DETAILED ACTION
This action is in response to the submission filed on 11/18/2025. Claims 1-17, 21-23 are presented for examination.
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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-6, 11-15 and 23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. To determine if a claim is directed to patent ineligible subject matter, the Court has guided the Office to apply the Alice/Mayo test, which requires:
1. Determining if the claim falls within a statutory category;
2A. Determining if the claim is directed to a patent ineligible judicial exception consisting of a law of
nature, a natural phenomenon, or abstract idea; and
2B. If the claim is directed to a judicial exception, determining if the claim recites limitations or elements
that amount to significantly more than the judicial exception. (See MPEP 2106).
Step 1: With respect to claims 1-6, 11-15 and 23, applying step 1, the preamble of independent claims 1 and 11 claim a method and a system. As such these claims fall within the statutory categories of process and machine.
Step 2A, prong one: In order to apply step 2A, a recitation of claim 1 is copied below. The limitations of the claim that describe an abstract idea are bolded.
A method, comprising:
receiving time constants and at least one trained regression model determined during a training phase that applied machine learning to multi-dimensional simulation points of a simulation simulating an asset and temperatures associated with the respective simulation points;
receiving real-time measured current used by the asset;
receiving real-time measured temperatures measured at essential monitoring points;
predicting temperature for prediction points in real time to generate predicted temperatures by applying the at least one trained regression model and using the real-time measured current, previously predicted temperatures for the prediction points, a time lapse since the previously predicted temperatures were predicted, and the time constants, wherein the prediction points are selectable to include selected prediction points having same prediction points same as the essential monitoring points and different prediction points that are different than the essential monitoring points (mental process – observation, evaluation, judgement, opinion);
performing a comparison of the predicted temperatures for a subset of the prediction points with currently received temperatures for the essential monitoring points that correspond to the subset of the prediction points (mental process – observation, evaluation, judgement, opinion);
correcting the predicted temperatures for the selected prediction points to generate corrected predicted temperatures using a result of the comparison (mental process – observation, evaluation, judgement, opinion); and
outputting the corrected predicted temperatures in real time.
The limitations as analyzed include concepts directed to the "mental process" groupings
of abstract ideas performed in the human mind (including an observation, evaluation,
judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III). The claim involves predicting, comparing and correcting. The steps are simple enough/broadly claimed that they could be performed mentally or with pen and paper. Thus, limitations noted above also fall into the "mental process" groupings of abstract ideas.
Step 2A, prong two: Under step 2A prong two, this judicial exception is not integrated into a practical application because the additional claim limitations outside the abstract idea only present insignificant extra-solution activity. In particular, the claim recites the additional limitations: “receiving time constants and at least one trained regression model determined during a training phase that …”, “receiving real-time measured current used by the asset”, “receiving real-time measured temperatures measured at essential monitoring points”, “outputting the corrected predicted temperatures in real time”.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B: Moving on to step 2B of the analysis, the Examiner must consider whether each claim limitation individually or as an ordered combination amounts to significantly more than the abstract idea. This analysis includes determining whether an inventive concept is furnished by an element or a combination of elements that are beyond the judicial exception. For limitations that were categorized as "apply it" or generally linking the use of the abstract idea to a particular technological environment or field of use, the analysis is the same. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional limitations is considered directed towards data gathering and output. See MPEP 2106.04(d) referencing MPEP 2106.05(h). Furthermore, as Berkheimer evidence that the claim elements “receiving time constants and at least one trained regression model determined during a training phase that …”, “receiving real-time measured current used by the asset”, “receiving real-time measured temperatures measured at essential monitoring points”, “outputting the corrected predicted temperatures in real time” are Well-Understood, Routine, and Conventional, MPEP § 2106.05(d) (II) provides support that mere data collecting and data outputting is well understood, routine, and conventional: "The courts have recognized the following computer functions as well- understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra- solution activity:
• Receiving or transmitting data over a network, e.g., using the Internet to gather
data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary
computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d
607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image
transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d
1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google,
Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives
and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P.,
773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014)
• Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP
Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788
F.3d at 1363, 115 USPQ2d at 1092-93
• Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115
USPQ2d at 1092-93
For the foregoing reasons, claim 1 is directed to an abstract idea without significantly more, and is rejected as not patent eligible under 35 U.S.C. 101. Independent claim 1 is directed to substantially the same subject matter as independent claim 11 and is rejected under similar rationale and further failure to add significantly more. The same conclusion is reached for the dependent claims 2-6, 12-15 and 23.
Claims 2-6, 12-15 and 23 are further directed towards concepts directed to the "mental process" groupings of abstract ideas performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III). The steps are simple enough/broadly claimed that they could be performed mentally or with pen and paper. This judicial exception is not integrated into a practical application because the additional claim limitations outside the abstract idea only present insignificant extra-solution activity. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional limitations is considered directed towards data gathering and output. See MPEP 2106.04(d) referencing MPEP 2106.05(h). Furthermore, as Berkheimer evidence that the claim elements are Well-Understood, Routine, and Conventional, MPEP § 2106.05(d) (II) provides support that mere data collecting and data outputting is well understood, routine, and conventional. See above cited court cases.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 3-8, 10-11, 13-17, 21-23 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 2020/0356087 (“Elbsat”).
Regarding claims 1 and 11, Elbsat teaches:
A method, comprising:
receiving time constants and at least one trained regression model determined during a training phase that applied machine learning to multi-dimensional simulation points of a simulation simulating an asset and temperatures associated with the respective simulation points (Elbsat: para [0368]-[0369], [0373]-[0374] - "time series data may be a sequence taken at successive equally spaced points in time (e.g., 5 ms, 50 ms, 500 ms, etc.) and is thus B sequence of discrete-time data. In some embodiments, data 2010 includes information relating to compressor speeds, compressor current, pump speeds, power out, power Input, operating voltage, operating current, pump pressure, and temperature measurements"; "data 2010 will have a constant mean and variance, except for when a fault is injected into the data"; "temporal method 2006 is configured to calculate one or more linear regression coefficients at each time step of the provided data 2010 (e.g., time series data). Temporal method 2006 may then monitor how those coefficients change over time"; "Al detection method 2008 may be configured to use an auto-encoder neural network (NN) as a control model to calculate an output of the system");
receiving real-time measured current used by the asset (Elbsat: para [0134], [0319] "BMS 606 may receive Input from various sensors (e.g., temperature sensors, humidity sensors, airflow sensors, voltage sensors, etc.) distributed throughout the building and may report building conditions to MPM system"; "real-time sensor measurements and analytics");
receiving real-time measured temperatures measured at essential monitoring points (Elbsat: para [0325]-[0327] "model predictive optimizer 1120 may be configured to perform its functionality periodically (e.g., daily, weekly, bi-weekly, monthly, etc.) and runtime manager 1312 can be configured to initiate non-scheduled runs of model predictive optimizer 1120 based on real-time conditions, estimations, or predictions"; "performance indicator(s) include one or more performance variables of the connected equipment 1132 (e.g., efficiency, setpoints, temperature readings, humidity readings, sensor data, etc.)");
predicting temperature for prediction points in real time to generate predicted temperatures by applying the at least one trained regression model and using the real-time measured current, previously predicted temperatures for the prediction points, a time lapse since the previously predicted temperatures were predicted, and the time constants, wherein the prediction points are selectable to include selected prediction points having same prediction points as the essential monitoring points and different prediction points than the essential monitoring points (Elbsat: para [0114], [0298]-[0300], [0319], [0325]-[0327], [0341]-[0342], [0349] - "MPM system 602 can estimate a likelihood of various types of failures that could potentially occur based on the current operating conditions of connected equipment 610 and an amount of time that has elapsed since connected equipment 610 has been installed and/or since maintenance was last performed. In some embodiments, MPM system 602 estimates an amount of time until each failure is predicted to occur"; "values of the power model coefficients .sub.reg in the training data may be generated by performing a regression process"; "data preprocessor 1202 may modify the input data such that It fits an expected form for USE in the power regression model"; "performance variables y.sub.k may include any of a variety of variables that characterize the performance of connected equipment 1132 including for example, power consumption, natural gas consumption, water consumption, heating load produced, cooling load produced, temperature lift, or any other variable that indicates the resource consumption or production of connected equipment 1132 or characterizes the performance of connected equipment 1132. In some embodiments, data preprocessor 1202 generates 3 plurality of different sets of preprocessed data. Each set of preprocessed data may include a value of the estimated degradation state (circumfiex over ().sub.k and corresponding values of the performance variables"; "PHM seeks to estimate health status of assets (e.g., of the connected equipment 1132) based on real-time sensor measurements and analytics. in some embodiments, results of PHM techniques (e.g., degradation estimation or prediction) reflect a score of the system (e.g., of connected equipment 1132 or a health of a system of building equipment)"; "thresholds are based on user Inputs obtained from the user Input device 1314. For example, the user may set or select the thresholds for the runtime manager 1312. in some embodiments, the user may select between various levels of cautionary performance"; "rate of change manager 1404 is configured to estimate, calculate, determine, etc., a difference or a della between values of any of the performance indicator(s) at subsequent time steps"; "condition manager 1408 is configured to use any of the delta values or the slopes provided by rate of change manager 1404 to determine if a condition has been met that indicates model predictive optimizer 1120 should be activated or run");
performing a comparison of the predicted temperatures for a subset of the prediction points with currently received temperatures for the essential monitoring points that correspond to the subset of the prediction points (Elbsat: para [0093], [0349]-[0351] "AM&V layer 412 may compare a model-predicted output with an actual output from building subsystems 428 to determine an accuracy of the model"; "the Al model is based on real-time monitoring techniques (e.g., real-time sensor data) to determine critical boundaries (e.g., thresholds) for any of the performance indicators");
correcting the predicted temperatures for the selected prediction points to generate corrected predicted temperatures using a result of the comparison (Elbsat: para [0093]- [0096] - "FDD layer 416 is configured to provide "fault" events to integrated control layer 418 which executes control strategies and policies in response to the received fault events. According to some embodiments, FDD layer 416 (or a policy executed by an integrated control engine or business rules engine) may shut-down systems or direct control activities around faulty devices or systems to reduce energy waste, extend equipment life, or assure proper control response"; "building subsystems 428 may generate temporal (i.e., time-series) data indicating the performance of BMS 400 and the various components thereof. The data generated by building subsystems 428 can Include measured or calculated values that exhibit statistical characteristics and provide information about how the corresponding system or process (e.g., a temperature control process, a flow control process, etc.) is performing in terms of error from its setpoint. These processes can be examined by FDD layer 416 to expose when the system begins to degrade in performance and alert a user to repair the fault"); and
outputting the corrected predicted temperatures in real time (Elbsat: para [0153] "Monitoring and reporting applications 826 may be configured to generate real time "system health" dashboards that can be viewed and navigated by a user").
Regarding claims 3 and 13, Elbsat teaches:
The method of claim 1, further comprising:
determining whether a difference between the predicted temperatures for the subset of the prediction points and the currently received temperatures at the corresponding essential monitoring points exceed a threshold; and triggering an action to affect the asset in response to a determining that the difference exceeds the threshold (Elbsat: para [0347]-[0351], [0354]-[0356] - "condition manager 1408 is configured to compare the variance, covariance, deviation, etc., to a corresponding threshold amount (e.g., a threshold variance, a threshold covariance, etc.). If condition manager 1408 determines that the performance indicator is significant (e.g., that the variance, covariance, deviation, etc., exceeds the threshold variance, the threshold covariance, etc.), condition manager 1408 can determine that model predictive optimizer 1120 should be initiated"; "determining a rate of change of any of the performance indicators (e.g., an increase or decrease amount between different time steps").
Regarding claims 4 and 14, Elbsat teaches:
The method of claim 1, wherein the time constants are associated with respective clusters of the simulation points, and wherein the at least one trained regression model is determined from the respective clusters of the simulation data (Elbsat: para [0148]-[0149] - "optimizer 832 may optimize the objective function J for an entire set of connected equipment 610 (e.g., all of the devices within a building) or for a subset of connected equipment 610 (e.g., a single device, all of the devices of a subplant or building subsystem, etc.) to determine the optimal values for each of the decision variables"; "optimization results may include optimal power consumption values P.sub.op.i and/or load values Load.sub.i for each device or set of devices of connected equipment at each time step i in the optimization period").
Regarding claims 5 and 15, Elbsat teaches:
The method of claim 1, wherein the at least one trained regression model includes a steady-state regression model that uses polynomial regression and a transient-state regression model that uses exponential regression, and wherein predicting the temperature for the prediction points comprises:
predicting steady-state temperatures at the prediction points to generate predicted steady-state temperatures by applying the steady-state regression model (Elbsat: para [0298], [0319], [0341]-[0343], [0373] - "values of the power model coefficients .sub.reg in the training data may be generated by performing a regression process"; "to generate the power model coefficients and/or predict the resource consumption"; "models may Include regression models, polynomial models, physics-based models, linear or nonlinear models, static or dynamic models, discrete or continuous models, deterministic or stochastic models, or any other type of model that relates the estimated degradation state to the power model coefficients and/or the predicted resource consumption"); and
predicting transient-state temperatures at the prediction points to generate predicted transient-state temperatures by applying the transient-state regression model using the predicted steady-state temperatures at the prediction points, the real time measured current, previously predicted transient-state temperatures for the prediction points, the time lapse, and the time constants, wherein the predicted temperature for the prediction points in real time includes the predicted transient-state temperatures (Elbsat: para [0298], [0319]. [0341]-[0343], [0373] "runtime manager 1312 includes rate of change manager 1404, according to some embodiments. Rate of change manager 1404 is configured to receive any of the performance indicator(s) (e.g., the performance variable(s), the degradation prediction, the degradation estimate, etc.) and determine, calculate, estimate, etc., a rate of change (e.g., a time rate of change) of any of the performance indicator(s) (e.g., {dot over (y)).sub.k, custom-character.sub.k, or custom-character.sub.k+1) In some embodiments, rate of change manager 1404 is configured to estimate, calculate, determine, etc., a difference or a delta between values of any of the performance indicator(s) at subsequent time steps"; "It should be understood that condition manager 1408 can estimate delta values (e.g., increase or decrease amounts), time rate of changes, slopes, etc., or any of the performance indicators"; "temporal method 2006 is configured to calculate one or more linear regression coefficients at each time step of the provided data").
Regarding claims 6 and 23, Elbsat teaches:
The method of claim 1, wherein the simulation is a digital twin (Elbsat: para [0093], [0153], [0258], [0358] "AM&V layer 412 may compare a model-predicted output with an actual output from building subsystems"; "the GUI elements may summarize relative energy use and intensity across building management systems in different buildings (real or modeled), different campuses, or the like"; "one or more of the components of MPM system 1100 may be the same as or similar to the corresponding components of building system 600 and/or MPM system").
Regarding claims 7 and 21, Elbsat teaches:
The method of claim 1, wherein the simulation includes two or more steady-state simulations using different simulation parameters, and wherein the method further comprises, during the training phase, repeating a steady-state loop process until a steady-state prediction is determined to be acceptable (Elbsat: para [0250]-[0251] - "time steps i at which the efficiency .sub.i is updated may correspond to the predicted time steps"; "step 1018 can include resetting the value of .sub.i to .sub.cap, where .sub.cap is the efficiency value that is expected to result from purchasing a new device to supplement or replace one or more devices of the building equipment performed at time step i. Step 1018 can include resetting the efficiency .sub.l for one or more time steps while the optimization is being performed (e.g., with each iteration of the optimization)"), wherein the steady-state loop process comprises:
extracting, for the two or more steady-state simulations, steady-state simulation points of the simulation points and a temperature associated with each of the steady-state simulation points (Elbsat: para [0298]-[0301] - "values of the power model coefficients .sub.reg in the training data may be generated by performing a regression process"; "Although degradation Impact modeler 1114 is described primarily as using neural network 1212 to generate the power model coefficients and/or predict the resource consumption as a function of the estimated degradation state, it should be understood that any other type of model (i.e., other than neural network models) can be used in addition to or in place of neural network 1212. Examples of such models may include regression models, polynomial models, physics-based models, linear or nonlinear models, static or dynamic models, discrete or continuous models, deterministic or stochastic models, or any other type of model that relates the estimated degradation state to the power model coefficients and/or the predicted resource consumption"; "performance variables y.sub.k may include any of a variety of variables that characterize the performance of connected equipment 1132 including for example, power consumption, natural gas consumption, water consumption. heating load produced, cooling load produced, temperature lift, or any other variable that indicates the resource consumption or production of connected equipment"; "preprocessor 1202 can be configured to extract information from the raw data including a degradation state, a power value, a load value");
applying, for each of the two or more steady-state simulations, a clustering algorithm to the steady-state simulation points and their respective associated temperatures to form a plurality of steady-state clusters (Elbsat: para [0147]-[0149], [0298]- [0301] - "optimizer 832 may optimize the objective function J for an entire set of connected equipment 610 (e.g., all of the devices within a building) or for a subset of connected equipment 610 (e.g., a single device, all of the devices of a subplant or building subsystem, etc.) to determine the optimal values for each of the decision variables"; "preprocessor 1202 may modify the input data such that it fits an expected form for use in the power regression model. Prior to being processed, a raw dataset can Include one or more files (e.g., an Excel file) which are a combination of both cooling and heating mode data. Each file in the rew dataset can be related to a specific degradation case that has been generated by simulation for an amount of time");
applying a steady-state regression model to each of the steady-state clusters to represent a relationship between the temperatures associated with the respective steady- state simulation points and the simulation parameters (Elbsat: para [0147]-[0149], [0298]-[0301]);
generating the steady-state prediction by applying the steady-state regression model to selected simulation parameters for predicting steady-state temperatures of the steady-state simulation points at the selected simulation parameters (Elbsat: para [0147]-[0149], [0298]-[0301] "performing the regression process"; "to generate the power model coefficients and/or predict the resource consumption");
determining a steady-state difference between the predicted steady-state temperatures of the steady-state simulation points and measured temperatures at a plurality of corresponding monitoring points of the asset, wherein the steady-state prediction is determined to be acceptable when the steady-state difference is below a steady-state threshold (Elbsat: para [0346]- [0347], [0348]-[0351] - "Variance estimator 1412 is configured to estimate a variance, a covariance, a deviation, a standard deviation. etc., of one or more of the performance indicators (e.g., over time). For example, variance estimator 1412 may be configured to calculate a covariance in a pressure ratio of the connected equipment 1132. in some embodiments, the covariance, variance, deviation, standard deviation, etc., indicates changes of any of the performance indicator(s) over time (e.g., across different time steps)"; "Threshold generator 1402 can generate the threshold values used by condition manager 1408 (e.g., the threshold values for the performance indicators and/or the thresholds for the rate of change of the performance indicators) based on expected or known information regarding the connected equipment 1132. In this way, the thresholds may be predetermined values that are determined analytically based on various parameters, operating settings, size, configuration, type, model, etc., of the connected equipment 1132 or expected values of any of the performance indicator(s) for normal operating or given a life of the connected equipment"); and
adjusting for use with a next repetition, if any, the selected simulation parameters to attempt to reduce the steady-state difference (Elbsat: para [0346]-[0347], [0348]-[0351] "thresholds are based on user inputs obtained from the user input device 1314. For example, the user may set or seject the thresholds for the runtime manager 1312. In some embodiments, the user may select between various levels of cautionary performance"; "threshold generator 1402 can generate a functional threshold (e.g., a function of time) that varies with an expected target value of any of the performance indicators").
Regarding claim 8, Elbsat teaches:
The method of claim 7, wherein the simulation includes a transient-state simulation, and wherein the method further comprises, during the training phase:
extracting transient-state simulation points of the simulation points and temperatures associated with each of the transient-state simulation points for a plurality of spaced time steps (Elbsat: para [0129]-[0130], [0298], [0319], [0341]-[0343], [0373] - "optimization results provided to BMS 606 may include the optimal values of the decision variables in the objective function/for each time step I in the optimization period"; "values of the power model coefficients .sub.reg in the training data may be generated by performing a regression process"; "monitored variables can include one or more measured or calculated temperatures, pressures, flow rates, valve positions, resource consumptions (e.g., power consumption, water consumption, electricity consumption, etc.), control setpoints, model parameters (e.g., equipment model coefficients), or any other variables that provide information about how the corresponding system, device, or process is performing"; "to generate the power model coefficients and/or predict the resource consumption"; "models may include regression models, polynomial models, physics-based models, linear or nonlinear models, static or dynamic models, discrete or continuous models, deterministic or stochastic models, or any other type of model that relates the estimated degradation state to the power model coefficients and/or the predicted resource consumption");
applying a clustering algorithm to the transient-state simulation points across the plurality of spaced time steps and their temperatures to form a plurality of transient-state clusters (Elbsat: para [0148]-[0149] "optimizer 832 may optimize the objective function J for an entire set of connected equipment 610 (e.g., all of the devices within a building) or for a subset of connected equipment 610 (e.g., a single device, all of the devices of a subplant or building subsystem, etc.) to determine the optimal values for each of the decision variables"; "optimization results may include optimal power consumption values P.sub.op.i and/or load values Load.sub.l for each device or set of devices of connected equipment at each time step I in the optimization period"); and
repeating a transient-state loop process until a transient-state prediction is determined to be acceptable (Elbsat: para [0250]-[0251] - "time steps i at which the efficiency .sub.i is updated may correspond to the predicted time steps"; "step 1018 can include resetting the value of .sub.i to .sub.cap, where .sub.cap is the efficiency value that is expected to result from purchasing a new device to supplement or replace one or more devices of the building equipment performed at time step I. Step 1018 can Include resetting the efficiency .sub.l for one or more time steps while the optimization is being performed (e.g., with each iteration of the optimization)"), wherein the transient-state loop process comprises:
applying a transient-state regression model to each of the transient-state clusters using the a most recent time constant associated with each of the transient-state clusters (Elbsat: para [0180]-[0182] - "reliability estimator 924 receives operating data from a plurality of devices of connected equipment 610 distributed across multiple buildings. The operating data can include, for example, current operating conditions, fault indications, failure times, or other data that characterize the operation and performance of connected equipment 610. Reliability estimator 924 can use the set of operating data to develop a reliability model"; "Reliability estimator 924 may determine the amount of time t.sub.main,i that has elapsed since maintenance was last performed on connected equipment 610 based on the values of the binary decision variables B.sub.main,i. For each time step i, reliability estimator 924 can examine the corresponding values of B.sub.main at time step I and each previous time step (e.g., time steps i-1, I-2, 1). Reliability estimator 924 can calculate the value of t.sub.main,i by subtracting the time at which maintenance was last performed (i.e., the most recent time at which B.sub.main,(=1) from the time associated with time step");
generating the transient-state prediction for predicting a latest temperature associated with respective transient-state clusters by applying the transient-state regression model using the steady-state prediction once it is determined to be acceptable, a previously predicted transient-state temperature for the respective transient-state clusters obtained at an earlier simulated time, amount of simulated time elapsed since the earlier simulated time, and the most recent time constant for the corresponding transient-state cluster (Elbsat: para [0180]-[0182], [0185] "For each time step i, reliability estimator 924 can examine the corresponding values of B.sub.main at time step i and each previous time step (e.g., time steps I-1, I-2, 1). Reliability estimator 924 can calculate the value of t.sub.main,i by subtracting the time at which maintenance was last performed (i.e., the most recent time at which B.sub.main,i=1) from the time associated with time step 19: "Operating under more strenuous conditions (e.g., high load, extreme temperatures, etc.) may result in a lower reliability, whereas operating under less strenuous conditions (e.g., low load, moderate temperatures, etc.) may result in a higher reliability"; "maintenance estimator 922 can be configured to compare the probability that connected equipment 610 will require replacement at a given time step to a critical value. Maintenance estimator 922 can be configured to set the value of B.sub.cap,i=1 in response to a determination that the probability that connected equipment 610 will require replacement at time step i exceeds the critical value");
determining a transient-state difference between the predicted transient- state temperatures of the transient-state simulation points and the measured temperatures at the plurality of corresponding monitoring points of the asset, wherein the transient-state prediction is determined to be acceptable when the transient-state difference is below a transient-state threshold (Elbsat: para [0180]-[0182]. [0185] - "Maintenance estimator 922 can be configured to set the value of B.sub.cap,i=1 in response to 8 determination that the probability that connected equipment 610 will require replacement at time step i exceeds the critical value"); and
adjusting for use with a next repetition, if any, the time constant to attempt to reduce the transient-state difference (Elbsat: para [0180]-[0182], [0185], [0347]-[0351] "Variance estimator 1412 can provide any of the variance, the covariance, the deviation, the standard deviation, etc., to condition manager 1408. In some embodiments, condition manager 1408 is configured to compare the variance, covariance, deviation, etc., to a corresponding threshold amount (e.g., a threshold variance, a threshold covariance, etc.). If condition manager 1408 determines that the performance indicator is significant (e.g., that the variance, covariance, deviation, etc., exceeds the threshold variance, the threshold covariance, etc.), condition manager 1408 can determine that model predictive optimizer 1120 should be initiated").
Regarding claim 10, Elbsat teaches:
The method of claim 8, further comprising:
obtaining the measured temperatures at the plurality of corresponding monitoring points; and continually updating the measured temperatures with measurements obtained at a subset of the monitoring points for use when determining the transient-state difference (Elbsat: para [0153]-[0154], [0347]-[0351], [0354]-[0356] "MPM system 602 may be configured to maintain detailed historical databases (e.g., relational databases, XML databases, etc.) of relevant data and includes computer code modules that continuously, frequently, or infrequently query, aggregate, transform, search, or otherwise process the data maintained in the detailed databases. MPM system 602 may be configured to provide the results of any such processing to other databases, tables, XML files, or other data structures for further querying, calculation, or access by, for example, external monitoring and reporting applications").
Regarding claim 16, Elbsat teaches:
The thermal monitoring system of claim 11, wherein the simulation includes two or more steady-state simulations using different simulation parameters, and wherein during the training phase, the at least one processing device upon execution of the instructions is further configured to repeat a steady-state loop process until a steady-state prediction is determined to be acceptable (Elbsat: para [0250]-[0251]).
Regarding claim 17, Elbsat teaches:
The thermal monitoring system of claim 11, wherein the simulation includes a transient-state simulation, and wherein during the training phase, the at least one processing device upon execution of the instructions is further configured to:
extract transient-state simulation points of the simulation points and temperatures associated with each of the transient-state simulation points for a plurality of spaced time steps (Elbsat: para [0129]-[0130]. [0298], [0319]. [0341]-[0343], [0373]);
apply a clustering algorithm to the extracted transient-state simulation points across the plurality of spaced time steps and their temperatures to form a plurality of transient-state clusters (Elbsat: para [0148]-[0149]); and
repeat a transient loop process until a transient-state prediction is determined to be acceptable (Elbsat: para [0250]-[0251]).
Regarding claim 22, Elbsat teaches:
The thermal monitoring system of claim 17, wherein the transient-state loop process comprises:
applying a transient-state regression model to each of the transient-state clusters using a most recent time constant associated with each of the transient-state clusters (Elbsat: para [0180]-[0182]);
generating the transient-state prediction for predicting a latest temperature associated with respective transient-state clusters by applying the transient-state regression model using the steady-state prediction once it is determined to be acceptable, a previously predicted transient-state temperature for the respective transient-state clusters obtained at an earlier simulated time, an amount of simulated time elapsed since the earlier simulated time, and the most recent time constant for the corresponding transient-state cluster (Elbsat: para [0180]-[0182], [0185]);
determining a transient-state difference between the predicted transient-state temperatures of the transient-state simulation points and the measured temperatures at the plurality of corresponding monitoring points of the asset, wherein the transient-state prediction is determined to be acceptable when the transient-state difference is below a transient-state threshold (Elbsat: para [0180]-[0182], [0185]); and
adjusting for use with a next repetition, if any, the time constant to attempt to reduce the transient-state difference (Elbsat: para [0180]-[0182], [0185], [0347]-[0351]).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 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 2, 9 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over US 2020/0356087 (“Elbsat”) in view of US 20210157312 A1 (“Cella”).
Regarding claims 2 and 12, Elbsat teaches:
The method of claim 1, further comprising updating an visualization of the asset in real time using the corrected predicted temperatures (Elbsat: para [0128], [0153] - "MPM system 602 includes a data analytics and visualization platform"; "to generate real time "system health" dashboards that can be viewed and navigated by a user").
Elbsat does not teach but Cella does teach:
augmented reality (Cella: para [0025] - "environmental digital twin and the set of discrete digital twins are visual digital twins that are configured to be rendered in a visual manner"; "the present disclosure includes outputting the visual digital twins to B client application that displays the visual digital twins via an augmented reality-enabled device");
It would have been obvious to one of ordinary skill in the art to utilize augmented reality as taught by Cella in order to provide a model predictive maintenance system for building equipment as taught by Elbsat capable of improved user interactivity and data visualization.
Regarding claim 9, Elbsat teaches:
The method of claim 8, further comprising updating an visualization of the asset in real time using at least one of the transient-state prediction or the steady- state prediction (Elbsat: para [0128], [0153], [0348]-[0351]).
Elbsat does not teach but Cella does teach:
augmented reality (Cella: para [0025] - "environmental digital twin and the set of discrete digital twins are visual digital twins that are configured to be rendered in a visual manner"; "the present disclosure includes outputting the visual digital twins to B client application that displays the visual digital twins via an augmented reality-enabled device");
It would have been obvious to one of ordinary skill in the art to utilize augmented reality as taught by Cella in order to provide a model predictive maintenance system for building equipment as taught by Elbsat capable of improved user interactivity and data visualization.
Additional References Cited
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and are cited in the attached PTOL-892.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NITHYA J. MOLL whose telephone number is (571)270-1003. The examiner can normally be reached Monday-Friday 10am-6pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rehana Perveen can be reached at 571-272-3676. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/NITHYA J. MOLL/Primary Examiner, Art Unit 2189
Prosecution Insights