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 .
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 120 as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Application No. 16437308, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. Claims 1-3 recite subject matter not supported by the earlier filed parent applications (the Gradient Boosting Machine (GBM) model in Claims 1-3 and Multi-Seasonal-series using LOESS (MSTL) decomposition in Claim 3)[See MPEP 211.05 – “if a claim in a continuation-in-part application recites a feature which was not disclosed or adequately supported by a proper disclosure under 35 U.S.C. 112 in the parent nonprovisional application, but which was first introduced or adequately supported in the continuation-in-part application, such a claim is entitled only to the filing date of the continuation-in-part application. See, e.g., In re Chu, 66 F.3d 292, 36 USPQ2d 1089 (Fed. Cir. 1995); Transco Products, Inc. v. Performance Contracting Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994); In re Van Lagenhoven, 458 F.2d 132, 136, 173 USPQ 426, 429 (CCPA 1972).”]. As such, the instant Claims 1-3 are afforded an effective filing date of 4/17/2023.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1 and 2 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites, in the 11th element, the term “the scheduling error.” This term lacks antecedent basis. Possibly the Applicant intended to recite “the efficiency error.”
Claim 2 recites, in the 6th element, the term “the acquired previous GBM model.” This term lacks antecedent basis.
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.
Claim 3 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) a mathematical algorithm for managing weather related energy usage (the algorithm explicitly uses a Gradient Boosting Machine and Multi-Seasonal-series using LOESS decomposition, which are mathematical operations).
This judicial exception is not integrated into a practical application because no specific actions that would result in energy consumption improvements are recited.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because receiving the data needed for implementing the algorithm is necessary in the implementation of the algorithm. Generating an alert amounts to insignificant extra-solution activity (See Parker v. Flook, 437 U.S. 584 (1978). From Page 595 – “Here it is absolutely clear that respondent's application contains no claim of patentable invention. The chemical processes involved in catalytic conversion of hydrocarbons are well known, as are the practice of monitoring the chemical process variables, the use of alarm limits to trigger alarms, the notion that alarm limit values must be recomputed and readjusted, and the use of computers for "automatic monitoring alarming."”).
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.
Claim(s) 1 and 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mitterhofer (US 20190154873 A1) and Remini et al. (US 20190122132 A1)[hereinafter “Remini”].
Regarding Claim 1, Mitterhofer discloses an energy management system [Abstract – “A weather related energy information system is connected to a network, weather stations portals and energy portals.”] comprising:
a plurality of physical structures that generates energy usage data for the respective physical structures [Abstract – “A server communicates with smart meters mounted to structures defined by an address.”], the energy usage data being gathered by a pre-existing energy portal website [Paragraph [0092] – “The term “energy portals” refers to a website that provides interval energy data from smart meters or interval data recorders (IDR) by a unique electrical service identifier or electric service identifier (ESI), such as Smart Meter Texas and CenterPoint Energy Demand & Energy Information Systems (DEIS).”];
at least one weather station distanced from the physical structures and generating weather data for the physical locations of the physical structures [Paragraph [0047] – “The crawling through the energy portals and the temperature portals and harvesting of the energy interval and the temperature interval data by structure defined by an address includes weather data from airports or a weather station within from 60 miles to 100 miles of the structure defined an address.”];
a server [Paragraph [0034]] configured to:
recursively crawl the Internet to search for the plurality of energy portals websites that provide a fifteen minute interval data [Paragraph [0034] – “The server crawls through the energy portals and temperature portals and harvests energy interval data and temperature interval data identified by the structure defined by an address forming harvested energy interval and temperature interval data.”Paragraph [0063] – “The server automatically and on 15 minutes intervals verifies if there is new data available in the energy and temperature portals and harvests energy interval data and temperature interval data either identified by the structure defined by an address forming harvested energy interval and temperature interval data.”Paragraph [0136] – “The server is set to perform the harvesting on 15 minutes intervals continuously.”],
recursively crawl the Internet to search for a pre-existing weather portal website that provides a sixty minutes interval weather data that are collected at locations within a predetermined distance from an address of the respective one of the plurality of physical structures [Paragraph [0104] – “The phrase “resample interval energy data and interval temperature data to one day periods” refers to a method of frequency conversion by frequency of time, such as an initial sampling of every 15 minutes, then resampling to every hour.”], and a dynamic energy model to process and analyze the generated energy usage data and the generated weather data to generate weather-related energy usage data [Paragraph [0088]],
acquire a previous weather-related energy usage data generated at a same time during a previous year at a same day of the previous year, compare the generated weather-related energy usage data with the acquired weather-related energy usage data [Paragraph [0059] – “The weather related energy information system includes mapping resampled energy interval data to comparable days of a week and overlapping annual data sets to compare as current year versus previous years, by month, by day, and by hour.”],
identify a scheduling error in an operation of an energy consumption system contributing to the generated weather-related energy usage data, generate an alert report, based on the identified error [Paragraph [0060] – “The weather related energy information system includes calculating changes in energy metrics and mapping changes in energy metrics to startup and shutdown times of equipment using energy in a structure defined by an address.”Paragraph [0070] – “The embodiments prevent environmental harm by creating and sending automated alert reports to clients when alert thresholds have been met, therefore, reducing the wasted energy consumption by fostering energy usage savings, using substantially less energy and reducing the amount of carbon dioxide produced to the environment. Issues may include but not limited to client's issues with HVAC systems scheduling process, or human error, a facility manager forgetting to change setting on HVAC units, and leaving the units on for one for months a time.”], and
generate a heat map representing energy usage during a predetermined time period to identify the error [Paragraphs [0093] and [0153]],
wherein the error is identified by:
comparing a current day energy usage data during which the weather-related energy usage data is generated, with the energy usage data acquired during the same day of the previous or another year, and
identifying the error upon the current day energy usage data exceeding the weather-related energy usage data over a predetermined threshold, and
wherein it is determined that the energy consumption system is not operating based on a pre-determined maximum percentage change; and a minimum change in the fifteen minute interval data that is captured for a day during which the fifteen minute interval data is obtained [Paragraphs [0085] and [0148]-[0149]]; and
a client device communicating with the server through which the generated alert or measuring and verification report is sent [Abstract – “The server harvests energy interval data and temperature interval data either conditions the interval data, generates groups of interactive graphs, generates alert reports using stored energy metrics for each structure defined by an address while using a dynamic energy model that performs the steps of: calculating energy metrics for each structure by day or groups of days, determining percent changes in energy metrics for each structure defined by an address by day and groups of days, and comparing percent changes to stored threshold values automatically generating a daily report and optionally generate an alert report to client device connected to the network.”Paragraph [0070] – “The embodiments prevent environmental harm by creating and sending automated alert reports to clients when alert thresholds have been met, therefore, reducing the wasted energy consumption by fostering energy usage savings, using substantially less energy and reducing the amount of carbon dioxide produced to the environment.”].
Mitterhofer fails to disclose (see specifically the underlined portions) to:
acquire a previous weather-related energy usage data generated by a Gradient Boosting Machine (GBM) model at a same time during a previous year at a same day of the previous year,
compare the generated weather-related energy usage data with the acquired GBM model weather-related energy usage data,
identify an efficiency error in an operation of an energy consumption system contributing to the generated weather-related energy usage data,
generate an alert report, based on the identified efficiency error, and
generate a heat map representing energy usage during a predetermined time period to identify the efficiency error,
wherein the scheduling error is identified by:
identifying the efficiency error upon the current day energy usage data exceeding the GBM model weather-related energy usage data over a predetermined threshold.
However, Remini discloses the use of GBM modeling in the determination of energy usage abnormalities (i.e., efficiency errors) [Abstract – “The present invention provides a method for identifying abnormalities in personal energy consumption/usage. The method comprising the steps of: generating personal dynamic forecast model of energy usage patterns in sub-hourly resolution for defined time periods, based on historical personal usage data considering environmental/weather condition (temp, humidity), wherein the dynamic model apply adaptive gradient boost iterative learning algorithm using predefined periodical features and determining abnormalities of actual energy usage in defined time period by comparing predictions of the forecast model, wherein the predictions are calculated by applying the generated forecast model with the actual environmental condition at the relevant time period and identifying delta between the actual usage and the predicted usage patterns which exceeds a predefined threshold for predefined duration.”See Paragraphs [0071]-[0077], particularly – “Pre-processing may include iterative running of a machine-learning gradient boosting algorithm on specific training data of historical consumption usage for identifying the technical parameters explanatory features, including environmental condition or periodical personal feature (e.g. activating the HVAC at a specific hour each day) which provides more accurate forecast results (step 110).”].
It would have been obvious to utilize such an approach in order to ascertain whether or not energy/HVAC systems are operating effectively.
Regarding Claim 2, Mitterhofer discloses an automated method for managing weather related energy usage [Paragraph [0067] – “The embodiments automatically perform and improve the energy management functions and analysis by finding waste in energy consumption.”] comprising:
crawling the Internet to search for a plurality of energy portals websites that provide data [Paragraph [0092] – “The term “energy portals” refers to a website that provides interval energy data from smart meters or interval data recorders (IDR) by a unique electrical service identifier or electric service identifier (ESI), such as Smart Meter Texas and CenterPoint Energy Demand & Energy Information Systems (DEIS).”Paragraph [0034] – “The server crawls through the energy portals and temperature portals and harvests energy interval data and temperature interval data identified by the structure defined by an address forming harvested energy interval and temperature interval data.”] that are collected at locations within a predetermined distance from an address of respective one of a plurality of physical structures [Paragraph [0047] – “The crawling through the energy portals and the temperature portals and harvesting of the energy interval and the temperature interval data by structure defined by an address includes weather data from airports or a weather station within from 60 miles to 100 miles of the structure defined an address.”];
using a dynamic energy model to acquire energy usage data for the respective one of the plurality of physical structures [Paragraph [0088]], each energy portal associated with the respective one of the physical structures [Abstract – “A server communicates with smart meters mounted to structures defined by an address.”];
recursively crawling the Internet to search for at least one weather station using the dynamic energy model to acquire weather data for the physical locations of the physical structures [Paragraph [0104] – “The phrase “resample interval energy data and interval temperature data to one day periods” refers to a method of frequency conversion by frequency of time, such as an initial sampling of every 15 minutes, then resampling to every hour.”], the weather station being physically distanced from the physical structures [Paragraph [0047] – “The crawling through the energy portals and the temperature portals and harvesting of the energy interval and the temperature interval data by structure defined by an address includes weather data from airports or a weather station within from 60 miles to 100 miles of the structure defined an address.”];
analyzing the acquired energy usage data and the acquired weather data to generate weather-related energy usage data for the physical structures [Paragraph [0088]];
acquiring a previous weather-related energy usage data generated during a previous year at a same day of the previous year; comparing the generated weather-related energy usage data with the acquired data [Paragraph [0059] – “The weather related energy information system includes mapping resampled energy interval data to comparable days of a week and overlapping annual data sets to compare as current year versus previous years, by month, by day, and by hour.”];
identifying a scheduling error in an operation of an energy consumption system contributing to the generated weather-related energy usage data; generating an alert and measuring and verification reports, based on the identified error [Paragraph [0060] – “The weather related energy information system includes calculating changes in energy metrics and mapping changes in energy metrics to startup and shutdown times of equipment using energy in a structure defined by an address.”Paragraph [0070] – “The embodiments prevent environmental harm by creating and sending automated alert reports to clients when alert thresholds have been met, therefore, reducing the wasted energy consumption by fostering energy usage savings, using substantially less energy and reducing the amount of carbon dioxide produced to the environment. Issues may include but not limited to client's issues with HVAC systems scheduling process, or human error, a facility manager forgetting to change setting on HVAC units, and leaving the units on for one for months a time.”], wherein the weather data includes one of temperature, dew point, humidity, wind speed, wind direction, or pressure precipitation [Paragraph [0047] – “The crawling through the energy portals and the temperature portals and harvesting of the energy interval and the temperature interval data by structure defined by an address includes weather data from airports or a weather station within from 60 miles to 100 miles of the structure defined an address.”]; and
generating a heat map representing energy usage during a predetermined time period to identify the error [Paragraphs [0093] and [0153]],
wherein the error is identified by:
comparing a current day energy usage data during which the weather-related energy usage data is generated, with a previous year usage data acquired during the same day of a previous year; and
identifying an error upon the current day energy usage data exceeding the data of a previous year weather related energy usage data over a predetermined threshold, and
wherein it is determined that the energy consumption system is not operating based on a pre-determined maximum percentage change in the energy usage data that is captured for a day during which the energy usage data is obtained [Paragraphs [0085] and [0148]-[0149]].
Mitterhofer fails to disclose (see specifically the underlined portions):
acquiring a previous weather-related energy usage data generated by a Gradient Boosting Machine (GBM) model during a previous year at a same day of the previous year;
comparing the generated weather-related energy usage data with the acquired previous GBM model;
identifying an efficiency error in an operation of an energy consumption system contributing to the generated weather-related energy usage data;
generating an alert and measuring and verification reports, based on the identified efficiency error, wherein the weather data includes one of temperature, dew point, humidity, wind speed, wind direction, or pressure precipitation; and
generating a heat map representing energy usage during a predetermined time period to identify the efficiency error,
wherein the efficiency error is identified by:
comparing a current day energy usage data during which the weather-related energy usage data is generated, with a previous year GBM model usage data acquired during the same day of a previous year; and
identifying an efficiency error upon the current day energy usage data exceeding the GBM model of a previous year weather related energy usage data over a predetermined threshold, and
wherein it is determined that the energy consumption system is not operating based on a pre-determined GBM model maximum percentage change in the energy usage data that is captured for a day during which the energy usage data is obtained.
However, Remini discloses the use of GBM modeling in the determination of energy usage abnormalities (i.e., efficiency errors) [Abstract – “The present invention provides a method for identifying abnormalities in personal energy consumption/usage. The method comprising the steps of: generating personal dynamic forecast model of energy usage patterns in sub-hourly resolution for defined time periods, based on historical personal usage data considering environmental/weather condition (temp, humidity), wherein the dynamic model apply adaptive gradient boost iterative learning algorithm using predefined periodical features and determining abnormalities of actual energy usage in defined time period by comparing predictions of the forecast model, wherein the predictions are calculated by applying the generated forecast model with the actual environmental condition at the relevant time period and identifying delta between the actual usage and the predicted usage patterns which exceeds a predefined threshold for predefined duration.”See Paragraphs [0071]-[0077], particularly – “Pre-processing may include iterative running of a machine-learning gradient boosting algorithm on specific training data of historical consumption usage for identifying the technical parameters explanatory features, including environmental condition or periodical personal feature (e.g. activating the HVAC at a specific hour each day) which provides more accurate forecast results (step 110).”].
It would have been obvious to utilize such an approach in order to ascertain whether or not energy/HVAC systems are operating effectively.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mitterhofer (US 20190154873 A1); Remini et al. (US 20190122132 A1)[hereinafter “Remini”]; and Zhang et al., A Novel Decomposition and Combination Technique for Forecasting Monthly Electricity Consumption, Frontiers in Energy Research, 12.2021 [hereinafter “Zhang”].
Regarding Claim 3, Mitterhofer discloses a method of detecting energy consumption anomaly [Abstract – “A weather related energy information system is connected to a network, weather stations portals and energy portals.”Paragraph [0075] – “The term “alert reports” refers to a daily report that tells a client's device that an issue or anomaly is occurring in the structure defined by an address, such as a heating ventilation and air conditioning has been left on after hours.”], comprising:
receiving an energy consumption data of an energy usage system of a building [Abstract – “A server communicates with smart meters mounted to structures defined by an address.”];
analyzing the energy consumption data of the energy usage system by breaking down the energy consumption data into frequency components [Paragraph [0104] – “The phrase “resample interval energy data and interval temperature data to one day periods” refers to a method of frequency conversion by frequency of time, such as an initial sampling of every 15 minutes, then resampling to every hour.”], and
identifying an energy usage of the building [Paragraph [0118] – “Each smart meter is mounted to or proximate structures defined by an address 199a-199e. Structure defined by an address 199a is a warehouse, structure defined by an address 199b is an office building, structure defined by an address 199c is a hospital, structure defined by an address 199d is a convenience store, and structure defined by an address 199e is a residential house.”],
calculating a recommended conservation level based on predicted energy usage of the building [Paragraph [0091] – “The term “energy conservation’ refers to reducing energy through the use. For example not forgetting to turn off heating ventilation and air conditioner systems when not used or not needed is energy conservation.”];
comparing a recommended conservation level to an actual energy consumption data [Paragraph [0199] – “FIG. 6E chart 170e shows historical heap map by day and hour for 2018 to present. Overall, the lower energy consumption in blue shows optimal conservation when no units were left on during afterhours or weekends. Except for Sep. 30, 2018 6040 and a couple of more days. These events turn out to be the weekend request for AC.”]; and
alerting a user upon determining that a deviation between the actual energy consumption data and time series consumption data exceeds a predetermined threshold, wherein: the predetermined threshold is a difference between the actual consumption data and the energy usage of the building predicted [Paragraph [0200] – “The server stores threshold values of kilowatt degree days inserted by an agent of the property management and the server generate alert reports using those inserted threshold values.”].
Mitterhofer fails to disclose:
analyzing the received energy consumption data of the energy usage system of the building using a Multi-Seasonal- series using Loess (MSTL) decomposition,
wherein the analyzing of the received energy consumption data further includes:
receiving the time-series energy consumption data of the energy system, and
analyzing the received time-series energy consumption data using the MSTL decomposition by separating the received time-series energy consumption data into frequency components,
graphically displaying the frequency components on a display;
identifying patterns and trends from the graphically displayed frequency component;
determining a target value for a non-operating time of the building, wherein the target value is determined by performing a box plot analysis;
creating a conservation model based on the determined target value for the non-operating time of the building and a schedule hour based on the MSTL decomposition.
However, Zhang discloses the use and display of MSTL decomposition of energy consumption data [See Abstract and Fig. 4]. It would have been obvious to use such a technique to develop a conservation model for a building in order to more effectively optimize its energy consumption.
Mitterhofer also fails to disclose:
generating a Gradient Boosting Machine (GBM) model based on the created conservation model;
predicting, using the generated GBM model, the energy usage of the building, based on a current weather condition and a current time;
the predetermined threshold is a difference between the actual consumption data and the energy usage of the building predicted by the generated GBM model, and
the generated GBM model is updated on a periodic basis, the periodic basis being one of a monthly or a weekly basis.
However, Remini discloses the use of GBM modeling in the determination of energy usage abnormalities (i.e., efficiency errors) [Abstract – “The present invention provides a method for identifying abnormalities in personal energy consumption/usage. The method comprising the steps of: generating personal dynamic forecast model of energy usage patterns in sub-hourly resolution for defined time periods, based on historical personal usage data considering environmental/weather condition (temp, humidity), wherein the dynamic model apply adaptive gradient boost iterative learning algorithm using predefined periodical features and determining abnormalities of actual energy usage in defined time period by comparing predictions of the forecast model, wherein the predictions are calculated by applying the generated forecast model with the actual environmental condition at the relevant time period and identifying delta between the actual usage and the predicted usage patterns which exceeds a predefined threshold for predefined duration.”See Paragraphs [0071]-[0077], particularly – “Pre-processing may include iterative running of a machine-learning gradient boosting algorithm on specific training data of historical consumption usage for identifying the technical parameters explanatory features, including environmental condition or periodical personal feature (e.g. activating the HVAC at a specific hour each day) which provides more accurate forecast results (step 110).”].
It would have been obvious to utilize such an approach in order to set appropriate thresholds in establishing whether energy usage anomalies are present in order to more effectively determine whether anomalies are present. It would have been obvious to update the model periodically to ensure that it remains accurate.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1 and 2 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 18 and 19 of U.S. Patent No. US 11630236 B2 in view of Remini et al. (US 20190122132 A1)[hereinafter “Remini”].
Claim 1 recites the same limitations as claim 18 of US 11630236 B2 except for (see the underlined portions below) to:
acquire a previous weather-related energy usage data generated by a Gradient Boosting Machine (GBM) model at a same time during a previous year at a same day of the previous year,
compare the generated weather-related energy usage data with the acquired GBM model weather-related energy usage data,
identify an efficiency error in an operation of an energy consumption system contributing to the generated weather-related energy usage data,
generate an alert report, based on the identified efficiency error, and
generate a heat map representing energy usage during a predetermined time period to identify the efficiency error,
wherein the scheduling error is identified by:
identifying the efficiency error upon the current day energy usage data exceeding the GBM model weather-related energy usage data over a predetermined threshold.
However, Remini discloses the use of GBM modeling in the determination of energy usage abnormalities (i.e., efficiency errors) [Abstract – “The present invention provides a method for identifying abnormalities in personal energy consumption/usage. The method comprising the steps of: generating personal dynamic forecast model of energy usage patterns in sub-hourly resolution for defined time periods, based on historical personal usage data considering environmental/weather condition (temp, humidity), wherein the dynamic model apply adaptive gradient boost iterative learning algorithm using predefined periodical features and determining abnormalities of actual energy usage in defined time period by comparing predictions of the forecast model, wherein the predictions are calculated by applying the generated forecast model with the actual environmental condition at the relevant time period and identifying delta between the actual usage and the predicted usage patterns which exceeds a predefined threshold for predefined duration.”See Paragraphs [0071]-[0077], particularly – “Pre-processing may include iterative running of a machine-learning gradient boosting algorithm on specific training data of historical consumption usage for identifying the technical parameters explanatory features, including environmental condition or periodical personal feature (e.g. activating the HVAC at a specific hour each day) which provides more accurate forecast results (step 110).”].
It would have been obvious to utilize such an approach in order to ascertain whether or not energy/HVAC systems are operating effectively.
Claim 2 recites the same limitations as claim 19 of US 11630236 B2 except for (see the underlined portions below):
acquiring a previous weather-related energy usage data generated by a Gradient Boosting Machine (GBM) model during a previous year at a same day of the previous year;
comparing the generated weather-related energy usage data with the acquired previous GBM model;
identifying an efficiency error in an operation of an energy consumption system contributing to the generated weather-related energy usage data;
generating an alert and measuring and verification reports, based on the identified efficiency error, wherein the weather data includes one of temperature, dew point, humidity, wind speed, wind direction, or pressure precipitation; and
generating a heat map representing energy usage during a predetermined time period to identify the efficiency error,
wherein the efficiency error is identified by:
comparing a current day energy usage data during which the weather-related energy usage data is generated, with a previous year GBM model usage data acquired during the same day of a previous year; and
identifying an efficiency error upon the current day energy usage data exceeding the GBM model of a previous year weather related energy usage data over a predetermined threshold, and
wherein it is determined that the energy consumption system is not operating based on a pre-determined GBM model maximum percentage change in the energy usage data that is captured for a day during which the energy usage data is obtained.
However, Remini discloses the use of GBM modeling in the determination of energy usage abnormalities (i.e., efficiency errors) [Abstract – “The present invention provides a method for identifying abnormalities in personal energy consumption/usage. The method comprising the steps of: generating personal dynamic forecast model of energy usage patterns in sub-hourly resolution for defined time periods, based on historical personal usage data considering environmental/weather condition (temp, humidity), wherein the dynamic model apply adaptive gradient boost iterative learning algorithm using predefined periodical features and determining abnormalities of actual energy usage in defined time period by comparing predictions of the forecast model, wherein the predictions are calculated by applying the generated forecast model with the actual environmental condition at the relevant time period and identifying delta between the actual usage and the predicted usage patterns which exceeds a predefined threshold for predefined duration.”See Paragraphs [0071]-[0077], particularly – “Pre-processing may include iterative running of a machine-learning gradient boosting algorithm on specific training data of historical consumption usage for identifying the technical parameters explanatory features, including environmental condition or periodical personal feature (e.g. activating the HVAC at a specific hour each day) which provides more accurate forecast results (step 110).”].
It would have been obvious to utilize such an approach in order to ascertain whether or not energy/HVAC systems are operating effectively.
Claim 19 of US 11630236 B2 also recites that it is determined that the energy consumption system is not operating based on a minimum change in the fifteen minute interval data.
However, the instant claim limitation would be broader in scope than the patented claim as this limitation is not present. As such, the narrower patent claim would read on the broader limitation of the instant claim.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Rashid et al., Monitor: An Abnormality Detection Approach in Buildings Energy Consumption, IEEE, 2018
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/KYLE R QUIGLEY/Primary Examiner, Art Unit 2857