DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 5/14/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-34 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1: Claims 1-34 are directed to statutory categories, namely a process (claims 1-17), and a machine (claims 18-34).
Step 2A, Prong 1: Claims 1 and 18, in part, recite the following abstract idea:
…A method of providing energy analytics comprising: collecting energy consumption data for a site having a plurality of loads using one or more … each associated with one or more of the loads, the energy consumption data collected from each of … comprising a plurality of data points each having a timestamp and a value representative of a level of energy usage by the one or more of the loads associated therewith; disaggregating the data points into a plurality of granular classifications as a function of the data point values and the data point timestamps; analyzing the energy consumption data in view of the classifications and in view of additional site information and historical energy consumption data to identify one or more periods of over consumption and associated over consumption patterns for the site; analyzing the periods of over consumption and associated over consumption patterns; and providing information of the periods of over consumption and associated over consumption patterns in at least one visualization, the at least one visualization including a recommendation for improving energy consumption efficiency for the site [Claim 1],
A system for energy management comprising: one or more …configured to collect energy consumption data for a site, …each associated with one or more of the loads, the energy consumption data collected from each of … comprising a plurality of data points each having a timestamp and a value representative of a level of energy usage by the one or more of the loads associated therewith; and … to: disaggregate the data points into a plurality of granular classifications as a function of the data point values and the data point timestamps; analyze the energy consumption data in view of the classifications and in view of additional site information and historical energy consumption data; analyze the periods of over consumption and associated over consumption patterns to identify which of the one or more loads is a root source of over consumption; and provide information of the periods of over consumption and associated over consumption patterns in at least one visualization, the at least one visualization including a recommendation for improving energy consumption efficiency for the site [Claim 18].
These concepts are not meaningfully different than the following concepts identified by the MPEP:
Concepts relating to certain methods of organizing human activity. The aforementioned limitations describe steps for performing fundamental economic principles or practices, which includes hedging, insurance, and mitigating risk. Specifically, analyzing energy consumption data to provide efficiency recommendations is considered to describe steps for mitigating risk of excessive energy consumption. Further, the aforementioned limitations describe steps for managing personal behavior or relationships or interactions between people, including social activities, teaching, and following rules or instructions. Specifically, analyzing energy consumption data to provide efficiency recommendations is considered to describe steps for managing personal behavior.
Concepts relating to mental processes. The aforementioned limitations describe steps for concepts performed in the human mind which includes an observation, evaluation, judgment, or an opinion. Specifically, analyzing energy consumption data to provide efficiency recommendations is considered to describe steps for an evaluation of energy efficiency. As such, claims 1 and 18 recite concepts identified as abstract ideas.
The dependent claims recite limitations relative to the independent claims, including, for example:
…performing at least one action responsive to the recommendation and then triggering the at least one action [Claim 2],
…wherein the recommendation includes a confidence level that the action performed in response thereto will reduce energy consumption at the site [Claim 3],
… wherein the at least one action includes at least one of: alarming, generating and a sending report, generating and sending data to…, generating and sending data to…, generating and sending data to …, triggering a control change in at least one device or system, and triggering a setting change in at least one device or system [Claim 4],
…wherein analyzing the periods of over consumption and associated over consumption patterns comprises identifying which of the one or more loads is a root source of over consumption, and wherein the recommendation for improving energy consumption efficiency is based on the identified root source [Claim 5],
…wherein the recommendation of the visualization includes a detailed plan for reducing energy consumption at the site… [Claim 6],
The limitations of these dependent claims are merely narrowing the abstract idea identified in the independent claims, and thus, the dependent claims also recite abstract ideas.
Step 2A, Prong 2: This judicial exception is not integrated into a practical application. In particular, claims 1 and 18 only recite the following additional elements –
…energy consumption meters, the energy consumption meters… the energy consumption meters… [Claim 1],
…energy consumption meters… the energy consumption meters… the energy consumption meters…; …a controller in communication with the one or more energy consumption meters, the controller having a processor and a memory component, the memory component storing processor-executable instructions that, when executed, configure the processor… [Claim 18].
The dependent claims only recite the following new additional elements –
…an external control system… an external analysis system… an external management system… [Claim 4],
…a machine learning algorithm [Claim 11],
The apparatus and executable instructions are recited at a high-level of generality (see MPEP § 2106.05(a)), like the following MPEP examples:
iii. Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017) or speeding up a loan-application process by enabling borrowers to avoid physically going to or calling each lender and filling out a loan application, LendingTree, LLC v. Zillow, Inc., 656 Fed. App'x 991, 996-97 (Fed. Cir. 2016) (non-precedential);
iii. Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48;
Furthermore, the computer implemented element is considered to amount to no more than mere instructions to apply the exception using a generic computer component (see MPEP 2106.05(f)), like the following MPEP example:
i. A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
Accordingly, these additional elements do not integrate the abstract idea into a practical application.
The remaining dependent claims do not recite any new additional elements, and thus do not integrate the abstract idea into a practical application.
Step 2B: Claims 1 and 18 and their underlying limitations, steps, features and terms, considered both individually and as a whole, do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the following reasons:
Independent claims 1 and 18 only recite the following additional elements –
…energy consumption meters, the energy consumption meters… the energy consumption meters… [Claim 1],
…energy consumption meters… the energy consumption meters… the energy consumption meters…; …a controller in communication with the one or more energy consumption meters, the controller having a processor and a memory component, the memory component storing processor-executable instructions that, when executed, configure the processor… [Claim 18].
These elements do not amount to significantly more than the abstract idea for the reasons discussed in 2A prong 2 with regard to MPEP 2106.05(a) and MPEP 2106.05(f). By the failure of the elements to integrate the abstract idea into a practical application there, the additional elements likewise fail to amount to an inventive concept that is significantly more than an abstract idea here, in Step 2B.
As such, both individually or in combination, these limitations do not add significantly more to the judicial exception.
The remaining dependent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the dependent claims do not recite any new additional elements other than those mentioned in the independent claims, which amount to no more than mere instructions to apply the exception using a generic computer component (see MPEP 2106.05(f)). As such, these claims are not patent eligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-13, 15-30 and 32-34 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Koval et al., U.S. Publication No. 2019/0260204 [hereinafter Koval].
Regarding Claim 1, Koval anticipates …A method of providing energy analytics comprising: collecting energy consumption data for a site having a plurality of loads using one or more energy consumption meters, the energy consumption meters each associated with one or more of the loads, the energy consumption data collected from each of the energy consumption meters comprising a plurality of data points each having a timestamp and a value representative of a level of energy usage by the one or more of the loads associated therewith (Koval, ¶ 2, The present disclosure relates generally to intelligent electronic devices (IEDs) and utility metering systems, and more particularly, to devices, systems and methods for the collection of meter data in a common, globally accessible, group of servers, to provide simpler configuration, collection, viewing, and analysis of the meter data (discloses energy analytics)), (Id., ¶ 50, As used herein, intelligent electronic devices (“IEDs”) can be any device that senses electrical parameters and computes data including, but not limited to, Programmable Logic Controllers (“PLC's”), Remote Terminal Units (“RTU's”), electric power meters, panel meters, protective relays, fault recorders, phase measurement units, serial switches, smart input/output devices and other devices which are coupled with power distribution networks to manage and control the distribution and consumption of electrical power. A meter is a device that records and measures power events, power quality, current, voltage waveforms, harmonics, transients and other power disturbances. Revenue accurate meters (“revenue meter”) relate to revenue accuracy electrical power metering devices with the ability to detect, monitor, report, quantify and communicate power quality information about the power that they are metering. (discloses energy consumption meters)), (Id., ¶ 249, Additionally, the above information can be combined to provide a comprehensive view of a circuit and a facility. Thus, infrared apparatus temperatures combined with ambient temperatures, load and other circuit characteristics to provide a complete picture of the electrical circuit above and beyond the traditional electrical a parameters), (Id., ¶ 305, Another implementation may be where module 1104 takes the outputs of other trained sets, such as one based on the historical readings (e.g., voltage, current, frequency, etc.) of meters, and one based on the live readings of the meter, along with information about the environment to predict the energy usage for a predetermined future time interval. Action module 1106 may then use the predicted energy usage to send an alert, notification, or control signal, as described above. One example may be a historical input set of meter data that is used by module 1104 to compute daily interval predictions and combine the predictions with the live readings of the meter, along with the current weather conditions, such as temperature, precipitation, and humidity, to predict the energy usage for the next day. Another example may be where module 1104 uses the historical input set of meter data, coupled with another historical input set of real time pricing for one or more locations of one or more meters, and combines it with live meter environmental conditions, to both predict energy usage for the next day, predict real time pricing costs for the next day, as well as make recommendations as to when the ideal periods within the predetermined future time interval to lower and increase usage are to reduce costs. The predictions and recommendations may be sent to one or more clients by action module 1106, where the trend prediction may be presented as a graph. Action module 1106 may further use the predictions to send one or more control signals to IEDs and/or facilities (e.g., the control signals are sent to client devices within each of the facilities having control of various loads) to turn on and off loads increase or decrease energy usage in a way that lowers cost (discloses energy loads)), (Id., ¶ 149, As described above, JSON files may be employed for the communication between IEDs, e.g., IED 410, 412, 414, and servers, e.g., servers 424, 524. In one embodiment, the overhead size of JSON files and JSON bodies sent by IEDs and/or servers across networks 422, 522, 622, described above, may be improved by reducing the size of the data transferred by encoding the data in two separate fields, one of which contains the list of all values, the other of which describes each of the values. One example may be to represent a set of historical data, wherein the header contains the JSON array [“timestamp”,“voltage”], (discloses timestamped usage data) which describes the format of the body, and the body contains the JSON array [[1509451200000,120.1], [1509452100000,120.2]], which contains the actual values. Another example may be to represent a sequence of limit events, wherein the header contains the JSON array [“index”, “channel”, “type”, “duration”, “excursion_value”], and the body contains the JSON array [[1,“voltage an”,“above”,24.3,153.3],[2,“voltage cn”,“below”,12.7, 67.4]]. It should be appreciated that such an array may represent other combinations of values as well, such as more channels in the historical array, or other logs);
disaggregating the data points into a plurality of granular classifications as a function of the data point values and the data point timestamps (Id., ¶ 312, Another implementation may use the historical readings and power quality events of a meter as input to module 1104 to classify which fault scenario a given meter belongs to (discloses classifying data points) and subsequently use the classified fault scenario to determine which fault prediction algorithm is best for that meter. It is envisioned that each meter installation would have different environmental conditions, but that these conditions would fall within limited groups of similarities, and that these environmental conditions are unlikely to change after installation. One example may be a residential meter, which is exposed to neighborhood upstream faults, such as seasonal brownouts, and simple downstream faults, such as short circuits. Another example may be a meter in a manufacturing building, which is primarily exposed to downstream faults caused by the machinery used in the manufacturing. Another example may be an office building in the neighborhood of a large energy consumer, such as a steel manufactory or super collider, which inject large amounts of noise on the upstream signal. It is to be appreciated that such lists are not meant to be exhaustive), (Id., ¶ 149, As described above, JSON files may be employed for the communication between IEDs, e.g., IED 410, 412, 414, and servers, e.g., servers 424, 524. In one embodiment, the overhead size of JSON files and JSON bodies sent by IEDs and/or servers across networks 422, 522, 622, described above, may be improved by reducing the size of the data transferred by encoding the data in two separate fields, one of which contains the list of all values, the other of which describes each of the values. One example may be to represent a set of historical data, wherein the header contains the JSON array [“timestamp”,“voltage”], (discloses timestamped usage data) which describes the format of the body, and the body contains the JSON array [[1509451200000,120.1], [1509452100000,120.2]], which contains the actual values. Another example may be to represent a sequence of limit events, wherein the header contains the JSON array [“index”, “channel”, “type”, “duration”, “excursion_value”], and the body contains the JSON array [[1,“voltage an”,“above”,24.3,153.3],[2,“voltage cn”,“below”,12.7, 67.4]]. It should be appreciated that such an array may represent other combinations of values as well, such as more channels in the historical array, or other logs);
analyzing the energy consumption data in view of the classifications and in view of additional site information and historical energy consumption data to identify one or more periods of over consumption and associated over consumption patterns for the site (Id., ¶ 149, As described above, JSON files may be employed for the communication between IEDs, e.g., IED 410, 412, 414, and servers, e.g., servers 424, 524. In one embodiment, the overhead size of JSON files and JSON bodies sent by IEDs and/or servers across networks 422, 522, 622, described above, may be improved by reducing the size of the data transferred by encoding the data in two separate fields, one of which contains the list of all values, the other of which describes each of the values. One example may be to represent a set of historical data, wherein the header contains the JSON array [“timestamp”,“voltage”], which describes the format of the body, and the body contains the JSON array [[1509451200000,120.1], [1509452100000,120.2]], which contains the actual values. Another example may be to represent a sequence of limit events, wherein the header contains the JSON array [“index”, “channel”, “type”, “duration”, “excursion_value”], and the body contains the JSON array [[1,“voltage an”,“above”,24.3,153.3],[2,“voltage cn”,“below”,12.7, 67.4]]. It should be appreciated that such an array may represent other combinations of values as well, such as more channels in the historical array, or other logs), (Id., ¶ 246, In one embodiment, as will be described in greater detail below, a Meter Data Cloud server 424/524 may be improved by including a service that analyzes the data points stored for a multitude of meters, and determines trends (discloses identifying patterns) and predictions about the conditions of the data and meters, based off that data. Such analysis is often called the training phase of Machine Learning. It is envisioned that such a Machine Learning service may then be applied by server 424/524 to new data points being posted to the Meter Data Cloud server 424/524, using the analysis generated previously, to provide predictions about the condition of unrelated data and meters. One example may be analyzing the measured data points by meters 702 to identify the conditions that occur right before a fault on the power grid. Another example may be analyzing the energy usage for an entire year, correlated to the weather information, to estimate the usage for any particular meter in the near future), (Id., ¶ 284, In another embodiment of a Recurrent Neural Network employed by module 1104, some components are composed of a value storage, and three functions that regulate the values input, output, and the update of the value storage. Such an arrangement may be used to augment the long term temporal retention of events input to a Recurrent Neural Network, and is often called a Long Short-Term Memory components. One example may be where module includes a Recurrent Neural Network using Long Short-Term Memory nodes to take a series of power values over the course of a predetermined time interval (e.g., a day) as input from library 1102, and use the data to predict the power conditions in the next hour. Another example may be that module 1104 takes a series of events recorded by a meter or IED as input, such as system events, or security events, over the course of the day, to detect or predict attempts at intrusion or tampering (e.g., of one or more IEDs at a facility) by a malicious user. Action module 1106 may use the detected attempts of intrusion or tampering to send one or more alerts to one or more clients indicative of the detected attempts of intrusion or tampering (discloses additional site information). Action module 1106 may further use the detected attempts of intrusion or tampering to increase a security state (e.g., require more factors of authentication at an IED or facility) to reduce the risk of intrusion or tampering until the intrusion or tampering is otherwise dealt with), (Id., ¶ 300, Another set of data that may be stored in library 1102 and used as inputs to such algorithms in module 1104 may be user configured information. One example may be the geo-location of a meter or facility. Another example may be facility information, such as number of employees, number of residents, square footage of the facility, or number of rooms in the facility. Another example may be the ratings of the CT's connected to the current inputs of each meter. Another example may be an install date of peripheral hardware, such as CT's (i.e., current transformers) and PT's (i.e., potential transformers). Another example may information relative to peripheral hardware, such as rated lifespan and model of hardware. This information may be used by module 1104 to determine various correlations in making predictions and/or recommendation), (Id., ¶ 302, One application of system 1100 may be to predict energy usage for one or more meters, locations, or facilities in the future based on data stored in library 1102 and to use action module 1106 to send communications to one or more clients communicating these predictions and/or send control signals to one or more facilities or IEDs to prevent and/or act upon one of these predictions. In one embodiment, based on data stored in library 1102, module 1104 predicts the energy usage of a location for every 15 minute interval in the next hour. In another embodiment, based on data stored in library 1102, module 1104 predicts the total energy usage and cost of a facility or building, for the next month. Action module 1106 may be configured to receive the predicted energy usages from module 1104, and if the predicted energy usages for a given future time frame (e.g., the next 15 minutes, the next day) is above a predetermined threshold (discloses identifying periods and patterns of over consumption), module 1106 send a notification or alert to one or more clients warning of an expected increased demand. Action module 1106 may further send a control signal to shut off or otherwise limit one or more of the loads which are predicted to cause energy consumption above the predetermined threshold. If the predicted energy usages are below a second predetermined threshold, action module 1106 may send a communication signal to one or more clients that additional energy may be consumed by a load or action module 1106 may send a control signal to cause additional energy to be consumed by a load. In this way, action module 1106 may use the predictions of module 1104 to perform load balancing across a network);
analyzing the periods of over consumption and associated over consumption patterns (Id., ¶ 246, In one embodiment, as will be described in greater detail below, a Meter Data Cloud server 424/524 may be improved by including a service that analyzes the data points stored for a multitude of meters, and determines trends (discloses identifying patterns) and predictions about the conditions of the data and meters, based off that data. Such analysis is often called the training phase of Machine Learning. It is envisioned that such a Machine Learning service may then be applied by server 424/524 to new data points being posted to the Meter Data Cloud server 424/524, using the analysis generated previously, to provide predictions about the condition of unrelated data and meters. One example may be analyzing the measured data points by meters 702 to identify the conditions that occur right before a fault on the power grid. Another example may be analyzing the energy usage for an entire year, correlated to the weather information, to estimate the usage for any particular meter in the near future), (Id., ¶ 302, One application of system 1100 may be to predict energy usage for one or more meters, locations, or facilities in the future based on data stored in library 1102 and to use action module 1106 to send communications to one or more clients communicating these predictions and/or send control signals to one or more facilities or IEDs to prevent and/or act upon one of these predictions. In one embodiment, based on data stored in library 1102, module 1104 predicts the energy usage of a location for every 15 minute interval in the next hour. In another embodiment, based on data stored in library 1102, module 1104 predicts the total energy usage and cost of a facility or building, for the next month. Action module 1106 may be configured to receive the predicted energy usages from module 1104, and if the predicted energy usages for a given future time frame (e.g., the next 15 minutes, the next day) is above a predetermined threshold (discloses identifying periods and patterns of over consumption), module 1106 send a notification or alert to one or more clients warning of an expected increased demand. Action module 1106 may further send a control signal to shut off or otherwise limit one or more of the loads which are predicted to cause energy consumption above the predetermined threshold. If the predicted energy usages are below a second predetermined threshold, action module 1106 may send a communication signal to one or more clients that additional energy may be consumed by a load or action module 1106 may send a control signal to cause additional energy to be consumed by a load. In this way, action module 1106 may use the predictions of module 1104 to perform load balancing across a network);
and providing information of the periods of over consumption and associated over consumption patterns in at least one visualization, the at least one visualization including a recommendation for improving energy consumption efficiency for the site (Id., ¶ 58, The multimedia user interface 22 is shown coupled to the CPU 50 in FIG. 1 for interacting with a user and for communicating events, such as alarms and instructions to the user. The multimedia user interface 22 may include a display for providing visual indications to the user. The display may be embodied as a touch screen, a liquid crystal display (LCD), a plurality of LED number segments, individual light bulbs or any combination. The display may provide information to the user in the form of alpha-numeric lines, computer-generated graphics, videos, animations, etc. The multimedia user interface 22 further includes a speaker or audible output means for audibly producing instructions, alarms, data, etc. The speaker is coupled to the CPU 50 via a digital-to-analog converter (D/A) for converting digital audio files stored in a memory 18 or non-volatile memory 20 to analog signals playable by the speaker. An exemplary interface is disclosed and described in commonly owned U.S. Pat. No. 8,442,660, entitled “POWER METER HAVING AUDIBLE AND VISUAL INTERFACE”, which claims priority to expired U.S. Provisional Patent Appl. No. 60/731,006, filed Oct. 28, 2005, the contents of which are hereby incorporated by reference), (Id., ¶ 294, In any of the examples above, each of the data stored in library 1102 and inputted to module 1104 may be used by module 1104 to make predictions and/or recommendations as to faults, energy usage, device failure, etc. and then by action module 1106 to send communications indicating the predictions and/or send control signals to a client or IED to cause a desired change (e.g., a shutoff of a component or IED, a restart, etc.) based on the predictions), (Id., ¶ 305, Another implementation may be where module 1104 takes the outputs of other trained sets, such as one based on the historical readings (e.g., voltage, current, frequency, etc.) of meters, and one based on the live readings of the meter, along with information about the environment to predict the energy usage for a predetermined future time interval. Action module 1106 may then use the predicted energy usage to send an alert, notification, or control signal, as described above. One example may be a historical input set of meter data that is used by module 1104 to compute daily interval predictions and combine the predictions with the live readings of the meter, along with the current weather conditions, such as temperature, precipitation, and humidity, to predict the energy usage for the next day. Another example may be where module 1104 uses the historical input set of meter data, coupled with another historical input set of real time pricing for one or more locations of one or more meters, and combines it with live meter environmental conditions, to both predict energy usage for the next day, predict real time pricing costs for the next day, as well as make recommendations as to when the ideal periods within the predetermined future time interval to lower and increase usage are to reduce costs. The predictions and recommendations may be sent to one or more clients by action module 1106, where the trend prediction may be presented as a graph. Action module 1106 may further use the predictions to send one or more control signals to IEDs and/or facilities (e.g., the control signals are sent to client devices within each of the facilities having control of various loads) to turn on and off loads increase or decrease energy usage in a way that lowers cost).
Regarding Claim 2, Koval anticipates …The method of claim 1…
Koval further anticipates …further comprising: performing at least one action responsive to the recommendation and then triggering the at least one action (Koval, ¶ 294, In any of the examples above, each of the data stored in library 1102 and inputted to module 1104 may be used by module 1104 to make predictions and/or recommendations as to faults, energy usage, device failure, etc. and then by action module 1106 to send communications indicating the predictions and/or send control signals to a client or IED to cause a desired change (e.g., a shutoff of a component or IED, a restart, etc.) based on the predictions).
Regarding Claim 3, Koval anticipates …The method of claim 2…
Koval further anticipates …wherein the recommendation includes a confidence level that the action performed in response thereto will reduce energy consumption at the site (Koval, ¶ 313, Another implementation may where module 1104 takes a proposed action to the meter or grid, as input from a user (e.g., inputted via a UI coupled to system 1100), along with other fault predictive inputs, and output the likelihood of that action causing a fault. The action module may provide the likelihood to a client device (e.g., including a UI used by a user to input proposed actions). In this way, a user may test proposed actions using system 1100 to predict the likelihood of a given proposed action causing a fault. One example of a proposed action may be a planned meter downtime, wherein the user disconnects the meter from the power grid, temporarily shutting off power. Another example of a proposed action may be power line service, wherein that segment of the power grid may be shut down temporarily, increasing the load on other segments), (Id., ¶ 314, Another application of machine learning system 1100 may be use module 1104 for the determination of the cause of faults, after they have occurred. One example may be where module 1104 determines based on data stored in library 1102 the likelihood that a fault was of any particular type, such as, but not limited to, a downstream short circuit, an upstream brownout, an upstream short circuit, upstream noise on the power line, or in-rush current caused by devices downstream. Another example may be where module 1104 determines the likelihood came from a particular source, such as, but not limited to, manufacturing loads, a power line drop out, air conditioner usage, or downstream wiring. Another example may be where module 1104 identifies where a fault might have occurred, in relation to the meters in a facility. In any of these determinations, action module 1106 may send communication signals to one or more client devices indicating the determined causes, locations, and/or likelihoods. Action module 1106 may further send control signals (e.g., software updates, restart signals, shutoff signals) to IEDs and/or facilities to correct and/or prevent the causes from occurring. In some embodiment, module 1106 sends the communication signals and/or control signals if the determined likelihood is above a predetermined threshold).
Regarding Claim 4, Koval anticipates …The method of claim 2…
Koval further anticipates … wherein the at least one action includes at least one of: alarming, generating and a sending report, generating and sending data to an external control system, generating and sending data to an external analysis system, generating and sending data to an external management system, triggering a control change in at least one device or system, and triggering a setting change in at least one device or system (Koval, ¶ 294, In any of the examples above, each of the data stored in library 1102 and inputted to module 1104 may be used by module 1104 to make predictions and/or recommendations as to faults, energy usage, device failure, etc. and then by action module 1106 to send communications indicating the predictions and/or send control signals to a client or IED to cause a desired change (e.g., a shutoff of a component or IED, a restart, etc.) (discloses control change) based on the predictions).
Regarding Claim 5, Koval anticipates …The method of claim 1…
Koval further anticipates … wherein analyzing the periods of over consumption and associated over consumption patterns comprises identifying which of the one or more loads is a root source of over consumption, and wherein the recommendation for improving energy consumption efficiency is based on the identified root source (Id., ¶ 279, One machine learning algorithm that may be used by module 1104 is one which takes a set of input values from library 1102, transfers those values though a connected graph of nodes, here called a network, where each node applies a summation function between its inputs, and applies a weighting function on the output, to generate a set of output values to be provided to action module 1102. In such a network, during the training phase, the value of the weighting function is adjusted to make the known input set match the known output set. Such an algorithm is often called an Artificial Neural Network, or ANN. One example of an Artificial Neural Network may be a set of 7 inputs provided to module 1104 by library 1102, which include 3 voltage phases, 3 current phases, and frequency readings, an interior network of 20 connected nodes, and a single output value outputted by module 1104 that gives the noise on a power distribution system monitored. If the noise distribution is above a predetermined threshold, action module 1106 may send a communication signal to one or more clients indicating the noise distribution. It is to be appreciated that the communication signal may be, but is not limited to, an e-mail, a text message, a tweet, etc. Another example may be a set of 60 voltage inputs received from library 1102, one for each minute in the previous hour, and two output values outputted by module 1104, one that predicts the likelihood of a fault in the next 10 minutes, and the other the magnitude of the fault. If the likelihood of the fault is above a predetermined threshold and/or the predicted magnitude is above a predetermined threshold, action module 1106 may send a communication signal to one or more clients. Alternatively, the action module 1106 may send a control signal to one or more IEDs and/or control devices to turn off or shut down at least one load (discloses recommendation based on identified root source load of overconsumption) that is associated to a location of the fault. In certain embodiments, the action module 1106 may send the communication signal and control signal substantially simultaneously to alert the user of the client of the shutdown. Alternatively, the communication signal may be sent first with a predetermined time delay before sending the control signal, so a user may have the predetermined time to rectify the fault before the shutdown of equipment).
Regarding Claim 6, Koval anticipates …The method of claim 1…
Koval further anticipates … wherein the recommendation of the visualization includes a detailed plan for reducing energy consumption at the site (Id., ¶ 58, The multimedia user interface 22 is shown coupled to the CPU 50 in FIG. 1 for interacting with a user and for communicating events, such as alarms and instructions to the user. The multimedia user interface 22 may include a display for providing visual indications to the user. The display may be embodied as a touch screen, a liquid crystal display (LCD), a plurality of LED number segments, individual light bulbs or any combination. The display may provide information to the user in the form of alpha-numeric lines, computer-generated graphics, videos, animations, etc. The multimedia user interface 22 further includes a speaker or audible output means for audibly producing instructions, alarms, data, etc. The speaker is coupled to the CPU 50 via a digital-to-analog converter (D/A) for converting digital audio files stored in a memory 18 or non-volatile memory 20 to analog signals playable by the speaker. An exemplary interface is disclosed and described in commonly owned U.S. Pat. No. 8,442,660, entitled “POWER METER HAVING AUDIBLE AND VISUAL INTERFACE”, which claims priority to expired U.S. Provisional Patent Appl. No. 60/731,006, filed Oct. 28, 2005, the contents of which are hereby incorporated by reference), (Id., ¶ 294, In any of the examples above, each of the data stored in library 1102 and inputted to module 1104 may be used by module 1104 to make predictions and/or recommendations as to faults, energy usage, device failure, etc. and then by action module 1106 to send communications indicating the predictions and/or send control signals to a client or IED to cause a desired change (e.g., a shutoff of a component or IED, a restart, etc.) based on the predictions).
Regarding Claim 7, Koval anticipates …The method of claim 1…
Koval further anticipates …further comprising extracting the historical energy consumption data from at least one historical data database (Id., ¶ 130, Hosted data services, such as a hosted database, cloud data storage, Drop-Box, or web service hosting, could be used as an external server to store the meter's data, called Hosting. Each of these Hosts, e.g., servers 440, 540, 640, could then be accessed by the clients to query the Hosted Data. Many of these hosted data services support HTTP Push messages to upload the data, or direct SQL messages. As many web service and cloud hosts allow their users to use their own software, a hosted data service could be further extended by placing proprietary software on them, thus allowing them to act as the external meter server for any of the previously mentioned methods (e.g., servers 424, 524)).
Regarding Claim 8, Koval anticipates …The method of claim 1…
Koval further anticipates …wherein analyzing the energy consumption data in view of the classifications and analyzing the periods of over consumption and associated over consumption patterns is performed at least one of in real time, on demand, and on a scheduled basis (Id., ¶ 246, In one embodiment, as will be described in greater detail below, a Meter Data Cloud server 424/524 may be improved by including a service that analyzes the data points stored for a multitude of meters, and determines trends (discloses identifying patterns) and predictions about the conditions of the data and meters, based off that data. Such analysis is often called the training phase of Machine Learning. It is envisioned that such a Machine Learning service may then be applied by server 424/524 to new data points being posted to the Meter Data Cloud server 424/524, using the analysis generated previously, to provide predictions about the condition of unrelated data and meters. One example may be analyzing the measured data points by meters 702 to identify the conditions that occur right before a fault on the power grid. Another example may be analyzing the energy usage for an entire year, correlated to the weather information, to estimate the usage for any particular meter in the near future), (Id., ¶ 302, One application of system 1100 may be to predict energy usage for one or more meters, locations, or facilities in the future based on data stored in library 1102 and to use action module 1106 to send communications to one or more clients communicating these predictions and/or send control signals to one or more facilities or IEDs to prevent and/or act upon one of these predictions. In one embodiment, based on data stored in library 1102, module 1104 predicts the energy usage of a location for every 15 minute interval (discloses analyzing on a scheduled basis) in the next hour. In another embodiment, based on data stored in library 1102, module 1104 predicts the total energy usage and cost of a facility or building, for the next month. Action module 1106 may be configured to receive the predicted energy usages from module 1104, and if the predicted energy usages for a given future time frame (e.g., the next 15 minutes, the next day) is above a predetermined threshold (discloses identifying periods and patterns of over consumption), module 1106 send a notification or alert to one or more clients warning of an expected increased demand. Action module 1106 may further send a control signal to shut off or otherwise limit one or more of the loads which are predicted to cause energy consumption above the predetermined threshold. If the predicted energy usages are below a second predetermined threshold, action module 1106 may send a communication signal to one or more clients that additional energy may be consumed by a load or action module 1106 may send a control signal to cause additional energy to be consumed by a load. In this way, action module 1106 may use the predictions of module 1104 to perform load balancing across a network).
Regarding Claim 9, Koval anticipates …The method of claim 1…
Koval further anticipates …further comprising generating a per meter model of normal energy consumption from the energy consumption data as a function of the classifications, and wherein analyzing the energy consumption data in view of the classifications includes identifying one or more incoherences in the energy consumption data relative to the model (Id., ¶ 283, Another algorithm that may be used by module 1104 is one in which the outputs of some nodes are fed as inputs to previous layers, adjusting algorithm parameters, thereby adjusting the result over a temporal sequence of input sets during detection. Such an algorithm is often called a Recurrent Neural Network. One example may be a sequence of voltage, current, and frequency values (i.e., measured by one or more IEDs) inputted to module 1104 via library 1102 to predict what the energy usage will be in the next interval. The predication may be outputted to action module 1106 and action module 1106 may communicate the predicted energy usage to one or more clients. Action module 1106 may be configured to communicate the predicted energy usage to the one or more clients if the predicted energy usage is above a predetermined threshold. Action module 1106 may further be configured to send a control signal to a client, one or more IEDs, or a control facility, etc., to shut off one or more loads if the predicted energy usage is above the predetermined threshold. Thereafter, if the predicted energy usage falls below the predetermined threshold, action module 1106 may send a control signal to cause the loads to continue to consume power. (discloses model for identifying incoherences in energy consumption) It is to be appreciated that the predetermined threshold may be selected by any machine learning algorithm or function described herein. (discloses normal energy consumption) For example, the predetermined threshold may be based on load shedding parameters including, but not limited to, time of day, designation of equipment as essential or non-essential, real-time pricing issued by a utility, etc.), (Id., ¶ 287, One set of data stored in library 1102 that may be used as input to such algorithms in module 1104 may be a meter's or IED's live readings. One example may be to use electrical parameters, such as, but not limited to, the voltage, current, and frequency channels measured by one or more meters. Another example may be to use the instantaneous power computed by one or more meters. Another example may be to use the accumulated energy or interval energy computed by one or more meters. Another example may be to use the internal temperature of each meter, measured by each meter), (Id., ¶ 246, In one embodiment, as will be described in greater detail below, a Meter Data Cloud server 424/524 may be improved by including a service that analyzes the data points stored for a multitude of meters, and determines trends and predictions about the conditions of the data and meters, based off that data. Such analysis is often called the training phase of Machine Learning. It is envisioned that such a Machine Learning service may then be applied by server 424/524 to new data points being posted to the Meter Data Cloud server 424/524, using the analysis generated previously, to provide predictions about the condition of unrelated data and meters. One example may be analyzing the measured data points by meters 702 to identify the conditions that occur right before a fault on the power grid. Another example may be analyzing the energy usage for an entire year, correlated to the weather information, (discloses historical energy consumption and weather data used to determine normal energy consumption) to estimate the usage for any particular meter in the near future).
Regarding Claim 10, Koval anticipates …The method of claim 9…
Koval further anticipates …wherein analyzing the energy consumption data in view of the classifications includes identifying co-occurrences in the energy consumption data relative to the model (Id., ¶ 283, Another algorithm that may be used by module 1104 is one in which the outputs of some nodes are fed as inputs to previous layers, adjusting algorithm parameters, thereby adjusting the result over a temporal sequence of input sets during detection. Such an algorithm is often called a Recurrent Neural Network. One example may be a sequence of voltage, current, and frequency values (i.e., measured by one or more IEDs) inputted to module 1104 via library 1102 to predict what the energy usage will be in the next interval. The predication may be outputted to action module 1106 and action module 1106 may communicate the predicted energy usage to one or more clients. Action module 1106 may be configured to communicate the predicted energy usage to the one or more clients if the predicted energy usage is above a predetermined threshold. Action module 1106 may further be configured to send a control signal to a client, one or more IEDs, or a control facility, etc., to shut off one or more loads if the predicted energy usage is above the predetermined threshold. Thereafter, if the predicted energy usage falls below the predetermined threshold, action module 1106 may send a control signal to cause the loads to continue to consume power. (discloses model for identifying incoherences in energy consumption) It is to be appreciated that the predetermined threshold may be selected by any machine learning algorithm or function described herein. For example, the predetermined threshold may be based on load shedding parameters including, but not limited to, time of day, designation of equipment as essential or non-essential, real-time pricing issued by a utility, etc.), (Id., ¶ 250, Another implementation of a time series optimization may be to use a fixed layout table in a conventional database, i.e., fixed table SQL Db, such as PostGreSQL, MySQL, MariaDb, SQLServer, or Oracle Db. A fixed layout table is one in which all the data points are known beforehand, and the table is specifically constructed to match the known data points. Additionally, a single timestamp is associated with each collection of data points at any particular point in time. One example may be a system where the only data points recorded are Voltage AN, Voltage BN, Voltage CN, Current A, Current B, and Current C, and each of those points are known to occur at the same time. (discloses identifying co-occurrences) In such a system, a fixed table which associates a timestamp with the six channels may be constructed, which would minimize the impact of the timestamp on inserts, queries, and storage).
Regarding Claim 11, Koval anticipates …The method of claim 9…
Koval further anticipates …wherein generating the model of normal energy consumption comprise executing a machine learning algorithm (Id., ¶ 283, Another algorithm that may be used by module 1104 is one in which the outputs of some nodes are fed as inputs to previous layers, adjusting algorithm parameters, thereby adjusting the result over a temporal sequence of input sets during detection. Such an algorithm is often called a Recurrent Neural Network. (discloses machine learning algorithm) One example may be a sequence of voltage, current, and frequency values (i.e., measured by one or more IEDs) inputted to module 1104 via library 1102 to predict what the energy usage will be in the next interval. The predication may be outputted to action module 1106 and action module 1106 may communicate the predicted energy usage to one or more clients. Action module 1106 may be configured to communicate the predicted energy usage to the one or more clients if the predicted energy usage is above a predetermined threshold. Action module 1106 may further be configured to send a control signal to a client, one or more IEDs, or a control facility, etc., to shut off one or more loads if the predicted energy usage is above the predetermined threshold. Thereafter, if the predicted energy usage falls below the predetermined threshold, action module 1106 may send a control signal to cause the loads to continue to consume power. (discloses model for identifying incoherences in energy consumption) It is to be appreciated that the predetermined threshold may be selected by any machine learning algorithm or function described herein. For example, the predetermined threshold may be based on load shedding parameters including, but not limited to, time of day, designation of equipment as essential or non-essential, real-time pricing issued by a utility, etc.).
Regarding Claim 12, Koval anticipates …The method of claim 9…
Koval further anticipates …further comprising collecting additional site information, the additional site information including at least one of weather data, an operating schedule, an event schedule, and historical energy consumption data, and wherein the model of normal energy consumption is a function of the additional site information (Id., ¶ 283, Another algorithm that may be used by module 1104 is one in which the outputs of some nodes are fed as inputs to previous layers, adjusting algorithm parameters, thereby adjusting the result over a temporal sequence of input sets during detection. Such an algorithm is often called a Recurrent Neural Network. One example may be a sequence of voltage, current, and frequency values (i.e., measured by one or more IEDs) inputted to module 1104 via library 1102 to predict what the energy usage will be in the next interval. The predication may be outputted to action module 1106 and action module 1106 may communicate the predicted energy usage to one or more clients. Action module 1106 may be configured to communicate the predicted energy usage to the one or more clients if the predicted energy usage is above a predetermined threshold. Action module 1106 may further be configured to send a control signal to a client, one or more IEDs, or a control facility, etc., to shut off one or more loads if the predicted energy usage is above the predetermined threshold. Thereafter, if the predicted energy usage falls below the predetermined threshold, action module 1106 may send a control signal to cause the loads to continue to consume power. (discloses model for identifying incoherences in energy consumption) It is to be appreciated that the predetermined threshold may be selected by any machine learning algorithm or function described herein. (discloses normal energy consumption) For example, the predetermined threshold may be based on load shedding parameters including, but not limited to, time of day, designation of equipment as essential or non-essential, real-time pricing issued by a utility, etc.), (Id., ¶ 246, In one embodiment, as will be described in greater detail below, a Meter Data Cloud server 424/524 may be improved by including a service that analyzes the data points stored for a multitude of meters, and determines trends and predictions about the conditions of the data and meters, based off that data. Such analysis is often called the training phase of Machine Learning. It is envisioned that such a Machine Learning service may then be applied by server 424/524 to new data points being posted to the Meter Data Cloud server 424/524, using the analysis generated previously, to provide predictions about the condition of unrelated data and meters. One example may be analyzing the measured data points by meters 702 to identify the conditions that occur right before a fault on the power grid. Another example may be analyzing the energy usage for an entire year, correlated to the weather information, (discloses historical energy consumption and weather data used to determine normal energy consumption) to estimate the usage for any particular meter in the near future).
Regarding Claim 13, Koval anticipates …The method of claim 1…
Koval further anticipates …wherein disaggregating the data points into the granular classifications comprises categorizing the data points into one of a baseload period, a running period, and a transition period (Id., ¶ 312, Another implementation may use the historical readings and power quality events of a meter as input to module 1104 to classify which fault scenario a given meter belongs to (discloses classifying data points) and subsequently use the classified fault scenario to determine which fault prediction algorithm is best for that meter. It is envisioned that each meter installation would have different environmental conditions, but that these conditions would fall within limited groups of similarities, and that these environmental conditions are unlikely to change after installation. One example may be a residential meter, which is exposed to neighborhood upstream faults, such as seasonal brownouts, and simple downstream faults, such as short circuits. Another example may be a meter in a manufacturing building, which is primarily exposed to downstream faults caused by the machinery used in the manufacturing. Another example may be an office building in the neighborhood of a large energy consumer, such as a steel manufactory or super collider, which inject large amounts of noise on the upstream signal. It is to be appreciated that such lists are not meant to be exhaustive), (Id., ¶ 149, As described above, JSON files may be employed for the communication between IEDs, e.g., IED 410, 412, 414, and servers, e.g., servers 424, 524. In one embodiment, the overhead size of JSON files and JSON bodies sent by IEDs and/or servers across networks 422, 522, 622, described above, may be improved by reducing the size of the data transferred by encoding the data in two separate fields, one of which contains the list of all values, the other of which describes each of the values. One example may be to represent a set of historical data, wherein the header contains the JSON array [“timestamp”,“voltage”], which describes the format of the body, and the body contains the JSON array [[1509451200000,120.1], [1509452100000,120.2]], which contains the actual values. Another example may be to represent a sequence of limit events, wherein the header contains the JSON array [“index”, “channel”, “type”, “duration”, “excursion_value”], and the body contains the JSON array [[1,“voltage an”,“above”,24.3,153.3],[2,“voltage cn”,“below”,12.7, 67.4]]. It should be appreciated that such an array may represent other combinations of values as well, such as more channels in the historical array, or other logs), (Id., ¶ 246, In one embodiment, as will be described in greater detail below, a Meter Data Cloud server 424/524 may be improved by including a service that analyzes the data points stored for a multitude of meters, and determines trends and predictions about the conditions of the data and meters, based off that data. Such analysis is often called the training phase of Machine Learning. It is envisioned that such a Machine Learning service may then be applied by server 424/524 to new data points being posted to the Meter Data Cloud server 424/524, using the analysis generated previously, to provide predictions about the condition of unrelated data and meters. One example may be analyzing the measured data points by meters 702 to identify the conditions that occur right before a fault on the power grid. Another example may be analyzing the energy usage for an entire year, correlated to the weather information, (discloses baseline energy consumption) to estimate the usage for any particular meter in the near future), (Id., ¶ 131, the IEDs can communicate to devices using Generic Object Oriented Substation Event (GOOSE) messages, as defined by the IEC-61850 standard, the content of which are herein incorporated by reference. A GOOSE message is a user-defined set of data that is “published” on detection of a change in any of the contained data items sensed or calculated by the IED. Any IED or device on the LAN or network that is interested in the published data can “subscribe” to the publisher's GOOSE message and subsequently use any of the data items in the message as desired. As such, GOOSE is known as a Publish-Subscribe message. With binary values, change detect is a False-to-True or True-to-False transition. (discloses transition periods) With analog measurements, IEC61850 defines a “deadband” whereby if the analog value changes greater than the deadband value, a GOOSE message with the changed analog value is sent. In situation where changes of state are infrequent, a “keep alive” message is periodically sent by the publisher to detect a potential failure. In the keepalive message, there is a data item that indicates “The NEXT GOOSE will be sent in XX Seconds” (where XX is a user definable time). If the subscriber fails to receive a message in the specified time frame, it can set an alarm to indicate either a failure of the publisher or the communication network), (Id., ¶ 229, Referring to FIG. 12B, another implementation of Load Balancing may be to store duplicate copies of data on multiple internal servers 808, and where each of the internal servers 808 notifies the others (e.g., Server A notifies Servers B, C, D, etc.) when they receive an update to their data, so that they can all remain in sync. For example, a meter 702 may post new data to the Voltage AN data point, which gets routed to Load Balanced Server A. (discloses load transition periods) Load Balanced Server A would then notify Load Balanced Server B and C that new data is now available, such that when a user requests data (e.g., request 802), they would get the same data, regardless of if their request was routed to Load Balanced Server A, B, or C), (Id., ¶ 117, In one embodiment, UPnP is employed to allow the network addresses of devices, such as meters, to automatically be discovered by a client. This enables the client software to display a list of all devices which are available. In addition, this could also allow the client software to enable the user to connect to these devices, without having to configure the network address of that device. In addition, the UPnP notify may be used to indicate the health status of the device, including starting up, running, (discloses transition and running periods) errors in configuration, and resetting).
Regarding Claim 15, Koval anticipates …The method of claim 1…
Koval further anticipates …further comprising normalizing the energy consumption data on a per day basis, and wherein analyzing the energy consumption data comprises analyzing the normalized energy consumption data to identify the one or more periods of over consumption and associated over consumption patterns for the site (Id., ¶ 257, In one embodiment, the problem of data storage is overcome by down sampling the data stored, i.e., reducing the actual number of records stored. One implementation of down sampling may be to reduce the number of values stored to a minimum interval. One example may be a Voltage AN data point, stored every minute, may be reduced to only keep every 15 minute value. Another example may be an Interval Watt-Hour data point, stored every 15 minutes, may be reduced to only keep a value every hour, where that value is the sum of the 15 minute data point values during that hour. Another example may be a Power Quality data point that only allows 20 events in a single day, and any event that occurs after that limit is reached is discarded. Another example may be a Waveform data point that only allows 2 events in a single minute, with a maximum of 20 events in a single day, (discloses normalizing energy consumption data on a per day basis) and any event that occurs after either of those limits is reached is discarded. Another example may be to only allow a maximum waveform sample resolution of 256 samples per cycle, and to down sample the waveform samples of any waveform event which exceeds this resolution. Another example may be a Waveform Event, which down samples the sampling rate of the samples, based on the amount of harmonic noise detected within the waveform, where more noise allows a higher sampling rate, and less noise forces a lower sampling rate. It is envisioned that such down sampling may be performed by the device posting the data (e.g., a meter, IED, etc.), by the server receiving the data, by the service storing the data, or by a process at a later date, after the data has been stored at full resolution), (Id., ¶ 302, One application of system 1100 may be to predict energy usage for one or more meters, locations, or facilities in the future based on data stored in library 1102 and to use action module 1106 to send communications to one or more clients communicating these predictions and/or send control signals to one or more facilities or IEDs to prevent and/or act upon one of these predictions. In one embodiment, based on data stored in library 1102, module 1104 predicts the energy usage of a location for every 15 minute interval in the next hour. In another embodiment, based on data stored in library 1102, module 1104 predicts the total energy usage and cost of a facility or building, for the next month. Action module 1106 may be configured to receive the predicted energy usages from module 1104, and if the predicted energy usages for a given future time frame (e.g., the next 15 minutes, the next day) is above a predetermined threshold (discloses identifying periods and patterns of over consumption), module 1106 send a notification or alert to one or more clients warning of an expected increased demand. Action module 1106 may further send a control signal to shut off or otherwise limit one or more of the loads which are predicted to cause energy consumption above the predetermined threshold. If the predicted energy usages are below a second predetermined threshold, action module 1106 may send a communication signal to one or more clients that additional energy may be consumed by a load or action module 1106 may send a control signal to cause additional energy to be consumed by a load. In this way, action module 1106 may use the predictions of module 1104 to perform load balancing across a network).
Regarding Claim 16, Koval anticipates …The method of claim 1…
Koval further anticipates … wherein the energy consumption data is periodically collected by the energy consumption meters (Id., ¶ 211, In another embodiment, a cache may be for the proxy server to periodically query the Meter Data Cloud server for information about the meters it tracks. One example may be for the proxy server to query the meter settings every 10 minutes from the Meter Data Cloud server, updating the cache appropriately. Another example may be when the proxy server gets a log data status query from a meter, if it has not updated the cache for that data within 10 minutes, the proxy server may query the log data status from the Meter Data Cloud server and update the cache, before responding to the meter. Another example may be after the proxy server forwards a log data point push to the Meter Data Cloud server, it queries the log data status from the Meter Data Cloud server to update the local cache).
Regarding Claim 17, Koval anticipates …The method of claim 1…
Koval further anticipates …wherein the energy consumption data is continuously collected by the energy consumption meters (Id., ¶ 119, In another embodiment, an automated server is configured to perform actions related to these automatically discovered services, such as retrieving real time information, downloading logs, or registering for notification of events. For example, as shown in FIG. 8, a server 530 could be on a network 516 to collect log information from meters 510, 512, 514, and whenever a meter broadcast that it provided log data, the server 530 could automatically collect that data from the meter. As another example, the server 530 could automatically poll and log the realtime readings of all meters on the network, automatically including them as they become available on the network. As described above, the server 530 may then post the data to server 524).
Regarding Claim 18, Koval anticipates …A system for energy management comprising: one or more energy consumption meters configured to collect energy consumption data for a site, the energy consumption meters each associated with one or more of the loads, the energy consumption data collected from each of the energy consumption meters comprising a plurality of data points each having a timestamp and a value representative of a level of energy usage by the one or more of the loads associated therewith (Koval, ¶ 2, The present disclosure relates generally to intelligent electronic devices (IEDs) and utility metering systems, and more particularly, to devices, systems and methods for the collection of meter data in a common, globally accessible, group of servers, to provide simpler configuration, collection, viewing, and analysis of the meter data (discloses energy analytics)), (Id., ¶ 50, As used herein, intelligent electronic devices (“IEDs”) can be any device that senses electrical parameters and computes data including, but not limited to, Programmable Logic Controllers (“PLC's”), Remote Terminal Units (“RTU's”), electric power meters, panel meters, protective relays, fault recorders, phase measurement units, serial switches, smart input/output devices and other devices which are coupled with power distribution networks to manage and control the distribution and consumption of electrical power. A meter is a device that records and measures power events, power quality, current, voltage waveforms, harmonics, transients and other power disturbances. Revenue accurate meters (“revenue meter”) relate to revenue accuracy electrical power metering devices with the ability to detect, monitor, report, quantify and communicate power quality information about the power that they are metering. (discloses energy consumption meters)), (Id., ¶ 249, Additionally, the above information can be combined to provide a comprehensive view of a circuit and a facility. Thus, infrared apparatus temperatures combined with ambient temperatures, load and other circuit characteristics to provide a complete picture of the electrical circuit above and beyond the traditional electrical a parameters), (Id., ¶ 305, Another implementation may be where module 1104 takes the outputs of other trained sets, such as one based on the historical readings (e.g., voltage, current, frequency, etc.) of meters, and one based on the live readings of the meter, along with information about the environment to predict the energy usage for a predetermined future time interval. Action module 1106 may then use the predicted energy usage to send an alert, notification, or control signal, as described above. One example may be a historical input set of meter data that is used by module 1104 to compute daily interval predictions and combine the predictions with the live readings of the meter, along with the current weather conditions, such as temperature, precipitation, and humidity, to predict the energy usage for the next day. Another example may be where module 1104 uses the historical input set of meter data, coupled with another historical input set of real time pricing for one or more locations of one or more meters, and combines it with live meter environmental conditions, to both predict energy usage for the next day, predict real time pricing costs for the next day, as well as make recommendations as to when the ideal periods within the predetermined future time interval to lower and increase usage are to reduce costs. The predictions and recommendations may be sent to one or more clients by action module 1106, where the trend prediction may be presented as a graph. Action module 1106 may further use the predictions to send one or more control signals to IEDs and/or facilities (e.g., the control signals are sent to client devices within each of the facilities having control of various loads) to turn on and off loads increase or decrease energy usage in a way that lowers cost (discloses energy loads)), (Id., ¶ 149, As described above, JSON files may be employed for the communication between IEDs, e.g., IED 410, 412, 414, and servers, e.g., servers 424, 524. In one embodiment, the overhead size of JSON files and JSON bodies sent by IEDs and/or servers across networks 422, 522, 622, described above, may be improved by reducing the size of the data transferred by encoding the data in two separate fields, one of which contains the list of all values, the other of which describes each of the values. One example may be to represent a set of historical data, wherein the header contains the JSON array [“timestamp”,“voltage”], (discloses timestamped usage data) which describes the format of the body, and the body contains the JSON array [[1509451200000,120.1], [1509452100000,120.2]], which contains the actual values. Another example may be to represent a sequence of limit events, wherein the header contains the JSON array [“index”, “channel”, “type”, “duration”, “excursion_value”], and the body contains the JSON array [[1,“voltage an”,“above”,24.3,153.3],[2,“voltage cn”,“below”,12.7, 67.4]]. It should be appreciated that such an array may represent other combinations of values as well, such as more channels in the historical array, or other logs);
and a controller in communication with the one or more energy consumption meters, the controller having a processor and a memory component, the memory component storing processor-executable instructions that, when executed, configure the processor to: disaggregate the data points into a plurality of granular classifications as a function of the data point values and the data point timestamps (Id., ¶ 47, It is further noted that, unless indicated otherwise, all functions described herein may be performed in either hardware or software, or some combination thereof. In one embodiment, however, the functions are performed by at least one processor, such as a computer or an electronic data processor, digital signal processor or embedded micro-controller, in accordance with code, such as computer program code, software, and/or integrated circuits that are coded to perform such functions, unless indicated otherwise), (Id., ¶ 48, It should be appreciated that the present disclosure can be implemented in numerous ways, including as a process, an apparatus, a system, a device, a method, or a computer readable medium such as a computer readable storage medium or a computer network where program instructions are sent over optical or electronic communication links), (Id., ¶ 312, Another implementation may use the historical readings and power quality events of a meter as input to module 1104 to classify which fault scenario a given meter belongs to (discloses classifying data points) and subsequently use the classified fault scenario to determine which fault prediction algorithm is best for that meter. It is envisioned that each meter installation would have different environmental conditions, but that these conditions would fall within limited groups of similarities, and that these environmental conditions are unlikely to change after installation. One example may be a residential meter, which is exposed to neighborhood upstream faults, such as seasonal brownouts, and simple downstream faults, such as short circuits. Another example may be a meter in a manufacturing building, which is primarily exposed to downstream faults caused by the machinery used in the manufacturing. Another example may be an office building in the neighborhood of a large energy consumer, such as a steel manufactory or super collider, which inject large amounts of noise on the upstream signal. It is to be appreciated that such lists are not meant to be exhaustive), (Id., ¶ 149, As described above, JSON files may be employed for the communication between IEDs, e.g., IED 410, 412, 414, and servers, e.g., servers 424, 524. In one embodiment, the overhead size of JSON files and JSON bodies sent by IEDs and/or servers across networks 422, 522, 622, described above, may be improved by reducing the size of the data transferred by encoding the data in two separate fields, one of which contains the list of all values, the other of which describes each of the values. One example may be to represent a set of historical data, wherein the header contains the JSON array [“timestamp”,“voltage”], (discloses timestamped usage data) which describes the format of the body, and the body contains the JSON array [[1509451200000,120.1], [1509452100000,120.2]], which contains the actual values. Another example may be to represent a sequence of limit events, wherein the header contains the JSON array [“index”, “channel”, “type”, “duration”, “excursion_value”], and the body contains the JSON array [[1,“voltage an”,“above”,24.3,153.3],[2,“voltage cn”,“below”,12.7, 67.4]]. It should be appreciated that such an array may represent other combinations of values as well, such as more channels in the historical array, or other logs);
analyze the energy consumption data in view of the classifications and in view of additional site information and historical energy consumption data (Id., ¶ 149, As described above, JSON files may be employed for the communication between IEDs, e.g., IED 410, 412, 414, and servers, e.g., servers 424, 524. In one embodiment, the overhead size of JSON files and JSON bodies sent by IEDs and/or servers across networks 422, 522, 622, described above, may be improved by reducing the size of the data transferred by encoding the data in two separate fields, one of which contains the list of all values, the other of which describes each of the values. One example may be to represent a set of historical data, wherein the header contains the JSON array [“timestamp”,“voltage”], which describes the format of the body, and the body contains the JSON array [[1509451200000,120.1], [1509452100000,120.2]], which contains the actual values. Another example may be to represent a sequence of limit events, wherein the header contains the JSON array [“index”, “channel”, “type”, “duration”, “excursion_value”], and the body contains the JSON array [[1,“voltage an”,“above”,24.3,153.3],[2,“voltage cn”,“below”,12.7, 67.4]]. It should be appreciated that such an array may represent other combinations of values as well, such as more channels in the historical array, or other logs), (Id., ¶ 246, In one embodiment, as will be described in greater detail below, a Meter Data Cloud server 424/524 may be improved by including a service that analyzes the data points stored for a multitude of meters, and determines trends (discloses identifying patterns) and predictions about the conditions of the data and meters, based off that data. Such analysis is often called the training phase of Machine Learning. It is envisioned that such a Machine Learning service may then be applied by server 424/524 to new data points being posted to the Meter Data Cloud server 424/524, using the analysis generated previously, to provide predictions about the condition of unrelated data and meters. One example may be analyzing the measured data points by meters 702 to identify the conditions that occur right before a fault on the power grid. Another example may be analyzing the energy usage for an entire year, correlated to the weather information, to estimate the usage for any particular meter in the near future), (Id., ¶ 284, In another embodiment of a Recurrent Neural Network employed by module 1104, some components are composed of a value storage, and three functions that regulate the values input, output, and the update of the value storage. Such an arrangement may be used to augment the long term temporal retention of events input to a Recurrent Neural Network, and is often called a Long Short-Term Memory components. One example may be where module includes a Recurrent Neural Network using Long Short-Term Memory nodes to take a series of power values over the course of a predetermined time interval (e.g., a day) as input from library 1102, and use the data to predict the power conditions in the next hour. Another example may be that module 1104 takes a series of events recorded by a meter or IED as input, such as system events, or security events, over the course of the day, to detect or predict attempts at intrusion or tampering (e.g., of one or more IEDs at a facility) by a malicious user. Action module 1106 may use the detected attempts of intrusion or tampering to send one or more alerts to one or more clients indicative of the detected attempts of intrusion or tampering (discloses additional site information). Action module 1106 may further use the detected attempts of intrusion or tampering to increase a security state (e.g., require more factors of authentication at an IED or facility) to reduce the risk of intrusion or tampering until the intrusion or tampering is otherwise dealt with), (Id., ¶ 300, Another set of data that may be stored in library 1102 and used as inputs to such algorithms in module 1104 may be user configured information. One example may be the geo-location of a meter or facility. Another example may be facility information, such as number of employees, number of residents, square footage of the facility, or number of rooms in the facility. Another example may be the ratings of the CT's connected to the current inputs of each meter. Another example may be an install date of peripheral hardware, such as CT's (i.e., current transformers) and PT's (i.e., potential transformers). Another example may information relative to peripheral hardware, such as rated lifespan and model of hardware. This information may be used by module 1104 to determine various correlations in making predictions and/or recommendation), (Id., ¶ 302, One application of system 1100 may be to predict energy usage for one or more meters, locations, or facilities in the future based on data stored in library 1102 and to use action module 1106 to send communications to one or more clients communicating these predictions and/or send control signals to one or more facilities or IEDs to prevent and/or act upon one of these predictions. In one embodiment, based on data stored in library 1102, module 1104 predicts the energy usage of a location for every 15 minute interval in the next hour. In another embodiment, based on data stored in library 1102, module 1104 predicts the total energy usage and cost of a facility or building, for the next month. Action module 1106 may be configured to receive the predicted energy usages from module 1104, and if the predicted energy usages for a given future time frame (e.g., the next 15 minutes, the next day) is above a predetermined threshold (discloses identifying periods and patterns of over consumption), module 1106 send a notification or alert to one or more clients warning of an expected increased demand. Action module 1106 may further send a control signal to shut off or otherwise limit one or more of the loads which are predicted to cause energy consumption above the predetermined threshold. If the predicted energy usages are below a second predetermined threshold, action module 1106 may send a communication signal to one or more clients that additional energy may be consumed by a load or action module 1106 may send a control signal to cause additional energy to be consumed by a load. In this way, action module 1106 may use the predictions of module 1104 to perform load balancing across a network);
analyze the periods of over consumption and associated over consumption patterns to identify which of the one or more loads is a root source of over consumption (Id., ¶ 246, In one embodiment, as will be described in greater detail below, a Meter Data Cloud server 424/524 may be improved by including a service that analyzes the data points stored for a multitude of meters, and determines trends (discloses identifying patterns) and predictions about the conditions of the data and meters, based off that data. Such analysis is often called the training phase of Machine Learning. It is envisioned that such a Machine Learning service may then be applied by server 424/524 to new data points being posted to the Meter Data Cloud server 424/524, using the analysis generated previously, to provide predictions about the condition of unrelated data and meters. One example may be analyzing the measured data points by meters 702 to identify the conditions that occur right before a fault on the power grid. Another example may be analyzing the energy usage for an entire year, correlated to the weather information, to estimate the usage for any particular meter in the near future), (Id., ¶ 302, One application of system 1100 may be to predict energy usage for one or more meters, locations, or facilities in the future based on data stored in library 1102 and to use action module 1106 to send communications to one or more clients communicating these predictions and/or send control signals to one or more facilities or IEDs to prevent and/or act upon one of these predictions. In one embodiment, based on data stored in library 1102, module 1104 predicts the energy usage of a location for every 15 minute interval in the next hour. In another embodiment, based on data stored in library 1102, module 1104 predicts the total energy usage and cost of a facility or building, for the next month. Action module 1106 may be configured to receive the predicted energy usages from module 1104, and if the predicted energy usages for a given future time frame (e.g., the next 15 minutes, the next day) is above a predetermined threshold (discloses identifying periods and patterns of over consumption), module 1106 send a notification or alert to one or more clients warning of an expected increased demand. Action module 1106 may further send a control signal to shut off or otherwise limit one or more of the loads which are predicted to cause energy consumption above the predetermined threshold. If the predicted energy usages are below a second predetermined threshold, action module 1106 may send a communication signal to one or more clients that additional energy may be consumed by a load or action module 1106 may send a control signal to cause additional energy to be consumed by a load. In this way, action module 1106 may use the predictions of module 1104 to perform load balancing across a network), (Id., ¶ 279, One machine learning algorithm that may be used by module 1104 is one which takes a set of input values from library 1102, transfers those values though a connected graph of nodes, here called a network, where each node applies a summation function between its inputs, and applies a weighting function on the output, to generate a set of output values to be provided to action module 1102. In such a network, during the training phase, the value of the weighting function is adjusted to make the known input set match the known output set. Such an algorithm is often called an Artificial Neural Network, or ANN. One example of an Artificial Neural Network may be a set of 7 inputs provided to module 1104 by library 1102, which include 3 voltage phases, 3 current phases, and frequency readings, an interior network of 20 connected nodes, and a single output value outputted by module 1104 that gives the noise on a power distribution system monitored. If the noise distribution is above a predetermined threshold, action module 1106 may send a communication signal to one or more clients indicating the noise distribution. It is to be appreciated that the communication signal may be, but is not limited to, an e-mail, a text message, a tweet, etc. Another example may be a set of 60 voltage inputs received from library 1102, one for each minute in the previous hour, and two output values outputted by module 1104, one that predicts the likelihood of a fault in the next 10 minutes, and the other the magnitude of the fault. If the likelihood of the fault is above a predetermined threshold and/or the predicted magnitude is above a predetermined threshold, action module 1106 may send a communication signal to one or more clients. Alternatively, the action module 1106 may send a control signal to one or more IEDs and/or control devices to turn off or shut down at least one load (discloses recommendation based on identified root source load of overconsumption) that is associated to a location of the fault. In certain embodiments, the action module 1106 may send the communication signal and control signal substantially simultaneously to alert the user of the client of the shutdown. Alternatively, the communication signal may be sent first with a predetermined time delay before sending the control signal, so a user may have the predetermined time to rectify the fault before the shutdown of equipment);
and provide information of the periods of over consumption and associated over consumption patterns in at least one visualization, the at least one visualization including a recommendation for improving energy consumption efficiency for the site (Id., ¶ 58, The multimedia user interface 22 is shown coupled to the CPU 50 in FIG. 1 for interacting with a user and for communicating events, such as alarms and instructions to the user. The multimedia user interface 22 may include a display for providing visual indications to the user. The display may be embodied as a touch screen, a liquid crystal display (LCD), a plurality of LED number segments, individual light bulbs or any combination. The display may provide information to the user in the form of alpha-numeric lines, computer-generated graphics, videos, animations, etc. The multimedia user interface 22 further includes a speaker or audible output means for audibly producing instructions, alarms, data, etc. The speaker is coupled to the CPU 50 via a digital-to-analog converter (D/A) for converting digital audio files stored in a memory 18 or non-volatile memory 20 to analog signals playable by the speaker. An exemplary interface is disclosed and described in commonly owned U.S. Pat. No. 8,442,660, entitled “POWER METER HAVING AUDIBLE AND VISUAL INTERFACE”, which claims priority to expired U.S. Provisional Patent Appl. No. 60/731,006, filed Oct. 28, 2005, the contents of which are hereby incorporated by reference), (Id., ¶ 294, In any of the examples above, each of the data stored in library 1102 and inputted to module 1104 may be used by module 1104 to make predictions and/or recommendations as to faults, energy usage, device failure, etc. and then by action module 1106 to send communications indicating the predictions and/or send control signals to a client or IED to cause a desired change (e.g., a shutoff of a component or IED, a restart, etc.) based on the predictions), (Id., ¶ 305, Another implementation may be where module 1104 takes the outputs of other trained sets, such as one based on the historical readings (e.g., voltage, current, frequency, etc.) of meters, and one based on the live readings of the meter, along with information about the environment to predict the energy usage for a predetermined future time interval. Action module 1106 may then use the predicted energy usage to send an alert, notification, or control signal, as described above. One example may be a historical input set of meter data that is used by module 1104 to compute daily interval predictions and combine the predictions with the live readings of the meter, along with the current weather conditions, such as temperature, precipitation, and humidity, to predict the energy usage for the next day. Another example may be where module 1104 uses the historical input set of meter data, coupled with another historical input set of real time pricing for one or more locations of one or more meters, and combines it with live meter environmental conditions, to both predict energy usage for the next day, predict real time pricing costs for the next day, as well as make recommendations as to when the ideal periods within the predetermined future time interval to lower and increase usage are to reduce costs. The predictions and recommendations may be sent to one or more clients by action module 1106, where the trend prediction may be presented as a graph. Action module 1106 may further use the predictions to send one or more control signals to IEDs and/or facilities (e.g., the control signals are sent to client devices within each of the facilities having control of various loads) to turn on and off loads increase or decrease energy usage in a way that lowers cost).
Regarding Claims 19, 21-30 and 32-34, these claims recite limitations substantially similar to those in claims 2, 4-13, and 15-17, and are rejected for the same reasons as stated above.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 14 and 31 are rejected under 35 U.S.C. 103 as being unpatentable over Koval et al., U.S. Publication No. 2019/0260204 [hereinafter Koval] in view of Hurst et al., U.S. Publication No. 2019/0180389 [hereinafter Hurst].
Regarding Claim 14, Koval anticipates …The method of claim 1…
While suggested in at least Fig. 1 and related text, Koval does not explicitly disclose …further comprising classifying the energy consumption data as associated with either an open day or a closed day at the site.
However, Hurst discloses …further comprising classifying the energy consumption data as associated with either an open day or a closed day at the site (Hurst, ¶ 87, The output can be used by behavioural classifiers 26. In embodiments, these classifiers can generate an indication of whether the device on/off usage within data describing energy usage is normal or abnormal, for example (although it will be appreciated that for non-health monitoring embodiments, the classifications may vary, e.g. building empty/occupied; no one/one person present/more than one person present, etc). The normal/abnormal classification may be based on analysis results such as the time or day and/or day of week a particular device/class of device/utility is being used; a combination/sequence of usage of devices/utilities, or any other appropriate behavioural analysis, e.g. behaviours that indicate that a person is not eating regularly, visiting the bathroom very frequently, etc.).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified the energy efficiency elements of Koval to include the site operation status elements of Hurst in the analogous art of analysing energy/utility usage.
The motivation for doing so would have been to provide an improved method for determining a “classification indicating an energy/utility user's normal energy/utility usage pattern and a second said classification indicating the energy/utility user's abnormal energy/utility usage pattern” [Hurst, ¶ 26], wherein such improvements would benefit Koval’s system which provide an improved method “ that analyzes the data points stored for a multitude of meters, and determines trends and predictions about the conditions of the data and meters, based off that data” [Hurst, ¶ 26; Koval, ¶ 246].
Regarding Claim 31, this claim recites limitations substantially similar to those in claim 14, and is rejected for the same reasons as stated above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Aggarwala et al., U.S. Publication No.2020/0175534, discloses methods, systems, and media for energy management.
Tang et al., U.S. Publication No. 2015/0268062, discloses detecting a selected mode of household use.
Kagan, U.S. Publication No. 2023/0162123, discloses devices, systems and methods for cost management and risk mitigation in power distribution systems.
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/NICHOLAS D BOLEN/ Examiner, Art Unit 3624 /PATRICIA H MUNSON/Supervisory Patent Examiner, Art Unit 3624
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