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 .
DETAIL OFFICE ACTIONS
The United States Patent & Trademark Office appreciates the response filed for the current application that is submitted on 07/08/2026. The United States Patent & Trademark Office reviewed the following documents submitted and has made the following comments below.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/29/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Amendment
Applicant submitted amendments on 07/08/2026. The Examiner acknowledges the amendment and has reviewed the claims accordingly.
Status of Claims
Claims 1-20 are pending.
Applicant Arguments:
Regarding Argument 1, see remarks (page 8), filed 07/08/2026, Applicant/s state/s “Specification paragraph [0021] is amended to recite "includes" on line 4, per the Office Action suggestion. No new matter is added by way of the amendments to the specification.”
Regarding Argument 2, see remarks (page 8), filed 07/08/2026, Applicant/s state/s “Replacement sheets of drawings including FIG. 1 and FIG. 4 are being submitted. FIG. 1 now includes references item "100". FIG. 4 shows reference items "402", "404", "406" and "408." No new matter is added by way of the amendments to the drawings.”
Regarding Argument 3, see remarks (page 9), filed 07/08/2026, Applicant/s state/s “Claims 8-14 stand rejected under 35 U.S.C. §101 as allegedly being directed to non- statutory subject matter. Independent claim 8 is being amended to recite "non-transitory" computer readable storage medium. At least for the amendment, it is respectfully requested that the rejection of the claims under this section be withdrawn.”
Regarding Argument 4, see remarks (pages 9-10), filed 07/08/2026, Applicant/s state/s “Amended claim 1 recites, inter alia, "analyzing the received metrics, wherein patterns of components of the metrics are analyzed based on performing Fourier encoding and evaluating different waveforms in the metrics, and based on time synchronizations across different components of a digital twin rendering performed using different synchronization protocols; ... running simulations of the physical entity using the digital twin; and updating the physical entity based on results of the simulations." The cited references do not appear to disclose or suggest the amended features.”
Regarding Argument 5, see remarks (page 10), filed 07/08/2026, Applicant/s state/s “However, Cella even if combined with the rest of the cited references do not appear to disclose or suggest, "analyzing the received metrics, wherein patterns of components of the metrics are analyzed based on performing Fourier encoding and evaluating different waveforms in the metrics, and based on time synchronizations across different components of a digital twin rendering performed using different synchronization protocols." The same reasons apply to independent claims 8 and 15, and pending dependent claims at least by virtue of their dependencies. At least for the amendment, it is respectfully requested that the rejection of the claims under this section be withdrawn.”
Examiner’s Responses:
In response to Argument 1, see remarks (page 8), filed 07/08/2026, with respect to the objection to paragraph [0021] of the specification has been fully considered and is persuasive. Therefore, the objection to the specification has been withdrawn.
In response to Argument 2, see remarks (page 8), filed 07/08/2026, with respect to the drawing objections have been fully considered and are persuasive. Therefore, the drawing objections have been withdrawn.
In response to Argument 3, see remarks (page 9), filed 07/08/2026, with respect to the 35 U.S.C. §101 rejection of claims 8-14 has been fully considered and is persuasive. Therefore, the U.S.C. §101 rejection of claims 8-14 has been withdrawn.
In response to Argument 4, see remarks (pages 9-10), filed 07/08/2026, with respect to the teachings of the references, specifically Cella et al. (U.S. Patent Pub. No. 20240144141 A1, hereafter referred to as Cella) and Roper, JR. et al. (U.S. Patent Pub No. 20250217114 A1, hereafter referred to as Roper, JR.), has been fully considered but they are not persuasive.
Specifically, the Examiner finds that previously cited references, Cella and Roper, JR. teach the following amended claim language: “outfitted with a plurality of sensors associated with different functionalities of the physical entity”. Cella teaches there may be hundreds to thousands of IoT sensors that provide metrics such as vibration data signatures of important machinery, temperatures throughout the facility, motion sensors that track throughput, asset tracking sensors and beacons to locate items, cameras and optical sensors, chemical and biological sensors, and other sensors (Paragraph [0222]). Next, the Examiner finds that Roper, JR. teaches the amended claim language “and based on time synchronizations across different components of a digital twin rendering”. Specifically, Roper, JR. teaches digital twins are continuously updated and refined in real-time using operational data collected from monitoring the performance of the physical assembly or its components. Digital twins stay synchronized with the actual system (Paragraph [0137]. Further, the Examiner interprets the word “assembly” implies there are multiple components/parts “assembled” to form/encompass the physical entity. Therefore, since the “physical assembly” performance is updated/synchronized in real-time with the digital twin, then the synchronizations are “across different components” (i.e., all the components that make up the assembly and therefore are represented in the digital twin and are continuously updated.). Next, the Examiner finds that Cella teaches “performed using different synchronization protocols”. Specifically, Cella teaches time-synchronization by aligning data (sensor data) with a common clock or performing alignment with other data, such as a stream of data produced by a physical entity such as a vehicle, ship, robot, etc. (Paragraph [0252]). Further, Cella teaches “running simulations of the physical entity using the digital twin”. Cella teaches running simulations such as testing packaging for the packaging and physical asset (Paragraph [0538]). Lastly, Cella teaches “updating the physical entity based on results of the simulations.” Cella discloses outputting results of the simulations to a user. The user may then make adjustments based on the results of the digital twin simulation (Paragraphs [0538-539]. Cella in view of Roper, JR. does not explicitly disclose the limitation, “wherein patterns of components of the metrics are analyzed based on performing Fourier encoding and evaluating different waveforms in the metrics”. However, upon further consideration, a new ground(s) of rejection is made in view of Cella et al. (U.S. Patent Pub. No. 20240144141 A1, hereafter referred to as Cella) in view of Roper, JR. et al. (U.S. Patent Pub No. 20250217114 A1, hereafter referred to as Roper, JR.) in further view of Cella et al. (U.S. Patent Pub. No. 20230186200 A1, hereafter referred to as Cella 2).
The Examiner finds that Cella 2 teaches the limitation “wherein patterns of components of the metrics are analyzed based on performing Fourier encoding and evaluating different waveforms in the metrics”. Cella 2 teaches a signal evaluation circuit may perform frequency analysis using techniques such as digital Fast Fourier transform (FFT) or other frequency domain transform or digital analog signal analysis techniques. Frequencies of interest and abrupt changes in signal amplitude may be identified (Paragraphs [0705-706]). The Examiner interprets “Fourier Transform” to be synonymous to “Fourier Encoding” in light of paragraph [0063] of Applicant’s specification, which states, “‘Fourier Encoding,’ also known as ‘Fourier Transform,’ is used to analyze and represent input data in terms of their frequency components.” Additionally, the Examiner interprets “signal amplitude” to be a feature of the waveform of the signal. Further, Cella 2 teaches performing frequency analysis using techniques such as wavelet transform (Paragraphs [0705-706]). The Examiner will maintain prior art Cella and Roper, JR. and the details of the rejection are below.
In response to Argument 5, see remarks (page 10), filed 07/08/2026, with respect to the 35 U.S.C §103 rejections of claims 1-20 have been fully considered but are moot in view of new ground(s) of rejection caused by amendments. Therefore, the original rejections of Claims 1-20 are withdrawn under 35 U.S.C §103. However, a new grounds of rejection is issued for Claims 1-20 in view of Cella et al. (U.S. Patent Pub. No. 2024/0144141 A1, hereafter referred to as Cella) in view of Roper, JR. et al. (U.S. Patent Pub No. 2025/0217114 A1, hereafter referred to as Roper, JR.) in further view of Cella et al. (U.S. Patent Pub. No. 20230186200 A1, hereafter referred to as Cella 2).
See the Examiner’s response (above) to Argument 4 regarding the teachings of prior art references Cella, Roper, JR., and Cella 2 on the newly amended claim language/limitations. The Examiner will maintain prior art Cella and Roper, JR. and the details of the rejection are below.
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 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(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103(a) as being unpatentable over Cella et al. (U.S. Patent Pub. No. 20240144141 A1, hereafter referred to as Cella) in view of Roper JR. et al. (U.S. Patent Pub No. 20250217114 A1, hereafter referred to as Roper, JR.) in further view of Cella et al. (U.S. Patent Pub. No. 20230186200 A1, hereafter referred to as Cella 2).
Regarding Claim 1, Cella teaches the following:
A computer-implemented method (Claim 1, Cella teaches a computer-implemented method.) comprising:
receiving metrics associated with a physical entity (Paragraphs [0866], [0222], Fig. 68, Cella teaches receiving data from a connected data source (8020), such as a sensor system (8022) having sensors that collect data from facilities (e.g. manufacturing facilities, shipping facilities, warehouse facilities, data center facilities, and other physical entities of the enterprise). For example, in a manufacturing facility, IoT sensors may provide metrics such as vibration data that measure the vibration signatures of important machinery, temperatures throughout the facility, motion sensors, asset tracking sensors, cameras and optical sensors, chemical and biological sensors, and other types of sensors.)
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outfitted with a plurality of sensors associated with different functionalities of the physical entity (Paragraph [0222], Cella teaches in a manufacturing facility or other operating environment, there may be hundreds to thousands of IoT sensors that provide metrics such as vibration data that measure vibration signatures of important machinery, temperatures throughout the facility, motion sensors that track throughput, asset tracking sensors and beacons to locate items, cameras and optical sensors, chemical and biological sensors, and others.),
analyzing the received metrics (Paragraphs [1289-1290], Fig. 111, Cella teaches a diagnostic chip which may perform diagnostics based on data from one or more sensors, including biological sensors, chemical sensors, and/or electromechanical sensors, and to generate reports including analyses and recommended actions based on the diagnostics. The diagnostic chip may perform certain diagnostics by receiving, storing, and leveraging corresponding analytics libraries that may be used to configure and perform one or more analyses.),
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(Paragraph [0252], Cella teaches a monitoring systems layer facilitates alignment, such as time-synchronization. For example, one or more video streams or other sensor data collected with respect to an entity in a value chain network facility or environment, such as from a set of camera-enabled IoT devices, may be aligned with a common clock, so that the relative timing of a set of videos or other data can be understood by systems that may process the videos. The monitoring systems layer may further align a set of videos, images, sensor data, or the like, with other data, such as a stream of data produced by value chain network systems. The Examiner interprets synchronizing data with a common clock is one type of synchronization protocol and synchronizing data with another set of data is a second type of synchronization protocol. Therefore, “different synchronization protocol(s)” are used.);
based on the analysis of the received metrics, updating a digital twin corresponding to the physical entity (Paragraph [0854], Cella teaches updating one or more enterprise digital twins with some or all of the received data. The data is real-time sensor data collected from one or more sensor systems (8022) of an enterprise.), the digital twin being a virtual representation of the physical entity (Paragraph [0434], Fig. 8, Cella teaches a digital twin system which creates a digital replica of one or more of the value chain entities (652). The digital replica of the value chain entities may use substantially real-time sensor data to provide for substantially real-time virtual representation of the value chain entity. The Examiner interprets “value chain entities” are physical entities since the claim is silent to the specific type of “physical entity.” Fig. 8 (below) shows examples of “value chain entities”/physical entities such as machines, shipyards, roadways, railways, port infrastructure, drones, autonomous vehicles, robotics, and others.);
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running simulations of the physical entity using the digital twin (Paragraphs [0538], [0623], Cella teaches the digital twin of the packaging and physical asset may be used to run simulations that test the packaging (e.g., whether the packaging holds up in shipping, whether the packaging provides adequate insulation/padding, and the like).); and
updating the physical entity based on results of the simulations (Paragraphs [0538-539], Cella teaches the results of the simulation may be returned to the packaging design system, which may output the results to the user. The user may accept the packaging design, may adjust the packaging design, or may reject the packaging design.).
Cella does not explicitly disclose the following:
the metrics received using an established real-time data synchronization protocol;
and wherein patterns of components of the metrics are analyzed based on performing Fourier encoding and evaluating different waveforms in the metrics, and based on time synchronizations across different components of a digital twin rendering.
Roper, JR. is in the same field of art of using artificial intelligence for workflow enhancement/automation. Further, Roper, JR. teaches the metrics received using an established real-time data synchronization protocol (Paragraphs [0137], [0203], Fig. 1, Roper, JR. teaches continuously updating and refining the digital twins in real-time using the operational data collected from monitoring the performance of the physical assembly of components. This data may include processed sensory data, performance indicators, and other relevant information. By incorporating this real-time operational data, digital twins stay synchronized with the physical system and provide an accurate representation of its operational performance. Spatial computing interfaces enable real-time synchronization between digital twin and physical twin.).
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and based on time synchronizations across different components of a digital twin rendering (Paragraph [0137], Roper JR teaches the digital twins are created based on specific performance metrics and are continuously updated and refined in real-time using the operational data collected from monitoring the performance of the physical assembly or its components/individual component parts. The data may include sensory data, performance indicators, and other relevant information. By incorporating this real-time operational data, digital twins stay synchronized with the actual system and provide an accurate representation of its operational performance. This ensures that the digital twins remain up to date and aligned with the current state of the physical system.).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Cella by receiving the operational data associated with the physical system in real-time that is taught by Roper, JR., to make the invention that incorporates real-time operational data into the digital twin to stay continuously synchronized with the physical twin/entity; thus, one of ordinary skilled in the art would be motivated to combine the references since this ensures that the digital twins remains synchronized and aligned with the current state of the physical system allowing the digital twins to provide reliable predictions of future performance and assist in making informed decisions (Roper, JR., Paragraphs [0137-138]).
Cella in view of Roper, JR. does not explicitly disclose the following:
wherein patterns of components of the metrics are analyzed based on performing Fourier encoding and evaluating different waveforms in the metrics.
Cella 2 is in the same field of art of data collection in an industrial environment to generate a digital twin representing/modeling a real-world physical thing, such as an operating environment, a building, or a factory, for example. Further, Cella 2 teaches wherein patterns of components of the metrics are analyzed based on performing Fourier encoding (Paragraphs [0705-706], [0823], Cella 2 teaches a signal evaluation circuit may perform frequency analysis using techniques such as a digital Fast Fourier transform (FFT) or other frequency domain transform or digital analog signal analysis techniques. Frequencies of interest may be identified. Abrupt changes in signal amplitude may also be identified. The Examiner interprets “Fourier Transform” to be synonymous to “Fourier Encoding” in light of paragraph [0063] of Applicant’s specification, which states, “‘Fourier Encoding,’ also known as ‘Fourier Transform,’ is used to analyze and represent input data in terms of their frequency components.”) and evaluating different waveforms in the metrics (Paragraphs [0705-706], Cella 2 teaches the signal evaluation circuit may perform frequency analysis using techniques such as wavelet transform. Time-based detection values may alternatively be used to perform transitory signal analysis. These may include identifying abrupt changes in signal amplitude including changes where the change in amplitude exceeds a predetermined value or exists for a certain duration.).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Cella in view of Roper, JR. by analyzing patterns of the collected metrics based on performing Fourier transform to evaluate the metrics that is taught by Cella 2, to make the invention that analyzes signals/metrics received from various components of the physical entity to obtain information about the component or piece of equipment being monitored; thus, one of ordinary skilled in the art would be motivated to combine the references since evaluating signals received from equipment/physical entities may help with identifying deficiencies or failures in the equipment/entity. For example, performing frequency analyses may be used to filter out dominant frequency signals such as the overall rotation of a component/piece of equipment/machinery, and may help enable the evaluation of low amplitude signals at frequencies associated with torsion, bearing failure, and the like (Cella 2, Paragraph [0703]). Therefore, by analyzing frequencies and other waveform properties (amplitude) of the signals of the physical entity (machinery, equipment, component, etc.), problems may be detected earlier and prior to complete failure by identifying abnormal frequencies that are present.
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
In regards to Claim 2, Cella in view of Roper, JR. in further view of Cella 2 discloses the computer-implemented method of claim 1, wherein the metrics include data generated by at least one of sensors, devices and databases associated with the physical entity (Paragraphs [0866], [0887], Fig. 70, Cella teaches a sensor system (8022) having sensors that collect data from facilities. For example, the sensor system (8022) may include an edge device (8042) that reports data relating to physical assets in the facility. Under Broadest Reasonable Interpretation, the Examiner interprets “at least one of” to mean only one of sensors, devices, or databases is required to meet the limitation. However, Cella teaches all three: sensor (sensor systems), devices (edge device), databases (data warehouse/data lake, shown in Fig. 70 below).),
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the data capturing information about the physical entity (Paragraph [0866], Cella teaches the data captured by devices such as edge devices (8042) relates to physical assets such as smart machinery/manufacturing equipment, sensor kits, autonomous vehicles of the enterprise, wearable devices, and the like.).
In regards to Claim 3, Cella in view of Roper, JR. in further view of Cella 2 discloses the computer-implemented method of claim 1, wherein the real-time data synchronization protocol includes publish-subscribe protocol established between the physical entity and the digital twin (Paragraphs [0866], [3213], [0051], Cella teaches a digital twin I/O system (8104) which may subscribe to sensor system (8022) streams from facilities (e.g., manufacturing facilities, shipping facilities, data center facilities, etc.) and/or other physical entities of the enterprise. Sensors may be configured to publish data relating to the value chain network entities or a particular item (e.g., an engine part, for example). The information received by the value chain network digital twin associated with the physical data items of the value chain network is dynamic and real-time.).
In regards to Claim 4, Cella in view of Roper, JR. in further view of Cella 2 discloses the computer-implemented method of claim 1, wherein the analysis includes analyzing trends of data change in the received metrics (Paragraphs [0624], [1736], Cella teaches continuously capturing key operational metrics of the machines and may be used to monitor and optimize machine performance in real time. Machine digital twins may generate an alert or other warning based on a change in operating characteristics of the machine. The Examiner interprets identifying a change in operating characteristics to be a analyzing a “trend” since the claim is silent to the specific meaning of trend.), and based on the trends of data change, dynamically adjusting an interval of data streaming received from the physical entity (Paragraphs [2388], [2394], Cella teaches the time interval may be dynamically adjusted based on applicable conditions, such as increasing the time interval when no movable elements are detected, decreasing the time interval as or when the number of movable elements within an environment increases (e.g., increasing the number of robots and robot interactions), increasing the time interval during periods of reduced activity, decreasing the time interval during periods of abnormal activity (e.g., inspections or maintenance), decreasing the time interval when unexpected or uncharacteristic movement is detected (e.g., frequent movement by a typical sedentary element or uncoordinated movement, for example, of robots approaching an exit or moving cooperatively to carry a large object), combinations thereof, and the like.).
In regards to Claim 5, Cella in view of Roper, JR. in further view of Cella 2 discloses the computer-implemented method of claim 1, wherein the analysis includes estimating data to fill missing data points in the received metrics (Paragraphs [2737], [2420], Cella teaches using augmentation models to generate interpolated values from data streams that may be missing data (e.g. due to network interruptions), may generate predicted sensor readings for a sensor (e.g., a broken sensor) based on sensor readings from other nearby sensors, and may segment data received from data sources with additional data.), and wherein the digital twin is updated using the estimated data (Paragraphs [2420-2421], Cella teaches updating the digital twin by populating the asset digital twin with the calculated interpolated values.).
In regards to Claim 6, Cella in view of Roper, JR. in further view of Cella 2 discloses the computer-implemented method of claim 1, wherein the analysis includes predicting future data points based on historical trends and available data (Paragraphs [2343], [2420], [0684], Cella teaches a digital twin update module which provides the sensor data from one or more sensors to the digital twin dynamic model system, which can model a behavior of a shipping environment and/or shipping entity to extrapolate additional state data. Dynamic models associated with a digital twin of an asset can extrapolate values based on input data collected from sensors and/or devices disposed in the industrial setting and/or any other suitable data. For example, events or state data about value chain entities are stored as well as historical or other data stored in the data storage layer.), and wherein the digital twin is updated using the predicted future data points (Paragraphs [2420-2421], Cella teaches updating the digital twin by populating the asset digital twin with the calculated extrapolated values.).
In regards to Claim 7, Cella in view of Roper, JR. in further view of Cella 2 teaches the computer-implemented method of claim 1, wherein the updating the digital twin includes rendering visual updates to the digital twin (Paragraph [2430], Cella teaches updating properties of a digital twin in order to enable a digital representation of a shipping entity and/or environment wherein the real-time digital representation is a visualization of the digital twin. A digital twin may be rendered such that a human user can view the digital representations of real-world shipping entities, devices, workers, processes, and/or environments.).
In regards to Claim 8, Cella discloses the following:
A computer program product (Paragraph [0007], Cella teaches a computer program product which may reside on a computer readable storage medium having instructions stored thereon.) comprising a non-transitory computer readable storage medium having program instructions embodied therewith (Paragraph [2551], Cella teaches the data storage may be any type of non-transitory storage medium. The data storage stores methods, programs, codes, program instructions, or other types of instructions capable of being executed by processors.), the program instructions readable by a device to (Paragraph [0007], Cella teaches a plurality of instructions executed across one or more processors.) to cause the device to:
receive metrics associated with a physical entity (Paragraphs [0866], [0222], Fig. 68, Cella teaches receiving data from a connected data source (8020), such as a sensor system (8022) having sensors that collect data from facilities (e.g. manufacturing facilities, shipping facilities, warehouse facilities, data center facilities, and other physical entities of the enterprise). For example, in a manufacturing facility, IoT sensors may provide metrics such as vibration data that measure the vibration signatures of important machinery, temperatures throughout the facility, motion sensors, asset tracking sensors, cameras and optical sensors, chemical and biological sensors, and other types of sensors.) outfitted with a plurality of sensors associated with different functionalities of the physical entity (Paragraph [0222], Cella teaches in a manufacturing facility or other operating environment, there may be hundreds to thousands of IoT sensors that provide metrics such as vibration data that measure vibration signatures of important machinery, temperatures throughout the facility, motion sensors that track throughput, asset tracking sensors and beacons to locate items, cameras and optical sensors, chemical and biological sensors, and others.),
analyze the received metrics (Paragraphs [1289-1290], Fig. 111, Cella teaches a diagnostic chip which may perform diagnostics based on data from one or more sensors, including biological sensors, chemical sensors, and/or electromechanical sensors, and to generate reports including analyses and recommended actions based on the diagnostics. The diagnostic chip may perform certain diagnostics by receiving, storing, and leveraging corresponding analytics libraries that may be used to configure and perform one or more analyses.), (Paragraph [0252], Cella teaches a monitoring systems layer facilitates alignment, such as time-synchronization. For example, one or more video streams or other sensor data collected with respect to an entity in a value chain network facility or environment, such as from a set of camera-enabled IoT devices, may be aligned with a common clock, so that the relative timing of a set of videos or other data can be understood by systems that may process the videos. The monitoring systems layer may further align a set of videos, images, sensor data, or the like, with other data, such as a stream of data produced by value chain network systems. The Examiner interprets synchronizing data with a common clock is one type of synchronization protocol and synchronizing data with another set of data is a second type of synchronization protocol. Therefore, “different synchronization protocol(s)” are used.);
based on analysis of the received metrics, update a digital twin corresponding to the physical entity (Paragraph [0854], Cella teaches updating one or more enterprise digital twins with some or all of the received data. The data is real-time sensor data collected from one or more sensor systems (8022) of an enterprise.), the digital twin being a virtual representation of the physical entity (Paragraph [0434], Fig. 8, Cella teaches a digital twin system which creates a digital replica of one or more of the value chain entities (652). The digital replica of the value chain entities may use substantially real-time sensor data to provide for substantially real-time virtual representation of the value chain entity. The Examiner interprets “value chain entities” are physical entities since the claim is silent to the specific type of “physical entity.” Fig. 8 (below) shows examples of “value chain entities”/physical entities such as machines, shipyards, roadways, railways, port infrastructure, drones, autonomous vehicles, robotics, and others.);
run simulations of the physical entity using the digital twin (Paragraphs [0538], [0623], Cella teaches the digital twin of the packaging and physical asset may be used to run simulations that test the packaging (e.g., whether the packaging holds up in shipping, whether the packaging provides adequate insulation/padding, and the like).); and
update the physical entity based on results of the simulations (Paragraphs [0538-539], Cella teaches the results of the simulation may be returned to the packaging design system, which may output the results to the user. The user may accept the packaging design, may adjust the packaging design, or may reject the packaging design.).
Cella does not explicitly disclose the following:
the metrics received using an established real-time data synchronization protocol;
wherein patterns of components of the metrics are analyzed based on performing Fourier encoding and evaluating different waveforms in the metrics, and based on time synchronizations across different components of a digital twin rendering
Roper, JR. is in the same field of art of using artificial intelligence for workflow enhancement/automation. Further, Roper, JR. teaches the metrics received using an established real-time data synchronization protocol (Paragraphs [0137], [0203], Fig. 1, Roper, JR. teaches continuously updating and refining the digital twins in real-time using the operational data collected from monitoring the performance of the physical assembly of components. This data may include processed sensory data, performance indicators, and other relevant information. By incorporating this real-time operational data, digital twins stay synchronized with the physical system and provide an accurate representation of its operational performance. Spatial computing interfaces enable real-time synchronization between digital twin and physical twin.).
and based on time synchronizations across different components of a digital twin rendering (Paragraph [0137], Roper JR teaches the digital twins are created based on specific performance metrics and are continuously updated and refined in real-time using the operational data collected from monitoring the performance of the physical assembly or its components/individual component parts. The data may include sensory data, performance indicators, and other relevant information. By incorporating this real-time operational data, digital twins stay synchronized with the actual system and provide an accurate representation of its operational performance. This ensures that the digital twins remain up to date and aligned with the current state of the physical system.).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Cella by receiving the operational data associated with the physical system in real-time that is taught by Roper, JR., to make the invention that incorporates real-time operational data into the digital twin to stay continuously synchronized with the physical twin/entity; thus, one of ordinary skilled in the art would be motivated to combine the references since this ensures that the digital twins remains synchronized and aligned with the current state of the physical system allowing the digital twins to provide reliable predictions of future performance and assist in making informed decisions (Roper, JR., Paragraphs [0137-138]).
Cella in view of Roper, JR. does not explicitly disclose the following:
wherein patterns of components of the metrics are analyzed based on performing Fourier encoding and evaluating different waveforms in the metrics.
Cella 2 is in the same field of art of data collection in an industrial environment to generate a digital twin representing/modeling a real-world physical thing, such as an operating environment, a building, or a factory, for example. Further, Cella 2 teaches wherein patterns of components of the metrics are analyzed based on performing Fourier encoding (Paragraphs [0705-706], [0823], Cella 2 teaches a signal evaluation circuit may perform frequency analysis using techniques such as a digital Fast Fourier transform (FFT) or other frequency domain transform or digital analog signal analysis techniques. Frequencies of interest may be identified. Abrupt changes in signal amplitude may also be identified. The Examiner interprets “Fourier Transform” to be synonymous to “Fourier Encoding” in light of paragraph [0063] of Applicant’s specification, which states, “‘Fourier Encoding,’ also known as ‘Fourier Transform,’ is used to analyze and represent input data in terms of their frequency components.”) and evaluating different waveforms in the metrics (Paragraphs [0705-706], Cella 2 teaches the signal evaluation circuit may perform frequency analysis using techniques such as wavelet transform. Time-based detection values may alternatively be used to perform transitory signal analysis. These may include identifying abrupt changes in signal amplitude including changes where the change in amplitude exceeds a predetermined value or exists for a certain duration.).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Cella in view of Roper, JR. by analyzing patterns of the collected metrics based on performing Fourier transform to evaluate the metrics that is taught by Cella 2, to make the invention that analyzes signals/metrics received from various components of the physical entity to obtain information about the component or piece of equipment being monitored; thus, one of ordinary skilled in the art would be motivated to combine the references since evaluating signals received from equipment/physical entities may help with identifying deficiencies or failures in the equipment/entity. For example, performing frequency analyses may be used to filter out dominant frequency signals such as the overall rotation of a component/piece of equipment/machinery, and may help enable the evaluation of low amplitude signals at frequencies associated with torsion, bearing failure, and the like (Cella 2, Paragraph [0703]). Therefore, by analyzing frequencies and other waveform properties (amplitude) of the signals of the physical entity (machinery, equipment, component, etc.), problems may be detected earlier and prior to complete failure by identifying abnormal frequencies that are present.
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
In regards to Claim 9, Cella in view of Roper, JR. in further view of Cella 2 discloses the computer program product of claim 8, wherein the metrics include data generated by at least one of sensors, devices and databases associated with the physical entity (Paragraph [0866], Cella teaches a sensor system (8022) having sensors that collect data from facilities. For example, the sensor system (8022) may include an edge device (8042) that reports data relating to physical assets in the facility. Under Broadest Reasonable Interpretation, the Examiner interprets “at least one of” to mean only one of sensors, devices, or databases is required to meet the limitation.), the data capturing information about the physical entity (Paragraph [0866], Cella teaches the data captured by edge devices (8042) relates to physical assets (e.g. smart machinery/manufacturing equipment, sensor kits, autonomous vehicles of the enterprise, wearable devices, and the like).).
In regards to Claim 10, Cella in view of Roper, JR. in further view of Cella 2 discloses the computer program product of claim 8, wherein the real-time data synchronization protocol includes publish-subscribe protocol established between the physical entity and the digital twin (Paragraphs [0866], [3213], [0051], Cella teaches a digital twin I/O system (8104) which may subscribe to sensor system (8022) streams from facilities (e.g., manufacturing facilities, shipping facilities, data center facilities, etc.) and/or other physical entities of the enterprise. Sensors may be configured to publish data relating to the value chain network entities or a particular item (e.g., an engine part). The information received by the value chain network digital twin associated with the physical data items of the value chain network is dynamic and real-time.).
In regards to Claim 11, Cella in view of Roper, JR. in further view of Cella 2 discloses the computer program product of claim 8, wherein the analysis includes analyzing trends of data change in the received metrics (Paragraph [0624], Cella teaches continuously capturing key operational metrics of the machines. Machine digital twins may generate an alert or other warning based on a change in operating characteristics of the machine. The Examiner interprets “a change in” operating characteristics to be a trend since the claim is silent to the meaning of trend.), and based on the trends of data change, dynamically adjusting an interval of data streaming received from the physical entity (Paragraphs [2388], [2394], Cella teaches the time interval may be dynamically adjusted based on applicable conditions, such as increasing the time interval when no movable elements are detected, decreasing the time interval as or when the number of movable elements within an environment increases (e.g., increasing the number of robots and robot interactions), increasing the time interval during periods of reduced activity, decreasing the time interval during periods of abnormal activity (e.g., inspections or maintenance), decreasing the time interval when unexpected or uncharacteristic movement is detected (e.g., frequent movement by a typical sedentary element or uncoordinated movement, for example, of robots approaching an exit or moving cooperatively to carry a large object), combinations thereof, and the like.).
In regards to Claim 12, Cella in view of Roper, JR. in further view of Cella 2 discloses the computer program product of claim 8, wherein the analysis includes estimating data to fill missing data points in the received metrics (Paragraphs [2737], [2420], Cella teaches using augmentation models to generate interpolated values from data streams that may be missing data (e.g. due to network interruptions), may generate predicted sensor readings for a sensor (e.g., a broken sensor) based on sensor readings from other nearby sensors, and may segment data received from data sources with additional data.), and wherein the digital twin is updated using the estimated data (Paragraphs [2420-2421], Cella teaches updating the digital twin by populating the digital twin with the calculated interpolated values.).
In regards to Claim 13, Cella in view of Roper, JR. in further view of Cella 2 discloses the computer program product of claim 8, wherein the analysis includes predicting future data points based on historical trends and available data (Paragraphs [2343], [2420], [0684], Cella teaches a digital twin update module which provides the sensor data from one or more sensors to the digital twin dynamic model system, which can model a behavior of a shipping environment and/or shipping entity to extrapolate additional state data. Dynamic models associated with a digital twin of an asset can extrapolate values based on input data collected from sensors and/or devices disposed in the industrial setting and/or any other suitable data. For example, events or state data about value chain entities are stored as well as historical or other data stored in the data storage layer.), and wherein the digital twin is updated using the predicted future data points (Paragraphs [2420-2421], Cella teaches updating the digital twin by populating the digital twin with the calculated extrapolated values.).
In regards to Claim 14, Cella in view of Roper, JR. in further view of Cella 2 discloses the computer program product of claim 8, wherein the device is caused to update the digital twin by rendering visual updates to the digital twin (Paragraph [2430], Cella teaches updating properties of a digital twin in order to enable a digital representation of a shipping entity and/or environment wherein the real-time digital representation is a visualization of the digital twin. A digital twin may be rendered such that a human user can view the digital representations of real-world shipping entities, devices, workers, processes, and/or environments.).
In regards to Claim 15, Cella discloses the following:
A system (Paragraph [0005], Cella teaches a computing system.) comprising:
at least one computer processor (Paragraph [0005], Cella teaches a computing system with one or more processors.);
at least one memory device coupled with the at least one computer processor (Paragraphs [0005], [2492], Fig. 151, Cella teaches a computing system with one or more memories. The memory stores methods, programs, codes, program instructions or other type of instructions capable of being executed by the processor.);
the at least one computer processor configured to at least:
receive metrics associated with a physical entity (Paragraphs [0866], [0222], Fig. 68, Cella teaches receiving data from a connected data source (8020), such as a sensor system (8022) having sensors that collect data from facilities (e.g. manufacturing facilities, shipping facilities, warehouse facilities, data center facilities, and other physical entities of the enterprise). For example, in a manufacturing facility, IoT sensors may provide metrics such as vibration data that measure the vibration signatures of important machinery, temperatures throughout the facility, motion sensors, asset tracking sensors, cameras and optical sensors, chemical and biological sensors, and other types of sensors.) outfitted with a plurality of sensors associated with different functionalities of the physical entity (Paragraph [0222], Cella teaches in a manufacturing facility or other operating environment, there may be hundreds to thousands of IoT sensors that provide metrics such as vibration data that measure vibration signatures of important machinery, temperatures throughout the facility, motion sensors that track throughput, asset tracking sensors and beacons to locate items, cameras and optical sensors, chemical and biological sensors, and others.),
analyze the received metrics (Paragraphs [1289-1290], Fig. 111, Cella teaches a diagnostic chip which may perform diagnostics based on data from one or more sensors, including biological sensors, chemical sensors, and/or electromechanical sensors, and to generate reports including analyses and recommended actions based on the diagnostics. The diagnostic chip may perform certain diagnostics by receiving, storing, and leveraging corresponding analytics libraries that may be used to configure and perform one or more analyses.), (Paragraph [0252], Cella teaches a monitoring systems layer facilitates alignment, such as time-synchronization. For example, one or more video streams or other sensor data collected with respect to an entity in a value chain network facility or environment, such as from a set of camera-enabled IoT devices, may be aligned with a common clock, so that the relative timing of a set of videos or other data can be understood by systems that may process the videos. The monitoring systems layer may further align a set of videos, images, sensor data, or the like, with other data, such as a stream of data produced by value chain network systems. The Examiner interprets synchronizing data with a common clock is one type of synchronization protocol and synchronizing data with another set of data is a second type of synchronization protocol. Therefore, “different synchronization protocol(s)” are used.);
based on analysis of the received metrics, update a digital twin corresponding to the physical entity (Paragraph [0854], Cella teaches updating one or more enterprise digital twins with some or all of the received data. The data is real-time sensor data collected from one or more sensor systems (8022) of an enterprise.), the digital twin being a virtual representation of the physical entity (Paragraph [0434], Fig. 8, Cella teaches a digital twin system which creates a digital replica of one or more of the value chain entities (652). The digital replica of the value chain entities may use substantially real-time sensor data to provide for substantially real-time virtual representation of the value chain entity. The Examiner interprets “value chain entities” are physical entities since the claim is silent to the specific type of “physical entity.” Fig. 8 (below) shows examples of “value chain entities”/physical entities such as machines, shipyards, roadways, railways, port infrastructure, drones, autonomous vehicles, robotics, and others.);
run simulations of the physical entity using the digital twin (Paragraphs [0538], [0623], Cella teaches the digital twin of the packaging and physical asset may be used to run simulations that test the packaging (e.g., whether the packaging holds up in shipping, whether the packaging provides adequate insulation/padding, and the like).); and
update the physical entity based on results of the simulations (Paragraphs [0538-539], Cella teaches the results of the simulation may be returned to the packaging design system, which may output the results to the user. The user may accept the packaging design, may adjust the packaging design, or may reject the packaging design.).
Cella does not explicitly disclose the following:
the metrics received using an established real-time data synchronization protocol;
wherein patterns of components of the metrics are analyzed based on performing Fourier encoding and evaluating different waveforms in the metrics, and based on time synchronizations across different components of a digital twin rendering.
Roper, JR. is in the same field of art of using artificial intelligence for workflow enhancement/automation. Further, Roper, JR. teaches the metrics received using an established real-time data synchronization protocol (Paragraphs [0137], [0203], Fig. 1, Roper, JR. teaches continuously updating and refining the digital twins in real-time using the operational data collected from monitoring the performance of the physical assembly of components. This data may include processed sensory data, performance indicators, and other relevant information. By incorporating this real-time operational data, digital twins stay synchronized with the physical system and provide an accurate representation of its operational performance. Spatial computing interfaces enable real-time synchronization between digital twin and physical twin.); and based on time synchronizations across different components of a digital twin rendering (Paragraph [0137], Roper JR teaches the digital twins are created based on specific performance metrics and are continuously updated and refined in real-time using the operational data collected from monitoring the performance of the physical assembly or its components/individual component parts. The data may include sensory data, performance indicators, and other relevant information. By incorporating this real-time operational data, digital twins stay synchronized with the actual system and provide an accurate representation of its operational performance. This ensures that the digital twins remain up to date and aligned with the current state of the physical system.).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Cella by receiving the operational data associated with the physical system in real-time that is taught by Roper, JR., to make the invention that incorporates real-time operational data into the digital twin to stay continuously synchronized with the physical twin/entity; thus, one of ordinary skilled in the art would be motivated to combine the references since this ensures that the digital twins remains synchronized and aligned with the current state of the physical system allowing the digital twins to provide reliable predictions of future performance and assist in making informed decisions (Roper, JR., Paragraphs [0137-138]).
Cella in view of Roper, JR. does not explicitly disclose the following:
wherein patterns of components of the metrics are analyzed based on performing Fourier encoding and evaluating different waveforms in the metrics.
Cella 2 is in the same field of art of data collection in an industrial environment to generate a digital twin representing/modeling a real-world physical thing, such as an operating environment, a building, or a factory, for example. Further, Cella 2 teaches wherein patterns of components of the metrics are analyzed based on performing Fourier encoding (Paragraphs [0705-706], [0823], Cella 2 teaches a signal evaluation circuit may perform frequency analysis using techniques such as a digital Fast Fourier transform (FFT) or other frequency domain transform or digital analog signal analysis techniques. Frequencies of interest may be identified. Abrupt changes in signal amplitude may also be identified. The Examiner interprets “Fourier Transform” to be synonymous to “Fourier Encoding” in light of paragraph [0063] of Applicant’s specification, which states, “‘Fourier Encoding,’ also known as ‘Fourier Transform,’ is used to analyze and represent input data in terms of their frequency components.”) and evaluating different waveforms in the metrics (Paragraphs [0705-706], Cella 2 teaches the signal evaluation circuit may perform frequency analysis using techniques such as wavelet transform. Time-based detection values may alternatively be used to perform transitory signal analysis. These may include identifying abrupt changes in signal amplitude including changes where the change in amplitude exceeds a predetermined value or exists for a certain duration.).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Cella in view of Roper, JR. by analyzing patterns of the collected metrics based on performing Fourier transform to evaluate the metrics that is taught by Cella 2, to make the invention that analyzes signals/metrics received from various components of the physical entity to obtain information about the component or piece of equipment being monitored; thus, one of ordinary skilled in the art would be motivated to combine the references since evaluating signals received from equipment/physical entities may help with identifying deficiencies or failures in the equipment/entity. For example, performing frequency analyses may be used to filter out dominant frequency signals such as the overall rotation of a component/piece of equipment/machinery, and may help enable the evaluation of low amplitude signals at frequencies associated with torsion, bearing failure, and the like (Cella 2, Paragraph [0703]). Therefore, by analyzing frequencies and other waveform properties (amplitude) of the signals of the physical entity (machinery, equipment, component, etc.), problems may be detected earlier and prior to complete failure by identifying abnormal frequencies that are present.
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
In regards to Claim 16, Cella in view of Roper, JR. in further view of Cella 2 discloses the system of claim 15, wherein the metrics include data generated by at least one of sensors, devices and databases associated with the physical entity (Paragraph [0866], Cella teaches a sensor system (8022) having sensors that collect data from facilities. For example, the sensor system (8022) may include an edge device (8042) that reports data relating to physical assets in the facility. Under Broadest Reasonable Interpretation, the Examiner interprets “at least one of” to mean only one of sensors, devices, or databases is required to meet the limitation.), the data capturing information about the physical entity (Paragraph [0866], Cella teaches the data captured by edge devices (8042) relates to physical assets (e.g. smart machinery/manufacturing equipment, sensor kits, autonomous vehicles of the enterprise, wearable devices, and the like).).
In regards to Claim 17, Cella in view of Roper, JR. in further view of Cella 2 discloses The system of claim 15, wherein the real-time data synchronization protocol includes publish-subscribe protocol established between the physical entity and the digital twin (Paragraphs [0866], [3213], [0051], Cella teaches a digital twin I/O system (8104) which may subscribe to sensor system (8022) streams from facilities (e.g., manufacturing facilities, shipping facilities, data center facilities, etc.) and/or other physical entities of the enterprise. Sensors may be configured to publish data relating to the value chain network entities or a particular item (e.g., an engine part). The information received by the value chain network digital twin associated with the physical data items of the value chain network is dynamic and real-time.).
In regards to Claim 18, Cella in view of Roper, JR. in further view of Cella 2 discloses the system of claim 15, wherein the analysis includes analyzing trends of data change in the received metrics (Paragraph [0624], Cella teaches continuously capturing key operational metrics of the machines. Machine digital twins may generate an alert or other warning based on a change in operating characteristics of the machine.), and based on the trends of data change, dynamically adjusting an interval of data streaming received from the physical entity (Paragraphs [2388], [2394], Cella teaches the time interval may be dynamically adjusted based on applicable conditions, such as increasing the time interval when no movable elements are detected, decreasing the time interval as or when the number of movable elements within an environment increases (e.g., increasing the number of robots and robot interactions), increasing the time interval during periods of reduced activity, decreasing the time interval during periods of abnormal activity (e.g., inspections or maintenance), decreasing the time interval when unexpected or uncharacteristic movement is detected (e.g., frequent movement by a typical sedentary element or uncoordinated movement, for example, of robots approaching an exit or moving cooperatively to carry a large object), combinations thereof, and the like.).
In regards to Claim 19, Cella in view of Roper, JR. in further view of Cella 2 discloses the system of claim 15, wherein the analysis includes estimating data to fill missing data points in the received metrics (Paragraphs [2737], [2420], Cella teaches using augmentation models to generate interpolated values from data streams that may be missing data (e.g. due to network interruptions), may generate predicted sensor readings for a sensor (e.g., a broken sensor) based on sensor readings from other nearby sensors, and may segment data received from data sources with additional data.), and wherein the digital twin is updated using the estimated data (Paragraphs [2420-2421], Cella teaches updating the digital twin by populating the asset digital twin with the calculated interpolated values.).
In regards to Claim 20, Cella in view of Roper, JR. in further view of Cella 2 discloses the system of claim 15, wherein the analysis includes predicting future data points based on historical trends and available data (Paragraphs [2343], [2420], [0684], Cella teaches a digital twin update module which provides the sensor data from one or more sensors to the digital twin dynamic model system, which can model a behavior of a shipping environment and/or shipping entity to extrapolate additional state data. Dynamic models associated with a digital twin of an asset can extrapolate values based on input data collected from sensors and/or devices disposed in the industrial setting and/or any other suitable data. For example, events or state data about value chain entities are stored as well as historical or other data stored in the data storage layer.), and wherein the digital twin is updated using the predicted future data points (Paragraphs [2420-2421], Cella teaches updating the digital twin by populating the asset digital twin with the calculated extrapolated values.).
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Rakshit (U.S. Patent Pub. No. 20210374032 A1) teaches simulating workflows of physical assets using digital twin models. User-defined simulations are performed by selecting digital twin components being analyzed during the simulation and concentrating the analysis on the selected components.
B R et al. (U.S. Patent Pub. No. 20230315043 A1) teaches a system, apparatus, and method for instantaneous performance management of a machine tool. The method includes receiving real-time condition data associated with one or more components of the machine tool from one or more sources. At least one parameter value associated with the one or more components likely to affect a performance of the machine tool, is computed based on the condition data. A digital twin of the machine tool is configured based on the parameter value to simulate a behavior of one or more critical components in a simulation environment. An impact on the performance of the machine tool is predicted based on the simulated behavior of the one or more critical components. Further, a vibration spectrum may be generated using a FFT.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SYDNEY L BLACKSTEN whose telephone number is (571)272-7651. The examiner can normally be reached 8:30am-4:30pm.
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/SYDNEY L BLACKSTEN/Examiner, Art Unit 2674
/ONEAL R MISTRY/Supervisory Patent Examiner, Art Unit 2674
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