DETAILED ACTION
The following is a Final Office action. In response to Examiner’s communication of 12/2/25, Applicant, on 2/27/26, presented additional arguments for consideration. Claims 1-13 and 22-28 are pending in this application and have been rejected below.
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 .
Response to Amendment
Applicant’s amendments are acknowledged.
The 35 USC § 103 rejections of claims 1-13 and 22-28 are maintained in light of Applicant’s explanations.
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-9 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication Number 2015/0268281 to Haghighat-Kashani (hereafter referred to as Haghighat-Kashani) in view of U.S. Patent Application Publication 2010/0286937 to Hedley et al. (hereafter referred to as Hedley).
As per claim 1, Haghighat-Kashani teaches:
A method of managing a power distribution system comprising: monitoring. via one or more first facility electric meters. the energy consumption of a first facility associated with an enterprise: (Paragraph Number [0027] teaches the electrical wiring in buildings has been likened to a nervous system that connects all electronics, including electrical devices, to a central place such as the breaker panel or the meter box. The system described herein introduces artificial intelligence to all existing electronic devices by monitoring the electricity patterns of the building's electrical network. Paragraph Number [0030] teaches the system described herein may be configured, in at least one example, to gather electricity data relating to a building or premises, including energy used, real power usage, reactive power usage, power factor, current, and voltage. This information can be obtained from one or multiple sensors installed across the electrical network. One way to implement this is to place a sensor inside the breaker panel to monitor the main electrical lines entering the premises. Another way would be to utilize smart metering infrastructure that exists in many households. There could also be sensors placed at one or more individual plugs. The system may report total aggregate information, as well as individual phase data, or individual plug data, depending on the setup).
transmitting. via the one or more first facility electric meters. first facility energy consumption data to a meter data system. the meter data system including one or more servers: (Paragraph Number [0007] teaches various load disaggregation algorithms have been suggested in the literature. One technique of disaggregating the power signal measured at the incoming power meter into its constituent individual loads is known as Single Point End-use Energy Disaggregation (SPEED.TM.), and is available from Enetics, Inc. of New York. The SPEED.TM. product includes logging a premises' load data and then transferring the data via telephone, walk-ups, or alternative communications to a master station that processes the recorded data into individual load data, and acts as a server and database manager for pre- and post-processed energy consumption data, temperature data, queries from analysis stations, and queries from other information systems).
monitoring. via one or more second facility electric meters. the energy consumption of a second facility associated with the enterprise: (Paragraph Number [0027] teaches the electrical wiring in buildings has been likened to a nervous system that connects all electronics, including electrical devices, to a central place such as the breaker panel or the meter box. The system described herein introduces artificial intelligence to all existing electronic devices by monitoring the electricity patterns of the building's electrical network. Paragraph Number [0030] teaches the system described herein may be configured, in at least one example, to gather electricity data relating to a building or premises, including energy used, real power usage, reactive power usage, power factor, current, and voltage. This information can be obtained from one or multiple sensors installed across the electrical network. One way to implement this is to place a sensor inside the breaker panel to monitor the main electrical lines entering the premises. Another way would be to utilize smart metering infrastructure that exists in many households. There could also be sensors placed at one or more individual plugs. The system may report total aggregate information, as well as individual phase data, or individual plug data, depending on the setup. Paragraph Number [0047] teaches to an end-user, the monitoring and intelligence capability described here brings together a user's device experience into a single platform, which he can access through a variety of interfaces described earlier in order to observe the devices and manage the experience. Therefore, this technology provides a homepage for locations such as homes or offices. The single platform may be a central application for the occupants of a given location, allowing them to observe and manage their experience with the host of electronic devices present. Such a central application unifies the management of both smart and non-smart devices. (Examiner asserts that the plurality of locations mentioned indicates the monitoring capability of more than a single location)).
transmitting. via the one or more second facility electric meters. second facility energy consumption data to the meter data system: (Paragraph Number [0007] teaches various load disaggregation algorithms have been suggested in the literature. One technique of disaggregating the power signal measured at the incoming power meter into its constituent individual loads is known as Single Point End-use Energy Disaggregation (SPEED.TM.), and is available from Enetics, Inc. of New York. The SPEED.TM. product includes logging a premises' load data and then transferring the data via telephone, walk-ups, or alternative communications to a master station that processes the recorded data into individual load data, and acts as a server and database manager for pre- and post-processed energy consumption data, temperature data, queries from analysis stations, and queries from other information systems. Paragraph Number [0047] teaches to an end-user, the monitoring and intelligence capability described here brings together a user's device experience into a single platform, which he can access through a variety of interfaces described earlier in order to observe the devices and manage the experience. Therefore, this technology provides a homepage for locations such as homes or offices. The single platform may be a central application for the occupants of a given location, allowing them to observe and manage their experience with the host of electronic devices present. Such a central application unifies the management of both smart and non-smart devices. (Examiner asserts that the plurality of locations mentioned indicates the monitoring capability of more than a single location)).
receiving. via the meter data system. the first facility energy consumption data and the second facility energy consumption data. the first facility energy consumption data and the second facility energy consumption data (Paragraph Number [0047] teaches to an end-user, the monitoring and intelligence capability described here brings together a user's device experience into a single platform, which he can access through a variety of interfaces described earlier in order to observe the devices and manage the experience. Therefore, this technology provides a homepage for locations such as homes or offices. The single platform may be a central application for the occupants of a given location, allowing them to observe and manage their experience with the host of electronic devices present. Such a central application unifies the management of both smart and non-smart devices. (Examiner asserts that the plurality of locations mentioned indicates the monitoring capability of more than a single location)).
each including receiving parameters related to the consumption of energy at the respective facilities (Paragraph Number [0066] teaches a process is shown of a gamified electricity consumption monitor running as an app on a user device. In step 300, the system 10 determines the power or electricity consumption of a location, such as a user's home. The consumption may be the real-time consumption, an average consumption, a minimum consumption or a baseload consumption. In step 302, the system displays the electricity consumption via a user interface 30, such as a user interface of a user's smart phone. In step 304, which may be optional, the app outputs an audible and/or visible message that instructs the user to switch off or power down devices in the user's home. 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).
based on the received parameters, calculating. via one or more processors associated with the one or more servers of the meter data system. a grading index for each facility (Paragraph Number [0066] teaches 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).
the grading index including an energy efficiency value for each facility (Paragraph Number [0064] teaches to further encourage users to educate themselves using this tool as well as to make the information more meaningful to them, this process can be gamified by introducing comparable measurements from other users. For example, a user can be presented with their ranking in their community in terms of how efficient their baseload is (i.e. baseload is the amount of energy consumed when home is at rest and only always-on devices remain powered). Besides the baseload value, a scoring and leaderboard approach can be applied to other measurements such as the home's minimum power usage in a given period of time, the home's average energy usage in a given amount of time, etc. Paragraph Number [0065] teaches one specific implementation of a gamified educational tool, for understanding how energy is used at home, is an application that displays the real-time power and the minimum power ever achieved. The users are then instructed to walk around the home and turn off all lights and appliances, then unplug remaining devices, and continue until the power draw reaches the smallest possible number. Their minimum power score is compared against that of other users in real-time to put their home's energy efficiency in the context of other homes. Through this process, users are empowered to identify devices that use more power than they expected, or draw power while they're off).
normalizing the energy efficiency value for each facility with respect to one or more of facility size and occupancy (Paragraph Number [0064] teaches to further encourage users to educate themselves using this tool as well as to make the information more meaningful to them, this process can be gamified by introducing comparable measurements from other users. For example, a user can be presented with their ranking in their community in terms of how efficient their baseload is (i.e. baseload is the amount of energy consumed when home is at rest and only always-on devices remain powered). Besides the baseload value, a scoring and leaderboard approach can be applied to other measurements such as the home's minimum power usage in a given period of time, the home's average energy usage in a given amount of time, etc. Paragraph Number [0065] teaches one specific implementation of a gamified educational tool, for understanding how energy is used at home, is an application that displays the real-time power and the minimum power ever achieved. The users are then instructed to walk around the home and turn off all lights and appliances, then unplug remaining devices, and continue until the power draw reaches the smallest possible number. Their minimum power score is compared against that of other users in real-time to put their home's energy efficiency in the context of other homes. Through this process, users are empowered to identify devices that use more power than they expected, or draw power while they're off).
ranking. via the meter data system. the facilities based on the calculated grading index (Paragraph Number [0064] teaches to further encourage users to educate themselves using this tool as well as to make the information more meaningful to them, this process can be gamified by introducing comparable measurements from other users. For example, a user can be presented with their ranking in their community in terms of how efficient their baseload is (i.e. baseload is the amount of energy consumed when home is at rest and only always-on devices remain powered). Besides the baseload value, a scoring and leaderboard approach can be applied to other measurements such as the home's minimum power usage in a given period of time, the home's average energy usage in a given amount of time, etc. Paragraph Number [0065] teaches one specific implementation of a gamified educational tool, for understanding how energy is used at home, is an application that displays the real-time power and the minimum power ever achieved. The users are then instructed to walk around the home and turn off all lights and appliances, then unplug remaining devices, and continue until the power draw reaches the smallest possible number. Their minimum power score is compared against that of other users in real-time to put their home's energy efficiency in the context of other homes. Through this process, users are empowered to identify devices that use more power than they expected, or draw power while they're off).
predicting. via the meter data system. a positive expected result in response to improving at least one grading value of the grading index of one of the facilities: (Paragraph Number [0046] teaches the technology described herein may be used to observe existing (non-smart as well as smart) devices within location, and additionally, by sharing the knowledge obtained from this process, to introduce artificial intelligence to devices. The intelligence leads to timely notifications and alerts to users, and seamless adjustments to the device states (for devices with connectivity) based on user behavior, previous or current actions, and predicted desires. Paragraph Number [0061] teaches analyses can be used for a number of services. First, they can be used to offer users targeted advertising. Leads can be created for services and products, and presented to users through the variety of user interfaces listed above (e.g., mobile, web, wearable, etc.). The products and services may relate to what is used by users within the location, or be relevant to them as predicted by their general demographic and predicted interest. For example, a user with an old fridge may be provided with promotions for a new energy saving fridge. This is shown in FIG. 11, where in step 210, the system detects that a particular device is old, either by determining that it consumes significantly more energy than currently available fridges, by detecting one or more malfunctions, by determining that its energy consumption has steadily increased over time, or by having recorded how long the fridge has been in service. In step 212, the system 10 provides targeted ads to the user interface that relate to offerings of a new, replacement device. As another example, a user with many connected devices may be presented with ads for a new internet service; and all users can be presented with contact information of service providers and tradesmen such as electricians, carpenters, plumbers, etc. based on a variety of observations and information obtained about the users and the location).
outputting, via the meter data system, one or more reports to a client device. the one more reports including the at least one grading value (Paragraph Number [0030] teaches the system described herein may be configured, in at least one example, to gather electricity data relating to a building or premises, including energy used, real power usage, reactive power usage, power factor, current, and voltage. This information can be obtained from one or multiple sensors installed across the electrical network. One way to implement this is to place a sensor inside the breaker panel to monitor the main electrical lines entering the premises. Another way would be to utilize smart metering infrastructure that exists in many households. There could also be sensors placed at one or more individual plugs. The system may report total aggregate information, as well as individual phase data, or individual plug data, depending on the setup. Paragraph Number [0066] teaches referring to FIG. 12, a process is shown of a gamified electricity consumption monitor running as an app on a user device. In step 300, the system 10 determines the power or electricity consumption of a location, such as a user's home. The consumption may be the real-time consumption, an average consumption, a minimum consumption or a baseload consumption. In step 302, the system displays the electricity consumption via a user interface 30, such as a user interface of a user's smart phone. In step 304, which may be optional, the app outputs an audible and/or visible message that instructs the user to switch off or power down devices in the user's home).
executing, via the one or more processors, at least one machine learning algorithm to process the received parameters and the historical readings stored in the data library (Paragraph Number [0044] teaches the tools used to match new signatures against existing models and libraries include statistical analysis as well as machine learning. The learning capabilities in the system enables the addition of artificial intelligence to existing non-smart devices, as well as to new smart ones. A self-learning home, for instance, can adjust itself to user needs, like adjusting lighting and temperature as soon as the garage door is opened and its signature detected by this system. [0046] The technology described herein may be used to observe existing (non-smart as well as smart) devices within location, and additionally, by sharing the knowledge obtained from this process, to introduce artificial intelligence to devices. The intelligence leads to timely notifications and alerts to users, and seamless adjustments to the device states (for devices with connectivity) based on user behavior, previous or current actions, and predicted desires. Paragraph Number [0055] teaches the monitoring and artificial intelligence capabilities in this presented system can transform the collection of electronics in a given location to become aware of each other’s state and of the occupants' actions, habits, and desires).
and output at least one prediction of energy usage in a predetermined future time interval based on the received parameters and the historical readings (Paragraph Number [0055] teaches the monitoring and artificial intelligence capabilities in this presented system can transform the collection of electronics in a given location to become aware of each other’s state and of the occupants' actions, habits, and desires. For instance, a smart coffee maker can receive a notification every morning right before the users are expected to wake up, if the users are observed to brew coffee every morning. This is shown in FIG. 8, where in step 180 the system 10 determines a pattern of usage of a particular device. Following this, in step 182, the system 10 sends an advance notification to the particular device, informing it to switch on. Paragraph Number [0052] teaches the sensing and analytics presented here can be used to manage multiple energy sources such as homes that have solar panels, storage batteries, EV (electric vehicle) batteries, as well as the grid. The system 10 can be used to decide, based on consumption patterns, available energy and generation potential, when the best times are to charge batteries or draw from them. The system 10 can also be used to decide when solar generation should be output to the grid and when to use the grid for consumption and battery charging. The system 10 can be used for providing solar consumers with intelligence on how their electricity consumption compares to their electricity generation, and intelligence on how to optimize their electricity network to pull energy from the most cost-efficient source at a given time. Paragraph Number [0053] teaches also the monitoring and management of these sources can also benefit energy trading markets by controlling the grid at a micro level to optimize supply and demand).
Haghighat-Kashani teaches grading a facility based on its consumption of energy and determining a future action that will improve its grade with respect to other facilities but does not explicitly teach storing historical information in tables and using that information to provide control signals to balance loads as described by the following citations from Hedley:
storing the received parameters as historical readings in a data library in at least one table (Paragraph Number [0139] teaches the system EEMS solution will meet the company minimum requirements and offers an even better data-collection capability. The system may collect real-time data, which may be defined as having a one-minute interval. The real time collection interval is adjustable, e.g., up to a 15-minute interval or other interval. The table below lists a specific data collection and frequency schedule example. (See Table 00001) Paragraph Number [0520] teaches a table showing monthly date in any desired (e.g., mm/yyyy) format, kWh consumption, kBTU consumption, monthly CDD, monthly HDD, monthly CDD 2, Log CDD, Log HDD, HDD 2, monthly average Occupancy, monthly average Relative Humidity, monthly average Wind Speed, monthly average Global Solar Radiation, or other variables; and Paragraph Number [0521] teaches a table which shows a summary of the top regression outputs, including R 2, Significance F, Regression Equation, Intercept and coefficients for each independent variable for each regression output).
in accordance with a time series optimization scheme, wherein the time series optimization scheme includes (Paragraph Number [0522] teaches the machine 2500 may additionally or alternatively include comparison logic 2544 (e.g., as one of the energy analysis programs 2524). The comparison logic 2544 may include instructions that when executed by the processor 2502 cause the processor 2502 to perform a kilowatt hour consumption and exception rank analysis, for example. One example of a comparison analysis 2700 that results from the comparison logic 2544 is shown in FIG. 27. The comparison analysis 2700 extends in 30 minute intervals over an entire day for a particular building under analysis, but the comparison logic 2544 may perform analyses over shorter or longer time periods at different intervals. Furthermore, the comparison may be done with respect to a single building (e.g., to compare energy consumption data historically for the building), or with respect to multiple buildings (e.g., to compare a building under analysis to a different control building). In addition, there may be multiple buildings in a control building group that each contribute energy data for defining (e.g., by averaging or according to another statistical treatment) the control building data described below. (See Table 00007 scenario 3 teaching optimization of schedules and energy)).
at least one indexed table for each of the stored at least one first table of historical readings (Paragraph Number [0139] teaches the system EEMS solution will meet the company minimum requirements and offers an even better data-collection capability. The system may collect real-time data, which may be defined as having a one-minute interval. The real time collection interval is adjustable, e.g., up to a 15-minute interval or other interval. The table below lists a specific data collection and frequency schedule example. (See Table 00001) Paragraph Number [0520] teaches a table showing monthly date in any desired (e.g., mm/yyyy) format, kWh consumption, kBTU consumption, monthly CDD, monthly HDD, monthly CDD 2, Log CDD, Log HDD, HDD 2, monthly average Occupancy, monthly average Relative Humidity, monthly average Wind Speed, monthly average Global Solar Radiation, or other variables; and Paragraph Number [0521] teaches a table which shows a summary of the top regression outputs, including R 2, Significance F, Regression Equation, Intercept and coefficients for each independent variable for each regression output).
each indexed table tracks one selected field of a particular value measured and associated timestamps in the at least one first table such that retrieval of the particular value over a period of time does not look at every record in the at least one first table (Paragraph Number [0522] teaches the machine 2500 may additionally or alternatively include comparison logic 2544 (e.g., as one of the energy analysis programs 2524). The comparison logic 2544 may include instructions that when executed by the processor 2502 cause the processor 2502 to perform a kilowatt hour consumption and exception rank analysis, for example. One example of a comparison analysis 2700 that results from the comparison logic 2544 is shown in FIG. 27. The comparison analysis 2700 extends in 30 minute intervals over an entire day for a particular building under analysis, but the comparison logic 2544 may perform analyses over shorter or longer time periods at different intervals. Furthermore, the comparison may be done with respect to a single building (e.g., to compare energy consumption data historically for the building), or with respect to multiple buildings (e.g., to compare a building under analysis to a different control building). In addition, there may be multiple buildings in a control building group that each contribute energy data for defining (e.g., by averaging or according to another statistical treatment) the control building data described below).
and generating, via the one or more processors, at least one control signal based on the at least one prediction of energy usage and outputting the at least one control signal to implement load balancing in at least one of the first facility and the second facility (Paragraph Number [0075] teaches at the building (e.g., company facility): a mediator 1102 (e.g., a Richards-Zeta Mediator 2500) that provides a connectivity interface for connecting to one or more meters and sub-systems in a facility. The meters may include utility meters as well as any Building Automation System (BAS), lighting or security control system, or other systems. Paragraph Number [0080] teaches the system 100 energy management data services offer a unique approach at delivering a comprehensive view of a facility's operations. The system 100 may implement continuous optimized control through real time/interval data acquisition and analysis of all relevant facility data. The System Enterprise Energy Management System (EEMS) may include or involve: (1) a physical site assessment, (2) historical utility bill analysis, (3) utility meter interval data analysis, (4) holistic facility controls analysis, (5) real-time automated equipment fault detection and (6) energy sourcing and demand-response energy management. From these inputs the System energy management system generates insight in the form of reports, dashboards, and alerts that provide actionable information that leads to realized energy reduction and cost savings. Paragraph Number [0086] teaches the mediator 1102 provides for bi-directional (read/write) capability with any integrated system. This facilitates for 24/7 continuous optimized control of the systems connected to it. The mediator 1102 has an intelligence layer allowing for full closed loop advanced math and logic between any of the previously disparate systems. The mediator 1102 also sends and consumes Web services. An example Web service would be an Automated Demand Response (ADR) notice and pricing level signal from Constellation New Energy (CNE) triggering the mediator 1102 on board logic and control to automatically shed electrical loads by turning off non-essential lighting and changing set points on chilled water and HVAC zones).
Both Haghighat-Kashani and Hedley are directed to power monitoring and optimization. Haghighat-Kashani discloses grading a facility based on its consumption of energy and determining a future action that will improve its grade with respect to other facilities. Hedley improves upon Haghighat-Kashani by disclosing storing historical information in tables and using that information to provide control signals to balance loads. One of ordinary skill in the art would be motivated to further include storing historical information in tables and using that information to provide control signals to balance loads, to efficiently monitor and control a power supply so as to be optimized based on prior data and current needs. Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method of grading a facility based on its consumption of energy and determining a future action that will improve its grade with respect to other facilities in Haghighat-Kashani to further utilize storing historical information in tables and using that information to provide control signals to balance loads as disclosed in Hedley, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 2, the combination of Haghighat-Kashani and Hedley teaches each of the limitations of claim 1.
In addition, Haghighat-Kashani teaches:
wherein the one or more reports includes the positive expected result (Paragraph Number [0050] teaches using the technology described above, users can be provided with energy management features that display household energy use, break it down by individual devices and behaviors, compare it against other users, and provide tips and relevant content on managing energy. For instance, when an AC (air conditioner) is left on, the user can be notified to take action to preserve energy and costs. Paragraph Number [0065] teaches one specific implementation of a gamified educational tool, for understanding how energy is used at home, is an application that displays the real-time power and the minimum power ever achieved. The users are then instructed to walk around the home and turn off all lights and appliances, then unplug remaining devices, and continue until the power draw reaches the smallest possible number. Their minimum power score is compared against that of other users in real-time to put their home's energy efficiency in the context of other homes. Through this process, users are empowered to identify devices that use more power than they expected, or draw power while they're off).
As per claim 3, the combination of Haghighat-Kashani and Hedley teaches each of the limitations of claim 1.
In addition, Haghighat-Kashani teaches:
wherein the one or more reports includes a comparison of the facilities with respect to the grading index. (Paragraph Number [0050] teaches using the technology described above, users can be provided with energy management features that display household energy use, break it down by individual devices and behaviors, compare it against other users, and provide tips and relevant content on managing energy. For instance, when an AC (air conditioner) is left on, the user can be notified to take action to preserve energy and costs. Paragraph Number [0066] teaches a process is shown of a gamified electricity consumption monitor running as an app on a user device. In step 300, the system 10 determines the power or electricity consumption of a location, such as a user's home. The consumption may be the real-time consumption, an average consumption, a minimum consumption or a baseload consumption. In step 302, the system displays the electricity consumption via a user interface 30, such as a user interface of a user's smart phone).
As per claim 4, the combination of Haghighat-Kashani and Hedley teaches each of the limitations of claim 1.
In addition, Haghighat-Kashani teaches:
wherein the one or more reports includes one or more of a comparison of a plurality of electric circuits at a selected facility and energy usage details of one or more electric circuits at the selected facility. (Paragraph Number [0050] teaches using the technology described above, users can be provided with energy management features that display household energy use, break it down by individual devices and behaviors, compare it against other users, and provide tips and relevant content on managing energy. For instance, when an AC (air conditioner) is left on, the user can be notified to take action to preserve energy and costs. Paragraph Number [0066] a process is shown of a gamified electricity consumption monitor running as an app on a user device. In step 300, the system 10 determines the power or electricity consumption of a location, such as a user's home. The consumption may be the real-time consumption, an average consumption, a minimum consumption or a baseload consumption. In step 302, the system displays the electricity consumption via a user interface 30, such as a user interface of a user's smart phone. Paragraph Number [0065] teaches a gamified educational tool, for understanding how energy is used at home, is an application that displays the real-time power and the minimum power ever achieved. The users are then instructed to walk around the home and turn off all lights and appliances, then unplug remaining devices, and continue until the power draw reaches the smallest possible number. Their minimum power score is compared against that of other users in real-time to put their home's energy efficiency in the context of other homes. Through this process, users are empowered to identify devices that use more power than they expected, or draw power while they're off).
As per claim 5, the combination of Haghighat-Kashani and Hedley teaches each of the limitations of claim 1.
In addition, Haghighat-Kashani teaches:
wherein the step of receiving the parameters includes each of the first facility electric meters are configured to measure energy usage with respect to one or more electric circuits. (Paragraph Number [0030] teaches the system described herein may be configured, in at least one example, to gather electricity data relating to a building or premises, including energy used, real power usage, reactive power usage, power factor, current, and voltage. This information can be obtained from one or multiple sensors installed across the electrical network. One way to implement this is to place a sensor inside the breaker panel to monitor the main electrical lines entering the premises. Another way would be to utilize smart metering infrastructure that exists in many households. There could also be sensors placed at one or more individual plugs. The system may report total aggregate information, as well as individual phase data, or individual plug data, depending on the setup).
As per claim 6, the combination of Haghighat-Kashani and Hedley teaches each of the limitations of claims 1 and 5.
In addition, Haghighat-Kashani teaches:
wherein the energy usage information includes one or more of voltage information, current information, and frequency information (Paragraph Number [0030] teaches the system described herein may be configured, in at least one example, to gather electricity data relating to a building or premises, including energy used, real power usage, reactive power usage, power factor, current, and voltage. This information can be obtained from one or multiple sensors installed across the electrical network. One way to implement this is to place a sensor inside the breaker panel to monitor the main electrical lines entering the premises. Another way would be to utilize smart metering infrastructure that exists in many households. There could also be sensors placed at one or more individual plugs. The system may report total aggregate information, as well as individual phase data, or individual plug data, depending on the setup).
As per claim 7, the combination of Haghighat-Kashani and Hedley teaches each of the limitations of claim 1.
Haghighat-Kashani teaches grading a facility based on its consumption of energy and determining a future action that will improve its grade with respect to other facilities but does not explicitly teach normalizing degree days as described by the following citations from Hedley:
normalizing further includes normalizing for degree days by comparing how much the outdoor temperature differs from a base temperature at a given location. (Paragraph Number [0157] teaches calculations range from simple (e.g., Delta T) to complex (regression-based Baseline Energy Model normalized for weather and occupancy patterns). The EEMS may support industry standards such as indices for thermal comfort (ASHRAE 55-2004) and minimum ventilation requirements (ASHRAE 62.1-2007). The ventilation index is an example where the system first will integrate data from external systems (space planning data for room sizes and uses). Paragraph Number [0497] teaches the balance points may include a Heating Balance Point (HBP) 2530 and a Cooling Balance Point (CBP) 2532 to help identify the number of heating degree days (HDDs) and cooling degree days (CDDs). The HBP 2530 may be interpreted as the temperature above which the building is not heating, while the CBP 2532 may be interpreted as the temperature below which the building is not cooling. In one model, the building is neither heating nor cooling between the HBP 2530 and the CBP 2532. Thus, identifying both the HBP 2530 and CBP 2532 may significantly increase the accuracy of the count of the number of HDDs and CDDs for the building, particularly as compared to finding a single balance point for both heating and cooling, or compared to assuming a standard and usually inaccurate balance point (e.g., 65 degrees F.)).
A person with ordinary skill would have been motivated to combine these references as described in regard to claim 1.
As per claim 8, the combination of Haghighat-Kashani and Hedley teaches each of the limitations of claims 1 and 7.
Haghighat-Kashani teaches grading a facility based on its consumption of energy and determining a future action that will improve its grade with respect to other facilities but does not explicitly teach normalizing degree days as described by the following citations from Hedley:
wherein degree days include heating degree days and cooling degree days. (Paragraph Number [0497] teaches the balance points may include a Heating Balance Point (HBP) 2530 and a Cooling Balance Point (CBP) 2532 to help identify the number of heating degree days (HDDs) and cooling degree days (CDDs). The HBP 2530 may be interpreted as the temperature above which the building is not heating, while the CBP 2532 may be interpreted as the temperature below which the building is not cooling. In one model, the building is neither heating nor cooling between the HBP 2530 and the CBP 2532. Thus, identifying both the HBP 2530 and CBP 2532 may significantly increase the accuracy of the count of the number of HDDs and CDDs for the building, particularly as compared to finding a single balance point for both heating and cooling, or compared to assuming a standard and usually inaccurate balance point (e.g., 65 degrees F.)).
A person with ordinary skill would have been motivated to combine these references as described in regard to claim 1.
As per claim 9, the combination of Haghighat-Kashani and Hedley teaches each of the limitations of claims 1 and 7.
In addition, Haghighat-Kashani teaches:
wherein the step of predicting the positive expected result includes predicting a potential cost savings value (Paragraph Number [0046] teaches the technology described herein may be used to observe existing (non-smart as well as smart) devices within location, and additionally, by sharing the knowledge obtained from this process, to introduce artificial intelligence to devices. The intelligence leads to timely notifications and alerts to users, and seamless adjustments to the device states (for devices with connectivity) based on user behavior, previous or current actions, and predicted desires. Paragraph Number [0061] teaches such analyses can be used for a number of services. First, they can be used to offer users targeted advertising. Leads can be created for services and products, and presented to users through the variety of user interfaces listed above (e.g., mobile, web, wearable, etc.). The products and services may relate to what is used by users within the location, or be relevant to them as predicted by their general demographic and predicted interest. For example, a user with an old fridge may be provided with promotions for a new energy saving fridge. This is shown in FIG. 11, where in step 210, the system detects that a particular device is old, either by determining that it consumes significantly more energy than currently available fridges, by detecting one or more malfunctions, by determining that its energy consumption has steadily increased over time, or by having recorded how long the fridge has been in service. In step 212, the system 10 provides targeted ads to the user interface that relate to offerings of a new, replacement device. As another example, a user with many connected devices may be presented with ads for a new internet service; and all users can be presented with contact information of service providers and tradesmen such as electricians, carpenters, plumbers, etc. based on a variety of observations and information obtained about the users and the location).
As per claim 13, the combination of Haghighat-Kashani and Hedley teaches each of the limitations of claim 1.
In addition, Haghighat-Kashani teaches:
wherein the step of predicting the positive expected result includes using an Artificial Intelligence (Al) function (Paragraph Number [0027] teaches the electrical wiring in buildings has been likened to a nervous system that connects all electronics, including electrical devices, to a central place such as the breaker panel or the meter box. The system described herein introduces artificial intelligence to all existing electronic devices by monitoring the electricity patterns of the building's electrical network. Paragraph Number [0044] teaches the tools used to match new signatures against existing models and libraries include statistical analysis as well as machine learning. The learning capabilities in the system enables the addition of artificial intelligence to existing non-smart devices, as well as to new smart ones. A self-learning home, for instance, can adjust itself to user needs, like adjusting lighting and temperature as soon as the garage door is opened and its signature detected by this system).
Claims 10-12, 22, 23, and 26-28 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication Number 2015/0268281 to Haghighat-Kashani (hereafter referred to as Haghighat-Kashani) in view of U.S. Patent Application Publication 2010/0286937 to Hedley et al. (hereafter referred to as Hedley) and in further view of U.S. Patent Application Publication Number 2003/0042794 to Jarrett, JR (hereafter referred to as Jarrett, JR).
As per claim 10, the combination of Haghighat-Kashani and Hedley teaches each of the limitations of claim 1.
Haghighat-Kashani teaches grading a facility based on its consumption of energy and determining a future action that will improve its grade with respect to other facilities but does not explicitly teach determining a risk factor related to power quality as described by the following citations from Jarrett, JR:
wherein the step of calculating the grading index includes calculating a risk factor for each facility, the risk factor related to power quality issues (Paragraph Number [0012] teaches prior to executing the transfer of loads to the generator, the control logic may provide a logic signal that "notifies" the loads of an upcoming power disturbance during the transfer process. For example, if the load is an air conditioning system, the control logic may open a relay in the thermostat circuit, allowing air conditioning blowers and pumps to come to a halt under their standard timing. This avoids the likelihood of inducing power spikes into the equipment during the transfer process. These power quality problems at transfer time, especially in "open transition" transfer switches represent one of the primary impediments to the use of standby power generators in load reduction programs. That is, the potential for financial loss due to equipment damage by power surges may seem to be a greater risk than the value of the savings in power costs associated with the load reduction program incentives. Thus, the ability to "power down" loads prior to transfer is an important aspect of the present invention. Paragraph Number [0047] teaches these power quality problems at transfer time, especially in "open transition" transfer switches represent one of the primary impediments to the use of standby power generators 15 in load reduction programs. Thus, the potential for financial loss due to equipment damage by power surges may seem to be a greater risk than the value of the savings in power costs associated with the load reduction program incentives. Thus, the ability to "power down" loads prior to transfer is an important aspect of the present invention. Paragraph Number [0052] teaches methods and apparatus have been disclosed that make existing standby power available for use in load reduction programs to assist utilities in avoiding power shortages and the resultant blackouts).
Both the combination of Haghighat-Kashani and Hedley and Jarrett, JR are directed to power monitoring and optimization. The combination of Haghighat-Kashani and Hedley discloses grading a facility based on its consumption of energy and determining a future action that will improve its grade with respect to other facilities. Jarrett, JR improves upon the combination of Haghighat-Kashani and Hedley by disclosing determining a risk factor related to power quality. One of ordinary skill in the art would be motivated to further include determining a risk factor related to power quality, to efficiently take into consideration for ranking purposes whether the power flow is consistent and sufficient to meet the power needs of a facility. Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method of grading a facility based on its consumption of energy and determining a future action that will improve its grade with respect to other facilities in the combination of Haghighat-Kashani and Hedley to further utilize determining a risk factor related to power quality as disclosed in Jarrett, JR, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 11, the combination of Haghighat-Kashani, Hedley, and Jarrett, JR teaches each of the limitations of claims 1 and 10.
Haghighat-Kashani teaches grading a facility based on its consumption of energy and determining a future action that will improve its grade with respect to other facilities but does not explicitly teach determining a risk factor related to power quality as described by the following citations from Jarrett, JR:
wherein the risk factor is related to one or more of a number of voltage surges, a number of voltage transients, voltage harmonics, and current harmonics. (Paragraph Number [0012] teaches prior to executing the transfer of loads to the generator, the control logic may provide a logic signal that "notifies" the loads of an upcoming power disturbance during the transfer process. For example, if the load is an air conditioning system, the control logic may open a relay in the thermostat circuit, allowing air conditioning blowers and pumps to come to a halt under their standard timing. This avoids the likelihood of inducing power spikes into the equipment during the transfer process. These power quality problems at transfer time, especially in "open transition" transfer switches represent one of the primary impediments to the use of standby power generators in load reduction programs. That is, the potential for financial loss due to equipment damage by power surges may seem to be a greater risk than the value of the savings in power costs associated with the load reduction program incentives. Thus, the ability to "power down" loads prior to transfer is an important aspect of the present invention. Paragraph Number [0047] teaches these power quality problems at transfer time, especially in "open transition" transfer switches represent one of the primary impediments to the use of standby power generators 15 in load reduction programs. Thus, the potential for financial loss due to equipment damage by power surges may seem to be a greater risk than the value of the savings in power costs associated with the load reduction program incentives. Thus, the ability to "power down" loads prior to transfer is an important aspect of the present invention. Paragraph Number [0052] teaches methods and apparatus have been disclosed that make existing standby power available for use in load reduction programs to assist utilities in avoiding power shortages and the resultant blackouts.).
A person with ordinary skill would have been motivated to combine these references as described in regard to claim 10.
As per claim 12, the combination of Haghighat-Kashani, Hedley, and Jarrett, JR teaches each of the limitations of claims 1 and 10.
In addition, Haghighat-Kashani teaches:
a cost saving on maintenance and repairs (Paragraph Number [0046] teaches the technology described herein may be used to observe existing (non-smart as well as smart) devices within location, and additionally, by sharing the knowledge obtained from this process, to introduce artificial intelligence to devices. The intelligence leads to timely notifications and alerts to users, and seamless adjustments to the device states (for devices with connectivity) based on user behavior, previous or current actions, and predicted desires. Paragraph Number [0061] teaches such analyses can be used for a number of services. First, they can be used to offer users targeted advertising. Leads can be created for services and products, and presented to users through the variety of user interfaces listed above (e.g., mobile, web, wearable, etc.). The products and services may relate to what is used by users within the location, or be relevant to them as predicted by their general demographic and predicted interest. For example, a user with an old fridge may be provided with promotions for a new energy saving fridge. This is shown in FIG. 11, where in step 210, the system detects that a particular device is old, either by determining that it consumes significantly more energy than currently available fridges, by detecting one or more malfunctions, by determining that its energy consumption has steadily increased over time, or by having recorded how long the fridge has been in service. In step 212, the system 10 provides targeted ads to the user interface that relate to offerings of a new, replacement device. As another example, a user with many connected devices may be presented with ads for a new internet service; and all users can be presented with contact information of service providers and tradesmen such as electricians, carpenters, plumbers, etc. based on a variety of observations and information obtained about the users and the location).
Haghighat-Kashani teaches grading a facility based on its consumption of energy and determining a future action that will improve its grade with respect to other facilities but does not explicitly teach determining a risk factor related to power quality as described by the following citations from Jarrett, JR:
wherein the step of predicting the positive expected result includes predicting one or more of a reduction in risk of an outage. (Paragraph Number [0012] teaches prior to executing the transfer of loads to the generator, the control logic may provide a logic signal that "notifies" the loads of an upcoming power disturbance during the transfer process. For example, if the load is an air conditioning system, the control logic may open a relay in the thermostat circuit, allowing air conditioning blowers and pumps to come to a halt under their standard timing. This avoids the likelihood of inducing power spikes into the equipment during the transfer process. These power quality problems at transfer time, especially in "open transition" transfer switches represent one of the primary impediments to the use of standby power generators in load reduction programs. That is, the potential for financial loss due to equipment damage by power surges may seem to be a greater risk than the value of the savings in power costs associated with the load reduction program incentives. Thus, the ability to "power down" loads prior to transfer is an important aspect of the present invention. Paragraph Number [0047] teaches these power quality problems at transfer time, especially in "open transition" transfer switches represent one of the primary impediments to the use of standby power generators 15 in load reduction programs. Thus, the potential for financial loss due to equipment damage by power surges may seem to be a greater risk than the value of the savings in power costs associated with the load reduction program incentives. Thus, the ability to "power down" loads prior to transfer is an important aspect of the present invention. Paragraph Number [0052] teaches methods and apparatus have been disclosed that make existing standby power available for use in load reduction programs to assist utilities in avoiding power shortages and the resultant blackouts).
A person with ordinary skill would have been motivated to combine these references as described in regard to claim 10.
As per claim 22, Haghighat-Kashani teaches:
A method of managing a power distribution system comprising: monitoring, via one or more first facility electric meters, the energy consumption ... of a first facility associated with an enterprise (Paragraph Number [0027] teaches the electrical wiring in buildings has been likened to a nervous system that connects all electronics, including electrical devices, to a central place such as the breaker panel or the meter box. The system described herein introduces artificial intelligence to all existing electronic devices by monitoring the electricity patterns of the building's electrical network. Paragraph Number [0030] teaches the system described herein may be configured, in at least one example, to gather electricity data relating to a building or premises, including energy used, real power usage, reactive power usage, power factor, current, and voltage. This information can be obtained from one or multiple sensors installed across the electrical network. One way to implement this is to place a sensor inside the breaker panel to monitor the main electrical lines entering the premises. Another way would be to utilize smart metering infrastructure that exists in many households. There could also be sensors placed at one or more individual plugs. The system may report total aggregate information, as well as individual phase data, or individual plug data, depending on the setup).
transmitting, via the one or more first facility electric meters, first facility energy consumption data ... to a meter data system, the meter data system including one or more servers (Paragraph Number [0007] teaches various load disaggregation algorithms have been suggested in the literature. One technique of disaggregating the power signal measured at the incoming power meter into its constituent individual loads is known as Single Point End-use Energy Disaggregation (SPEED.TM.), and is available from Enetics, Inc. of New York. The SPEED.TM. product includes logging a premises' load data and then transferring the data via telephone, walk-ups, or alternative communications to a master station that processes the recorded data into individual load data, and acts as a server and database manager for pre- and post-processed energy consumption data, temperature data, queries from analysis stations, and queries from other information systems).
monitoring, via one or more second facility electric meters, the energy consumption ...of a second facility associated with the enterprise (Paragraph Number [0027] teaches the electrical wiring in buildings has been likened to a nervous system that connects all electronics, including electrical devices, to a central place such as the breaker panel or the meter box. The system described herein introduces artificial intelligence to all existing electronic devices by monitoring the electricity patterns of the building's electrical network. Paragraph Number [0030] teaches the system described herein may be configured, in at least one example, to gather electricity data relating to a building or premises, including energy used, real power usage, reactive power usage, power factor, current, and voltage. This information can be obtained from one or multiple sensors installed across the electrical network. One way to implement this is to place a sensor inside the breaker panel to monitor the main electrical lines entering the premises. Another way would be to utilize smart metering infrastructure that exists in many households. There could also be sensors placed at one or more individual plugs. The system may report total aggregate information, as well as individual phase data, or individual plug data, depending on the setup. Paragraph Number [0047] teaches to an end-user, the monitoring and intelligence capability described here brings together a user's device experience into a single platform, which he can access through a variety of interfaces described earlier in order to observe the devices and manage the experience. Therefore, this technology provides a homepage for locations such as homes or offices. The single platform may be a central application for the occupants of a given location, allowing them to observe and manage their experience with the host of electronic devices present. Such a central application unifies the management of both smart and non-smart devices. (Examiner asserts that the plurality of locations mentioned indicates the monitoring capability of more than a single location)).
transmitting, via the one or more second facility electric meters, second facility energy consumption data ... to the meter data system (Paragraph Number [0007] teaches various load disaggregation algorithms have been suggested in the literature. One technique of disaggregating the power signal measured at the incoming power meter into its constituent individual loads is known as Single Point End-use Energy Disaggregation (SPEED.TM.), and is available from Enetics, Inc. of New York. The SPEED.TM. product includes logging a premises' load data and then transferring the data via telephone, walk-ups, or alternative communications to a master station that processes the recorded data into individual load data, and acts as a server and database manager for pre- and post-processed energy consumption data, temperature data, queries from analysis stations, and queries from other information systems. Paragraph Number [0047] teaches to an end-user, the monitoring and intelligence capability described here brings together a user's device experience into a single platform, which he can access through a variety of interfaces described earlier in order to observe the devices and manage the experience. Therefore, this technology provides a homepage for locations such as homes or offices. The single platform may be a central application for the occupants of a given location, allowing them to observe and manage their experience with the host of electronic devices present. Such a central application unifies the management of both smart and non-smart devices. (Examiner asserts that the plurality of locations mentioned indicates the monitoring capability of more than a single location)).
receiving, via the meter data system, the first facility energy consumption ... and the second facility energy consumption… (Paragraph Number [0047] teaches to an end-user, the monitoring and intelligence capability described here brings together a user's device experience into a single platform, which he can access through a variety of interfaces described earlier in order to observe the devices and manage the experience. Therefore, this technology provides a homepage for locations such as homes or offices. The single platform may be a central application for the occupants of a given location, allowing them to observe and manage their experience with the host of electronic devices present. Such a central application unifies the management of both smart and non-smart devices. (Examiner asserts that the plurality of locations mentioned indicates the monitoring capability of more than a single location)).
outputting, via the meter data system, one or more reports to a client device, the one more reports … (Paragraph Number [0030] teaches the system described herein may be configured, in at least one example, to gather electricity data relating to a building or premises, including energy used, real power usage, reactive power usage, power factor, current, and voltage. This information can be obtained from one or multiple sensors installed across the electrical network. One way to implement this is to place a sensor inside the breaker panel to monitor the main electrical lines entering the premises. Another way would be to utilize smart metering infrastructure that exists in many households. There could also be sensors placed at one or more individual plugs. The system may report total aggregate information, as well as individual phase data, or individual plug data, depending on the setup. Paragraph Number [0066] teaches referring to FIG. 12, a process is shown of a gamified electricity consumption monitor running as an app on a user device. In step 300, the system 10 determines the power or electricity consumption of a location, such as a user's home. The consumption may be the real-time consumption, an average consumption, a minimum consumption or a baseload consumption. In step 302, the system displays the electricity consumption via a user interface 30, such as a user interface of a user's smart phone. In step 304, which may be optional, the app outputs an audible and/or visible message that instructs the user to switch off or power down devices in the user's home).
executing, via the one or more processors, at least one machine learning algorithm to process the first facility energy consumption and power quality data and the second facility energy consumption and power quality data and the historical readings stored in the data library (Paragraph Number [0044] teaches the tools used to match new signatures against existing models and libraries include statistical analysis as well as machine learning. The learning capabilities in the system enables the addition of artificial intelligence to existing non-smart devices, as well as to new smart ones. A self-learning home, for instance, can adjust itself to user needs, like adjusting lighting and temperature as soon as the garage door is opened and its signature detected by this system. [0046] The technology described herein may be used to observe existing (non-smart as well as smart) devices within location, and additionally, by sharing the knowledge obtained from this process, to introduce artificial intelligence to devices. The intelligence leads to timely notifications and alerts to users, and seamless adjustments to the device states (for devices with connectivity) based on user behavior, previous or current actions, and predicted desires. Paragraph Number [0055] teaches the monitoring and artificial intelligence capabilities in this presented system can transform the collection of electronics in a given location to become aware of each other’s state and of the occupants' actions, habits, and desires).
output at least one risk prediction in a predetermined future time interval based on the first facility energy consumption and power quality data and the second facility energy consumption and power quality data and the historical readings (Paragraph Number [0055] teaches the monitoring and artificial intelligence capabilities in this presented system can transform the collection of electronics in a given location to become aware of each other’s state and of the occupants' actions, habits, and desires. For instance, a smart coffee maker can receive a notification every morning right before the users are expected to wake up, if the users are observed to brew coffee every morning. This is shown in FIG. 8, where in step 180 the system 10 determines a pattern of usage of a particular device. Following this, in step 182, the system 10 sends an advance notification to the particular device, informing it to switch on. Paragraph Number [0052] teaches the sensing and analytics presented here can be used to manage multiple energy sources such as homes that have solar panels, storage batteries, EV (electric vehicle) batteries, as well as the grid. The system 10 can be used to decide, based on consumption patterns, available energy and generation potential, when the best times are to charge batteries or draw from them. The system 10 can also be used to decide when solar generation should be output to the grid and when to use the grid for consumption and battery charging. The system 10 can be used for providing solar consumers with intelligence on how their electricity consumption compares to their electricity generation, and intelligence on how to optimize their electricity network to pull energy from the most cost-efficient source at a given time. Paragraph Number [0053] teaches also the monitoring and management of these sources can also benefit energy trading markets by controlling the grid at a micro level to optimize supply and demand).
Haghighat-Kashani teaches grading a facility based on its consumption of energy and determining a future action that will improve its grade with respect to other facilities but does not explicitly teach storing historical information in tables and using that information to provide control signals to balance loads as described by the following citations from Hedley:
storing the first facility energy consumption and power quality data and the second facility energy consumption and power quality data as historical readings in a data library in at least one table (Paragraph Number [0139] teaches the system EEMS solution will meet the company minimum requirements and offers an even better data-collection capability. The system may collect real-time data, which may be defined as having a one-minute interval. The real time collection interval is adjustable, e.g., up to a 15-minute interval or other interval. The table below lists a specific data collection and frequency schedule example. (See Table 00001) Paragraph Number [0520] teaches a table showing monthly date in any desired (e.g., mm/yyyy) format, kWh consumption, kBTU consumption, monthly CDD, monthly HDD, monthly CDD 2, Log CDD, Log HDD, HDD 2, monthly average Occupancy, monthly average Relative Humidity, monthly average Wind Speed, monthly average Global Solar Radiation, or other variables; and Paragraph Number [0521] teaches a table which shows a summary of the top regression outputs, including R 2, Significance F, Regression Equation, Intercept and coefficients for each independent variable for each regression output).
in accordance with a time series optimization scheme, wherein the time series optimization scheme includes (Paragraph Number [0522] teaches the machine 2500 may additionally or alternatively include comparison logic 2544 (e.g., as one of the energy analysis programs 2524). The comparison logic 2544 may include instructions that when executed by the processor 2502 cause the processor 2502 to perform a kilowatt hour consumption and exception rank analysis, for example. One example of a comparison analysis 2700 that results from the comparison logic 2544 is shown in FIG. 27. The comparison analysis 2700 extends in 30 minute intervals over an entire day for a particular building under analysis, but the comparison logic 2544 may perform analyses over shorter or longer time periods at different intervals. Furthermore, the comparison may be done with respect to a single building (e.g., to compare energy consumption data historically for the building), or with respect to multiple buildings (e.g., to compare a building under analysis to a different control building). In addition, there may be multiple buildings in a control building group that each contribute energy data for defining (e.g., by averaging or according to another statistical treatment) the control building data described below. (See Table 00007 scenario 3 teaching optimization of schedules and energy)).
at least one indexed table for each of the stored at least one first table of historical readings (Paragraph Number [0139] teaches the system EEMS solution will meet the company minimum requirements and offers an even better data-collection capability. The system may collect real-time data, which may be defined as having a one-minute interval. The real time collection interval is adjustable, e.g., up to a 15-minute interval or other interval. The table below lists a specific data collection and frequency schedule example. (See Table 00001) Paragraph Number [0520] teaches a table showing monthly date in any desired (e.g., mm/yyyy) format, kWh consumption, kBTU consumption, monthly CDD, monthly HDD, monthly CDD 2, Log CDD, Log HDD, HDD 2, monthly average Occupancy, monthly average Relative Humidity, monthly average Wind Speed, monthly average Global Solar Radiation, or other variables; and Paragraph Number [0521] teaches a table which shows a summary of the top regression outputs, including R 2, Significance F, Regression Equation, Intercept and coefficients for each independent variable for each regression output).
each indexed table tracks one selected field of a particular value measured and associated timestamps in the at least one first table such that retrieval of the particular value over a period of time does not look at every record in the at least one first table (Paragraph Number [0522] teaches the machine 2500 may additionally or alternatively include comparison logic 2544 (e.g., as one of the energy analysis programs 2524). The comparison logic 2544 may include instructions that when executed by the processor 2502 cause the processor 2502 to perform a kilowatt hour consumption and exception rank analysis, for example. One example of a comparison analysis 2700 that results from the comparison logic 2544 is shown in FIG. 27. The comparison analysis 2700 extends in 30 minute intervals over an entire day for a particular building under analysis, but the comparison logic 2544 may perform analyses over shorter or longer time periods at different intervals. Furthermore, the comparison may be done with respect to a single building (e.g., to compare energy consumption data historically for the building), or with respect to multiple buildings (e.g., to compare a building under analysis to a different control building). In addition, there may be multiple buildings in a control building group that each contribute energy data for defining (e.g., by averaging or according to another statistical treatment) the control building data described below).
and generating, via the one or more processors, at least one control signal based on the at least one risk prediction and outputting the at least one control signal to shut off one or more loads in at least one of the first facility and the second facility (Paragraph Number [0075] teaches at the building (e.g., company facility): a mediator 1102 (e.g., a Richards-Zeta Mediator 2500) that provides a connectivity interface for connecting to one or more meters and sub-systems in a facility. The meters may include utility meters as well as any Building Automation System (BAS), lighting or security control system, or other systems. Paragraph Number [0080] teaches the system 100 energy management data services offer a unique approach at delivering a comprehensive view of a facility's operations. The system 100 may implement continuous optimized control through real time/interval data acquisition and analysis of all relevant facility data. The System Enterprise Energy Management System (EEMS) may include or involve: (1) a physical site assessment, (2) historical utility bill analysis, (3) utility meter interval data analysis, (4) holistic facility controls analysis, (5) real-time automated equipment fault detection and (6) energy sourcing and demand-response energy management. From these inputs the System energy management system generates insight in the form of reports, dashboards, and alerts that provide actionable information that leads to realized energy reduction and cost savings. Paragraph Number [0086] teaches the mediator 1102 provides for bi-directional (read/write) capability with any integrated system. This facilitates for 24/7 continuous optimized control of the systems connected to it. The mediator 1102 has an intelligence layer allowing for full closed loop advanced math and logic between any of the previously disparate systems. The mediator 1102 also sends and consumes Web services. An example Web service would be an Automated Demand Response (ADR) notice and pricing level signal from Constellation New Energy (CNE) triggering the mediator 1102 on board logic and control to automatically shed electrical loads by turning off non-essential lighting and changing set points on chilled water and HVAC zones).
A person with ordinary skill would have been motivated to combine these references as described in regard to claim 1.
Haghighat-Kashani teaches grading a facility based on its consumption of energy and determining a future action that will improve its grade with respect to other facilities but does not explicitly teach determining a risk factor related to power quality as described by the following citations from Jarrett, JR:
and power quality issues (Paragraph Number [0012] teaches prior to executing the transfer of loads to the generator, the control logic may provide a logic signal that "notifies" the loads of an upcoming power disturbance during the transfer process. For example, if the load is an air conditioning system, the control logic may open a relay in the thermostat circuit, allowing air conditioning blowers and pumps to come to a halt under their standard timing. This avoids the likelihood of inducing power spikes into the equipment during the transfer process. These power quality problems at transfer time, especially in "open transition" transfer switches represent one of the primary impediments to the use of standby power generators in load reduction programs. That is, the potential for financial loss due to equipment damage by power surges may seem to be a greater risk than the value of the savings in power costs associated with the load reduction program incentives. Thus, the ability to "power down" loads prior to transfer is an important aspect of the present invention).
and first facility power quality data (Paragraph Number [0012] teaches prior to executing the transfer of loads to the generator, the control logic may provide a logic signal that "notifies" the loads of an upcoming power disturbance during the transfer process. For example, if the load is an air conditioning system, the control logic may open a relay in the thermostat circuit, allowing air conditioning blowers and pumps to come to a halt under their standard timing. This avoids the likelihood of inducing power spikes into the equipment during the transfer process. These power quality problems at transfer time, especially in "open transition" transfer switches represent one of the primary impediments to the use of standby power generators in load reduction programs. That is, the potential for financial loss due to equipment damage by power surges may seem to be a greater risk than the value of the savings in power costs associated with the load reduction program incentives. Thus, the ability to "power down" loads prior to transfer is an important aspect of the present invention).
and power quality issues (Paragraph Number [0012] teaches prior to executing the transfer of loads to the generator, the control logic may provide a logic signal that "notifies" the loads of an upcoming power disturbance during the transfer process. For example, if the load is an air conditioning system, the control logic may open a relay in the thermostat circuit, allowing air conditioning blowers and pumps to come to a halt under their standard timing. This avoids the likelihood of inducing power spikes into the equipment during the transfer process. These power quality problems at transfer time, especially in "open transition" transfer switches represent one of the primary impediments to the use of standby power generators in load reduction programs. That is, the potential for financial loss due to equipment damage by power surges may seem to be a greater risk than the value of the savings in power costs associated with the load reduction program incentives. Thus, the ability to "power down" loads prior to transfer is an important aspect of the present invention).
and second facility power quality data (Paragraph Number [0012] teaches prior to executing the transfer of loads to the generator, the control logic may provide a logic signal that "notifies" the loads of an upcoming power disturbance during the transfer process. For example, if the load is an air conditioning system, the control logic may open a relay in the thermostat circuit, allowing air conditioning blowers and pumps to come to a halt under their standard timing. This avoids the likelihood of inducing power spikes into the equipment during the transfer process. These power quality problems at transfer time, especially in "open transition" transfer switches represent one of the primary impediments to the use of standby power generators in load reduction programs. That is, the potential for financial loss due to equipment damage by power surges may seem to be a greater risk than the value of the savings in power costs associated with the load reduction program incentives. Thus, the ability to "power down" loads prior to transfer is an important aspect of the present invention).
and power quality data… and power quality data (Paragraph Number [0012] teaches prior to executing the transfer of loads to the generator, the control logic may provide a logic signal that "notifies" the loads of an upcoming power disturbance during the transfer process. For example, if the load is an air conditioning system, the control logic may open a relay in the thermostat circuit, allowing air conditioning blowers and pumps to come to a halt under their standard timing. This avoids the likelihood of inducing power spikes into the equipment during the transfer process. These power quality problems at transfer time, especially in "open transition" transfer switches represent one of the primary impediments to the use of standby power generators in load reduction programs. That is, the potential for financial loss due to equipment damage by power surges may seem to be a greater risk than the value of the savings in power costs associated with the load reduction program incentives. Thus, the ability to "power down" loads prior to transfer is an important aspect of the present invention).
based on the received first facility power quality data, calculating, via one or more processors associated with the one or more servers of the meter data system, a first risk factor score for the first facility and a second risk factor score for the second facility (Paragraph Number [0012] teaches prior to executing the transfer of loads to the generator, the control logic may provide a logic signal that "notifies" the loads of an upcoming power disturbance during the transfer process. For example, if the load is an air conditioning system, the control logic may open a relay in the thermostat circuit, allowing air conditioning blowers and pumps to come to a halt under their standard timing. This avoids the likelihood of inducing power spikes into the equipment during the transfer process. These power quality problems at transfer time, especially in "open transition" transfer switches represent one of the primary impediments to the use of standby power generators in load reduction programs. That is, the potential for financial loss due to equipment damage by power surges may seem to be a greater risk than the value of the savings in power costs associated with the load reduction program incentives. Thus, the ability to "power down" loads prior to transfer is an important aspect of the present invention. Paragraph Number [0029] teaches certain electrical loads designated "load reduction loads" 14c are further segregated from the critical and non-critical loads 14b, 14a. The load reduction loads 14c are specifically chosen based on certain criteria such as high energy consumption and tolerance to low power quality. The load reduction loads 14c are targeted for removal from the primary utility source 11 to reduce peak demand on the utility. Paragraph Number [0047] teaches these power quality problems at transfer time, especially in "open transition" transfer switches represent one of the primary impediments to the use of standby power generators 15 in load reduction programs. Thus, the potential for financial loss due to equipment damage by power surges may seem to be a greater risk than the value of the savings in power costs associated with the load reduction program incentives. Thus, the ability to "power down" loads prior to transfer is an important aspect of the present invention).
including the first risk factor score and the second risk factor score (Paragraph Number [0029] teaches certain electrical loads designated "load reduction loads" 14c are further segregated from the critical and non-critical loads 14b, 14a. The load reduction loads 14c are specifically chosen based on certain criteria such as high energy consumption and tolerance to low power quality. The load reduction loads 14c are targeted for removal from the primary utility source 11 to reduce peak demand on the utility. Paragraph Number [0047] teaches these power quality problems at transfer time, especially in "open transition" transfer switches represent one of the primary impediments to the use of standby power generators 15 in load reduction programs. Thus, the potential for financial loss due to equipment damage by power surges may seem to be a greater risk than the value of the savings in power costs associated with the load reduction program incentives. Thus, the ability to "power down" loads prior to transfer is an important aspect of the present invention).
A person with ordinary skill would have been motivated to combine these references as described in regard to claim 10.
As per claim 23, the combination of Haghighat-Kashani, Hedley, and Jarrett, JR teaches each of the limitations of claim 22.
Haghighat-Kashani teaches grading a facility based on its consumption of energy and determining a future action that will improve its grade with respect to other facilities but does not explicitly teach determining a risk factor related to power quality as described by the following citations from Jarrett, JR:
wherein the risk factor is related to one or more of a number of voltage surges, a number of voltage transients, voltage harmonics, and current harmonics (Paragraph Number [0029] teaches certain electrical loads designated "load reduction loads" 14c are further segregated from the critical and non-critical loads 14b, 14a. The load reduction loads 14c are specifically chosen based on certain criteria such as high energy consumption and tolerance to low power quality. The load reduction loads 14c are targeted for removal from the primary utility source 11 to reduce peak demand on the utility. Paragraph Number [0047] teaches these power quality problems at transfer time, especially in "open transition" transfer switches represent one of the primary impediments to the use of standby power generators 15 in load reduction programs. Thus, the potential for financial loss due to equipment damage by power surges may seem to be a greater risk than the value of the savings in power costs associated with the load reduction program incentives. Thus, the ability to "power down" loads prior to transfer is an important aspect of the present invention).
A person with ordinary skill would have been motivated to combine these references as described in regard to claim 10.
As per claim 26, the combination of Haghighat-Kashani, Hedley, and Jarrett, JR teaches each of the limitations of claim 22.
In addition, Haghighat-Kashani teaches:
outputting the positive expected result to a user device (Paragraph Number [0030] teaches the system described herein may be configured, in at least one example, to gather electricity data relating to a building or premises, including energy used, real power usage, reactive power usage, power factor, current, and voltage. This information can be obtained from one or multiple sensors installed across the electrical network. One way to implement this is to place a sensor inside the breaker panel to monitor the main electrical lines entering the premises. Another way would be to utilize smart metering infrastructure that exists in many households. There could also be sensors placed at one or more individual plugs. The system may report total aggregate information, as well as individual phase data, or individual plug data, depending on the setup. Paragraph Number [0066] teaches referring to FIG. 12, a process is shown of a gamified electricity consumption monitor running as an app on a user device. In step 300, the system 10 determines the power or electricity consumption of a location, such as a user's home. The consumption may be the real-time consumption, an average consumption, a minimum consumption or a baseload consumption. In step 302, the system displays the electricity consumption via a user interface 30, such as a user interface of a user's smart phone. In step 304, which may be optional, the app outputs an audible and/or visible message that instructs the user to switch off or power down devices in the user's home).
Haghighat-Kashani teaches grading a facility based on its consumption of energy and determining a future action that will improve its grade with respect to other facilities but does not explicitly teach determining a risk factor related to power quality as described by the following citations from Jarrett, JR:
further comprising predicting, via the meter data system, a positive expected result in response to improving the risk factor score for at least one of the first facility or the second facility (Paragraph Number [0012] teaches prior to executing the transfer of loads to the generator, the control logic may provide a logic signal that "notifies" the loads of an upcoming power disturbance during the transfer process. For example, if the load is an air conditioning system, the control logic may open a relay in the thermostat circuit, allowing air conditioning blowers and pumps to come to a halt under their standard timing. This avoids the likelihood of inducing power spikes into the equipment during the transfer process. These power quality problems at transfer time, especially in "open transition" transfer switches represent one of the primary impediments to the use of standby power generators in load reduction programs. That is, the potential for financial loss due to equipment damage by power surges may seem to be a greater risk than the value of the savings in power costs associated with the load reduction program incentives. Thus, the ability to "power down" loads prior to transfer is an important aspect of the present invention. Paragraph Number [0047] teaches these power quality problems at transfer time, especially in "open transition" transfer switches represent one of the primary impediments to the use of standby power generators 15 in load reduction programs. Thus, the potential for financial loss due to equipment damage by power surges may seem to be a greater risk than the value of the savings in power costs associated with the load reduction program incentives. Thus, the ability to "power down" loads prior to transfer is an important aspect of the present invention. Paragraph Number [0052] teaches methods and apparatus have been disclosed that make existing standby power available for use in load reduction programs to assist utilities in avoiding power shortages and the resultant blackouts).
A person with ordinary skill would have been motivated to combine these references as described in regard to claim 10.
As per claim 27, the combination of Haghighat-Kashani, Hedley, and Jarrett, JR teaches each of the limitations of claims 22 and 26.
In addition, Haghighat-Kashani teaches:
and a cost saving on maintenance and repairs (Paragraph Number [0046] teaches the technology described herein may be used to observe existing (non-smart as well as smart) devices within location, and additionally, by sharing the knowledge obtained from this process, to introduce artificial intelligence to devices. The intelligence leads to timely notifications and alerts to users, and seamless adjustments to the device states (for devices with connectivity) based on user behavior, previous or current actions, and predicted desires. Paragraph Number [0061] teaches such analyses can be used for a number of services. First, they can be used to offer users targeted advertising. Leads can be created for services and products, and presented to users through the variety of user interfaces listed above (e.g., mobile, web, wearable, etc.). The products and services may relate to what is used by users within the location, or be relevant to them as predicted by their general demographic and predicted interest. For example, a user with an old fridge may be provided with promotions for a new energy saving fridge. This is shown in FIG. 11, where in step 210, the system detects that a particular device is old, either by determining that it consumes significantly more energy than currently available fridges, by detecting one or more malfunctions, by determining that its energy consumption has steadily increased over time, or by having recorded how long the fridge has been in service. In step 212, the system 10 provides targeted ads to the user interface that relate to offerings of a new, replacement device. As another example, a user with many connected devices may be presented with ads for a new internet service; and all users can be presented with contact information of service providers and tradesmen such as electricians, carpenters, plumbers, etc. based on a variety of observations and information obtained about the users and the location).
Haghighat-Kashani teaches grading a facility based on its consumption of energy and determining a future action that will improve its grade with respect to other facilities but does not explicitly teach determining a risk factor related to power quality as described by the following citations from Jarrett, JR:
wherein the step of predicting the positive expected result includes predicting one or more of a reduction in risk of an outage (Paragraph Number [0012] teaches prior to executing the transfer of loads to the generator, the control logic may provide a logic signal that "notifies" the loads of an upcoming power disturbance during the transfer process. For example, if the load is an air conditioning system, the control logic may open a relay in the thermostat circuit, allowing air conditioning blowers and pumps to come to a halt under their standard timing. This avoids the likelihood of inducing power spikes into the equipment during the transfer process. These power quality problems at transfer time, especially in "open transition" transfer switches represent one of the primary impediments to the use of standby power generators in load reduction programs. That is, the potential for financial loss due to equipment damage by power surges may seem to be a greater risk than the value of the savings in power costs associated with the load reduction program incentives. Thus, the ability to "power down" loads prior to transfer is an important aspect of the present invention. Paragraph Number [0047] teaches these power quality problems at transfer time, especially in "open transition" transfer switches represent one of the primary impediments to the use of standby power generators 15 in load reduction programs. Thus, the potential for financial loss due to equipment damage by power surges may seem to be a greater risk than the value of the savings in power costs associated with the load reduction program incentives. Thus, the ability to "power down" loads prior to transfer is an important aspect of the present invention. Paragraph Number [0052] teaches methods and apparatus have been disclosed that make existing standby power available for use in load reduction programs to assist utilities in avoiding power shortages and the resultant blackouts).
A person with ordinary skill would have been motivated to combine these references as described in regard to claim 10.
As per claim 28, the combination of Haghighat-Kashani, Hedley, and Jarrett, JR teaches each of the limitations of claims 22 and 26.
In addition, Haghighat-Kashani teaches:
wherein the step of predicting the positive expected result includes using an Artificial Intelligence (AI) function. (Paragraph Number [0027] teaches the electrical wiring in buildings has been likened to a nervous system that connects all electronics, including electrical devices, to a central place such as the breaker panel or the meter box. The system described herein introduces artificial intelligence to all existing electronic devices by monitoring the electricity patterns of the building's electrical network. Paragraph Number [0044] teaches the tools used to match new signatures against existing models and libraries include statistical analysis as well as machine learning. The learning capabilities in the system enables the addition of artificial intelligence to existing non-smart devices, as well as to new smart ones. A self-learning home, for instance, can adjust itself to user needs, like adjusting lighting and temperature as soon as the garage door is opened and its signature detected by this system).
Claims 24 and 25 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication Number 2015/0268281 to Haghighat-Kashani (hereafter referred to as Haghighat-Kashani) in view of U.S. Patent Application Publication 2010/0286937 to Hedley et al. (hereafter referred to as Hedley) in further view of U.S. Patent Application Publication Number 2003/0042794 to Jarrett, JR (hereafter referred to as Jarrett, JR) and in even further view of U.S. Patent Application Publication Number 2009/0073807 to Sitton et al. (hereafter referred to as Sitton).
As per claim 24, the combination of Haghighat-Kashani, Hedley, and Jarrett, JR teaches:
Haghighat-Kashani teaches grading a facility based on its consumption of energy and determining a future action that will improve its grade with respect to other facilities but does not explicitly teach where a risk factor is based on a harmonic distortion score as described by the following citations from Sitton:
wherein the risk factor is calculated based at least in part on a Total Harmonic Distortion, which includes both voltage harmonic distortion and current harmonic distortion, analyzed from the power quality data (Paragraph Number [0029] teaches variations in the allowable distortion level of generated signals may be accommodated by separation processing methods where the emitted signal signatures are distinct enough for recognition within the band of distortion. In general, the term "low distortion" as used herein relates to total harmonic distortion (THD). THD adds risk to the ability to separate recorded information, and the acceptable level of THD may vary and may depend on one or more factors. Some factors that may allow for higher or lower acceptable THD include the particular seismic record, recording methods used, filtering methods used, subtraction methods used and information processing methods. Therefore, the term "low distortion: should be considered herein as meaning an acceptable level of THD. In most cases, a THD of less than about 5% will be acceptable. In some embodiments, acceptable THD may be 5% or more. Source separation in any particular seismic information acquisition operation will be a function of the source geometry, signal strength, and uncorrected vibrator distortion).
Both the combination of Haghighat-Kashani, Hedley, and Jarrett, JR and Sitton are directed to power monitoring and optimization. The combination of Haghighat-Kashani, Hedley, and Jarrett, JR discloses grading a facility based on its consumption of energy and determining a future action that will improve its grade with respect to other facilities. Sitton improves upon the combination of Haghighat-Kashani, Hedley, and Jarrett, JR by disclosing where a risk factor is based on a harmonic distortion score. One of ordinary skill in the art would be motivated to further include where a risk factor is based on a harmonic distortion score, to efficiently create a more accurate regression and prediction model with respect to outages and the more accurate regression model means more accurate measurement and verification of savings obtained when energy management strategies are implemented based on the downstream analyses. Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method of grading a facility based on its consumption of energy and determining a future action that will improve its grade with respect to other facilities in the combination of Haghighat-Kashani, Hedley, and Jarrett, JR to further utilize where a risk factor is based on a harmonic distortion score as disclosed in Sitton, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 25, the combination of Haghighat-Kashani, Hedley, and Jarrett, JR teaches:
Haghighat-Kashani teaches grading a facility based on its consumption of energy and determining a future action that will improve its grade with respect to other facilities but does not explicitly teach where a risk factor is based on a harmonic distortion score as described by the following citations from Sitton:
wherein the risk factor is calculated based at least in part on a current harmonic score (Paragraph Number [0029] teaches variations in the allowable distortion level of generated signals may be accommodated by separation processing methods where the emitted signal signatures are distinct enough for recognition within the band of distortion. In general, the term "low distortion" as used herein relates to total harmonic distortion (THD). THD adds risk to the ability to separate recorded information, and the acceptable level of THD may vary and may depend on one or more factors. Some factors that may allow for higher or lower acceptable THD include the particular seismic record, recording methods used, filtering methods used, subtraction methods used and information processing methods. Therefore, the term "low distortion: should be considered herein as meaning an acceptable level of THD. In most cases, a THD of less than about 5% will be acceptable. In some embodiments, acceptable THD may be 5% or more. Source separation in any particular seismic information acquisition operation will be a function of the source geometry, signal strength, and uncorrected vibrator distortion).
A person with ordinary skill would have been motivated to combine these references as described in regard to claim 24.
Response to Arguments
Applicant’s arguments filed 2/27/2026 have been fully considered but they are not persuasive.
Applicant argues that the claim language is not taught by the combination of cited references. (See Applicant’s Remarks, 2/27/2026, pgs. 7-17). Examiner respectfully disagrees. In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Examiner will note with specificity how the claim language is taught by the cited reference.
Applicant argues that the Hedley reference does not teach “storing the received parameters as historical readings in a data library in at least one table in accordance with a time series optimization scheme, wherein the time series optimization scheme includes.” (See Applicant’s Remarks, 2/27/2026, pgs. 7-9). Examiner respectfully disagrees. The following citations from Hedley are applied:
Paragraph Number [0139] teaches the system EEMS solution will meet the company minimum requirements and offers an even better data-collection capability. The system may collect real-time data, which may be defined as having a one-minute interval. The real time collection interval is adjustable, e.g., up to a 15-minute interval or other interval. The table below lists a specific data collection and frequency schedule example. (See Table 00001) Paragraph Number [0520] teaches a table showing monthly date in any desired (e.g., mm/yyyy) format, kWh consumption, kBTU consumption, monthly CDD, monthly HDD, monthly CDD 2, Log CDD, Log HDD, HDD 2, monthly average Occupancy, monthly average Relative Humidity, monthly average Wind Speed, monthly average Global Solar Radiation, or other variables; and Paragraph Number [0521] teaches a table which shows a summary of the top regression outputs, including R 2, Significance F, Regression Equation, Intercept and coefficients for each independent variable for each regression output. Paragraph Number [0522] teaches the machine 2500 may additionally or alternatively include comparison logic 2544 (e.g., as one of the energy analysis programs 2524). The comparison logic 2544 may include instructions that when executed by the processor 2502 cause the processor 2502 to perform a kilowatt hour consumption and exception rank analysis, for example. One example of a comparison analysis 2700 that results from the comparison logic 2544 is shown in FIG. 27. The comparison analysis 2700 extends in 30 minute intervals over an entire day for a particular building under analysis, but the comparison logic 2544 may perform analyses over shorter or longer time periods at different intervals. Furthermore, the comparison may be done with respect to a single building (e.g., to compare energy consumption data historically for the building), or with respect to multiple buildings (e.g., to compare a building under analysis to a different control building). In addition, there may be multiple buildings in a control building group that each contribute energy data for defining (e.g., by averaging or according to another statistical treatment) the control building data described below. (See Table 00007 scenario 3 teaching optimization of schedules and energy)
The above emphasized portions of the Hedley reference teach the collection (storing) of parameters as historical readings (Table showing monthly readings). Further they show that the data is analyzed for optimization (comparison to a control building) utilizing a time series optimization scheme (comparison analysis is made every 30 min over a specified time period). The other details argued by Applicant are not present in the claim language. As noted above, although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. As such, Examiner asserts that the Hedley reference, in combination with the Haghighat-Kashani reference teaches the claim limitation “storing the received parameters as historical readings in a data library in at least one table in accordance with a time series optimization scheme, wherein the time series optimization scheme includes.” Examiner is not persuaded by the distinction Applicant is attempting to make.
Applicant argues that the Hedley reference does not teach “at least one indexed table for each of the stored at least one first table of historical readings.” (See Applicant’s Remarks, 2/27/2026, pgs. 9-11). Examiner respectfully disagrees. The following citations from Hedley are applied:
Paragraph Number [0139] teaches the system EEMS solution will meet the company minimum requirements and offers an even better data-collection capability. The system may collect real-time data, which may be defined as having a one-minute interval. The real time collection interval is adjustable, e.g., up to a 15-minute interval or other interval. The table below lists a specific data collection and frequency schedule example. (See Table 00001) Paragraph Number [0520] teaches a table showing monthly date in any desired (e.g., mm/yyyy) format, kWh consumption, kBTU consumption, monthly CDD, monthly HDD, monthly CDD 2, Log CDD, Log HDD, HDD 2, monthly average Occupancy, monthly average Relative Humidity, monthly average Wind Speed, monthly average Global Solar Radiation, or other variables; and Paragraph Number [0521] teaches a table which shows a summary of the top regression outputs, including R 2, Significance F, Regression Equation, Intercept and coefficients for each independent variable for each regression output.
The above emphasized portions of the Hedley reference teach at least one indexed table (See Table one with multiple categories of data) that contains historical readings (data is stored in small time increments and as a monthly reading). The other details argued by Applicant are not present in the claim language. As noted above, although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. As such, Examiner asserts that the Hedley reference, in combination with the Haghighat-Kashani reference teaches the claim limitation “at least one indexed table for each of the stored at least one first table of historical readings.” Examiner is not persuaded by the distinction Applicant is attempting to make.
Applicant argues that the Hedley reference does not teach “each indexed table tracks one selected field of a particular value measured and associated timestamps in the at least one first table such that retrieval of the particular value over a period of time does not look at every record in the at least one first table” (See Applicant’s Remarks, 2/27/2026, pgs. 11-12). Examiner respectfully disagrees. The following citations from Hedley are applied:
Paragraph Number [0522] teaches the machine 2500 may additionally or alternatively include comparison logic 2544 (e.g., as one of the energy analysis programs 2524). The comparison logic 2544 may include instructions that when executed by the processor 2502 cause the processor 2502 to perform a kilowatt hour consumption and exception rank analysis, for example. One example of a comparison analysis 2700 that results from the comparison logic 2544 is shown in FIG. 27. The comparison analysis 2700 extends in 30 minute intervals over an entire day for a particular building under analysis, but the comparison logic 2544 may perform analyses over shorter or longer time periods at different intervals. Furthermore, the comparison may be done with respect to a single building (e.g., to compare energy consumption data historically for the building), or with respect to multiple buildings (e.g., to compare a building under analysis to a different control building). In addition, there may be multiple buildings in a control building group that each contribute energy data for defining (e.g., by averaging or according to another statistical treatment) the control building data described below.
The above emphasized portions of the Hedley reference teach at least one indexed table (See Table one with multiple categories of data) that contains historical readings (data is stored in small time increments and as a monthly reading) and while the Hedley reference does teach taking readings and making calculation on a consistent basis it also teaches retrieval of data over a period of time that does not look at every record such as statistical analysis or averaging the data over time. The other details argued by Applicant are not present in the claim language. As noted above, although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. As such, Examiner asserts that the Hedley reference, in combination with the Haghighat-Kashani reference teaches the claim limitation “each indexed table tracks one selected field of a particular value measured and associated timestamps in the at least one first table such that retrieval of the particular value over a period of time does not look at every record in the at least one first table.” Examiner is not persuaded by the distinction Applicant is attempting to make.
Applicant argues that the Haghighat-Kashani reference does not teach “executing, via the one or more processors, at least one machine learning algorithm to process the received parameters and the historical readings stored in the data library and output at least one prediction of energy usage in a predetermined future time interval based on the received parameters and the historical readings,” (See Applicant’s Remarks, 2/27/2026, pgs. 13-15). Examiner respectfully disagrees. The following citations from Haghighat-Kashani are applied:
Paragraph Number [0044] teaches the tools used to match new signatures against existing models and libraries include statistical analysis as well as machine learning. The learning capabilities in the system enables the addition of artificial intelligence to existing non-smart devices, as well as to new smart ones. A self-learning home, for instance, can adjust itself to user needs, like adjusting lighting and temperature as soon as the garage door is opened and its signature detected by this system. [0046] The technology described herein may be used to observe existing (non-smart as well as smart) devices within location, and additionally, by sharing the knowledge obtained from this process, to introduce artificial intelligence to devices. The intelligence leads to timely notifications and alerts to users, and seamless adjustments to the device states (for devices with connectivity) based on user behavior, previous or current actions, and predicted desires. Paragraph Number [0055] teaches the monitoring and artificial intelligence capabilities in this presented system can transform the collection of electronics in a given location to become aware of each others' state and of the occupants' actions, habits, and desires. For instance, a smart coffee maker can receive a notification every morning right before the users are expected to wake up, if the users are observed to brew coffee every morning. This is shown in FIG. 8, where in step 180 the system 10 determines a pattern of usage of a particular device. Following this, in step 182, the system 10 sends an advance notification to the particular device, informing it to switch on. Paragraph Number [0052] teaches the sensing and analytics presented here can be used to manage multiple energy sources such as homes that have solar panels, storage batteries, EV (electric vehicle) batteries, as well as the grid. The system 10 can be used to decide, based on consumption patterns, available energy and generation potential, when the best times are to charge batteries or draw from them. The system 10 can also be used to decide when solar generation should be output to the grid and when to use the grid for consumption and battery charging. The system 10 can be used for providing solar consumers with intelligence on how their electricity consumption compares to their electricity generation, and intelligence on how to optimize their electricity network to pull energy from the most cost-efficient source at a given time. Paragraph Number [0053] teaches also the monitoring and management of these sources can also benefit energy trading markets by controlling the grid at a micro level to optimize supply and demand.
The above emphasized portions of the Haghighat-Kashani reference teach at least one machine learning algorithm to processes the various parameters and readings (See integration of data and smart devices into the network to determine usage patterns) and output predictions of energy usage based on those parameters and readings (monitoring and managing sources for controlling the gris to optimize supply and demand). The other details argued by Applicant are not present in the claim language. As noted above, although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. As such, Examiner asserts that the Haghighat-Kashani reference, in combination with the Hedley reference teaches the claim limitation “executing, via the one or more processors, at least one machine learning algorithm to process the received parameters and the historical readings stored in the data library and output at least one prediction of energy usage in a predetermined future time interval based on the received parameters and the historical readings.” Examiner is not persuaded by the distinction Applicant is attempting to make.
Applicant argues that the Hedley reference does not teach “generating, via the one or more processors, at least one control signal based on the at least one prediction of energy usage and outputting the at least one control signal to implement load balancing in at least one of the first facility and the second facility.” (See Applicant’s Remarks, 2/27/2026, pgs. 16-17). Examiner respectfully disagrees. The following citations from Hedley are applied:
Paragraph Number [0075] teaches at the building (e.g., company facility): a mediator 1102 (e.g., a Richards-Zeta Mediator 2500) that provides a connectivity interface for connecting to one or more meters and sub-systems in a facility. The meters may include utility meters as well as any Building Automation System (BAS), lighting or security control system, or other systems. Paragraph Number [0080] teaches the system 100 energy management data services offer a unique approach at delivering a comprehensive view of a facility's operations. The system 100 may implement continuous optimized control through real time/interval data acquisition and analysis of all relevant facility data. The System Enterprise Energy Management System (EEMS) may include or involve: (1) a physical site assessment, (2) historical utility bill analysis, (3) utility meter interval data analysis, (4) holistic facility controls analysis, (5) real-time automated equipment fault detection and (6) energy sourcing and demand-response energy management. From these inputs the System energy management system generates insight in the form of reports, dashboards, and alerts that provide actionable information that leads to realized energy reduction and cost savings. Paragraph Number [0086] teaches the mediator 1102 provides for bi-directional (read/write) capability with any integrated system. This facilitates for 24/7 continuous optimized control of the systems connected to it. The mediator 1102 has an intelligence layer allowing for full closed loop advanced math and logic between any of the previously disparate systems. The mediator 1102 also sends and consumes Web services. An example Web service would be an Automated Demand Response (ADR) notice and pricing level signal from Constellation New Energy (CNE) triggering the mediator 1102 on board logic and control to automatically shed electrical loads by turning off non essential lighting and changing set points on chilled water and HVAC zones.
The above emphasized portions of the Hedley reference teach at least one control signal (sending control signals to shed electrical loads) based on predictions of energy usage (control through real time data acquisition) and outputting the control signal to implement load balancing (shedding electrical loads by turning of lights and changing thermostat setpoints). The other details argued by Applicant are not present in the claim language. As noted above, although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. As such, Examiner asserts that the Hedley reference, in combination with the Haghighat-Kashani reference teaches the claim limitation “generating, via the one or more processors, at least one control signal based on the at least one prediction of energy usage and outputting the at least one control signal to implement load balancing in at least one of the first facility and the second facility.” Examiner is not persuaded by the distinction Applicant is attempting to make.
Conclusion
Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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/M.H.D/Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624
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