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 .
Response to Amendment
Applicant’s amendment to claims 4,7 and 8 have overcome each and every objection set forth in previous office action mailed on 03/02/2026. Therefore the objection regarding claims 4,7 and 8 are withdrawn. Note: Claim 6 is cancelled by the applicant therefore the previous objection for claim 6 is not applicable.
Applicant’s argument regarding 35 U.S.C. l 12(b) for claim 2 have been fully considered and persuasive and therefore the rejection made under 35 U.S.C.112(b) as set forth in previous office action mailed on 03/02/2026 is withdrawn.
Applicant’s argument that neither in combination nor individually Dress et al., Engelstein et al. and other cited prior arts of record teach the newly amended limitations, receiving a request to obtain block energy hedges for a predetermined
period of time, generating, based on the request, energy hedge information including an amount of block energy hedges to purchase for the period of time, and purchasing, based on the energy hedge information, the amount of block energy hedges for the period of time; at a second time, wherein executing the component control instructions causes the refrigeration system to automatically actuate to draw a quantity of energy from the energy storage system, the quantity of energy being determined, based on the purchased amount of block energy hedges and the facility energy consumption, to maintain facility operations at or below a facility operational set point have been fully considered but in moot in view of newly cited reference Naserimojarad. Newly cited prior art, Naserimojarad teaches, in [0113], [0119], [0120], [0123] and [0125] that based on current energy agreements which has purchased power blocks from the grid ahead of time as taught in [0122], determine the target energy consumption for the data center as taught in [0120] including load for cooling as taught in [0125], determine the allocation of energy resources such as from energy storage in addition to grid and behind the meter energy generating station to be drawn by the various loads of the data center including cooling load (refrigeration). Newly cited prior art CN59 teaches to purchase certain blocks of energy for a future time, based on received request from a user as taught in page 3, last paragraph and page 4, 1st and 2nd paragraph. Naserimojarad and CN59 are analogous art because they are from the same field of endeavor that is predicting energy scheduling for a future time period based on certain conditions. Therefore it would have been obvious before the effective filing date of the claimed invention to a person of ordinary skill in the art to modify the system for controlling components in a facility based energy market condition, energy capacity of the facility, current operating conditions and purchased energy hedge information as taught by Naserimojarad by applying the known technique of purchasing the energy hedges or blocks based on user request for a future time as taught by CN59 to yield predictable results of controlling consumption of the loads for a facility for a period of time while considering energy hedge information and target power consumption limit.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1,8,9,16 and 21 are rejected under 35 U.S.C.103 as being unpatentable
over Naserimojarad (US 20260178403 A1) in view of CN59 (CN 112365059 A) and Drees et al.1 (US 20170102162 A1).
Regarding claim 1, Naserimojarad teaches, a system for controlling components in a facility based on energy hedges (automated control system associated with datacenter having datacenter controller controlling loads (components) in the datacenter based on purchased energy hedge, [0105], [0116] and [0122]), the system comprising:
a controller (datacenter controller, [0116]) configured to control the components in the facility (loads such as computing loads, non-computing loads such as cooling and others all of which are controlled by the datacenter controller, [0116] and [0125]), wherein the controller comprises processors and memory storing instructions that, when executed by the processors (controller having microprocessor and memory to implement system control, [0140]), causes the controller to perform a process comprising:
at a second time (over a scheduling horizon, [0130]), a facility energy consumption (controller monitoring actual compute usage and actual energy consumption of the datacenter, [0126] and [0131]);
monitoring real-time energy market conditions based on real-time energy
information from an energy grid data source (“…to receive information regarding energy price2, updates regarding the energy sources 150 and 152 (e.g., interruptions, announcements, notifications, price fluctuations, new incentive programs, and details about existing programs such as ancillary services or response programs) and energy agreements related to the energy grid…”, [0134]);
generating component control instructions based on the facility energy
consumption, the energy hedge information, and the monitored real-time energy market conditions (based on real time energy consumption and actual compute usage as taught in [0126] and [0131], energy price and energy hedge information from the energy contract, determine how to allocate and route computing tasks and energy sources for non-computing tasks such as cooling, [0122], [0125], [0126] and [0134]); and
executing the component control instructions (based energy hedge information and other conditions, modulate the loads on the datacenter such task computations and cooling to meet the target power consumption derived from the above cited conditions including purchased energy hedge information such energy blocks, [0115], [0119]-[0122] and [0125], see also [0161]),
wherein the components in the facility comprise a refrigeration system and an energy storage system (cooling system cooling the servers of the datacenter and energy storage system powering up the data center when needed, [0126] and [0201]), and
wherein executing the component control instructions causes the refrigeration system to automatically actuate to draw a quantity of energy from the energy storage system (the cooling system in addition to servers performing computational tasks will draw certain amount of energy from the energy storage system based on the target energy consumption since the controller determines to allocate capacity and route computing task based on current usage, energy storage information and energy hedge information, [0119],[0122] and [0125]),
the quantity of energy being determined (target power consumption, [0120]), based on the purchased amount of block energy hedges and the facility energy consumption, to maintain facility operations at or below a facility operational set point3 (based on energy hedge information, current energy usage as taught in [0131],the target power consumption for the datacenter is determined, such that the datacenter loads are modulated below the target power consumption, [0119] and [0120]).
Naserimojarad does not teach the details of receiving a request to obtain block energy hedges for a predetermined period of time. However Naserimorajad explicitly teaches in [0113], [0116], [0122] that based purchased energy blocks as energy hedges, the loads of the datacenter are modulated such that the total load consumption does not go above target power consumption as taught in [0119] and [0120].
On the other hand CN59 teaches, at a first time (at time t0, page 4, 2nd paragraph), receiving a request to obtain block energy hedges for a predetermined period of time (at time t0, a user issues a request to buy energy blocks for a future period of time, page 3, last paragraph and page 4, 1st paragraph),
generating, based on the request , energy hedge information including an
amount of block energy hedges to purchase for the period of time (based on the issued user request, the system returns a final result of energy block that can be bought for the time slot, page 3, last paragraph and page 4, 1st and 2nd paragraph ), and
purchasing, based on the energy hedge information, the amount of block
energy hedges for the period of time (based on user issued request, the final result of the energy block including all the energy block transaction amount (purchased energy block) is published on the network that is energy blocks purchased per received user request).
Naserimojarad and CN59 are analogous art because they are from the same field of endeavor that is predicting energy scheduling for a future time period based on certain conditions.
Therefore it would have been obvious before the effective filing date of the claimed invention to a person of ordinary skill in the art to modify the system for controlling components in a facility based energy market condition, energy capacity of the facility, current operating conditions and purchased energy hedge information as taught by Nasermojarad by applying the known technique of purchasing the energy hedges or blocks based on user request for a future time as taught by CN59 to yield predictable results of controlling consumption of the loads for a facility for a period of time while considering energy hedge information. Neither in combination nor individually Nasermojarad and CN59 teach the details of determining, based on current operating conditions and energy capacity of the facility, energy consumption for over at least a portion of the period of time. However Naserimojarad teaches to monitor actual power usage and SOC of the energy storage system in real time instead of future time-portion of the predetermined period of time.
Drees et al. teaches, determining, based on current operating conditions and energy capacity of the facility, over at least a portion of the period of time (the central controller predicts energy loads for plurality of time steps (portion of the period of time) of the optimization period based on real-time power consumption of the building (current operating condition of the facility) and SOC (energy capacity) of battery as taught in [0114] and [0264]).
Drees et al., Naserimojarad and CN59 are analogous art because they are from the same field of endeavor that is predicting energy scheduling for a future time period based on certain conditions.
Therefore it would have been obvious before the effective filing date of the claimed invention to a person of ordinary skill in the art to modify the system for controlling components in a facility based on energy hedges where facility consumption is determined in real time as taught by combination of Naserimojarad and CN59 by applying the known technique of predicting energy usage for each time step of future optimization period or portion of predetermined period of time based on current operating conditions and energy capacity of the facility as taught by Drees et al. to yield predictable results of controlling energy in a facility based on predicted energy usage.
Regarding claim 8, combination of Nasermojarad, CN59 and Drees et al. teach the system of claim 1. In addition, Drees et al. teaches, wherein the process performed by the controller (controller in view of Nasermojarad) further comprises:
determining, based on monitoring the real-time energy market conditions
(actual utility rates and other data from the utility are received and monitored by the
BMS controller to monitor energy market condition, [0086], [0087] and [0127]), an
upcoming energy event (demand response layer of the BMS controller predicts the
building is close to peak demand-upcoming energy event, [0087] and [0112]), the
upcoming energy event being at least one of an energy shortage and an energy surplus in the energy market (the demand response layer knows when is the off-peak hours so the energy market will have surplus energy to charge batteries at lower cost and also when the market will have peak demand that is energy shortage, so the building will use stored energy during energy market shortage hours, [0087] and
[0103]);
generating, based on the upcoming energy event and the current operating
conditions, a recommendation for a facility adjustment to maintain facility
operations at or below a predetermined facility operational setpoint during a time
corresponding to the upcoming energy event (based on the received inputs, the
demand response layer knows when there will be peak demand and where there will be
surplus energy in non-peak hours during an optimization window. Based on these
information, the demand response layer generate setpoints to control the building
components by turning some of the components off, or operating the chiller at variable
capacity during peak to curtail energy usage or with stored electrical energy during peaks hours to reduce electricity usage from the grid (load shifting) that is operating the
facility at or below operational setpoint and purchase energy from the grid during nonpeak hours to charge electrical energy storages, [0086], [0087], [0103]-[0105], [0107] and [0108]);and
returning the recommendation to a centralized controller (BMS controller in
communication with the demand response layer, [0074] and [0086]) of the facility for
automatic execution by components in the facility (" ... In some embodiments,
demand response layer 414 includes a control module configured to actively initiate
control actions (e.g., automatically changing setpoints4 which minimize energy costs
based on one or more inputs representative of or based on demand (e.g., price. a
curtailment signal. a demand level. etc.). .. ", [0088] and [0087], see also [0115]).
Regarding claim 9, combination of Nasermojarad, CN59 and Drees et al. teach the system of claim 1. In addition, Drees et al. teaches, wherein generating the
recommendation comprises at least one of:
(i) determining a threshold amount of energy usage to cut during the time
corresponding to the upcoming energy event (the controller determines the
predetermined amount of power (threshold amount) to be shed during peak usage
(upcoming energy event), [0111 ],[0112] and [0092]),
(ii) determining a threshold quantity of energy hedges of the facility to sell
back to the energy market while maintaining execution of the facility operations
at or below the predetermined facility operational setpoint (demand response optimizer of the controller determines for each time step of the optimization window
exactly what amount of energy to be sold to the utility (threshold quantity of energy
hedge), control setpoints for various components of the sub-plant of the building such
the reducing energy usage while still meeting required load setpoint (operational
setpoint) of the sub-plant of the building, [0112], [0131] and [0137]), or
(iii) determining a threshold quantity of energy hedges to store during the
time corresponding to the upcoming energy event to maintain execution of the
facility operations at or below the predetermined facility operational set point
(demand response optimizer of the controller determines for each time step of the
optimization window exactly what amount of energy to be purchased from the utility and
charge/discharge rates of the storage sub-plant (threshold quantity of energy hedge to
store5), control setpoints for various components of the sub-plant of the building such the reducing energy usage while still meeting required load setpoint (operational setpoint6) of the sub-plant of the building, [0112], [0131] and [0137]).
Regarding claim 16, combination of Nasermojarad, CN59 and Drees et al. teach the system of claim 1. In addition, Drees et al. teaches, wherein the controller is further configured to:
receive facility information and energy market conditions information (the
controller receives occupancy status, room schedules, energy use of the building, price
of energy usage and other data, [0086] and [0114]), wherein the facility information
indicates an amount of energy that was used to control the components in the
facility to perform the operational tasks (controller receives electrical use, thermal load measurements of the building and other additional data from various sources,
[0086], [0114] and [0134]);
determine a quantity of energy hedges to sell back to the energy market
based on the amount of energy that was used to control the components in the
facility to perform the operational tasks and the market conditions information
(based on the received inputs including energy use of the building as taught in [0086],
demand response optimizer of the controller determines for each time step of the
optimization window exactly what amount of energy to be sold7 to the utility (energy
hedge to sell back), control setpoints for various components of the sub-plant of the
building such the reducing energy usage while still meeting required load setpoint
(operational setpoint) of the sub-plant of the building, [0112], [0131] and [0137]); and
return a recommendation to sell the quantity of the energy hedges back to
an energy market over a future period of time (decisions made by the controller
including the amount of energy to sell back, store and others are presented to the user
in real time as system health dashboard, [0137], [0134], [0131] and [0240]).
Regarding claim 21, combination of Nasermojarad, CN59 and Drees et al. teach the system of claim 1. In addition Naserimojarad teaches, wherein the quantity of energy is determined, based on the purchased amount of block energy hedges and the facility energy consumption, to maintain the facility energy consumption at or below the purchased amount of block energy hedges for the period of time (the target power consumption is determined based purchased energy block information based of which the loads of the datacenter are modulated to stay below the target power consumption, [0119],[0120] and [0125]).
Claims 2-4 and 7 are rejected under 35 U.S.C.103 as being unpatentable
over Naserimojarad (US 20260178403 A1) in view of CN59 (CN 112365059 A) and Drees et al. (US 20170102162 A1) and Sun et al. (US 20220284458 A1).
Regarding claim 2, combination of Naserimojarad, CN59 and Drees et al. teach the system of claim 1. In addition Drees et al. teaches, wherein the controller is configured to generate the energy hedge information8 based on:
receiving facility information and energy market conditions information for a predetermined period of time (the demand response layer of the BMS controller
receiving room schedules, thermal load measurement of the building, energy price data
and energy availability data from the utility plurality of time steps of the optimization
window - predetermined period of time, [0086], [0114], [0116] and [0127]);
generating, based on the output, recommendations for purchasing or
selling a portion of the predicted block energy hedges over the future period of
time (based on the received inputs, the demand response layer knows when there will
be peak demand and where there will be surplus energy in non-peak hours during an
optimization window-future period of time. Based on these information, the demand
response layer generate setpoints to control the building components by turning some
of the components off, or operating the chiller at variable capacity during peak to curtail
energy usage or with stored electrical energy during peaks hours to reduce electricity
usage from the grid (load shifting) that is operating the facility at or below operational
setpoint and purchase energy from the grid during non-peak hours to charge electrical
energy storages and also determine exact amount to energy to sell back to the utility
during a particular time step of the optimization window and purchase exact amount of
energy from the utility for a certain time step of the optimization window, [0086], [0087],
[0103]-[0105], [0108] and [0112]); and
returning the recommendations for purchasing the portion of the predicted
block energy hedges over the future period of time (demand response optimizer of the controller determines for each time step of the optimization window exactly what
amount of energy to be purchased from the utility9 (purchasing energy from utility in a
certain time in future that is a time step of the optimization window, [0105], [0112],
[0131] and [0137]).
Neither in combination nor individually Naserimojarad, CN59 and Drees et al. teach the details of retrieving, from a data store, a model that was trained to predict energy market conditions over a future period of time; providing, to the model, at least a portion of the received information as input; receiving, from the model, output indicating the predicted energy market conditions over the future period of time, wherein the model was trained to predict block energy hedges over the future period of time and quantify energy prices based on the predicted block energy hedges. However Drees et al. teaches to receive utility price data in real time and during optimization window to in
addition to other data to determine control decisions to run the building as taught in
[0127] and [0086].
On the other hand Sun et al. teaches, retrieving, from a data store (memory,
[0069]), a model that was trained to predict energy market conditions over a future
period of time (VPP optimization models for determining optimal scheduling and
trading strategy for the VPP (virtual power plant) retrieved from the memory, [0064] and
[0069]);
providing, to the model, at least a portion of the received information as
input (the VPP optimization models receive data related to power consumption and
production forecast, future market pricing, future market uncertainty and other data,
[0019], [0065] and [0069]);
receiving, from the model, output indicating the predicted energy market
conditions over the future period of time (forecasting selling price for the pool
market, forecasting buying and selling strip prices in the future market during the future
time horizon-future period of time, [0032] and [0084]), wherein the model was trained
to predict block energy hedges over the future period of time and quantify energy
prices based on the predicted block energy hedges (" ... Specifically, the algorithm10
outputs the amount of energy that should be charged or discharged, the amount of
power consumption that should be curtailed, as well as how much powers should be
bought or sold at the pool or future markets during the time horizon to maximize total
revenue for energy reading while minimizing total cost for production11. This process or
portions thereof may be repeated at the beginning of each future/pool time interval to
maximize profits while satisfying the constraints such as keeping the storage level within
its minimum and maximum capacity at all times ... ", [0084], [0019], [0032], [0065] and
[0069]).
Drees et al., Naserimojarad, CN59 and Sun et al. are analogous art because they are from the same field of endeavor that is predicting energy scheduling for a future time period based on certain conditions.
Therefore it would have been obvious before the effective filing date of the
claimed invention to a person of ordinary skill in the art to modify the system controlling
components of a facility based on facility information and energy market condition as
taught by combination of Drees et al., Naserimojarad, CN59 by applying the known technique of using a trained model for predicting energy market condition and quantify
energy prices based on the predicted block energy hedges (buying and selling energy
strip price over a future time) as taught by Sun et al. to yield predictable results of predicting future market energy price information for buying or selling energy to maximize total future market revenue by strategically mixing selling and buying decisions for future Option and strip contracts as taught by Sun et al. in [0032].
Regarding claim 3, combination of Naserimojarad, CN59, Drees et al. and Sun et al. teach the system of claim 2. In addition Drees et al. teaches, wherein returning the recommendations comprises:
transmitting, to a user device, the recommendations and at least a portion
of the predicted energy market conditions (system health dashboard provided to user with information related energy market condition, building energy usage, setpoints and others, [0134] and [0241]),
wherein the user device is configured to present, in the GUls (dashboard,
[0134]), the recommendations, the at least portion of the predicted energy market
conditions, and a recommendation to purchase a predetermined quantity of the
predicted block energy hedges over the future period of time (the dashboard also
presents recommended optimized amount of energy to be sold or purchased by the
system as taught in [0086], [0105] and [0134]).
Regarding claim 4, combination of Naserimojarad, CN59, Drees et al. and Sun et al. teach the system of claim 2. In addition Sun et al. teaches, wherein based
on the model output, the process further comprises:
determining, based on identifying that the facility is operating at or below a
facility operational set point (in view of Drees et al., reducing energy usage while still
meeting required load setpoint (operational setpoint) of the sub-plant of the building,
[0112], [0131] and [0137]), (i) a quantity of the predicted block energy hedges to
buy in the energy market (forecasting buying and selling strip prices in future market,
[0032]) and (ii) a segment of time over the future period of time at which to buy the
quantity of the predicted block energy hedges (optimized energy trading and
scheduling showing how much energy should be sold or purchased during certain time
horizon of the future market, [0084] and [0032]).
Regarding claim 7, combination of Naserimojarad, CN59, Drees et al. and Sun et al. teach the system of claim 2. In addition Drees et al. teaches, wherein the
Process performed by the controller further comprises: determining at least one of (i) a facility operational setpoint to reduce facility load over the future period of time and (ii) component operational setpoints to reduce respective component loads over the future period of time (based on the received inputs, the demand response layer knows when there will be peak demand and where there will be surplus energy in non-peak hours during an optimization window-future period of time. Based on these information, the demand response layer generate setpoints to control the building components by turning some of the components off, or operating the chiller at variable capacity during upcoming peak (future time) to curtail energy usage or with stored electrical energy during upcoming peaks hours to reduce electricity usage from the grid (load shifting) that is operating the facility at or below operational setpoint and purchase energy from the grid during non-peak hours in the future to charge electrical energy storages and also determine exact amount to energy to sell back to the utility during a particular time step of the optimization window and purchase exact amount of energy from the utility for a certain time step of the optimization window, [0086], [0087], [0103]-[0105], [0108] and [0112]).
Claim 20 is rejected under 35 U.S.C.103 as being unpatentable over Naserimojarad (US 20260178403 A1) in view of CN59 (CN 112365059 A) and Drees et al. (US 20170102162 A1) and Green et al. (US 20170288402 A1).
Regarding claim 20 combination of Naserimojarad, CN59 and Drees et al. teach the system of claim 1. In addition Drees et al. teaches, executing the component control instructions (controller automatically changing setpoints,[0088] and [0074]).
Neither in combination nor individually Nasermojarad, CN59 and Drees et al. teach the details of activating a blast cell to perform item freezing operations using energy that is stored by an energy storage system in response to detecting an energy surplus in the energy market.
Green teaches, activating a blast cell to perform item freezing operations
using energy that is stored by an energy storage system in response to detecting
an energy surplus in the energy market (turning on a freezer (blast cell) when excess power is available especially when energy is stored as some physical parameter or
variable, [0008]).
Naserimojarad, CN59 and Drees et al. and Green are analogous art because they are from same field of endeavor that is predicting energy scheduling for a future/desired time period based on certain conditions.
Therefore it would have been obvious before effective filing date of the claimed
invention to a person of ordinary skill in the art to modify the system controlling
components in a facility based on energy hedges as taught by combination Naserimojarad, CN59 and Drees et al. by applying the known technique of turning on/activating a freezer (blast cell) when surplus energy is available as taught by Green to yield predictable results of controlling power load distributions to the loads with additional degree of freedom during times of power shortage and excess power as taught by Green in [0006] and [0008].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Jakagnanam et al. (US 20110071882 A1) teaches a method for forecasting energy demand and energy price for a future horizon where a set of hedge contract for a future horizon are selected for purchase and to sell back when forecasted unused energy as taught in [0135]-[0139].
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANZUMAN SHARMIN whose telephone number is (571)272-7365. The examiner can normally be reached M and Th 7:00am - 3:00pm and Tue 8:00am-12:00pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, KAMINI SHAH can be reached at (571)272-2279. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANZUMAN SHARMIN/ Examiner, Art Unit 2115
/MARK A CONNOLLY/ Primary Examiner, Art Unit 2115
7/31/26
1 Cited prior art of record.
2 Real time market condition.
3 Target power consumption.
4 Automatic execution of the setpoints by the components in facility/building by the demand response
layer in communication with the BMS controller (central controller).
5 Energy hedge information in view of Naserimojarad, [0122].
6 Total power consumption in view of Naserimojarad.
7 Energy hedge in view of Naserimojarad, [0122]-energy hedges or energy selling back to the energy grid.
8 Energy hedge information in view of Naserimojarad.
9 Energy block in view of CN59.
10 The model.
11 Predicting block energy hedge over future period of time and forecasting buying and selling strip price
(hedge price) in the future market as taught in [0032].
Prosecution Insights