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 .
Notice to Applicant
The following is a Final Office action. In response to Examiner’s Non-Final Rejection of 02/27/2026, Applicant, on 04/20/2026, amended claims, canceled claims 12-17, added claims 25-26. Claims 1-11 and 18-26 are pending in this application and have been rejected below.
Response to Arguments
Applicant's arguments filed 04/20/2026 have been fully considered, but they are not fully persuasive. The 35 USC § 101 has been removed. However, the updated 35 USC § 103 rejection of claims 1-11 and 18-26 are applied in light of Applicant's amendments.
The Applicant argues “Fujita does not teach "the one or more networked devices further configured to receive energy delivered from the local grid based on a plan that is generated by the orchestration service". The Office admitted that Fujita does not explicitly teach "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..." (Office Action, 13), and therefore, Fujita cannot also teach the claim16 amendment of "the one or more networked devices further configured to receive energy delivered from the local grid based on a plan that is generated by the orchestration service". Also, Fujita does not teach "determining, by the orchestration service, a current state of the one or more networked devices, the determining comprising: assessing whether one of the one or more networked devices is actively charging; for each actively charging networked device, determining its charging rate." (Remarks 04/20/2026)
The Examiner disagrees. The “active charging” sub limitation is taught by Gadh in 062: “, a determination is made whether the station's power current is less than a given threshold value (default value is 0 Amp and can be changed for a control box 12/15 when it is needed). If yes, then it is assumed that charging is finished, or the EV 16 detached from the system 25, and the algorithm proceeds it goes to step 166. If not, it skips to step 168.” Step 164 directly assesses whether a given station/EV is actively charging. Second, the claim that the “charging rate” must be measured in KW is not commensurate with BRI. The claim “ charging rate” carries no unit limitation, see MPEP §2111, claims are given the BRI. “Rate” denotes quantity per unit time; instantaneous charging current (amperes/second) is itself a rate. Further, Gadh teaches per-station measurement of “active power, apparent power” see 033: “instantaneous voltage, instantaneous current, instantaneous frequency, power factor, active power, apparent power, and the main energy/total energy consumed.” The limitation is met even under Applicant’s restrictive terms. Lastly, Gadh states 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.” The quotes clearly shoes Gadh teaches per-line rate sensing and the per-device charging rate determination. Thus, for the reasons above the rejection is maintained.
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, by an orchestration service, one or more conditions necessary to compute a rule set, the one or more conditions comprising current demands on the local power grid;
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.”
the orchestration service being communicatively coupled, via a network, to the one or more networked devices, the local power grid, and an electrical utility provider;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.”
the one or more networked devices further configured to receive energy delivered from the local grid based on a plan that is generated by the orchestration service; 0040: “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.
determining, by the orchestration service, a current state of the one or more networked devices, the determining comprising; 0026: “In the exemplary embodiment, outage management system 118 receives the planned outage rotation from DSM application 102 and distinguishes planned outages from unplanned outages. Distinguishing between planned outages and unplanned outages facilitates preventing outage management system 118 from dispatching maintenance crews to investigate outages that were pre-planned and executed by the energy provider. Furthermore, 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 networked 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 networked 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).”
dynamically increasing or decreasing the energy being delivered to the one or more networked 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:
and a past charging history of one or more networked devices..;062: “At step 164, a determination is made whether the station's power current is less than a given threshold value (default value is 0 Amp and can be changed for a control box 12/15 when it is needed). If yes, then it is assumed that charging is finished, or the EV 16 detached from the system 25, and the algorithm proceeds it goes to step 166. If not, it skips to step 168.”
assessing whether one of the one or more networked devices is actively charging; 062: “At step 164, a determination is made whether the station's power current is less than a given threshold value (default value is 0 Amp and can be changed for a control box 12/15 when it is needed). If yes, then it is assumed that charging is finished, or the EV 16 detached from the system 25, and the algorithm proceeds it goes to step 166. If not, it skips to step 168.”
for each actively charging networked device, determining its charging rate;033: “ 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.”
when the forecasted demand is greater than the demand threshold, generating [[a]] the plan by the orchestration service 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; 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.”
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 networked 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 the one or more networked devices also considers potential energy-saving modes for the one or more networked 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 teach the system, the same art and rationale apply.
As per claim 26, Fujita and Gadh teach all the limitations of claim 1.
In addition, Gadh teaches:
dynamically increasing or decreasing the energy being delivered to the one or more networked devices further comprises temporarily halting a charging of the one or more networked devices; 065: “ At step 174, the station is turned ‘off’ and the charging is changed into ‘waiting’ status.”:
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 charging status with the motivation to more efficiently and accurately provide power [Gadh 065].
Claim 25 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., in further view of U.S. PGPub 20130179061 to (hereinafter “Gadh 061”) et al.
As per claim 25, Fujita and Gadh teach all the limitations of claim 1.
Fujita and Gadh may not explicitly teach the following. However, Gadh 061 teaches:
wherein dynamically increasing or decreasing the energy being delivered to the one or more networked devices further comprises reducing a charging speed of the one or more networked devices; 0066: “The aggregator preferably directly communicates with and controls charging or backfilling stations 112, either by a proportional function of charge current control or simple on/off toggling at specific charge rates.”
Fujita, Gadh, and Gadh 061 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 and Gadh with the aforementioned teachings from Gadh 061 with a reasonable expectation of success, by adding steps that allow the software to utilize charging speed with the motivation to more efficiently and accurately provide power [Gadh 065].
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.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. THIS ACTION IS MADE FINAL. 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.
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
Prosecution Insights