceconnectoroccurence…”).
Claim 6. Weigman_2021 makes obvious “wherein some of the predefined labels include information identifying whether a corresponding one or more of the components are sensors” (COL 49: “… in some embodiments, aircraft performance model 800 may include a sensor model 840. A sensor model 840 may model at least a sensor of aircraft 808. Sensor may include any sensor described in this disclosure. In some cases, sensor model 840 may emulate and/or simulate expected measurements for at least a sensor on aircraft 808… in some embodiments, sensor model 840 may be informed by actual measurements… in some cases the difference between an expected measurement and an actual measurement may be found, for example by sensor model 840; the difference may be used to improve aircraft performance model 800 performance, for example through data-based model updating…” COL 32 lines 39 – 41: “… measurements as detected by one or more sensors configured to measure aircraft angle like the IMV, gyroscope, motion sensor, optical sensor, a combination thereof, or another sensor of combination of sensors…”; COL 3 lines 11 – 25: “… a lookup table used by the flight control system to determine the effectiveness of actuators under given operating conditions… the flight control system uses the lookup table to determine an optimal set of actuators and associated parameters (e.g., RPM, angle, etc., as applicable) to achieve a set of forces and moments determined to achieve an objective e.g., to respond to inputs received via one or more manual flight control devices (inceptors)…”;
EXAMINER NOTE: the above citation teaches to use data-based modeling where the data-based model may include a sensor model such as an angle sensor and the lookup table is taught to include parameters for measured values such as angle. Accordingly, it would be obvious for labels in the lookup table to include information identifying the parameters of the angle sensor. For example, it is at least obvious for the row in which the angle sensor parameter is stored to include a label called “angle sensor” in the column titled components. This is especially true in combination with Bhat_2021 which illustrates a component column in TABLE IV. Because, Weigman_2021 teaches to have a sensor model, it would be obvious to list the “angle sensor” in the component column similarly to the other components shown in TABLE IV (e.g., body, control surfaces).
Claim 7. Weigman_2021 makes obvious “wherein some of the predefined labels include information identifying whether corresponding data should be included in data sets used for training of machine learning models” (COL 21: “… wherein the machine-learning model is configured to receive the plurality of measured flight data as an input and output an aerodynamic model output as a function of training data… the training data may be received from a database…”).
Claim 8. Bhat_2021 makes obvious “wherein a plurality of the predefined labels indicative of the measured parameters is categorized according to one of the predefined labels indicatives of the components” ( TABLE IV shows α categorized according to a component known and “body.”)
Claim 9. Bhat_2021 makes obvious “wherein one of the predefined labels indicative of the measured parameters is categorized by a plurality of the predefined labels indicative of the components” (TABLE IV which, for example, categorizes α < 20 degrees according to Re < 3.9 X10^6 for the component “body.” EXAMINER NOTE: this claim is to a hierarchical table where each column is labeled for lower branches of a hierarchical tree.)
Sato_2020 makes obvious “wherein one of the predefined labels indicative of the measured parameters is categorized by a plurality of the predefined labels indicative of the components” (FIG. 5 illustrates components categorized by hierarchy and then further categorized by variation ID and then further categorized by product ID. This table is using column labels to categorize table entries by a plurality of labels indicative of the component. FIG. 7 also illustrates a hierarchical table where a value is categorized by a plurality of labels. The asset value is further categorized according to asset and then further categorized according to product ID. FIG. 10 illustrates a hierarchical table. FIG. 11 illustrates a table where, for example, components are categorized under asset and further categorized under configuration.)
Claim 10. Weigman_2021 makes obvious “wherein a plurality of the predefined labels indicative of the components is categorized according to one of the predefined labels indicative of the subsystem” (COL 48 lines 4: “… model 820 may model any flight controller system or subsystem described in this disclosure…”; COL 3 lines 10 – 25: “… a lookup table used by the flight control system to determine the effectiveness of actuators… the flight control system uses the lookup table to determine an optimal set of actuators and associated parameters..” EXAMNER NOTE: the above teaches to model any sub-system and further teaches to use the lookup table to contain information for controlling a sub-system known as an actuator. Accordingly, it would be obvious to have rows and/or columns of the lookup table labeled to indicate parameters indicative of an actuator.).
Bhat_2021 makes obvious “wherein a plurality of the predefined labels indicative of the components is categorized according to one of the predefined labels indicative of the subsystem” (page 1120: “… for all subcomponents in an assembly, full envelope hydrodynamic databases are synthesized as lookup tables… assemble components and actuator subsystems… compute hydrodynamic forces based on database lookup tables. The forces and moment acting on each component are individually calculated…”; EXAMINER NOTE: the above teaches to build a lookup table for each subcomponent of an assembly such as an actuator subsystem. Accordingly, it would be obvious to have rows and/or columns of the lookup table labeled to indicate parameters indicative of an actuator subsystem.).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRIAN S COOK whose telephone number is (571)272-4276. The examiner can normally be reached 8:00 AM - 5:00 PM.
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/BRIAN S COOK/Primary Examiner, Art Unit 2187
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