DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Applicant’s election without traverse of 1-11 and 18-24 in the reply filed on 11/18/2025 is acknowledged.
Information Disclosure Statement
The information disclosure statement (IDS) submitted are in compliance with the provisions of 37 CFR 1.97 and have been entered into the record. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-11 and 18-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
Claims 1-11 and 18-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the method (claims 1-11), system (claims 18-23), and CRM (claim 24) are directed to potentially eligible categories of subject matter (i.e., process, machine, and article of manufacture respectively). Thus, Step 1 is satisfied.
With respect to Step 2, and in particular Step 2A Prong One, it is next noted that the claims recite an abstract idea by reciting concepts performed in the human mind (including an observation, evaluation, judgment, opinion), which falls into the “Mental Process” group; and by reciting mathematical relationships, mathematical formulas or equations, mathematical calculations which falls into the “Mathematical concepts” group within the enumerated groupings of abstract ideas. The mere nominal recitation of a generic computer does not take the claim limitation out of methods of mathematical concepts or the mental processes grouping. Thus, the claim recites a mental process for performing math.
The limitations reciting the abstract idea(s) (Mental process and mathematical concepts), as set forth in exemplary claim 1, are: determining one or more conditions necessary to compute a rule set; determining a current state of one or more devices; determining both a forecasted demand and a demand threshold, based on the rule set, the current state… and the user inputs and overrides; when the forecasted demand is greater than the demand threshold, generating a plan to power off the one or more networked devices, one by one, in an order from the least important device to the most important device, until the forecasted demand no longer exceeds the demand threshold;. Independent claims 18 and 24 recite the system and CRM for performing the method of independent claim 1 without adding significantly more. Thus, the same rationale/analysis is applied.
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. The additional elements are directed to receiving user inputs and overrides, if any, via the one or more devices … of each of the one or more devices…; and delivering energy-related device commands for the one or more devices, based on the generated plan; A system for providing active demand management, comprising: a processor; and a memory coupled to the processor, the memory for storing instructions executable by the processor to perform a method comprising… (as recited in claims 1 and 18). However, these elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitation(s) is/are directed to: receiving user inputs and overrides, if any, via the one or more devices … of each of the one or more devices…; and delivering energy-related device commands for the one or more devices, based on the generated plan; A system for providing active demand management, comprising: a processor; and a memory coupled to the processor, the memory for storing instructions executable by the processor to perform a method comprising… (as recited in claims 1 and 18) for implementing the claim steps/functions. These elements have been considered, but merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation), though at a very high level of generality and without imposing meaningful limitation on the scope of the claim.
In addition, Applicant’s Specification (paragraph [0090]) describes generic off-the-shelf computer-based elements for implementing the claimed invention, and which does not amount to significantly more than the abstract idea, which is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. See, e.g., Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself. Further, the courts have found the presentation of data to be a well-understood, routine, conventional activity, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93 (see MPEP 2106.05(d)).
The dependent claims (2-11 and 19-23) are directed to the same abstract idea as recited in the independent claims, and merely incorporate additional details that narrow the abstract idea via additional details of the abstract idea. For example claims 2-11 “wherein the one or more devices includes one or more electrical vehicles; further comprising calculating the energy delivered to the one or more electrical vehicles; wherein the rule set is based on historical energy usage data, time-of-use tariffs, or grid demand patterns; wherein the user inputs and overrides comprise preferred device operational hours or specific times when a device must remain operational; wherein the energy delivered to the one or more electrical vehicles is prioritized based on user-defined vehicle usage schedules or a battery state of charge; wherein the demand threshold is adjustable and can be set either manually by a user or automatically based on historical grid demand data; wherein determining an operational status of a device includes checking if the device is in standby, active, or sleep mode; wherein a calculation of energy delivered considers both efficiency of an electrical vehicle's charging system and the state of a vehicle's battery; wherein the generated plan to power off devices also considers potential energy-saving modes for devices before completely turning them off; further comprising sending notifications to users about potential device power-offs and giving an option for manual overrides”, without additional elements that integrate the abstract idea into a practical application and without additional elements that amount to significantly more to the claims. The remaining dependent claims (19-24) recite the system for performing the method of claims 2-11. Thus, the same rationale/analysis is applied. Thus, all dependent claims have been fully considered, however, these claims are similarly directed to the abstract idea itself, without integrating it into a practical application and with, at most, a general purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims.
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-11 and 18-24 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPub 20100292856 (hereinafter “Fujita”) et al., in view of U.S. Patent 9620970 to (hereinafter “Gadh”) et al.
As per claim 1, Fujita teaches a method for providing active demand management, comprising:
determining one or more conditions necessary to compute a rule set;0020: “ In the exemplary embodiment, demand forecast module 130 interfaces with databases, for example, an historical demand database 140 and an historical weather database 142, as well as with a real-time weather database 144 and a real-time special event database 146…0039: The DSM system and method described herein determine whether DSM actions are necessary to be implemented and the duration of those actions. The determinations are provided to a utility operator to alert them of a recommended DSM action in several "look-ahead" periods such as a week ahead, a day ahead, and an hour ahead. Through rules configured by the utility, a set of recommendations are displayed to operators which may include a number of options that will shed the appropriate amount of load required to meet the energy supply forecast.”
determining a current state of one or more devices;0017: “In the exemplary embodiment, customer locations 16, 18, and 20 include electric loads, for example, loads 40, 42, and 44. Moreover, in the exemplary embodiment, customer locations 16, 18, and 20 also include an end user meter 46. In the exemplary embodiment, end user meter 46 is part of an advanced metering infrastructure (AMI). The AMI is an example of a bidirectional communication system that enables electric utility 12 to measure and collect information relevant to energy usage from customer locations 16, 18, and 20, as well as control loads 40, 42, and 44…0026: SCADA system 120 interfaces with DSM application 102, end user meters, and/or smart home devices, for example, AMI meter 46 (shown in FIG. 1), to regulate individual loads. SCADA system 120 also tracks voluntary end user load shedding and cases of end user load shedding overrides to continually update the load shedding schedule.”
receiving user inputs and overrides, if any, via the one or more devices;0025: “decision support system 116 receives data corresponding to recommended actions determined by DSM application 102 and transmits operator responses to DSM application 102. For example, data corresponding to a demand forecast, a supply forecast, and/or an energy transmission capability may be provided to decision support system 116 for use by an electric utility operator. In the exemplary embodiment, recommended actions are displayed by decision support system 116 along with a user interface that receives operator instructions. For example, the recommended actions may include transmitting an adjusted price signal and/or an electrical load shedding signal to predetermined customer locations and the user interface may enable the operator to authorize, disapprove, or edit the recommended action… 0041: customers may "opt-out" of the load control actions by simply overriding the control signals. For example, if one of the actions is to adjust the temperature set point of a thermostat a few degrees up or down, the consumer may elect to override that setting manually.”
determining both a forecasted demand and a demand threshold, based on the rule set, the current state of each of the one or more devices, and the user inputs and overrides;0020: “demand forecast module 130 interfaces with databases, for example, an historical demand database 140 and an historical weather database 142, as well as with a real-time weather database 144 and a real-time special event database 146. In the exemplary embodiment, demand forecast module 130 uses data stored in databases 140, 142, 144, and 146 to determine an electrical demand forecast for an upcoming predetermined time period. For example, demand forecast module 130 may determine an electrical demand forecast that includes the expected energy demand from customers, for example, customers 16, 18, and 20 (shown in FIG. 1) for the upcoming month, the upcoming week, the next fifteen minutes, or any predetermined future time period…0033-0034: . If demand is less than a corresponding forecasted energy supply at all times within the demand forecast, the method for managing electrical demand in response to electrical supply conditions is complete. In contrast, if at any time within the demand forecast the demand is greater than the corresponding forecasted energy supply, in the exemplary embodiment, DSM application 102 (shown in FIG. 2B) alerts 314 the utility operator. For example, FIG. 7 illustrates an exemplary energy supply forecast (i.e., shown in FIG. 6) overlaid upon an exemplary demand forecast (i.e., shown in FIG. 5).”
when the forecasted demand is greater than the demand threshold …until the forecasted demand no longer exceeds the demand threshold; and0024-0035: “customer information system 114 provides DSM application 102 with a list of critical loads and may be configured to determine an outage rotation schedule for use when DSM application 102 determines that an electrical load should be removed, i.e., "shed," from power grid 14 (shown in FIG. 1). In an alternative embodiment, customer information system 114 stores a predetermined outage rotation schedule created by, for example, an electrical utility operator… The method may also include determining 198 a difference between the second energy demand forecast and the first energy supply forecast and transmitting 200 the electrical load shedding signal to predetermined customers to shed a quantity of electrical loads greater than the difference between the second energy demand and the first energy supply. By determining 198 the difference between the second energy demand forecast and the first energy supply forecast, the method for managing electrical demand on the power grid becomes an iterative process, wherein transmitting 194 and 200 may be performed as many times as is needed to achieve the desired demand forecast… the method may include generating 202 a load shed schedule that rank orders loads available for shedding… DSM application 102 requests 330 a processed list of controllable loads from, for example, customer information system 114 (shown in FIG. 2B). In the exemplary embodiment, from the list of controllable loads received from customer information system 114, DSM application creates 332 an optimized load dispatch schedule. Furthermore, DSM application 102 displays 334 recommended load shedding options to the operator.”
delivering energy-related device commands for the one or more devices, based on the generated plan;0035-0040: “DSM application 102 transmits 340 the load control schedule to, for example, SCADA 120 (shown in FIG. 2B) and/or OMS 118 (shown in FIG. 2B). DSM application 102 also transmits 320 the adjusted generation strategy to the energy management system and the method is complete… The DSM system and method described herein facilitate delivery of direct load control signals by the SCADA system to controllable loads through the AMI network. In the exemplary embodiment, these signals are delivered to the AMI meter, to the HAN, and finally to the specific loads that are being controlled.”
Fujita may not explicitly teach the following. However, Gadh teaches:
generating a plan to power off the one or more networked devices, one by one, in an order from the least important device to the most important device…058-066: “ FIGS. 8A and 8B illustrate a schematic flow diagram of a round-robin charging algorithm 150 in accordance with the present invention. Generally, round-robin charging algorithm 150 is used when a control box's 12/15 power source 14 doesn't allow all stations get charging at the same time…064: If only one charging EV 16 is found at step 168, the algorithm proceeds to step 180 (this means the single charging can continue until it is finished or other charging is started). If not, it goes to step 170, where charging priority is determined. If the priority of the charging EV 16 is higher than all others, it proceeds to step 180. Otherwise, it proceeds to step 172. When a control box's 12/15 algorithm is round-robin, the charging control box's time quantum (how long a charging can get power in a turn) is 60 minutes (a default value that may be varied). The quantum of a control box can be changed as needed. If a charging time in the current turn is more than the quantum, then it proceeds to step 174. If not, it goes to step 180. At step 174, the station is turned ‘off’ and the charging is changed into ‘waiting’ status. At step 176, the algorithm finds the next waiting charging. If found, it goes to step 178. If not, it goes to step 180. At step 178, the station is turned ‘on,’ and the last start charging time and charging status is saved.”
Fujita and Gadh are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Fujita with the aforementioned teachings from Gadh with a reasonable expectation of success, by adding steps that allow the software to plan data with the motivation to more efficiently and accurately organize and analyze information [Gadh 064].
As per claim 2, Fujita and Gadh teach all the limitations of claim 1.
In addition, Gadh teaches:
wherein the one or more devices includes one or more electrical vehicles; Abstract: “A system for multiplexing charging of electric vehicles, comprising a server coupled to a plurality of charging control modules over a network. Each of said charging modules being connected to a voltage source such that each charging control module is configured to regulate distribution of voltage from the voltage source to an electric vehicle coupled to the charging control module. Data collection and control software is provided on the server for identifying a plurality of electric vehicles coupled to the plurality of charging control modules and selectively distributing charging of the plurality of charging control modules to multiplex distribution of voltage to the plurality of electric vehicles.”
Fujita and Gadh are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Fujita with the aforementioned teachings from Gadh with a reasonable expectation of success, by adding steps that allow the software to utilize electric vehicles with the motivation to more efficiently and accurately organize and analyze information [Gadh 064].
As per claim 3, Fujita and Gadh teach all the limitations of claim 2.
In addition, Gadh teaches:
further comprising calculating the energy delivered to the one or more electrical vehicles; 033: “The charging station map page 82 may show charging stations that are available and their status, e.g. stand by, available, occupied, etc. The user can turn the station on (start charging 84) and retrieve information for display, such as time stamp, instantaneous voltage, instantaneous current, instantaneous frequency, power factor, active power, apparent power, and the main energy/total energy consumed…024: Multiplexing control box 15 or meters 12a-12b are configured with sensors (not shown) to allow them to sense current, voltage, frequency and power quality monitoring for each dedicated power line 18 coupled to the site. The measurements taken by control box 15 or meters 12a-12b are then sent by way of wireless transceivers 23 (e.g. Zigbee or any wireless or wire line communications) onto a gateway 34. The gateway 34 then forwards this data by way of the local networks using local wireless networks such as Wifi, Zigbee, or wide-area networks. Robustness may be increased by having a memory within the gateway 34 in case the communications link stops functioning.”
Fujita and Gadh are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Fujita with the aforementioned teachings from Gadh with a reasonable expectation of success, by adding steps that allow the software to utilize electric vehicles with the motivation to more efficiently and accurately organize and analyze information [Gadh 064].
As per claim 4, Fujita and Gadh teach all the limitations of claim 2.
In addition, Fujita teaches:
wherein the rule set is based on historical energy usage data, time-of-use tariffs, or grid demand patterns; 0020: “ demand forecast module 130 interfaces with databases, for example, an historical demand database 140 and an historical weather database 142, as well as with a real-time weather database 144 and a real-time special event database 146. In the exemplary embodiment, demand forecast module 130 uses data stored in databases 140, 142, 144, and 146 to determine an electrical demand forecast for an upcoming predetermined time period. For example, demand forecast module 130 may determine an electrical demand forecast that includes the expected energy demand from customers, for example, customers 16, 18, and 20 (shown in FIG. 1) for the upcoming month, the upcoming week, the next fifteen minutes, or any predetermined future time period. Demand forecast module 130 may also determine an electrical demand forecast for a time period beginning at a future time, for example, an electrical demand forecast for a day after the demand forecast is generated, or an electrical demand forecast for an upcoming week beginning three days after the demand forecast is generated. In the exemplary embodiment, demand forecast module 130 may determine an electrical demand forecast for any time period and/or delay desired, although greater accuracy may be seen with more immediate forecasts. In the exemplary embodiment, demand forecast module 130 forecasts a change in energy demand over time, for example, an expected change in energy demand at each hour of an upcoming day. In some embodiments, demand forecast module 130 forecasts an aggregate demand amount, over time, for the grid. In the exemplary embodiment, demand forecast module 130 analyzes demand forecast accuracy by comparing a past demand forecast with the actual electrical demand over the same time period. In the exemplary embodiment, a database of these analyses is maintained to increase the accuracy of future demand forecasts…0015: A first technical effect of the energy production and transmission system described herein is to provide direct control of loads included within the transmission system. The first technical effect is at least partially achieved by transmitting an electrical load shedding signal to a customer over an advance metering infrastructure (AMI). A second technical effect of the energy production and transmission system described herein is to provide indirect control of loads included within the transmission system. The second technical effect is at least partially achieved by transmitting an adjusted price signal to a customer over an AMI.”
As per claim 5, Fujita and Gadh teach all the limitations of claim 4.
In addition, Fujita teaches:
wherein the user inputs and overrides comprise preferred device operational hours or specific times when a device must remain operational; 0041: “In the method described herein, customers may "opt-out" of the load control actions by simply overriding the control signals. For example, if one of the actions is to adjust the temperature set point of a thermostat a few degrees up or down, the consumer may elect to override that setting manually.”Note: matching, user override abilities with must remain operational.
As per claim 6, Fujita and Gadh teach all the limitations of claim 5.
In addition, Gadh teaches:
wherein the energy delivered to the one or more electrical vehicles is prioritized based on user-defined vehicle usage schedules or a battery state of charge; 030: “Monitoring page 56 allows display of station status which may be updated automatically according to a preset period of time interval, and allow for an administrator to manually check and control each gateway and/or station within the network 25. Control module 58 can turn on or off a selected station and retrieve its real-time status. The control module 58 for turning an EV bank on/off may be based on both manual and automatic input. Automatic control is set up ahead of time, and may be based on pricing signals, total energy available at a given time, personal preferences of EV owners and/or garage owners…064: If only one charging EV 16 is found at step 168, the algorithm proceeds to step 180 (this means the single charging can continue until it is finished or other charging is started). If not, it goes to step 170, where charging priority is determined. If the priority of the charging EV 16 is higher than all others, it proceeds to step 180. Otherwise, it proceeds to step 172. When a control box's 12/15 algorithm is round-robin, the charging control box's time quantum (how long a charging can get power in a turn) is 60 minutes (a default value that may be varied). The quantum of a control box can be changed as needed. If a charging time in the current turn is more than the quantum, then it proceeds to step 174. If not, it goes to step 18.”
Fujita and Gadh are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Fujita with the aforementioned teachings from Gadh with a reasonable expectation of success, by adding steps that allow the software to utilize electric vehicles with the motivation to more efficiently and accurately organize and analyze information [Gadh 064].
As per claim 7, Fujita and Gadh teach all the limitations of claim 1.
In addition, Fujita teaches:
wherein the demand threshold is adjustable and can be set either manually by a user or automatically based on historical grid demand data; 0020: “In the exemplary embodiment, demand forecast module 130 forecasts a change in energy demand over time, for example, an expected change in energy demand at each hour of an upcoming day. In some embodiments, demand forecast module 130 forecasts an aggregate demand amount, over time, for the grid. In the exemplary embodiment, demand forecast module 130 analyzes demand forecast accuracy by comparing a past demand forecast with the actual electrical demand over the same time period. In the exemplary embodiment, a database of these analyses is maintained to increase the accuracy of future demand forecasts.”
As per claim 8, Fujita and Gadh teach all the limitations of claim 7.
In addition, Fujita teaches:
wherein determining an operational status of a device includes checking if the device is in standby, active, or sleep mode; 0017: “For example, using the AMI, electric utility 12 may prevent load 40 from receiving electricity from power grid 14, an operational concept also referred to herein as "shedding" load 40 from power grid 14. In an alternative embodiment, at least one load 40, 42, and/or 44 may be a "smart device." As defined herein, smart devices include a communication device that facilitates receiving a shedding signal from electric utility 12 and turning-off the device after receiving the shedding signal. Loads 40, 42, and 44 may be communicatively coupled in any way that facilitates operation of the AMI as described herein, three of which are shown within customer locations 16, 18, and 20.”
As per claim 9, Fujita and Gadh teach all the limitations of claim 1.
In addition, Gadh teaches:
wherein a calculation of energy delivered considers both efficiency of an electrical vehicle's charging system and the state of a vehicle's battery; 024: “Multiplexing control box 15 or meters 12a-12b are configured with sensors (not shown) to allow them to sense current, voltage, frequency and power quality monitoring for each dedicated power line 18 coupled to the site. The measurements taken by control box 15 or meters 12a-12b are then sent by way of wireless transceivers 23 (e.g. Zigbee or any wireless or wire line communications) onto a gateway 34. The gateway 34 then forwards this data by way of the local networks using local wireless networks such as Wifi, Zigbee, or wide-area networks. Robustness may be increased by having a memory within the gateway 34 in case the communications link stops functioning… 033: The charging station map page 82 may show charging stations that are available and their status, e.g. stand by, available, occupied, etc. The user can turn the station on (start charging 84) and retrieve information for display, such as time stamp, instantaneous voltage, instantaneous current, instantaneous frequency, power factor, active power, apparent power, and the main energy/total energy consumed.”
Fujita and Gadh are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Fujita with the aforementioned teachings from Gadh with a reasonable expectation of success, by adding steps that allow the software to utilize electric vehicles with the motivation to more efficiently and accurately organize and analyze information [Gadh 064].
As per claim 10, Fujita and Gadh teach all the limitations of claim 1.
In addition, Fujita teaches:
wherein the generated plan to power off devices also considers potential energy-saving modes for devices before completely turning them off; 0015: “A first technical effect of the energy production and transmission system described herein is to provide direct control of loads included within the transmission system. The first technical effect is at least partially achieved by transmitting an electrical load shedding signal to a customer over an advance metering infrastructure (AMI). A second technical effect of the energy production and transmission system described herein is to provide indirect control of loads included within the transmission system. The second technical effect is at least partially achieved by transmitting an adjusted price signal to a customer over an AMI.”
As per claim 11, Fujita and Gadh teach all the limitations of claim 1.
In addition, Fujita teaches:
…and giving an option for manual overrides; 0020: “In the exemplary embodiment, demand forecast module 130 forecasts a change in energy demand over time, for example, an expected change in energy demand at each hour of an upcoming day. In some embodiments, demand forecast module 130 forecasts an aggregate demand amount, over time, for the grid. In the exemplary embodiment, demand forecast module 130 analyzes demand forecast accuracy by comparing a past demand forecast with the actual electrical demand over the same time period. In the exemplary embodiment, a database of these analyses is maintained to increase the accuracy of future demand forecasts…0041: customers may "opt-out" of the load control actions by simply overriding the control signals. For example, if one of the actions is to adjust the temperature set point of a thermostat a few degrees up or down, the consumer may elect to override that setting manually.”
further comprising sending notifications to users about potential device power-offs…; 063-088: “At step 166 the station is turned off and charging is closed, an email, text or other message may then be sent to the user and the algorithm proceeds to step 176…At step 264, if only one schedule is found, and that schedule allows continuing the charging after the end time has arrived, the charging will continue until it finishes or another schedule's start time has arrived. If a second schedule exists and its start time is earlier than or equal to the current time, it stops the first schedule and closes the charging, turns off the meter, and sends an email or other message to the user.”
Fujita and Gadh are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Fujita with the aforementioned teachings from Gadh with a reasonable expectation of success, by adding steps that allow the software to utilize electric vehicles with the motivation to more efficiently and accurately organize and analyze information [Gadh 064].
Claims 18-23 are directed to the system for performing the method of claims 1-11 above. Since Fujita and Gadh teach the system, the same art and rationale apply.
Claim 24 is directed to the CRM for performing the method of claim 1 above. Since Fujita and Gadh teaches the CRM, the same art and rationale apply.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Chen; Stephen Yuangi. SYSTEMS AND METHODS FOR MANAGING ELECTRICITY CONSUMPTION ON A POWER GRID, .U.S. PGPub 20170178158 The present disclosure relates generally to systems and methods for managing electricity consumption on a power grid.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Arif Ullah, whose telephone number is (571) 270-0161. The examiner can normally be reached from Monday to Friday between 9 AM and 5:30 PM.
If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Beth Boswell, can be reached at (571) 272-6737. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”)./Arif Ullah/Primary Examiner, Art Unit 3625