DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/17/2026 has been entered.
Status of the Claims
Claims 28-51, 53-64 and 66-73 are pending in the instant patent application. Claims 28, 46 and 59 are amended. Claims 1-27, 52 and 65 are cancelled. Claims 31-34, 36-38 and 41 have previously been deemed as distinguishable over the prior art. This Office Action is in response to the claims filed.
Response to Claim Amendments
Applicant’s amendments to the claims are insufficient to overcome the 35 U.S.C. §101 rejections. The rejections remain pending and are updated and addressed below in light of the amendments and per guidelines for 101 analysis (PEG 2019).
Applicant’s amendments to the claims are insufficient to overcome the 35 U.S.C. §103 rejections. The rejections remain pending and are updated and addressed below in light of the amendments.
Response to 35 U.S.C. §101 Arguments
Applicant’s arguments regarding 35 U.S.C. §101 rejection of the claims have been fully considered, but are not persuasive. Examiner will further note the Memorandums and future changes in light of said Memorandums and recent PTAB decisions have been taking into consideration in light of the Applicant’s arguments/remarks and amended claim language.
Regarding Applicant’s arguments that the claims are eligible under the clarified policy and legal principles, Examiner respectfully disagrees. As noted in Ex Parte Desjardins, the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting,” and that the claims reflected the improvement identified in the specification. There were clear improvements noted and further reflected in the claim language. The same cannot be said of the current claims in light of Ex Parte Desjardins.
Regarding Applicant’s arguments that the claims recite features that are not insignificant extra-solution activity, Examiner respectfully disagrees. Examiner will further remind Applicant that extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent. An example of post-solution activity is an element that is not integrated into the claim as a whole, e.g., a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent. Analyzing the claim features, Examiner maintains that they are mere data gathering activities.
Regarding Applicant’s arguments that the claims effect an improvement in the technical field of energy audits, Examiner respectfully disagrees. As previously stated, the claims as presently written still recite abstract ideas do nothing to help integrate the abstract idea into a practical application and/or recite an improvement to the technology/computer/technical field. The computing devices in this case, are only linked to the business process to automate it and nothing more. It does not improve the functioning of the computer itself or another technical field. These claimed features are generic components of the computer itself. The claims do not claim anything specific or that differentiates the limitations claimed from limitation of a generic computer. Therefore, any improvements or increased performance/efficiency claimed by the Applicant is an inherent quality of the linking of the abstract idea to these generic computer components and technological environment.
Furthermore, Examiner maintains that regardless of the complexity and/or granularity of the type of data, computational data analysis without meaningful limitations within the claims that amount to significantly more is a judicial exception (i.e. abstract idea). The claim limitations as currently written, taking into consideration the additional elements individually and in combination, still do not provide meaningful limitations that amount to significantly more. Amending the claim language to recite that its time-series energy usage data is collected ongoingly is only generally linking the use of the judicial exception to a specific technological environment.
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.
Regarding Claims 28-45 and 72-73, they are directed to a system, however the claims are directed to a judicial exception without significantly more. Claims 28-45 and 72-73 are directed to the abstract idea of providing energy audits.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 28, claim 28 recites retrieve input data corresponding to a target building, the input data including a first set of building- specific data that is generated on-site at the building by at least one of a utility providing energy or a power sensor or meter disposed at the target building and that is collected on an ongoing basis, the first set consisting of time-series energy usage data, the time-series energy usage data comprising total power usage of the target building and a second set of building-specific data that is generated off-site from the target building comprising characteristic data; wherein the only building-specific data in the input data that is collected on an ongoing basis is the time-series energy usage data; pre- process the input data to generate pre- processed input data: identifying and replacing missing values of the input data, detecting and replacing anomalous datapoints corresponding to outlier values of the input data, and imputing missing data points from the input data; generate a plurality of building markers for the target building based on the input data, automatically generate building efficiency diagnostics based on the plurality of building markers, wherein the building efficiency diagnostics include estimated on/off cycles of an HVAC system of the target building, the estimated on/off cycles of the HVAC system determined based on the time-series energy usage data; and automatically send the building efficiency diagnostics to be displayed.
These claim limitations fall within the Certain Methods of Organizing Human Activity due to the fundamental economic practices taking place. In addition, the claim limitations fall within the Mental Processes grouping of abstract ideas for they are concepts that can be practically performed in the human mind and/or with pen/paper. In addition, Claims 31-32, 35, 39, 42, and 44 fall within the Mathematical Concepts grouping of abstract ideas due to the mathematical calculations/relationships taking place. Also, Claims 36, 38, 40-41, and 45 recite Mental Processes for they are concepts that can be performed in the human mind (including an observation, evaluation, judgment, opinion) and/or with pen/paper. Furthermore, the courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind.
Accordingly, the claim recites an abstract idea and dependent claims 29-45 and 72-73 further recite the abstract idea.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of a processor, at least one memory and a user interface. The processor, at least one memory and a user interface are merely generic computing devices and do not integrate the judicial exception into a practical application.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claim 28 includes various elements that are not directed to the abstract idea under 2A. These elements include a processor, at least one memory, a user interface and the generic computing elements described in the Applicant's specification in at least Para 0043-0047. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions. In addition, the “retrieve”, and “send” limitation recites computer functions that the courts have recognized as well- understood, routine, and conventional functions when they are claimed ina merely generic manner (e.g., at a high level of generality) (See MPEP 2106.05(d)(ii)...at least, Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93).
Regarding Claims 46-51 and 53-58 they are directed to a method, however the claims are directed to a judicial exception without significantly more. Claims 46-51 and 53-58 are directed to the abstract idea of managing energy usage.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 46, claim 46 recites to receiving a request to monitor energy usage for at least one target building; retrieving input data corresponding to the at least one target building, including: obtaining a first set of building specific data that is generated off-site from the at least one target building, the first set comprising building-specific characteristic data for the at least one target building, the building-specific characteristic data including at least location data; retrieving, on an ongoing basis, a second set of building specific data that is generated on-site at the at least one target building by at least one of a utility providing energy power or a power sensor or meter disposed at the at least one target building, the second set consisting of time-series energy usage data for the at least one target building, wherein the time-series energy usage data reflects total electricity usage for the at least one target building, and wherein the only building-specific data in the input data that is collected on an ongoing basis is the time-series energy usage data; pre-processing the time-series energy usage data to generate pre- processed time-series energy usage data that reflects corrected total electricity usage for the at least one target building, the pre- processing including: identifying and replacing missing values of the time-series energy usage data, detecting and replacing anomalous datapoints corresponding to outlier values of the time-series energy usage data, and imputing missing data points from the input time-series energy usage; identifying, from fluctuations in the total electricity usage, on/off cycles for at least one apparatus associated with the at least one target building; periodically updating building efficiency diagnostics for the at least one target building using the time-series energy usage data; and automatically sending instructions for changing settings associated with the on/off cycles.
These claim limitations fall within the Certain Methods of Organizing Human Activity due to the fundamental economic practices taking place. In addition, the claim limitations fall within the Mental Processes grouping of abstract ideas for they are concepts that can be practically performed in the human mind and/or with pen/paper. In addition, Claim 47 recites Mental Processes for it is a concept that can be performed in the human mind or with pen/paper and Claim 57 recites Mathematical Concepts due to the mathematical relationships taking place. Furthermore, the courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind.
Accordingly, the claim recites an abstract idea and dependent claims 47-51 and 53-58 further recite the abstract idea.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim does not recite any elements that would integrate the judicial exception into a practical application.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claims 46 and 49 include various elements that are not directed to the abstract idea under 2A. These elements include at least one sensor and the generic computing elements described in the Applicant's specification in at least Para 0043-0047. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions. In addition, the “retrieving” and “sending” limitation recites computer functions that the courts have recognized as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) (See MPEP 2106.05(d)(ii)...at least, Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).
Therefore, Claims 46 and 49, alone or in combination, are not drawn to eligible subject matter as they are directed to abstract ideas without significantly more.
Regarding Claims 59-71 they are directed to a system, however the claims are directed to a judicial exception without significantly more. Claims 59-71 are directed to the abstract idea of managing energy usage.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 59, claim 59 recites to receive a request to monitor energy usage for at least one target building; retrieve input data corresponding to the at least one target building, including: obtaining a set of building specific data that is generated off-site from the at least one target building, the first set comprising building-specific characteristic data for the at least one target building, the building-specific characteristic data including at least location data; and retrieving, on an ongoing basis, a second set of building specific data that is generated on-site at the at least one target building by at least one of a utility providing energy or a power sensor or meter disposed at the at least one target building consisting of time-series energy usage data for the at least one target building, wherein the time-series energy usage data reflects total electricity usage for the at least one target building, and wherein the only building-specific data in the input data that is collected on an ongoing basis is the time-series energy usage data; pre-processing the time-series energy usage data to generate pre- processed time-series energy usage data that reflects corrected total electricity usage for the at least one target building, the pre- processing including: identifying and replacing missing values of the time-series energy usage data, detecting and replacing anomalous datapoints corresponding to outlier values of the time-series energy usage data, and imputing missing data points from the input time-series energy usage; identifying, from fluctuations in the total electricity usage, on/off cycles for at least one apparatus associated with the at least one target building; periodically updating building efficiency diagnostics for the at least one target building using the time-series energy usage data; and automatically sending instructions for changing settings associated with the at least one apparatus to change the on/off cycles of the at least one apparatus.
These claim limitations fall within the Certain Methods of Organizing Human Activity due to the fundamental economic practices taking place. In addition, the claim limitations fall within the Mental Processes grouping of abstract ideas for they are concepts that can be practically performed in the human mind and/or with pen/paper. In addition, Claim 60 recites Mental Processes for it is a concept that can be performed in the human mind or with pen/paper and Claim 70 recites Mathematical Concepts due to the mathematical relationships taking place. Furthermore, the courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind.
Accordingly, the claim recites an abstract idea and dependent claims 60-71 further recite the abstract idea.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of a processor and at least one memory. The processor and at least one memory are merely generic computing devices and do not integrate the judicial exception into a practical application.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claims 59 and 62 include various elements that are not directed to the abstract idea under 2A. These elements include a processor, at least one memory at least one sensor and the generic computing elements described in the Applicant's specification in at least Para 0043- 0047. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions. In addition, the “retrieve” and “send” limitation recites computer functions that the courts have recognized as well- understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) (See MPEP 2106.05(d)(ii)...at least, Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).
Therefore, Claims 59 and 62, alone or in combination, are not drawn to eligible subject matter as they are directed to abstract ideas without significantly more.
Response to 35 U.S.C. §103 Arguments
Applicant’s arguments regarding 35 U.S.C. §103 rejection of the claims have been fully considered, but are not persuasive. Furthermore, Applicant’s arguments are moot in light of newly amended language.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 28-30, 35, 40, 42-49, 54-55, 57-62, 67-68 and 70-72 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vitullo et al. (US 2019/0302157 A1) in view of Kishlock et al. (US 2001/0020219 A1) in view of Drees et al. (US 9,196,009 B2) further in view of Mashima (US 2016/0306373 A1).
Regarding Claim 28, Vitullo teaches the limitations of Claim 28 which state
a processor; and at least one memory coupled with the processor, the at least one memory having software stored thereon which, when executed by the processor, causes the processor to (Vitullo: Para 0009):
retrieve input data corresponding to a target building, the input data including: a first set of building-specific data that is generated on-site at the target building by at least one of a utility providing energy or a power sensor or meter disposed at the target building, the first set consisting of time-series energy usage data, the time-series energy usage data comprising total power usage of the target building (Vitullo: Para 0123, 0127, 0279, 0384 via each data sample is received with a timestamp indicating a time at which the corresponding data value was measured or calculated. In other embodiments, data collector 512 adds timestamps to the data samples based on the times at which the data samples are received. Data collector 512 can generate raw timeseries data for each of the data points for which data samples are received. Each timeseries can include a series of data values for the same data point and a timestamp for each of the data values… Data collector 512 can provide the raw timeseries data to data platform services 520 and/or store the raw timeseries data in local storage 514 or hosted storage 516. …any meters 5102-5104 associated with the new space may also be displayed in navigation pane 1902. Data provided by meters 5102-5104 may be shown in energy consumption widget 2702 and energy demand widget 2704, which may be the same or similar as previously described. For example, widgets 2702-2704 shown in FIG. 51 may be configured to display meter data for a current time period 5106 and a previous time period 5108. Current time period 5106 may be populated using real-time data received from meters 5102-5104. Previous time period 5108 may be unpopulated until historical data is retrieved for meters 5102-5104 (as described with reference to FIG. 45). After historical data is retrieved, dashboard 1900 may be automatically updated to display the historical data along with the current data in energy consumption widget 2702 and energy demand widget 2704... The various virtual audit metrics described herein for determining building characteristics can be implemented into the analytics service 524. The building data 9000 can be provided to the analytics service 524 via a cloud (e.g., provided every fifteen minutes). The building data 9000 can include building and meter level electric load and weather data. Some of the markers (e.g., virtual audit metrics) can be generated yearly although they can also be generated more frequently (e.g., every hour, day, week, month, quarter, etc.), and
a second set of building-specific data that is generated off-site from the target building, the second set comprising building characteristic data (Vitullo: Para 0127, 0303 via Data collector 512 can provide the raw timeseries data to data platform services 520 and/or store the raw timeseries data in local storage 514 or hosted storage 516. As shown in FIG. 5, local storage 514 can be data storage internal to BMS 500 (e.g., within memory 510) or other on-site data storage local to the building site at which the data samples are collected. Hosted storage 516 can include a remote database, cloud-based data hosting, or other remote data storage. For example, hosted storage 516 can include remote data storage located off-site relative to the building site at which the data samples are collected…Energy benchmarking module 5210 may receive building parameters from parameters database 5206. Building parameters may include various characteristics or attributes of the building such as building area (e.g., square feet), building type (e.g., one of a plurality of enumerated types), building location, and building benchmarks for the applicable building type and/or location. Building benchmarks can include benchmark energy consumption values for the building. The benchmarks can be ASHRAE benchmarks for buildings in the United States or other local standards for buildings in different countries. In some embodiments, the benchmarks specify an energy use intensity (EUI) value and/or energy density value for the building. EUI is a normalized metric which quantifies the energy consumption of a building per unit area over a given time period);
However, Vitullo does not explicitly disclose the limitations of Claim 28 which state pre-process the input data to generate pre-processed input data, including identifying and replacing missing values of the input data and imputing missing data points from the input data.
Kishlock though, with the teachings of Vitullo, teaches of
identifying and replacing missing values of the input data and imputing missing data points from the input data (Kishlock: Para 0014, 0035 via The energy usage data (consumption in volumes of fuel such as gallons, MCF's, and pounds, etc., or energy units such as kWh, therms, BTU's, etc.) with related information about the periods of consumption such as starting date and number of days ina period, ending date and number of days in a period, starting and ending dates ora series of days in a period with a beginning or ending offset sufficient to determine the starting and ending dates of each consumption period received from, for example, the utility or energy supplier, may contain known data structure problems such as overlapping or missing meter read periods, invalid dates, such as Feb. 30, invalid years such as 1901 appearing in a data set containing 2001 data, bad estimates, bad meter reads and accounting corrections, including those previously mentioned, and cancels and rebills. The present invention examines the data for these and other problems and repairs or removes problematic data elements using a variety of algorithms including but not limited to artificial intelligence, regression technology, analysis of variance, outlier analysis and human inspection... Weather Cleaning Module 36 examines the weather data for known data structure problems such as invalid dates or missing data. The Module 36 may also adjust for changing weather station data that are not consistent over time. The present invention fills missing data points using methods which may include but are not limited to averaging, regression, interpolation between neighboring weather stations, application of normals and application of known biases to data from neighboring stations).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Vitullo with the teachings of Kishlock, in order to have identifying and replacing missing values of the input data and imputing missing data points from the input data. The motivations behind this being to incorporate the teachings of fixing energy data structure problems. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Furthermore, Vitullo does not explicitly disclose the limitation of Claim 28 which state pre-process the input data to generate pre-processed input data, including detecting and replacing anomalous datapoints corresponding to outlier values of the input data.
Drees though, with the teachings of Vitullo/Kishlock, teaches of
detecting and replacing anomalous datapoints corresponding to outlier values of the input data (Drees: Col 9 Lines 8-20 via Outlier analysis module 256 is configured to test data points and determine if a data point is reliable. For example, if a data point is more than a threshold (e.g., three standard deviations, four standard deviations, or another set value) away from the an expected value (e.g., the mean) of all of the data points, the data point may be determined as unreliable and discarded. Outlier analysis module 256 may further calculate the expected value of the data points that each data point is to be tested against. Outlier analysis module 256 may be configured to replace the discarded data points in the data set with a NaN or another flag such that the new value will be skipped in further data analysis).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Vitullo/Kishlock with the teachings of Drees in order to have pre-process the input data to generate pre- processed input data, including: detecting and replacing anomalous datapoints corresponding to outlier values of the input data. The motivations behind this being to incorporate the teachings of identifying a change in a building's energy usage model based on data received from the building management system. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
The combination of Vitullo/Kishlock/Drees further teaches the limitations of Claim 28 which state
generate a plurality of building markers for the target building based on the pre-processed input data (Vitullo: Para 0383-0385 via Referring generally to FIG. 90, a system for integrating building markers into a dashboard is shown, according to various exemplary embodiments. The system shown in FIG. 90 may be an automated virtual audit generator, the analytics service 524. The analytics service 524 may receive the building data 9000 at a predefined frequency (e.g., a period of fifteen minutes or higher), analyze the building data 9000 to create building markers (e.g., the virtual audit metrics), and make recommendations to customers for energy improvements as well as be display the virtual audit metrics and/or recommendations to a user via a dashboard in order to provide building energy efficiency insights...The various virtual audit metrics described herein for determining building characteristics can be implemented into the analytics service 524. The building data 9000 can be provided to the analytics service 524 via a cloud (e.g., provided every fifteen minutes). The building data 9000 can include building and meter level electric load and weather data. Some of the markers (e.g., virtual audit metrics) can be generated yearly although they can also be generated more frequently (e.g., every hour, day, week, month, quarter, etc.)...),
automatically generate building efficiency diagnostics based on the plurality of building markers, wherein the building efficiency diagnostics include estimated on/off cycles of an HVAC system of the target building, the estimated on/off cycles of the HVAC system determined based on the time-series energy usage data (Vitullo: Para 0383, 0397 via Referring generally to FIG. 90, a system for integrating building markers into a dashboard is shown, according to various exemplary embodiments. The system shown in FIG. 90 may be an automated virtual audit generator, the analytics service 524. The analytics service 524 may receive the building data 9000 at a predefined frequency (e.g., a period of fifteen minutes or higher), analyze the building data 9000 to create building markers (e.g., the virtual audit metrics), and make recommendations to customers for energy improvements as well as be display the virtual audit metrics and/or recommendations to a user via a dashboard in order to provide building energy efficiency insights...the building management system can compare the first values and second values of the energy audit metrics. In some embodiments, the first values of the energy audit metrics may represent various characteristics (e.g., how efficient the building is in terms of consuming energy) of the building over the first period of time. Similarly, the second values of the energy audit metrics may represent various characteristics of the building over the second period of time. By automatically comparing the difference between the first values and second values and further displaying the difference and/or some of the first and second values on a dashboard...);
automatically send the building efficiency diagnostics to be displayed on a user interface (Vitullo: Para 0373 via The analytics service 524 is shown to provide the values of virtual audit metrics determined by the various components of the analytics service 524 and the audit recommendations determined by the recommendation generator 9020 to the monitoring and reporting service 534. The monitoring and reporting applications 534 can be configured to generate and/or provide a dashboard interface to an end user for viewing the virtual audit metrics and/or audit recommendations. In some embodiments, the analytics service 524 can be configured to generate the values of virtual audit metrics periodically (each metric may have its own period). The period may be every fifteen minutes, every hour, every day, every week, every month, and/or every year. The monitoring and reporting applications 534 can be configured to generate various trend graphs, bar graphs, pie charts, and/or any other visual representation of the virtual audit metrics. Furthermore, the monitoring and reporting applications 534 can be configured to cause the dashboard to include the various audit recommendations generated by the recommendation generator 9020. The dashboard can include a recommended audit recommendation, a performance increase that will result from the audit recommendation, evidence of the success of the audit recommendation (e.g., an indication of the performance of other buildings caused by the audit recommendation), etc. The recommendation manager 9022 can be configured to generate the audit recommendation features of the dashboard while the interface manager 9024 can be configured to generate the dashboard and cause the dashboard to include indications of the virtual audit metrics).
Furthermore, the combination fails to disclose the limitations of Claim 28 which state and that is collected on an ongoing basis and wherein the only building-specific data in the input data that is collected on an ongoing basis is the time-series energy usage data.
Mashima though, with the teachings of Vitullo/Kishlock/Drees, teaches of
and that is collected on an ongoing basis (Mashima: Para 0034, 0073 via The site 128 may include buildings, structures, equipment, or other objects that use electricity distributed by the utility 108. The site 128 may have adapted thereto a meter such as the smart meter 129 that measures the energy distributed to the site 128. The smart meter 129 may communicate the energy usage data to the utility 108. In some embodiments, the energy usage data may be communicated to the utility 108 via the network 122. Based on the energy usage data, the utility 108 may ascertain the energy usage of the site 128… Energy usage data may originate at the customer 102 (e.g., at the site 128 associated with the customer 102, as measured by the smart meter 129 or other suitable meter). The energy usage data may include meter readings 150. In some embodiments, the meter readings 150 may be sent periodically (e.g., at 15-minute intervals) or in real time).
and wherein the only building-specific data in the input data that is collected on an ongoing basis is the time-series energy usage data (Mashima: Para 0034 via The site 128 may include buildings, structures, equipment, or other objects that use electricity distributed by the utility 108. The site 128 may have adapted thereto a meter such as the smart meter 129 that measures the energy distributed to the site 128. The smart meter 129 may communicate the energy usage data to the utility 108. In some embodiments, the energy usage data may be communicated to the utility 108 via the network 122. Based on the energy usage data, the utility 108 may ascertain the energy usage of the site 128…).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Vitullo/Kishlock/Drees with the teachings of Mashima in order to have and that is collected on an ongoing basis and wherein the only building-specific data in the input data that is collected on an ongoing basis is the time-series energy usage data. The motivation behind this being to incorporate the teachings of analyzing time-series data of energy usage as taught by Mashima. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Regarding Claim 29, Vitullo/Kishlock/Drees/Mashima teaches the limitations of Claim 29 which state
periodically retrieve new energy usage data corresponding to the target building from at least one of: a utility providing energy or a power sensor disposed at the target building; add the new energy usage data to the time-series energy usage data in the at least one memory; (Vitullo: Para 0265-0266 via Energy consumption widget 3412 may display the energy consumption measured by the selected meter 3406 at various time intervals (e.g., weekly, daily, monthly, etc.). Energy consumption widget 3412 is shown to include a total current energy consumption 3420 for the selected time interval 3424 and the previous total energy consumption 3422 for a previous time interval 3426. In some embodiments, the previous time interval 3426 is the same month (or any other duration selected via time interval selector 3416) from a previous year (or any other time interval longer than the selected time interval). For example, the current time interval 3424 is shown as October 2015, and the previous time interval 3426 is shown as October 2014. By comparing the energy consumption during the same months of different years, changes in energy consumption due to weather differences can be reduced so that the comparison is more meaningful. Energy consumption widget 3412 may display an amount 3428 by which the energy consumption has increased or decreased (e.g., a percent change) from the previous time interval 3426 to the current time interval 3424... Energy demand widget 3414 may display the energy demand measured by the selected meter 3406 at various time intervals. Energy demand widget 3414 is shown to include a graph 3440. The bars 3430 displayed in graph 3440 may indicate the current energy demand measured by the selected meter 3406. For example, FIG. 34 shows the energy demand for building 2602 broken down by days, where the energy demand for each day is represented by a bar 3430 in graph 3440. In various embodiments, bars 3430 may represent average energy demand or peak energy demand. Dots 3432 displayed in graph 3440 represent the energy demand for the corresponding time period of the previous time interval, prior to the time interval displayed in graph 3440. For example, a monthly graph 3440 may display the current energy demand for each day of the month using bars 3430 and the previous energy demand for each day of the previous month using dots 3432. This allows the user to easily compare energy demand for each day of two consecutive months...);
retrieve the time-series energy usage data corresponding to the target building (Vitullo: Para 0266 via Energy demand widget 3414 may display the energy demand measured by the selected meter 3406 at various time intervals. Energy demand widget 3414 is shown to include a graph 3440. The bars 3430 displayed in graph 3440 may indicate the current energy demand measured by the selected meter 3406. For example, FIG. 34 shows the energy demand for building 2602 broken down by days, where the energy demand for each day is represented by a bar 3430 in graph 3440. In various embodiments, bars 3430 may represent average energy demand or peak energy demand. Dots 3432 displayed in graph 3440 represent the energy demand for the corresponding time period of the previous time interval, prior to the time interval displayed in graph 3440. For example, a monthly graph 3440 may display the current energy demand for each day of the month using bars 3430 and the previous energy demand for each day of the previous month using dots 3432. This allows the user to easily compare energy demand for each day of two consecutive months...); and
retrieve the building characteristic data corresponding to the target building (Vitullo: Para 0303 via Energy benchmarking module 5210 may receive building parameters from parameters database 5206. Building parameters may include various characteristics or attributes of the building such as building area (e.g., square feet), building type (e.g., one of a plurality of enumerated types), building location, and building benchmarks for the applicable building type and/or location. Building benchmarks can include benchmark energy consumption values for the building. The benchmarks can be ASHRAE benchmarks for buildings in the United States or other local standards for buildings in different countries).
Regarding Claim 30, Vitullo/Kishlock/Drees/Mashima teaches the limitations of Claim 30 which state
disaggregating the time-series energy usage data into a heating/cooling dataset and a load dataset (Vitullo: Para 0006 via the systems and methods can be configured to use a baseline virtual audit metric, heating load audit metrics, and/or cooling load audit metrics to provide insight into a building HVAC needs. The systems and methods can be configured to use the baseline load audit metric to determine whether electric consumption is above and/or below the baseline and can provide various insights into a building HVAC needs to an end user);
determining an interior heating load for a selected time period based on the load dataset, wherein the selected time period corresponds to a time period during which an interior temperature of the target building is substantially unchanging (Vitullo: Para 0088, 0314 via AHU controller 330 operates valves 346 and 352 via actuators 354-356 to modulate an amount of heating or cooling provided to supply air 310 (e.g., to achieve a setpoint temperature for supply air 310 or to maintain the temperature of supply air 310 within a setpoint temperature range)...Night/day comparison module 5214 can use the timeseries data to calculate a load ratio Q.sub.ratio for the one or more timeseries. In some embodiments, the load ratio Q.sub.ratio is a ratio of the minimum load during night hours (e.g., a minimum of the timeseries samples designated as nighttime samples) to the maximum load during day hours (e.g., a maximum of the timeseries samples designated as daytime samples);
determining an amount of energy being removed from an air-conditioned space of the target building based on the heating/cooling dataset for the selected time period (Vitullo: Para 0392 via the building management system (e.g., R-value service 9014 of FIG. 90) can calculate a first value of the thermal efficiency of the building during the first period of time based on the data samples collected during the first period of time and further based on electricity consumption of the building during the first period of time, one or more weather-related variables (indoor and/or outdoor temperatures around the building) during the first period of time, an area of a surface of the building during the first period of time, and a thermal conductivity of a construction material of the building during the first period of time);
determining an exterior temperature of the target building for the selected time period based on weather data (Vitullo: Para 0143, 0280 via In FIG. 7A, a data point 702 is shown. Data point 702 is an example of a measured data point for which timeseries values can be obtained. For example, data point 702 is shown as an outdoor air temperature point and has values which can be measured by a temperature sensor... For example, analytics service 524 can use the timeseries data from local storage 514 and/or hosted storage 516 in combination with weather data from weather service 5202 and meter data from meters 5204 to perform various energy analytics);
estimating the interior temperature of the target building for the selected time period within a predetermined range (Vitullo: Para 0392 via the building management system (e.g., R-value service 9014 of FIG. 90) can calculate a first value of the thermal efficiency of the building during the first period of time based on the data samples collected during the first period of time and further based on electricity consumption of the building during the first period of time, one or more weather-related variables (indoor and/or outdoor temperatures around the building) during the first period of time, an area of a surface of the building during the first period of time, and a thermal conductivity of a construction material of the building during the first period of time); and
generating the effective R-value building marker corresponding to a thermal insulation quality of the target building based on the amount of energy being removed, the interior heating load, the exterior temperature, and the interior temperature (Vitullo: Para 0379 via The R-value service 9014 can be configured to generate the value of an R-value virtual audit metric. In some embodiments, the R- value can sometimes be referred to as the thermal efficiency of a building. The R- value service 9014 can be configured to determine the R-values based on a total electricity consumption internal and external temperatures, an estimate of the building surface area, and an estimate of the conductivity value of the building construction materials).
Regarding Claim 40, Vitullo/Kishlock/Drees/Mashima teaches the limitations of Claim 40 which state
setting the building energy usage intensity building marker equal to an amount of energy used per square foot per year value based on the time-series energy usage data and the building characteristic data (Vitullo: Para 0246, 0303 via EUI chart 1918 may display the portfolio energy index as a function of the size of each facility. The dependent variable shown on the vertical axis 1924 (kWh/saqft) may be calculated by summing the total energy use for the facility and dividing by the size of the facility (e.g., square feet). A low EUI for a facility may indicate that the facility has a better energy performance, whereas a high EUI for a facility may indicate that the facility has a worse energy performance. The total energy use of the facility may be summed over a variety of different intervals by selecting different time intervals. For example, a user can click buttons 1926 above chart 1918 to select time intervals of one week, one month, three months, six months, one year, or a custom time interval (shown in FIG. 21)... Energy benchmarking module 5210 may receive building parameters from parameters database 5206. Building parameters may include various characteristics or attributes of the building such as building area (e.g., square feet), building type (e.g., one of a plurality of enumerated types), building location, and building benchmarks for the applicable building type and/or location. Building benchmarks can include benchmark energy consumption values for the building. The benchmarks can be ASHRAE benchmarks for buildings in the United States or other local standards for buildings in different countries); and
comparing the building energy usage intensity building marker to an average energy usage intensity building marker corresponding to a set of buildings, wherein a first climate zone of the set of buildings is equal to a second climate zone of the target building, and wherein a first building type of the set of buildings is equal to a second building type of the target building (Vitullo: Para 0246 via EUI chart 1918 may display the portfolio energy index as a function of the size of each facility. The dependent variable shown on the vertical axis 1924 (kWh/sqft) may be calculated by summing the total energy use for the facility and dividing by the size of the facility (e.g., square feet). A low EUI for a facility may indicate that the facility has a better energy performance, whereas a high EUI for a facility may indicate that the facility has a worse energy performance. The total energy use of the facility may be summed over a variety of different intervals by selecting different time intervals. For example, a user can click buttons 1926 above chart 1918 to select time intervals of one week, one month, three months, six months, one year, or a custom time interval (shown in FIG. 21). Hovering over a bar 1928 or 1930 in chart 1918 may display a pop-up that indicates the value of the EUI and the name of the facility).
Regarding Claim 43, Vitullo/Kishlock/Drees/Mashima teaches the limitations of Claim 43 which state
generate prognostics data based on the input data, the prognostics data comprising energy conservation measure recommendations and estimated impacts of implementing the energy conservation measure recommendations (Vitullo: Para 0373 via The analytics service 524 is shown to provide the values of virtual audit metrics determined by the various components of the analytics service 524 and the audit recommendations determined by the recommendation generator 9020 to the monitoring and reporting service 534. The monitoring and reporting applications 534 can be configured to generate and/or provide a dashboard interface to an end user for viewing the virtual audit metrics and/or audit recommendations. In some embodiments, the analytics service 524 can be configured to generate the values of virtual audit metrics periodically (each metric may have its own period). The period may be every fifteen minutes, every hour, every day, every week, every month, and/or every year. The monitoring and reporting applications 534 can be configured to generate various trend graphs, bar graphs, pie charts, and/or any other visual representation of the virtual audit metrics. Furthermore, the monitoring and reporting applications 534 can be configured to cause the dashboard to include the various audit recommendations generated by the recommendation generator 9020. The dashboard can include a recommended audit recommendation, a performance increase that will result from the audit recommendation, evidence of the success of the audit recommendation (e.g., an indication of the performance of other buildings caused by the audit recommendation);
generate energy conservation prognostics based on the prognostics data and the plurality of building markers (Vitullo: Para 0383, 0397 via a system for integrating building markers into a dashboard is shown, according to various exemplary embodiments. The system shown in FIG. 90 may be an automated virtual audit generator, the analytics service 524. The analytics service 524 may receive the building data 9000 at a predefined frequency (e.g., a period of fifteen minutes or higher), analyze the building data 9000 to create building markers (e.g., the virtual audit metrics), and make recommendations to customers for energy improvements as well as be display the virtual audit metrics and/or recommendations to a user via a dashboard in order to provide building energy efficiency insights... the building management system can compare the first values and second values of the energy audit metrics. In some embodiments, the first values of the energy audit metrics may represent various characteristics (e.g., how efficient the building is in terms of consuming energy) of the building over the first period of time. Similarly, the second values of the energy audit metrics may represent various characteristics of the building over the second period of time. By automatically comparing the difference between the first values and second values and further displaying the difference and/or some of the first and second values on a dashboard (e.g., 1900 shown in FIGS. 19-34) at operation 9114, the building management system may allow a user or an administrator to be provided with more insights into performing energy analytics for the building management system); and
send the energy conservation prognostics to be displayed on the user interface (Vitullo: Para 0373 via The analytics service 524 is shown to provide the values of virtual audit metrics determined by the various components of the analytics service 524 and the audit recommendations determined by the recommendation generator 9020 to the monitoring and reporting service 534. The monitoring and reporting applications 534 can be configured to generate and/or provide a dashboard interface to an end user for viewing the virtual audit metrics and/or audit recommendations. In some embodiments, the analytics service 524 can be configured to generate the values of virtual audit metrics periodically (each metric may have its own period). The period may be every fifteen minutes, every hour, every day, every week, every month, and/or every year. The monitoring and reporting applications 534 can be configured to generate various trend graphs, bar graphs, pie charts, and/or any other visual representation of the virtual audit metrics. Furthermore, the monitoring and reporting applications 534 can be configured to cause the dashboard to include the various audit recommendations generated by the recommendation generator 9020. The dashboard can include a recommended audit recommendation, a performance increase that will result from the audit recommendation, evidence of the success of the audit recommendation (e.g., an indication of the performance of other buildings caused by the audit recommendation), etc. The recommendation manager 9022 can be configured to generate the audit recommendation features of the dashboard while the interface manager 9024 can be configured to generate the dashboard and cause the dashboard to include indications of the virtual audit metrics).
Regarding Claim 44, Vitullo/Kishlock/Drees/Mashima teaches the limitations of Claim 44 which state
wherein the prognostics data is generated by processing weather data and the time-series energy usage data using at least one predictive model, and wherein the at least one predictive model is selected from at least one of: a neural network model, a random forest model, a support-vector machine model, GBRT model, or a diffusion index model (Vitullo: Para 0371 via The recommendation generator 9020 can be configured to perform various machine learning techniques with the values of virtual audit metrics generated by the analytics service 524. The machine learning techniques can include decision trees, neural networks (e.g., convolutional neural networks (CNN), recurrent neural networks (RNN), deep learning networks, etc.), Bayesian modeling, and/or any other type of machine learning. The recommendation generator 9020 can be configured to identify various recommendations implemented in a building and/or campus and determine the success of the recommendation).
Regarding Claim 45, Vitullo/Kishlock/Drees/Mashima teaches the limitations of Claim 45 which state
generate the energy conservation prognostics by: generating a recommendation for an energy conservation measure corresponding to an action that could be taken to improve energy efficiency of the building, identified based on at least one of the building markers; and generating a prediction of an effect the energy conservation measure would have on the energy efficiency of the target building; and send the recommendation and the prediction to be displayed at the user interface (Vitullo: Para 0373 via The analytics service 524 is shown to provide the values of virtual audit metrics determined by the various components of the analytics service 524 and the audit recommendations determined by the recommendation generator 9020 to the monitoring and reporting service 534. The monitoring and reporting applications 534 can be configured to generate and/or provide a dashboard interface to an end user for viewing the virtual audit metrics and/or audit recommendations. In some embodiments, the analytics service 524 can be configured to generate the values of virtual audit metrics periodically (each metric may have its own period). The period may be every fifteen minutes, every hour, every day, every week, every month, and/or every year. The monitoring and reporting applications 534 can be configured to generate various trend graphs, bar graphs, pie charts, and/or any other visual representation of the virtual audit metrics. Furthermore, the monitoring and reporting applications 534 can be configured to cause the dashboard to include the various audit recommendations generated by the recommendation generator 9020. The dashboard can include a recommended audit recommendation, a performance increase that will result from the audit recommendation, evidence of the success of the audit recommendation (e.g., an indication of the performance of other buildings caused by the audit recommendation), etc. The recommendation manager 9022 can be configured to generate the audit recommendation features of the dashboard while the interface manager 9024 can be configured to generate the dashboard and cause the dashboard to include indications of the virtual audit metrics).
Regarding Claim 46, Vitullo teaches the limitations of Claim 46 which state
receiving a request to monitor energy usage for at least one target building (Vitullo: Para 0373 via The monitoring and reporting applications 534 can be configured to generate and/or provide a dashboard interface to an end user for viewing the virtual audit metrics and/or audit recommendations. In some embodiments, the analytics service 524 can be configured to generate the values of virtual audit metrics periodically (each metric may have its own period). The period may be every fifteen minutes, every hour, every day, every week, every month, and/or every year);
retrieving input data corresponding to the at least one target building including: obtaining a first set of building-specific data that is generated off-site from the at least one target building, the first set comprising building-specific characteristic data for the at least one target building, the building-specific characteristic data including at least location data (Vitullo: Para 0127, 0303 via Data collector 512 can provide the raw timeseries data to data platform services 520 and/or store the raw timeseries data in local storage 514 or hosted storage 516. As shown in FIG. 5, local storage 514 can be data storage internal to BMS 500 (e.g., within memory 510) or other on-site data storage local to the building site at which the data samples are collected. Hosted storage 516 can include a remote database, cloud-based data hosting, or other remote data storage. For example, hosted storage 516 can include remote data storage located off-site relative to the building site at which the data samples are collected…Energy benchmarking module 5210 may receive building parameters from parameters database 5206. Building parameters may include various characteristics or attributes of the building such as building area (e.g., square feet), building type (e.g., one of a plurality of enumerated types), building location, and building benchmarks for the applicable building type and/or location. Building benchmarks can include benchmark energy consumption values for the building. The benchmarks can be ASHRAE benchmarks for buildings in the United States or other local standards for buildings in different countries);
retrieving, a second set of building-specific data that is generated on-site at the at least one target building by at least one of a utility providing energy or a power sensor or meter disposed at the at least one target building, the second set consisting of time- series energy usage data for the at least one target building, wherein the time-series energy usage data reflects total electricity usage for the at least one target building (Vitullo: Para 0123, 0127, 0279, 0384 via each data sample is received with a timestamp indicating a time at which the corresponding data value was measured or calculated. In other embodiments, data collector 512 adds timestamps to the data samples based on the times at which the data samples are received. Data collector 512 can generate raw timeseries data for each of the data points for which data samples are received. Each timeseries can include a series of data values for the same data point and a timestamp for each of the data values… Data collector 512 can provide the raw timeseries data to data platform services 520 and/or store the raw timeseries data in local storage 514 or hosted storage 516. …any meters 5102-5104 associated with the new space may also be displayed in navigation pane 1902. Data provided by meters 5102-5104 may be shown in energy consumption widget 2702 and energy demand widget 2704, which may be the same or similar as previously described. For example, widgets 2702-2704 shown in FIG. 51 may be configured to display meter data for a current time period 5106 and a previous time period 5108. Current time period 5106 may be populated using real-time data received from meters 5102-5104. Previous time period 5108 may be unpopulated until historical data is retrieved for meters 5102-5104 (as described with reference to FIG. 45). After historical data is retrieved, dashboard 1900 may be automatically updated to display the historical data along with the current data in energy consumption widget 2702 and energy demand widget 2704... The various virtual audit metrics described herein for determining building characteristics can be implemented into the analytics service 524. The building data 9000 can be provided to the analytics service 524 via a cloud (e.g., provided every fifteen minutes). The building data 9000 can include building and meter level electric load and weather data. Some of the markers (e.g., virtual audit metrics) can be generated yearly although they can also be generated more frequently (e.g., every hour, day, week, month, quarter, etc.).
However, Vitullo does not explicitly disclose the limitations of Claim 46 which state pre-processing the time-series energy usage data to generate pre-processed time-series energy usage data that reflects corrected total electricity usage for the at least one target building, the pre-processing including: identifying and replacing missing values of the time-series energy usage data, detecting and replacing anomalous datapoints corresponding to outlier values of the time-series energy usage data, and imputing missing data points from the input time-series energy usage.
Kishlock though, with the teachings of Vitullo, teaches of
pre-processing the time-series energy usage data to generate pre-processed time-series energy usage data that reflects corrected total electricity usage for the at least one target building, the pre-processing including: identifying and replacing missing values of the time-series energy usage data, detecting and replacing anomalous datapoints corresponding to outlier values of the time-series energy usage data, and imputing missing data points from the input time-series energy usage (Kishlock: Para 0014, 0035 via The energy usage data (consumption in volumes of fuel such as gallons, MCF's, and pounds, etc., or energy units such as kWh, therms, BTU's, etc.) with related information about the periods of consumption such as starting date and number of days in a period, ending date and number of days in a period, starting and ending dates or a series of days in a period with a beginning or ending offset sufficient to determine the starting and ending dates of each consumption period received from, for example, the utility or energy supplier, may contain known data structure problems such as overlapping or missing meter read periods, invalid dates, such as Feb. 30, invalid years such as 1901 appearing in a data set containing 2001 data, bad estimates, bad meter reads and accounting corrections, including those previously mentioned, and cancels and rebills. The present invention examines the data for these and other problems and repairs or removes problematic data elements using a variety of algorithms including but not limited to artificial intelligence, regression technology, analysis of variance, outlier analysis and human inspection... Weather Cleaning Module 36 examines the weather data for known data structure problems such as invalid dates or missing data. The Module 36 may also adjust for changing weather station data that are not consistent over time. The present invention fills missing data points using methods which may include but are not limited to averaging, regression, interpolation between neighboring weather stations, application of normals and application of known biases to data from neighboring stations).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Vitullo with the teachings of Kishlock, in order to have pre-processing the time-series energy usage data to generate pre-processed time-series energy usage data that reflects corrected total electricity usage for the at least one target building, the pre-processing including identifying and replacing missing values of the time-series energy usage data and imputing missing data points from the input time-series energy usage. The motivations behind this being to incorporate the teachings of fixing energy data structure problems. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Furthermore, Vitullo does not explicitly disclose the limitation of Claim 46 which state including detecting and replacing anomalous datapoints corresponding to outlier values of the time-series energy usage data.
Drees though, with the teachings of Vitullo/Kishlock, teaches of
including detecting and replacing anomalous datapoints corresponding to outlier values of the time-series energy usage data (Drees: Col 9 Lines 8-20 via Outlier analysis module 256 is configured to test data points and determine if a data point is reliable. For example, if a data point is more than a threshold (e.g., three standard deviations, four standard deviations, or another set value) away from the an expected value (e.g., the mean) of all of the data points, the data point may be determined as unreliable and discarded. Outlier analysis module 256 may further calculate the expected value of the data points that each data point is to be tested against. Outlier analysis module 256 may be configured to replace the discarded data points in the data set with a NaN or another flag such that the new value will be skipped in further data analysis).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Vitullo/Kishlock with the teachings of Drees in order to have including detecting and replacing anomalous datapoints corresponding to outlier values of the time-series energy usage data. The motivations behind this being to incorporate the teachings of identifying a change in a building's energy usage model based on data received from the building management system. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
The combination of Vitullo/Kishlock/Drees, further teaches the limitations of Claim 46 which state
identifying, from fluctuations in the corrected total electricity usage, on/off cycles for at least one apparatus associated with the at least one target building (Vitullo: Para 0380 via The occupancy schedule service 9016 can be configured to generate the value of an occupancy schedules virtual audit metric based on energy usage through the time of day for a daily consumption. The occupancy schedule service 9016 can be configured to determine the automatic morning turn on of HVAC and lighting systems and the automatic evening turnoff of HVAC and lighting systems based on the energy usage through time for a particular day and/or days);
periodically updating building efficiency diagnostics for the at least one target building using the pre-processed time-series energy usage data (Vitullo: Para 0373 via The analytics service 524 is shown to provide the values of virtual audit metrics determined by the various components of the analytics service 524 and the audit recommendations determined by the recommendation generator 9020 to the monitoring and reporting service 534. The monitoring and reporting applications 534 can be configured to generate and/or provide a dashboard interface to an end user for viewing the virtual audit metrics and/or audit recommendations. In some embodiments, the analytics service 524 can be configured to generate the values of virtual audit metrics periodically (each metric may have its own period). The period may be every fifteen minutes, every hour, every day, every week, every month, and/or every year. The monitoring and reporting applications 534 can be configured to generate various trend graphs, bar graphs, pie charts, and/or any other visual representation of the virtual audit metrics); and
automatically sending instructions for changing settings associated with the at least one apparatus to change the on/off cycles of the at least one apparatus (Vitullo: Para 0104 via In some embodiments, demand response layer 414 includes a control module configured to actively initiate control actions (e.g., automatically changing setpoints) which minimize energy costs based on one or more inputs representative of or based on demand (e.g., price, a curtailment signal, a demand level, etc.). In some embodiments, demand response layer 414 uses equipment models to determine an optimal set of control actions. The equipment models can include, for example, thermodynamic models describing the inputs, outputs, and/or functions performed by various sets of building equipment).
Furthermore, the combination fails to disclose the limitations of Claim 46 which state on an ongoing basis and wherein the only building specific data in the input data that is collected on an ongoing basis is the time-series energy usage data.
Mashima though, with the teachings of Vitullo/Kishlock/Drees, teaches of
on an ongoing basis and wherein the only building specific data in the input data that is collected on an ongoing basis is the time-series energy usage data (Mashima: Para 0034, 0073 via The site 128 may include buildings, structures, equipment, or other objects that use electricity distributed by the utility 108. The site 128 may have adapted thereto a meter such as the smart meter 129 that measures the energy distributed to the site 128. The smart meter 129 may communicate the energy usage data to the utility 108. In some embodiments, the energy usage data may be communicated to the utility 108 via the network 122. Based on the energy usage data, the utility 108 may ascertain the energy usage of the site 128… Energy usage data may originate at the customer 102 (e.g., at the site 128 associated with the customer 102, as measured by the smart meter 129 or other suitable meter). The energy usage data may include meter readings 150. In some embodiments, the meter readings 150 may be sent periodically (e.g., at 15-minute intervals) or in real time).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Vitullo/Kishlock/Drees with the teachings of Mashima in order to have on an ongoing basis and wherein the only building specific data in the input data that is collected on an ongoing basis is the time-series energy usage data. The motivation behind this being to incorporate the teachings of analyzing time-series data of energy usage as taught by Mashima. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Regarding Claim 47, Vitullo/Kishlock/Drees/Mashima teaches the limitations of Claim 47 which state
wherein the at least one apparatus is an HVAC system for the at least one target building, and the method further comprising: determining an HVAC turn on time building marker and an HVAC turn off time building marker from the pre- processed time-series energy usage data (Vitullo: Para 0380 via The occupancy schedule service 9016 can be configured to generate the value of an occupancy schedules virtual audit metric based on energy usage through the time of day for a daily consumption. The occupancy schedule service 9016 can be configured to determine the automatic morning turn on of HVAC and lighting systems and the automatic evening turnoff of HVAC and lighting systems based on the energy usage through time for a particular day and/or days).
Regarding Claim 48, Vitullo/Kishlock/Drees/Mashima teaches the limitations of Claim 48 which state
wherein the building-specific characteristic data is automatically obtained from a database of real estate records (Vitullo: Para 0303 via Energy benchmarking module 5210 may receive building parameters from parameters database 5206. Building parameters may include various characteristics or attributes of the building such as building area (e.g., square feet), building type (e.g., one of a plurality of enumerated types), building location, and building benchmarks for the applicable building type and/or location. Building benchmarks can include benchmark energy consumption values for the building. The benchmarks can be ASHRAE benchmarks for buildings in the United States or other local standards for buildings in different countries).
Regarding Claim 49, Vitullo/Kishlock/Drees/Mashima teaches the limitations of Claim 49 which state
wherein the time-series data is retrieved from at least one sensor associated with the at least one target building, the sensor installed to capture time-series total energy usage data for the at least one target building (Vitullo: Para 0264 via The meter data is shown to include energy consumption data which may be displayed in an energy consumption widget 3412, and energy demand data which may be displayed in an energy demand widget 3414. Each widget 3412-3414 may include a time interval selector 3416 or 3418 which allows the user to select a particular interval of data displayed in each widget 3412-3414. Like the other time selectors 1926, 1940, and 2710-2716, a user can click the buttons within time interval selectors 3414-3416 to select time intervals of one week, one month, three months, six months, one year, or a custom time interval. In some embodiments, the one month interval is selected by default).
Regarding Claim 54, Vitullo/Kishlock/Drees/Mashima teaches the limitations of Claim 54 which state
displaying, via a client device, the building efficiency diagnostics on a user interface (Vitullo: Para 0373 via The analytics service 524 is shown to provide the values of virtual audit metrics determined by the various components of the analytics service 524 and the audit recommendations determined by the recommendation generator 9020 to the monitoring and reporting service 534. The monitoring and reporting applications 534 can be configured to generate and/or provide a dashboard interface to an end user for viewing the virtual audit metrics and/or audit recommendations. In some embodiments, the analytics service 524 can be configured to generate the values of virtual audit metrics periodically (each metric may have its own period). The period may be every fifteen minutes, every hour, every day, every week, every month, and/or every year. The monitoring and reporting applications 534 can be configured to generate various trend graphs, bar graphs, pie charts, and/or any other visual representation of the virtual audit metrics. Furthermore, the monitoring and reporting applications 534 can be configured to cause the dashboard to include the various audit recommendations generated by the recommendation generator 9020. The dashboard can include a recommended audit recommendation, a performance increase that will result from the audit recommendation, evidence of the success of the audit recommendation).
Regarding Claim 55, Vitullo/Kishlock/Drees/Mashima teaches the limitations of Claim 55 which state
providing, via the user interface, a user an option to update and display the building efficiency diagnostics on demand (Vitullo: Para 0279 via As shown in FIG. 51, any meters 5102-5104 associated with the new space may also be displayed in navigation pane 1902. Data provided by meters 5102-5104 may be shown in energy consumption widget 2702 and energy demand widget 2704, which may be the same or similar as previously described. For example, widgets 2702-2704 shown in FIG. 51 may be configured to display meter data for a current time period 5106 and a previous time period 5108. Current time period 5106 may be populated using real-time data received from meters 5102-5104. Previous time period 5108 may be unpopulated until historical data is retrieved for meters 5102-5104 (as described with reference to FIG. 45). After historical data is retrieved, dashboard 1900 may be automatically updated to display the historical data along with the current data in energy consumption widget 2702 and energy demand widget 2704).
Regarding Claim 57, Vitullo/Kishlock/Drees/Mashima teaches the limitations of Claim 57 which state
determining an effective R-value for the at least one target building based on an interior heating load of the at least one target building, an amount of energy being removed from an air- conditioned space of the at least one target building, an interior temperature of the at least one target building, and an exterior temperature of the at least one target building (Vitullo: Para 0379 via The R-value service 9014 can be configured to generate the value of an R-value virtual audit metric. In some embodiments, the R-value can sometimes be referred to as the thermal efficiency of a building. The R-value service 9014 can be configured to determine the R- values based on a total electricity consumption, internal and external temperatures, an estimate of the building surface area, and an estimate of the conductivity value of the building construction materials).
Regarding Claim 58, Vitullo/Kishlock/Drees/Mashima teaches the limitations of Claim 58 which state
wherein the building efficiency diagnostics comprise: at least one of: a base load, a heating/cooling system load, a total heating time, a total cooling time, or a total water heater operation time (Vitullo: Para 0088, 0314 via In some embodiments, AHU controller 330 operates valves 346 and 352 via actuators 354- 356 to modulate an amount of heating or cooling provided to supply air 310 (e.g., to achieve a setpoint temperature for supply air 310 or to maintain the temperature of supply air 310 within a setpoint temperature range). The positions of valves 346 and 352 affect the amount of heating or cooling provided to supply air 310 by cooling coil 334 or heating coil 336 and may correlate with the amount of energy consumed to achieve a desired supply air temperature. AHU controller 330 can control the temperature of supply air 310 and/or building zone 306 by activating or deactivating coils 334-336, adjusting a speed of fan 338, or a combination of both... Night/day comparison module 5214 can use the timeseries data to calculate a load ratio Q.sub.ratio for the one or more timeseries. In some embodiments, the load ratio Q.sub.ratio is a ratio of the minimum load during night hours (e.g., a minimum of the timeseries samples designated as nighttime samples) to the maximum load during day hours (e.g., a maximum of the timeseries samples designated as daytime samples)... Night/day comparison module 5214 can generate a value of Q.sub.ratio for each day of each timeseries. In some embodiments, night/day comparison module 5214 stores the daily values of Q.sub.ratio as a new timeseries in local storage 514 and/or hosted storage 516. Each element of the new timeseries may correspond to a particular day and may include the calculated value of Q.sub.ratio for that day).
Regarding Claim 72, Vitullo/Kishlock/Drees/Mashima teaches the limitations of Claim 72 which state
wherein, to retrieve the input data corresponding to the target building, the software causes the processor to retrieve the first set of building-specific data without entry into the target building (Vitullo: Para 0121, Fig 5 via referring to FIG. 5, BMS 500 is shown to include a data collector 512. Data collector 512 is shown receiving data samples from building subsystems 428 via BMS interface 502. In some embodiments, the data samples include data values for various data points. The data values can be measured or calculated values, depending on the type of data point. For example, a data point received from a temperature sensor can include a measured data value indicating a temperature measured by the temperature sensor. A data point received from a chiller controller can include a calculated data value indicating a calculated efficiency of the chiller. Data collector 512 can receive data samples from multiple different devices within building subsystems 428).
Regarding Claims 59-62, 67-68 and 70-71, they are analogous to Claims 46- 49, 54-55 and 57-58 and are rejected for the same reasons.
Claim(s) 35, 42 and 73 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vitullo et al. (US 2019/0302157 A1) in view of Kishlock et al. (US 2001/0020219 A1) in view of Drees et al. (US 9,196,009 B2) in view of Mashima (US 2016/0306373 A1) further in view of Gaasch et al. (US 2016/0018835 A1).
Regarding Claim 35, Vitullo/Kishlock/Drees/Mashima teaches the limitation of
removing datapoints from the time-series energy usage data and weather data corresponding to holidays and weekends to produce modified energy usage data and modified weather data (Drees: Col 11 lines 25-33 via Missing days module 274 is configured to determine days for which is there is not enough data for proper integration performance. Missing days module 274 compares the amount of data for a variable for a given day (or other period of time) and compares the amount to a threshold (e.g., a fraction of a day) to make sure there is enough data to accurately calculate the integral. Workdays module 276 is configured to determine the number of work days in a given interval based on the start date and end date of the interval. For example, for a given start date and end date, workdays module 276 can determine weekend days and holidays that should not figure into the count of number of work days in a given interval. Modules 274, 276 may be used by data synchronization module 214 to, for example, identify the number of days within a time interval for which there exists sufficient data, identify days for which data should not be included in the calculation of the baseline model, etc.).
However, Vitullo/Kishlock/Drees/Mashima does not explicitly disclose the limitation of Claim 35 which states applying a piecewise linear regression model to the modified energy usage data and time- series exterior temperature data of the modified weather data to produce a heating season trendline and a cooling season trendline; determining a first slope of the heating season trendline; determining a second slope of the cooling season trendline; comparing the first slope to a first predetermined threshold to determine a heating type of the target building; comparing the second slope to a second predetermined threshold to determine a cooling type of the target building; setting the heating type building marker equal to the determined heating type; and setting the cooling type building marker equal to the determined cooling type.
Gaasch though, with the teachings of Vitullo/Kishlock/Drees/Mashima, teaches of
applying a piecewise linear regression model to the modified energy usage data and time- series exterior temperature data of the modified weather data to produce a heating season trendline and a cooling season trendline; determining a first slope of the heating season trendline; determining a second slope of the cooling season trendline; comparing the first slope to a first predetermined threshold to determine a heating type of the target building; comparing the second slope to a second predetermined threshold to determine a cooling type of the target building; setting the heating type building marker equal to the determined heating type; and setting the cooling type building marker equal to the determined cooling type (Gaasch: Para 0069 via facility 102 energy use data for Space heating and cooling are correlated to outdoor air temperature. Further, in some embodiments, correlation analyses such as the segmented linear regression can be performed between energy use and outdoor air temperature for each energy transference medium (e.g., electricity, natural gas, etc.) to determine if this energy transference medium is significantly used for facility heating or cooling. Taking electricity as an example, the plot 800 of FIG. 8 demonstrates an example in which the energy use data are in 15-minute intervals, and have two clusters of occupancy level. In some embodiments, each cluster of intervals and their corresponding dry bulb temperature values are correlated by a segmented linear regression line that has one inflection point (802 for the high cluster and 808 for the low cluster) and two line segments. In the high occupancy cluster, the slope of the line segment with lower temperature (804) can be defined as the heating indicator, and the slope of the line segment with higher temperature (806) can be defined as the cooling indicator. Similarly, in the low occupancy cluster, the slope of the line segment with lower temperature (810) is defined as the heating indicator, and the slope of the line segment with higher temperature (812) is the cooling indicator. In some embodiments, heating and cooling indicators are normalized by facility's floor area and time duration of each interval so that facilities 102 with different sizes and energy metering steps are comparable. In some further embodiments, if the heating indicator of a facility 102 is greater than a threshold, the facility 102 is most likely to have electric heating. On the contrary, in some other embodiments, if the heating indicator is smaller than the threshold, it is less likely to be electrically heated. In some further embodiments, the same approach can be applied to cooling as well).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Vitullo/Kishlock/Drees/Mashima with the teachings of Gaasch in order to have applying a piecewise linear regression model to the modified energy usage data and time- series exterior temperature data of the modified weather data to produce a heating season trendline and a cooling season trendline; determining a first slope of the heating season trendline; determining a second slope of the cooling season trendline; comparing the first slope to a first predetermined threshold to determine a heating type of the target building; comparing the second slope to a second predetermined threshold to determine a cooling type of the target building; setting the heating type building marker equal to the determined heating type; and setting the cooling type building marker equal to the determined cooling type. The motivations behind this being the teachings of analyzing energy consumption and demand management, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Regarding Claim 42, while Vitullo/Kishlock/Drees/Mashima teaches Claim 28, it does not explicitly disclose the limitations of Claim 42 which state generate the energy usage variability building marker by: generating a summer boxplot from first datapoints of the energy usage data corresponding to a summer time period; generating a winter boxplot from second datapoints of the energy usage data corresponding to a winter time period; generating a set of contiguous box plots for each hour represented in the summer boxplot and the winter boxplot; and calculating a smooth mean of energy usage values for each hour in both summer and winter.
Gaasch though, with the teachings of Vitullo/Kishlock/Drees/Mashima, teaches of
generate the energy usage variability building marker by: generating a summer boxplot from first datapoints of the energy usage data corresponding to a summer time period; generating a winter boxplot from second datapoints of the energy usage data corresponding to a winter time period; generating a set of contiguous box plots for each hour represented in the summer boxplot and the winter boxplot; and calculating a smooth mean of energy usage values for each hour in both summer and winter (Gaasch: Para 0081, Figs 16A-16C via representative facility load curves for individual energy meters as well as aggregated usage can be created for both actual energy use and for the energy model to visualize energy savings potential at different time periods. For example, FIG. 16A is an example visualization 1600 of a summer weekday average load demand curve 1601, FIG. 16B is an example visualization 1625 of shoulder weekday average load demand curve 1626, and FIG. 16C is an example visualization 1650 of winter weekday average load demand curve 1651 in accordance with some embodiments of the invention. As illustrated, the visualizations 1600, 1625, 1650 can comprise demand curves for actual energy use by the facility (curves 1601, 1626, 1651) and projected energy use (curves 1603, 1628, 1653 respectively) estimated by the efficient energy model).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Vitullo/Kishlock/Drees/Mashima with the teachings of Gaasch in order to have generate the energy usage variability building marker by: generating a summer boxplot from first datapoints of the energy usage data corresponding to a summer time period; generating a winter boxplot from second datapoints of the energy usage data corresponding to a winter time period; generating a set of contiguous box plots for each hour represented in the summer boxplot and the winter boxplot; and calculating a smooth mean of energy usage values for each hour in both summer and winter. In addition to being in the same CPC class, the motivations behind this being the teachings of analyzing energy consumption and demand management, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Regarding Claim 73, while Vitullo/Kishlock/Drees/Mashima teaches Claim 28, it does not explicitly disclose the limitations of Claim 73 which state wherein, with the exception of the time-series energy usage data, the input data is collected from public sources.
Gaasch though, with the teachings of Vitullo/Kishlock/Drees/Mashima, teaches of
wherein, with the exception of the time-series energy usage data, the input data is collected from public sources (Gaasch: Para 0064 via the system 100 can then retrieve text content about the facilities from varies sources (such as facility names, introductions and descriptions from their websites and public databases, web search results of their addresses, etc.,) in step 604).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Vitullo/Kishlock/Drees/Mashima with the teachings of Gaasch in order to have wherein, with the exception of the time-series energy usage data, the input data is collected from public sources. The motivation behind this being to incorporate readily available data, analyze energy consumption and support demand management. In addition to being in the same CPC class, the motivations behind this being the teachings of analyzing energy consumption and demand management, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Claim(s) 39 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vitullo et al. (US 2019/0302157 A1) in view of Kishlock et al. (US 2001/0020219 A1) in view of Drees et al. (US 9,196,009 B2) in view of Mashima (US 2016/0306373 A1) further in view of Stein et al. (US 2014/0058572 A1).
Regarding Claim 39, Vitullo/Kishlock/Drees/Mashima teaches the limitations of Claim 39 which state
sorting daily minimum energy usage values by magnitude to produce sorted daily minimum energy usage values; removing any anomalous and/or negative valued data points from the sorted daily minimum energy usage values to produce cleaned, sorted daily minimum energy usage values (Drees: Col 9 lines 14- 31 via baseline calculation module 210 is shown in greater detail, according to an exemplary embodiment. Baseline calculation module 210 includes data clean-up module 212. Data clean-up module 212 generally receives data from the BMS computer system of the building and pre-filters the data for data synchronization module 214 and the other modules of baseline calculation module 210. Data clean - up module 212 includes outlier analysis module 256, data formatting module 258, and sorting module 260 for pre-filtering the data. Data clean-up module 212 uses sub-modules 256-260 to discard or format bad data by normalizing any formatting inconsistencies with the data, removing statistical outliers, or otherwise preparing the data for further processing. Data formatting module 258 is configured to ensure that like data is in the same correct format (e.g., all time-based variables are in the same terms of hours, days, minutes, etc.). Sorting module 260 is configured to sort data for further analysis (e.g., place in chronological order, etc.).
However, Vitullo/Kishlock/Drees/Mashima does not explicitly disclose the limitations of Claim 39 which state applying a low-pass filter to the time-series energy usage data to produce filtered energy usage data; identifying daily minimum energy usage values from the filtered energy usage data.
Stein though, with the teachings of Vitullo/Kishlock/Drees/Mashima, teaches of
applying a low-pass filter to the time-series energy usage data to produce filtered energy usage data (Stein: Para 0136 via the pattern recognition process implemented in certain embodiments is generally outlined in FIG. 16. At step 1601, data is standardized for input into the pattern classification system. At step 1602, a high- pass or low-pass filter is applied to the data to help smooth extraneous features and help the features of interest stand out. At step 1603, the input data is mathematically characterized, transforming it from raw energy input into a higher - level description of energy use features. At step 1604, this higher-level characterization may then be classified according to the presence or absence of the pattern in question. Classification can be performed by a variety of techniques, ranging from rule-based systems to supervised machine-learning algorithms. At step 1605, a severity score is assigned to the classified pattern, allowing comparison of pattern events over times and across disparate meters );
identifying daily minimum energy usage values from the filtered energy usage data (Stein: Para 0143-0145 via the system may use a low pass filter, such as a moving median, to remove short period features from the load curve and mathematically isolate the longer period features of interest. Once the data series has been filtered, a Bayesian Change Point (BCP) algorithm may be used to find statistically significant changes in level and the system may determine periods of minimum nightly load by a rule-based classification model that identifies consecutive intervals between times showing statistically significantly higher levels than the observed minimum. Additionally, automated algorithms may determine building startup and shutdown times by using rule-based thresholds, or by using the Dynamic Time Warping technique to map an observed change point profile to a template with known startup and shutdown times. Finally, severity metrics are constructed to measure the length and magnitude of overnight levels above the local daily minimum (actual demand minus the minimum BCP predicted demand), as well as to compare the local daily minimum (predicted BCP demand) with respect to expected baseload levels (load decomposition baseload demand).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Vitullo/Kishlock/Drees/Mashima with the teachings of Stein in order to have applying a low-pass filter to the energy usage data to produce filtered energy usage data; identifying daily minimum energy usage values from the filtered energy usage data. The motivations behind this being the teachings of energy consumption and energy demand management, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Furthermore, the combination of Vitullo/Kishlock/Drees/Mashima/Stein teaches the limitation of Claim 39 which states
calculating an average of a predetermined number of lowest values of the cleaned, sorted daily minimum energy usage values (Stein: Para 0128-0130 via computing rate-related variance);
setting the baseload building marker equal to the average (Vitullo: Para 0308 via baseline comparison module 5212 can compare timeseries against various baselines. The baselines may be threshold values which can be generated in any of a variety of ways. For example, some baselines may be defined or set by a user. Some baselines can be calculated from historical data (e.g., average consumption, average demand, average EUI, average energy density, etc.) and other building parameters. Some baselines can be set by standards such as ASHRAE 90.1 (e.g., for building-level standards). Baseline comparison module 5212 may receive building parameters from parameters database 5206. Building parameters may include various characteristics or attributes of the building such as building area (e.g., square feet), building type (e.g., one of a plurality of enumerated types), building location, etc. Baseline comparison module 5212 can use the building parameters to identify appropriate benchmarks against which the timeseries can be compared).
Claim(s) 56 and 59 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vitullo et al. (US 2019/0302157 A1) in view of Kishlock et al. (US 2001/0020219 A1) in view of Drees et al. (US 9,196,009 B2) in view of Mashima (US 2016/0306373 A1) further in view of Steinberg (US 2010/0280667 A1).
Regarding Claim 56, while Vitullo/Kishlock/Drees/Mashima teaches the limitations of Claim 46, it does not explicitly disclose the limitation of Claim 56 which states receiving, via the user interface, unoccupied times for the at least one target building, to improve efficiency diagnostics.
Steinberg though, with the teachings of Vitullo/Kishlock/Drees/Mashima, teaches of
receiving, via the user interface, unoccupied times for the at least one target building, to improve efficiency diagnostics (Steinberg: Para 0039 via FIG. 7 represents a flowchart showing the steps involved in the operation of one embodiment of the subject invention. In step 1302, computer 104 transmits a message to server 106 via the Internet indicating that there is user activity on computer 104. This activity can be in the form of keystrokes, cursor movement, input via a television remote control, etc. In step 1304 the application queries database 300 to retrieve setting information for the HVAC system. In step 1306 the application determines whether the current HVAC program is intended to apply when the home is occupied or unoccupied. If the HVAC settings then in effect are intended to apply for an occupied home, then the application terminates for a specified interval. If the HVAC settings then in effect are intended to apply when the home is unoccupied, then in step 1308 the application will retrieve from database 300 the user's specific preferences for how to handle this situation. If the user has previously specified (at the time that the program was initially set up or subsequently modified) that the user prefers that the system automatically change settings under such circumstances, the application then proceeds to step 1316, in which it changes the programmed setpoint for the thermostat to the setting intended for the house when occupied. If the user has previously specified that the application should not make such changes without further user input, then in step 1310 the application transmits a command to computer 104 directing the browser to display a message informing the user that the current setting assumes an unoccupied house and asking the user in step 1312 to choose whether to either keep the current settings or revert to the pre-selected setting for an occupied home. If the user selects to retain the current setting, then in step 1314 the application will write to database 300 the fact that the users has so elected and terminate. If the user elects to change the setting, then in step 1316 the application transmits the revised setpoint to the thermostat. In step 1314 the application writes the updated setting information to database 300).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Vitullo/Kishlock/Drees/Mashima with the teachings of Steinberg in order to have receiving, via the user interface, unoccupied times for the at least one target building, to improve efficiency diagnostics. The motivations behind this being to incorporate the teachings of adjusting HVAC controls, and suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Regarding Claim 69, it is analogous to Claim 56 and is rejected for the same reasons.
Claim(s) 50-51 and 63-64 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vitullo et al. (US 2019/0302157 A1) in view of Kishlock et al. (US 2001/0020219 A1) in view of Drees et al. (US 9,196,009 B2) in view of Mashima (US 2016/0306373 A1) further in view of Kauffman et al. (US 2016/0266594 A1).
Regarding Claim 50, while Vitullo/Kishlock/Drees/Mashima teaches the limitations of Claim 46, it does not explicitly disclose the limitation of Claim 50 which state wherein the at least one target building comprises a group of target locations.
Kauffman though, with the teachings of Vitullo/Kishlock/Drees/Mashima, teaches of
wherein the at least one target building comprises a group of target locations (Kauffman: Para 0051 via The terms “dwelling”, “residential dwelling” and “home” are used herein to describe the building or dwelling under analysis. Some non- limiting examples of dwellings include single family detached, duplex, townhouse, apartment, and condominium. A dwelling can be a stand-alone building such as a detached home, or may constitute a fraction of a building, such as in the case of an apartment, condominium, duplex or townhouse. It is understood that though the present analysis is exemplified by energy usage in residential dwellings, the same or similar system and method can also be used for utility monitoring and improving energy efficiency in non-residential buildings, such as, for example, commercial buildings, multi-tenant residential buildings, educational buildings, institutional buildings, public sector buildings, religious buildings, hospital and health service buildings, and other building types).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Vitullo/Kishlock/Drees/Mashima with the teachings of Kauffman in order to have wherein the at least one target building comprises a group of target locations. The motivations behind this being to incorporate the teachings of determining energy efficiency, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Regarding Claim 51, the combination of Vitullo/Kishlock/Drees/Mashima/Kauffman, teaches the limitation of Claim 51 which states
wherein the group of target locations is an apartment (Kauffman: Para 0051 via The terms “dwelling”, “residential dwelling” and “home” are used herein to describe the building or dwelling under analysis. Some non-limiting examples of dwellings include single family detached, duplex, townhouse, apartment, and condominium. A dwelling can be a stand-alone building such as a detached home, or may constitute a fraction of a building, such as in the case of an apartment, condominium, duplex or townhouse. It is understood that though the present analysis is exemplified by energy usage in residential dwellings, the same or similar system and method can also be used for utility monitoring and improving energy efficiency in non-residential buildings, such as, for example, commercial buildings, multi-tenant residential buildings, educational buildings, institutional buildings, public sector buildings, religious buildings, hospital and health service buildings, and other building types).
Regarding Claims 63-64, they are analogous to Claims 50-51 and are rejected for the same reasons.
Claim(s) 53 and 66 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vitullo et al. (US 2019/0302157 A1) in view of Kishlock et al. (US 2001/0020219 A1) in view of Drees et al. (US 9,196,009 B2) in view of Mashima (US 2016/0306373 A1) further in view of Burge (US 11,100,465 B1).
Regarding Claim 53, while Vitullo/Kishlock/Drees/Mashima teaches the limitations of Claim 46, it does not explicitly disclose the limitation of Claim 53 which states updating thermostat settings based on the on/off cycles for the at least one apparatus.
Burge though, with the teachings of Vitullo/Kishlock/Drees/Mashima, teaches of
updating thermostat settings based on the on/off cycles for the at least one apparatus (Burge: Col 12 lines 7-23 via Based on this analysis, the system 100 may perform an energy management operation for at least one of the rental properties (270). In some implementations, the server 110 may perform energy management operations, such as verifying energy usage at the rental properties, generating reports related to energy usage at the rental properties, providing alerts related to energy usage at the rental properties based on determining that the rental properties do not align with owner settings and/or efficiency rules, providing suggestions based on determining that the rental properties do not align with efficiency rules, and automatically, without user input, adjusting settings of energy consuming devices at the rental properties. For example, the server 110 may determine that a rental property will likely be unoccupied in one hour and, in anticipation of the vacancy and in accordance with one or more efficiency rules, begin to reduce the temperature setting of the thermostat at the rental property).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Vitullo/Kishlock/Drees/Mashima with the teachings of Burge in order to have updating thermostat settings based on the on/off cycles for the at least one apparatus. In addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Regarding Claim 66, it is analogous to Claim 53 and is rejected for the same reasons.
Conclusion
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/T.E.S./ Examiner, Art Unit 3625
/BETH V BOSWELL/ Supervisory Patent Examiner, Art Unit 3625
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