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 .
Claims 1-20 are pending, of which claims 1, 9, and 17 are independent claims.
Priority
Applicant’s claim for the priority benefit of US provisional application No. 63/309,788 filed December 13, 2023 and US Provisional Application No. 63/633,572 filed April 12, 2024.
Information Disclosure Statement
The references cited in the information disclosure statements (IDS) submitted on December 11, 2024 and January 16, 2025 have been considered by the examiner.
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 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending U.S. Patent Application No. 18/946,145. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of U.S. Patent Application No. 18/946,145 recite very similar structure and functionality.
Present US Patent Application No. 18/946,131
U.S. Patent Application No. 18/946,145
Claim 1
A computing device for computing an energy score, the computing device comprising at least one processor and at least one memory device, the at least one processor configured to:
receive, from at least one energy tracking device configured to measure energy usage, energy data relating to a home;
compute, using an artificial intelligence model, an energy score based upon the received energy data, wherein the artificial intelligence model is trained based upon historical energy data relating to a plurality of homes; and
transmit content data to a user device that, when received by the user device, causes the user device to generate a user interface including at least the energy score.
Claim 1
A computing device for computing an energy score, the computing device comprising at least one processor and at least one memory device, the at least one processor configured to:
receive, from at least one data source, energy data relating to energy usage in a home;
compute, using an artificial intelligence model, an energy score based upon the received energy data, the energy score representing a comparison of the energy usage of the home to that of similar homes, wherein the artificial intelligence model is trained based upon historical energy data relating to a plurality of homes; and
transmit content data to a user device that, when received by the user device, causes the user device to generate a user interface including at least the energy score.
Claim 2
The computing device of Claim 1, wherein the at least one processor is further configured to: identify, using the artificial intelligence model, one or more devices present in the home; determine an energy usage associated with each of the one or more devices; and compute the energy score based further upon the determined energy usage associated with each of the one or more devices.
Claim 2
The computing device of Claim 1, wherein the at least one processor is further configured to: identify, using the artificial intelligence model, one or more devices present in the home; determine an energy usage associated with each of the one or more devices; and compute the energy score based further upon the determined energy usage associated with each of the one or more devices.
Claim 3
The computing device of Claim 2, wherein the at least one processor is further configured to cause the user interface to include the determined energy usage associated with each of the one or more devices.
Claim 3
The computing device of Claim 2, wherein the at least one processor is further configured to cause the user interface to include the determined energy usage associated with each of the one or more devices.
Claim 4
The computing device of Claim 1, wherein the at least one processor is further configured to: generate, using the artificial intelligence model, a recommendation increasing the energy score; and transmit recommendation data to the user device that, when received by the user device, causes the user interface to include the recommendation.
Claim 4
The computing device of Claim 1, wherein the at least one processor is further configured to: generate, using the artificial intelligence model, a recommendation increasing the energy score; and transmit recommendation data to the user device that, when received by the user device, causes the user interface to include the recommendation.
Claim 5
The computing device of Claim 4, wherein the user interface indicates a change in the energy score associated with performing the recommendation.
Claim 5
The computing device of Claim 4, wherein the user interface indicates a change in the energy score associated with performing the recommendation.
Claim 6
The computing device of Claim 4, wherein the user interface indicates a predicted change in energy cost associated with performing the recommendation.
Claim 6
The computing device of Claim 4, wherein the user interface indicates a predicted change in energy cost associated with performing the recommendation.
Claim 7
The computing device of Claim 1, wherein the at least one processor is further configured to train the artificial intelligence model using the historical energy data.
Claim 7
The computing device of Claim 1, wherein the at least one processor is further configured to train the artificial intelligence model using the historical energy data.
Claim 8
The computing device of Claim 1, wherein the at least one processor is further configured to: cause the user interface to prompt input of energy data by a user; receive an input of energy data by the user; and compute the energy score further based upon the input.
Claim 8
The computing device of Claim 1, wherein the at least one processor is further configured to: cause the user interface to prompt input of energy data by a user; receive an input of energy data by the user; and compute the energy score further based upon the input.
Claim 9
A computer-implemented method for computing an energy score, the computer-implemented method performed by a computing device including at least one processor and at least one memory device, the computer-implemented method comprising:
receiving, from at least one energy tracking device configured to measure energy usage, energy data relating to a home;
computing, using an artificial intelligence model, an energy score based upon the received energy data, wherein the artificial intelligence model is trained based upon historical energy data relating to a plurality of homes; and
transmitting content data to a user device that, when received by the user device, causes the user device to generate a user interface including at least the energy score.
Claim 9
A computer-implemented method for computing an energy score, the computer-implemented method performed by a computing device including at least one processor and at least one memory device, the computer-implemented method comprising:
receiving, from at least one data source, energy data relating to energy usage in a home;
computing, using an artificial intelligence model, an energy score based upon the received energy data, the energy score representing a comparison of the energy usage of the home to that of similar homes, wherein the artificial intelligence model is trained based upon historical energy data relating to a plurality of homes; and
transmitting content data to a user device that, when received by the user device, causes the user device to generate a user interface including at least the energy score.
Claim 10
The computer-implemented method of Claim 9, further comprising: identifying, using the artificial intelligence model, one or more devices present in the home; determining an energy usage associated with each of the one or more devices; and computing the energy score based further upon the determined energy usage associated with each of the one or more devices.
Claim 10
The computer-implemented method of Claim 9, further comprising: identifying, using the artificial intelligence model, one or more devices present in the home; determining an energy usage associated with each of the one or more devices; and computing the energy score based further upon the determined energy usage associated with each of the one or more devices.
Claim 11
The computer-implemented method of Claim 10, further comprising causing the user interface to include the determined energy usage associated with each of the one or more devices.
Claim 11
The computer-implemented method of Claim 10, further comprising causing the user interface to include the determined energy usage associated with each of the one or more devices.
Claim 12
The computer-implemented method of Claim 9, further comprising: generating, using the artificial intelligence model, a recommendation increasing the energy score; and transmitting recommendation data to the user device that, when received by the user device, causes the user interface to include the recommendation.
Claim 12
The computer-implemented method of Claim 9, further comprising: generating, using the artificial intelligence model, a recommendation increasing the energy score; and transmitting recommendation data to the user device that, when received by the user device, causes the user interface to include the recommendation.
Claim 13
The computer-implemented method of Claim 12, wherein the user interface indicates a change in the energy score associated with performing the recommendation.
Claim 13
The computer-implemented method of Claim 12, wherein the user interface indicates a change in the energy score associated with performing the recommendation.
Claim 14
The computer-implemented method of Claim 12, wherein the user interface indicates a predicted change in energy cost associated with performing the recommendation.
Claim 14
The computer-implemented method of Claim 12, wherein the user interface indicates a predicted change in energy cost associated with performing the recommendation.
Claim 15
The computer-implemented method of Claim 9, further comprising training the artificial intelligence model using the historical energy data.
Claim 15
The computer-implemented method of Claim 9, further comprising training the artificial intelligence model using the historical energy data.
Claim 16
The computer-implemented method of Claim 9, further comprising: causing the user interface to prompt input of energy data by a user; receiving an input of energy data by the user; and computing the energy score further based upon the input.
Claim 16
The computer-implemented method of Claim 9, further comprising: causing the user interface to prompt input of energy data by a user; receiving an input of energy data by the user; and computing the energy score further based upon the input.
Claim 17
At least one non-transitory computer-readable media having computer-executable instructions embodied thereon, wherein when executed by computing device including at least one processor and at least one memory device, the computer-executable instructions cause the at least one processor to:
receive, from at least one energy tracking device configured to measure energy usage, energy data relating to a home;
compute, using an artificial intelligence model, an energy score based upon the received energy data, wherein the artificial intelligence model is trained based upon historical energy data relating to a plurality of homes; and
transmit content data to a user device that, when received by the user device, causes the user device to generate a user interface including at least the energy score.
Claim 17
At least one non-transitory computer-readable media having computer-executable instructions embodied thereon, wherein when executed by computing device including at least one processor and at least one memory device, the computer-executable instructions cause the at least one processor to:
receive, from at least one data source, energy data relating to energy usage in a home;
compute, using an artificial intelligence model, an energy score based upon the received energy data, the energy score representing a comparison of the energy usage of the home to that of similar homes, wherein the artificial intelligence model is trained based upon historical energy data relating to a plurality of homes; and
transmit content data to a user device that, when received by the user device, causes the user device to generate a user interface including at least the energy score.
Claim 18
The at least one non-transitory computer-readable media of Claim 17, wherein the computer-executable instructions further cause the at least one processor to: identify, using the artificial intelligence model, one or more devices present in the home; determine an energy usage associated with each of the one or more devices; and compute the energy score based further upon the determined energy usage associated with each of the one or more devices.
Claim 18
The at least one non-transitory computer-readable media of Claim 17, wherein the computer-executable instructions further cause the at least one processor to: identify, using the artificial intelligence model, one or more devices present in the home; determine an energy usage associated with each of the one or more devices; and compute the energy score based further upon the determined energy usage associated with each of the one or more devices.
Claim 19
The at least one non-transitory computer-readable media of Claim 18, wherein the computer-executable instructions further cause the at least one processor to cause the user interface to include the determined energy usage associated with each of the one or more devices.
Claim 19
The at least one non-transitory computer-readable media of Claim 18, wherein the computer-executable instructions further cause the at least one processor to cause the user interface to include the determined energy usage associated with each of the one or more devices.
Claim 20
The at least one non-transitory computer-readable media of Claim 17, wherein the computer-executable instructions further cause the at least one processor to: generate, using the artificial intelligence model, a recommendation increasing the energy score; and transmit recommendation data to the user device that, when received by the user device, causes the user interface to include the recommendation.
Claim 20
The at least one non-transitory computer-readable media of Claim 17, wherein the computer-executable instructions further cause the at least one processor to: generate, using the artificial intelligence model, a recommendation increasing the energy score; and transmit recommendation data to the user device that, when received by the user device, causes the user interface to include the recommendation.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103, which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 4-9, 12-17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Japanese Sullivan (US Patent Publication No. 2018/0089143 A1) (“Sullivan”), in view of Khan et al. (US Patent Publication No. 2023/0280771 A1) (“Khan”), and further in view of Vega et al. (US Patent Publication No. 2021/0123771 A1) (“Vega”).
Regarding independent claim 1, Sullivan teaches:
A computing device for computing an energy score, the computing device comprising at least one processor and at least one memory device, the at least one processor configured to: Sullivan: Paragraph [0001] (“…a method and apparatus for generating energy models for similar structures and in particular to systems and methods for modeling the consumption of energy, such as electricity, heating oil or natural gas in a structure, and for generating meaningful comparisons of the energy consumption habits of the occupants of a structure compared to the energy consumption habits of the occupants of similar structures.”) Sullivan: Paragraph [0017] (“FIG. 2 illustrates a block diagram for the disclosed method for determining the energy factor for similar buildings, the method being implemented on a computer device 102 comprising one or more processors programmed with one or more computer program instructions that, when executed by the one or more processors, program the computer device 102 to generate a precision energy model 220 and provide means for quantifying and comparing the energy use of the tenants of various building in a group of buildings 110.”)
receive, from at least one energy tracking device configured to measure energy usage, energy data relating to a home; Sullivan: Paragraph [0024] (“The energy audit further comprises determining the energy efficiency said at least one control system by collecting data on the energy use of said at least one building's climate control system, for example, collecting data on the energy use of the building's air conditioning system. In another exemplarily embodiment the energy audit determines the energy efficiency of the buildings environmental control system by collecting data on the energy use of said at least one building's furnace. The energy audit can also include collecting data on the energy use of said a building's water heater.”)
compute, using an artificial intelligence model, an energy score based upon the received energy data,… Sullivan: Paragraph [0024] (“As shown is FIG. 4, a monthly energy model for each building in said selected group of buildings 320, 1320, 2320 is generated. Each structure's energy model 320, 1320, 2320 is used to predict the energy usage for that particular structure over the course for a given month. The predicted energy usage 350 is used to calculate an individual energy factor 330, 1330, 2330 for each individual structure. To generate the energy factor 230, the minimum value for predicted energy usage for all the homes in the selected group is divided by each home's predicted energy usage. This results in an individual energy factor 5330, 6330, 7330 with a value less than one for each home for each month for each fuel type. For example, homes in a neighborhood may use electricity and natural gas powered control systems for environmental controls. Each home therefore will have 12 monthly energy factors for its electricity usage and 12 monthly energy factors for its natural gas usage.”) Sullivan: Paragraph [0028] (“As shown is FIG. 4, a monthly energy model for each building in said selected group of buildings 320, 1320, 2320 is generated. Each structure's energy model 320, 1320, 2320 is used to predict the energy usage for that particular structure over the course for a given month. The predicted energy usage 350 is used to calculate an individual energy factor 330, 1330, 2330 for each individual structure. To generate the energy factor 230, the minimum value for predicted energy usage for all the homes in the selected group is divided by each home's predicted energy usage. This results in an individual energy factor 5330, 6330, 7330 with a value less than one for each home for each month for each fuel type. For example, homes in a neighborhood may use electricity and natural gas powered control systems for environmental controls. Each home therefore will have 12 monthly energy factors for its electricity usage and 12 monthly energy factors for its natural gas usage.”) Sullivan: Paragraph [0029] (“Referring to FIG. 5, with continued reference to FIGS. 2, and 4 the various monthly energy factor for an individual structure 530, 1530, 2530 is used to generate a weighted score measuring the energy use of the home's occupants by multiplying the calculated energy factor for a particular month by the actual energy used by that particular structure for a particular month. The weighted score, generated at the end of the end of each month, allows the energy lifestyle of the residents of each structure in a group to be compared 240 to other members of the group 110. The weighted score is an adjusted energy value that removes the inefficiency differences in the like structures.”) [Based on energy used by a particular structure, the structure's energy model used to calculate an individual energy factor and generate a weighted score reads on “compute, using an artificial intelligence model, an energy score based upon the received energy data”.]
Sullivan does not expressly teach “wherein the artificial intelligence model is trained based upon historical energy data relating to a plurality of homes; and transmit content data to a user device that, when received by the user device, causes the user device to generate a user interface including at least the energy score.” However, Khan describes a building automation system. Khan teaches:
…wherein the artificial intelligence model is trained based upon historical energy data relating to a plurality of homes; and… Khan: Paragraph [0105] (“As another example, the cloud manager 340 is configured to train a neural network (or other machine learning or artificial intelligence model), for example on historical data of configuration, events, performance, etc. of the equipment unit 106 and/or other equipment units (e.g., similar equipment units serving similar buildings). The cloud manager 340 may provide the trained neural network to the edge manager 324.”)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Sullivan and Khan before them, for the artificial intelligence model to be trained based upon historical energy data relating to a plurality of homes because the references are in the same field of endeavor as the claimed invention and they are focused on energy management.
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification to allow a user to determine where to focus attention for improvements, maintenance, and other interventions of air conditioning and include automated or user-selected adjustments on energy parameters. Khan Paragraphs [0105] and [0106]
Sullivan and Khan do not expressly teach “transmit content data to a user device that, when received by the user device, causes the user device to generate a user interface including at least the energy score.” However, Vega describes a optimizing utility consumption. Vega teaches:
…transmit content data to a user device that, when received by the user device, causes the user device to generate a user interface including at least the energy score. Vega: Paragraph [0293] (“The Energy Optimization Score (Energy IQ) integrates the information in the baseline model (energy fingerprint) and the on-going energy consumption data which can be gathered from multiple sources including smart devices and sensors in the customer premises. Initially only the information in the customer energy fingerprint is used to compute this score. As the continuous monitoring of energy consumption and performance indicators takes place the Energy Optimization Score (Energy IQ) is updated and displayed.”) Vega: Paragraph [0085] (“…a user interface for interacting with a user to allow user inputs to the system and at least displaying results in a plurality of preselected formats from said processor processing said preselected data and analysis of the preselected data stored in said memories and from comparisons and combinations of those sets of data in common time periods,…”)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Sullivan, Khan, and Vega before them, transmit content data to a user device that, when received by the user device, causes the user device to generate a user interface including at least the energy score because the references are in the same field of endeavor as the claimed invention and they are focused on energy management.
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification for obtaining and analyzing power consumption to establish a baseline for energy consumption and monitoring actual consumption for variances from the baseline and the determination of the cause for and correction of a variance. Vega Paragraph [0084]
Regarding claim 4, Sullivan, Khan, and Vega teach all the claimed features of claim 1, from which claim 4 depends. Vega further teaches:
The computing device of Claim 1, wherein the at least one processor is further configured to:
generate, using the artificial intelligence model, a recommendation increasing the energy score; and transmit recommendation data to the user device that, when received by the user device, causes the user interface to include the recommendation. Vega: Paragraphs [0377]-[0394] (“… an analytics and computation engine (energy optimization engine) executed by said at least one processor using a first portion of instructions stored in said associated instruction memory for performing: (i) statistical analysis of, aggregation of and disaggregation of said historical energy usage data, (ii) statistical analysis of historical weather data associated with historical energy usage data, (iii) machine learning and employing artificial intelligence models to identify data clustering, outliers and other data driven characteristics and incorporating feedback into selected portions of some of the analysis, …analyzing said usage data for energy consumption by one or more energy devices associated with said premises, … providing alternative representations of energy usage data associated with a source of energy for said premises, [0388] (ix) calculating a plurality of months of a baseline for usage data using at least a portion of said historical energy data as a basis for said calculating, [0389] (x) comparing said baseline usage to actual usage data, [0390] (xi) monitoring actual usage data for variances to said baseline, [0391] (xii) determining variances between actual and baseline [0392] (xiii) providing variance analysis for causation, [0393] (xiv) determining and providing recommendations for available energy reduction choices, [0394] (xv) calculating energy optimization scores based on the implementation of optimization recommendations and energy optimization performance indicators (e.g. Energy IQ), a display engine …”) Vega: Paragraph [0399] (“(v) displaying recommendations for available energy reduction choices, [0400] (vi) displaying recommendations and alternative representations for available environmental impact reduction choices (e.g., number of trees required to offset, renewable energy alternative …”)
The motivation to combine Sullivan, Khan, and Vega as provided in independent claim 1 is incorporated herein.
Regarding claim 5, Sullivan, Khan, and Vega teach all the claimed features of claim 4, from which claim 5 depends. Vega further teaches:
The computing device of Claim 4, wherein the user interface indicates a change in the energy score associated with performing the recommendation. Vega: Paragraph [0323] (“The user interface provides the customer the ability to modify the inputs for the optimization criteria, set points, preferences and schedules to recalculate the optimization recommendations until satisfied. The customer has the option to set the actuation of the optimization recommendations to manual or automatic, and to define automation settings, notifications and thresholds. The GUI (graphical User Interface) for the system 100 includes:”) Vega: Paragraph [0324] (“Display of the impact and alignment score for each available optimization opportunity based on consumer preferences”) Vega: Paragraph [0325] (“Interface to adjust selection criteria and recalculate weighted scores/re- rank optimization recommendations until satisfied”) Vega: Paragraph [0326] (“Interface to define and schedule personalized building modes”) Vega: Paragraph [0327] (“Interface to filter optimization recommendations by multiple criteria (e.g., cost, environmental footprint, changes to set points and schedules, etc.) ”) Vega: Paragraph [0328] (“Display and Side by side comparison of top optimization recommendations identified based on weighted scores calculated based on the customer's optimization criteria”) Vega: Paragraph [0329] (“Graphical comparison of the current condition and recommended optimization projected costs based on baseline historical and predicted consumption”) Vega: Paragraph [0330] (“Comparison of environmental impact of the current and recommended condition and recommended optimization projected costs based on baseline historical and predicted consumption”) Vega: Paragraph [0331] (“Interface to actuate the optimization recommendations”) Vega: Paragraph [0332] (“Interface to set the actuation mode for the optimization recommendations”) Vega: Paragraph [0333] (e.g. manual, automatic, hybrid), and to define automation settings, notifications and thresholds.”)
The motivation to combine Sullivan, Khan, and Vega as provided in independent claim 1 is incorporated herein.
Regarding claim 6, Sullivan, Khan, and Vega teach all the claimed features of claim 4, from which claim 6 depends. Vega further teaches:
The computing device of Claim 4, wherein the user interface indicates a predicted change in energy cost associated with performing the recommendation. Vega: Paragraph [0324] (“Display of the impact and alignment score for each available optimization opportunity based on consumer preferences”) Vega: Paragraph [0325] (“Interface to adjust selection criteria and recalculate weighted scores/re- rank optimization recommendations until satisfied”) Vega: Paragraph [0326] (“Interface to define and schedule personalized building modes”) Vega: Paragraph [0327] (“Interface to filter optimization recommendations by multiple criteria (e.g., cost, environmental footprint, changes to set points and schedules, etc.) ”) Vega: Paragraph [0328] (“Display and Side by side comparison of top optimization recommendations identified based on weighted scores calculated based on the customer's optimization criteria”) Vega: Paragraph [0329] (“Graphical comparison of the current condition and recommended optimization projected costs based on baseline historical and predicted consumption”) Vega: Paragraph [0330] (“Comparison of environmental impact of the current and recommended condition and recommended optimization projected costs based on baseline historical and predicted consumption”) Vega: Paragraph [0331] (“Interface to actuate the optimization recommendations”) Vega: Paragraph [0332] (“Interface to set the actuation mode for the optimization recommendations”) Vega: Paragraph [0333] (e.g. manual, automatic, hybrid), and to define automation settings, notifications and thresholds.”) [Displaying impact in cost based on recommendation reads on “the user interface indicates a predicted change in energy cost associated with performing the recommendation”.]
The motivation to combine Sullivan, Khan, and Vega as provided in independent claim 1 is incorporated herein.
Regarding claim 7, Sullivan, Khan, and Vega teach all the claimed features of claim 1, from which claim 7 depends. Khan further teaches:
The computing device of Claim 1, wherein the at least one processor is further configured to train the artificial intelligence model using the historical energy data. Khan: Paragraph [0089] (“The cloud tier 102 is shown as including a cloud system 110 and associated cloud applications 112 and remote services 114... As another example, the cloud applications 112 can include optimization applications that perform optimizations configured to reduce utility costs, energy usage, carbon emissions, or some combination thereof, for example subject to constraints that ensure occupant comfort, and provide control settings (e.g., zone temperature setpoints) to the equipment unit 106 as the output of such applications…”) Khan: Paragraph [0105] (“As another example, the cloud manager 340 is configured to train a neural network (or other machine learning or artificial intelligence model), for example on historical data of configuration, events, performance, etc. of the equipment unit 106 and/or other equipment units (e.g., similar equipment units serving similar buildings). The cloud manager 340 may provide the trained neural network to the edge manager 324.”)
The motivation to combine Sullivan and Khan as provided in independent claim 1 is incorporated herein.
Regarding claim 8, Sullivan, Khan, and Vega teach all the claimed features of claim 1, from which claim 8 depends. Vega further teaches:
The computing device of Claim 1, wherein the at least one processor is further configured to:
cause the user interface to prompt input of energy data by a user; receive an input of energy data by the user; and compute the energy score further based upon the input. Vega: Paragraph [0085] (“The present disclosure provides an energy analytics and optimization control system for use by an end-user for monitoring and controlling energy consumption, consisting of a processor; … a second memory for separately storing the preselected data from multiple sources that comprises historical energy usage data for preselected locations for the premises, … user preference, behavioral and schedule data for respective premises in the plurality of customer premises, … and user criteria preferences regarding optimizing consumption, and a user interface for interacting with a user to allow user inputs to the system and at least displaying results in a plurality of preselected formats from said processor processing said preselected data and analysis of the preselected data stored in said memories and from comparisons and combinations of those sets of data in common time periods, wherein the results comprise at least one of the following: comparisons of actual and historical energy usage in the same time period during different times, comparisons of energy usage in adjacent time periods, alternative representations of energy consumption for a preselected time period, energy consumption for preselected energy consumption devices for a preselected time period, determination of unintended energy consumption, efficiency of energy consumption calculated energy optimization score (e.g., Energy IQ), comparisons of energy usage for similar reference premises at the preselected locations for preselected time periods, baseline energy consumption including breakdowns for devices, periodic comparisons of baseline to actual consumption, listing of variances between baseline and actual usage, recommendations for correcting variances, recommendations for corrections of variances to reduce energy consumption to correct variances and reduce consumption, recommendations for adjustment in preference and schedule data for a user to control and reduce energy consumption and environmental impact to correct variances and reduce consumption.”) [The user inputs including user preference of energy usage reads on “cause the user interface to prompt input of energy data by a user; receive an input of energy data by the user”. In response, the results including the calculated energy optimization score reads on “compute the energy score further based upon the input”.]
The motivation to combine Sullivan and Khan as provided in independent claim 1 is incorporated herein.
Regarding independent claim 9, the claim recites similar limitations as corresponding claim 1 and is rejected using the same teachings and rationale.
Regarding claim 12, the claim recites similar limitations as corresponding claim 4 and is rejected using the same teachings and rationale.
Regarding claim 13, the claim recites similar limitations as corresponding claim 5 and is rejected using the same teachings and rationale.
Regarding claim 14, the claim recites similar limitations as corresponding claim 6 and is rejected using the same teachings and rationale.
Regarding claim 15, the claim recites similar limitations as corresponding claim 7 and is rejected using the same teachings and rationale.
Regarding claim 16, the claim recites similar limitations as corresponding claim 8 and is rejected using the same teachings and rationale.
Regarding independent claim 17, the functional limitations are similar as corresponding claim 1 and is rejected using the same teachings, rationale, and motivation.
In addition, Khan teaches:
At least one non-transitory computer-readable media having computer-executable instructions embodied thereon, wherein when executed by computing device including at least one processor and at least one memory device, the computer-executable instructions cause the at least one processor to:… Khan: Paragraph [0133] (“A processor also may be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. In some embodiments, particular processes and methods may be performed by circuitry that is specific to a given function. The memory (e.g., memory, memory unit, storage device) may include one or more devices (e.g., RAM, ROM, Flash memory, hard disk storage) for storing data and/or computer code for completing or facilitating the various processes, layers and modules described in the present disclosure. The memory may be or include volatile memory or non-volatile memory, and may include database components, object code components, script components, or any other type of information structure for supporting the various activities and information structures described in the present disclosure. According to an exemplary embodiment, the memory is communicably connected to the processor via a processing circuit and includes computer code for executing (e.g., by the processing circuit or the processor) the one or more processes described herein.”)
Regarding claim 20, the claim recites similar limitations as corresponding claim 4 and is rejected using the same teachings and rationale.
It is noted that any citations to specific paragraphs or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123.
Allowable Subject Matter
The subject matter of claims 2, 3, 10, 11, 18, and 19 is found to be allowable over the prior art of record and would be considered allowable pending the double patenting rejection given above.
While the prior art describes generating energy models for similar structures and in particular to systems and methods for modeling the consumption of energy (see Sullivan (US Patent Publication No. 2018/0089143 A1); Khan et al. (US Patent Publication No. 2023/0280771 A1); Vega et al. (US Patent Publication No. 2021/0123771 A1); Vega et al. (US Patent Publication No. 2021/0125253 A1); Yu et al. (US Patent Publication No. 2017/0242938 A1); Shklovskii et al. (US Patent Publication No. 2012/0216123 A1); and Haghighat-Kashani et al. (US Patent Publication No. 2015/0268281 A1)) the prior art, individually or combined, does not teach or suggest “identify, using the artificial intelligence model, one or more devices present in the home; determine an energy usage associated with each of the one or more devices; and compute the energy score based further upon the determined energy usage associated with each of the one or more devices,” as recited in claim 2 and similarly recited in claims 10 and 18. It is this concept that defines the present application over the prior art of record.
In view of their dependencies to an allowable claim, claims 3, 11, and 19 are found to be allowable over prior art.
As allowable subject matter has been indicated, applicant's reply must either comply with all formal requirements or specifically traverse each requirement not complied with. See 37 CFR 1.111(b) and MPEP § 707.07(a).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Haghighat-Kashani (US Patent Publication No. 2015/0268281 A1) describes in Paragraph [0066] (“Referring to FIG. 12, a process is shown of a gamified electricity consumption monitor running as an app on a user device…In step 306, the system, since it can be connected to multiple separate locations, retrieves electricity consumption levels from peers of the user, a peer being either literal or a user with a similar home, or a neighbor, or someone in the same city, for example. In step 308, the system 10 calculates a ranking and/or score of the user's electricity consumption compared to the consumption of the peers. Better scores or rankings will be calculated for lower electricity consumptions. In step 310, the results of the ranking and/or scoring are displayed on the user's smart phone. Rankings and/or scores may be based on real-time electricity consumption, average consumption, minimum consumption and/or baseload. There are also other ways in which scoring or ranking may be implemented. Calculating a score may be synonymous with calculating a ranking. The score and/or ranking may be updated as the user walks around the location unplugging various devices or powering them down, and as such the process may loop back to step 302 repeatedly.”)
Vega et al. (US Patent Publication No. 2021/0125253 A1) describes a system 100 for end-user energy analytics and optimization that is useful for selecting a utility energy supplier, having at least one processor and an associated instruction memory; at least one memory storage device configured to store: (i) historical energy usage data for a premises (facility), (ii) historical weather data for the zone associated with the premises (facility), (iii) data for unique and variable premises energy characteristics, … an analytics and computation engine executed by said at least one processor using a first portion of instructions stored in said associated instruction memory for performing: (i) conversion of and storing of historical energy usage data for a premises, (ii) statistical analysis of, aggregation of and disaggregation of said historical energy usage data, (iii) statistical analysis of historical weather data associated with historical energy usage data, (iv) machine learning and employing artificial intelligence models to identify data clustering, outliers and other data driven insights and incorporate ongoing feedback to the analysis, (v) time slice synchronization of selected portions of said data stored in said at least one memory storage device, (vi) analyzing said data for energy consumption by one or more energy devices associated with said premises, (vii) computation of energy costs using said converted and stored historical energy usage data, …(x) providing alternative representations of energy usage data associated with a source of energy for said premises, and (xi) determining/providing recommendations for available energy reduction choices; a display engine executed by said at least one processor using a second portion of instructions stored in said associated instruction memory for: (i) receiving end-user goals, lifestyle behaviors, and premises information and occupation data, (ii) displaying synchronized time slice data in one or more pre-selected formats, (iii) displaying alternative representations of energy usage data associated with a source of energy for said premises, (iv) displaying recommendations for available energy reduction choices, (v) displaying energy consumption for said energy devices associated with said premises, (vi) displaying rankings of computed electricity utility or supplier plans, and (vii) displaying and alerting an end-user of variances in energy use based on one or more of selected set points, excessive usage, and unintentional usage.”)
Shklovskii et al. (US Patent Publication No. 2012/0216123 A1) describes in Paragraph [0021] (“…an online energy audit system and method is provided to pose and collect responses to a list of survey questions regarding a subject house via a survey UI from a remote occupant. The survey responses are stored in a subject-home energy-use profile associated with the subject house and are used to populate model inputs to an energy-use software model, from which an energy-efficiency score is derived. To help a remote occupant choose the appropriate answers and facilitate the completion of the survey, the survey UI includes question-specific house-feature images associated with each question. In addition, the survey questions are designed to be simple and easy-to-understand, and the survey is kept as short as practicable. An energy-efficiency score of the subject house is presented to the remote occupant in comparison with energy-use data for similar houses, together with an action message to encourage the remote occupant to improve the energy score of the subject house.”)
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/ALICIA M. CHOI/Primary Patent Examiner, Art Unit 2117
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