DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
Claims 1-20 are pending.
Claims 1-20 are rejected, grounds follow.
THIS OFFICE ACTION IS FINAL, see additional information at the conclusion of this action.
Response to Arguments
Applicant’s arguments, see Remarks pages 8 et seq, filed 08 April 2026, with respect to the rejection(s) of claim(s) 1-20 (Claim 1 representative) under 35 USC 102/103 have been fully considered and are persuasive. Examiner notes that elements of Claim 4 were incorporated into Claim 1 along with additional limitations. Examiner thanks Applicant for addressing the rejection of Claim 4 in the remarks. Applicant Argues that neither Chen nor Bhargava teaches at least “proactively generat[ing]… recommended load curtailment information, the recommended load curtailment information included details for load curtailment in the power grid.” Examiner agrees that neither of these references appears to teach or fairly suggest the amended limitation. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made over Chen in view of Wenzel et al., US Pg-Pub 2013/0085614.
Because the new ground(s) of rejection are necessitated by Applicant’s Amendment, this action is made final. See additional information at the conclusion of this action.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al., US Pg-Pub 2013/0257372 in view of Wenzel et al., US Pg-Pub 2013/0085614.
Regarding Claim 1, Chen teaches:
A system (see figs. 1-4) comprising: a computer-readable storage medium (see e.g. fig. 4 and [0043]) having executable instructions ([0044] “processor 802 may retrieve (or fetch) the instructions”) for providing charging control to a plurality of electric controllable assets (fig. 1, EVs 115) which include at least some electric vehicles (EVs); (([0004] “determining optimal recharging schedules for electric vehicles within an electricity distribution network”) and one or more computer processors ([0044] “processor 802”, see fig. 1, server 120)
configured to execute the instructions to: determine, by a power flow analyzer, (e.g. [0033] “suitable power flow analysis method”) power flow information of a power grid, (fig. 1, grid 101; see [0019] “Particular embodiments may optimize electric vehicle recharging within the power system by optimizing the total demand of the power system. … Total Demand (t) and H(t), the base demand profile (e.g. in kW) for all households at a particular time step (t).”)
the power grid for providing power to the controllable assets for charging, ([0011] “An electrical substation 102 at the edge of power grid 101 may transform electricity from power grid 101 from a high voltage (e.g., 110 kV or higher) to an intermediate voltage (e.g., 50 kV or less), and deliver electricity to end customers through an electricity distribution network 120.”)
the power flow information including technical constraint information relating to the power grid; ([0033] “In particular embodiments, server 120 may determine whether any system constraint violation exists (at 303).”)
generate, by a charging controller, charging control information ([0031] “server 120 may determine optimal recharging schedules for electric vehicles within the power system without system constraints over a pre-determined period of time T (at 301).”) for providing charging control to the controllable assets based on the power flow information; ([0031] “For example, server 120 may determine optimal recharging schedules by optimizing the total demand of the power system based on customer recharging schedules (defined by the plug-in time TP.sub.i and the unplug time TUP.sub.i for each i-th node connecting to an electric vehicle) described earlier.”) …wherein the generating attempts to minimize violations of the technical constraint information in the power grid. ([0036] “In particular embodiments, server 120 may adjust recharging schedules for identified electric vehicles based on the sensitivity coefficients. For example, assume that electric vehicle recharging at the i-th node is identified as causing a system constraint violation for the m-th component at a particular time step t. Server 120 may adjust the recharging schedule at the i-th node by adjusting charging load E.sub.i(t) at the i-th node at the particular time step t based on sensitivity coefficient .alpha..sub.im”)
provide the charging control information to at least some of the controllable assets; ([0040] “server 120 may transmit messages to charging equipment 111; the messages may include optimal recharging schedules determined by the example method of FIG. 3, causing charging equipment 111 to recharge respectively connected electric vehicles accordingly.”)
provide at least part of the charging control information to the power flow analyzer; (see fig. 3, particularly the lines from 305 and 306 back to 302, and [0035] “for each system constraint violation for the m-th component, the charging loads for the i-th electric vehicles identified with the highest sensitivity coefficient .alpha..sub.im may be adjusted first. In other embodiments, the charging loads for all electric vehicles with positive sensitivity coefficients may be adjusted.”)
and determine, by the power flow analyzer, new power flow information of the power grid for a subsequent time period based on the at least part of the charging control information. (see fig. 3, and [0038] “After adjusting recharging schedules for identified electric vehicles at the current time step t, server 120 may determine system outputs for the current time step (at 302), and determine whether any system constraint violation exists (at 303). In some embodiments, if there are no system constraint violations, the adjusted recharging schedule is the optimal solution; in other embodiments, it may be the near-optimal solution. Server 120 may increment time step (at 306), determine system outputs (at 302), and determine whether any system constraint exists for the next time step (at 303).”)
Chen differs from the claimed invention in that:
Chen does not appear to clearly articulate: proactively generate, by a demand-response recommender, recommended load curtailment information, the recommended load curtailment information including details for load curtailment in the power grid;
Nor [generating charging controller information … based on power flow information] and the recommended load curtailment information
However, Wenzel teaches a demand response recommender (DR Layer 112) which proactively generates recommended load curtailment information ([0055] “The smart building manager 106 or the DR layer 112 may plan these activities or proactively begin load shedding based on grid services capacity forecasting conducted by a source on the smart grid or by a local algorithm (e.g., an algorithm of the demand response layer).”) and which includes details for load curtailment in the grid ([0051] “The DR layer 112 may be configured to bi-directionally communicate with the smart grid 104… to exchange … curtailable load calculations (e.g., the amount of load calculated by the DR layer to be able to be shed without exceeding parameters defined by the system or user), load profile forecasts, and the like”) as well as generating charging controller information based in part on the load curtailment information ([0056] “The DR layer 112 could then activate the PHEV charging station based upon that policy unless a curtailment event is expected (or occurs) or unless the DR layer 112 otherwise determines that charging should not occur (e.g., decides that electrical storage should be conducted instead to help with upcoming anticipated peak demand).”)
Wenzel is analogous art because it is from the same field of endeavor as the claimed invention and other references of EV charging scheduling in power distribution systems.
Accordingly, Examiner finds 1) the prior art contained a ‘base’ device (method, or product) upon which the claimed invention can be seen as an “improvement”– the system (method, etc.) of Chen, upon which the incorporation of proactive load curtailment features can be regarded as an “improvement”; 2) the prior art contained a “comparable” device (method, or product, that is not the same as the base device) that has been improved in the same way as the claimed invention; - the teachings of Wenzel, which includes a load consumer which may include PHEV assets that has been improved by the incorporation of proactive load curtailment analysis which may be reported back to the grid or used to adjust charging of PHEVs; 3) one of ordinary skill in the art before the effective filing date of the application could have applied the known “improvement” technique in the same way to be the “base” device (method or product) and the results would have been predictable to one of ordinary skill in the art because Wenzel teaches the generation and use of curtailable load information to support the grid during peak demand ([0055] “if energy is predicted to be expensive during a time when the DR layer 112 determines it can shed extra load or perhaps even enter a net-positive energy state using energy generated by solar arrays, or other energy sources of the building or campus, the DR layer 112 may offer units of energy during that period for sale back to the smart grid” [0056] “(e.g., decides that electrical storage should be conducted instead to help with upcoming anticipated peak demand). When such a decision is made, the DR layer 112 may pre-charge the vehicle… during such periods, the smart building manager 106 or the DR layer 112 may be configured to cause energy to be drawn from plugged-in connected vehicles to supplement or to provide back-up power to grid energy.”)
Regarding Claims 9 and 17, these claims recite substantively the same subject matter, except embodied as a method and a non-transitory computer readable medium, respectively; Mutatis mutandis, these claims are likewise obvious over Chen in view of Wenzel or the same reasons articulated with respect to claim 1.
Regarding Claims 2, 10, and 18, Chen in view of Wenzel teaches all of the limitations of parent claims 1, 9 and 17, respectively;
Chen further discloses:
(claim 2 representative) generate, by the charging controller, new charging control information associated with the subsequent time period based on the new power flow information. ([0034] “If there is no system constraint violation, server 120 may increment time step t (at 306). Server 120 may continue determining system outputs (at 302), and determining whether any system constraint violation exists for the next time step (at 303).”)
Regarding Claims 3 and 11, Chen in view of Wenzel teaches all of the limitations of parent claims 1, 9, and 10, respectively;
Chen further discloses:
(claim 3 representative) wherein the determining the power flow information of the power grid by the power flow analyzer ([0029] “The determining of the power system's outputs based on a set of inputs may be implemented using power flow analysis algorithms”) is based on topology information and electrical measurement information of the power grid. ([0026] “The example method of FIG. 2 may determine sensitivity of the power system (e.g., voltages or currents at bus nodes and components) caused by each potential electric vehicle-connecting node.” [0028] “Particular embodiments may determine a sensitivity coefficient .alpha..sub.im for the m-th bus node or component in response to electric vehicle recharging at the i-th node by calculating the ratio between the change in voltage or current at the m-th bus node or component and the change in load at the i-th node”)
Regarding Claims 4 and 12, Chen in view of Wenzel teaches all of the limitations of parent claims 1 and 9, respectively;
Wenzel further teaches:
Wherein the recommended load curtailment information is provided an operator of the power grid. ([0051] “The DR layer 112 may be configured to bi-directionally communicate with the smart grid 104 or energy providers and purchasers 102 (e.g., a utility, an energy retailer, a group of utilities, an energy broker, etc.) to exchange price information, demand information, curtailable load calculations (e.g., the amount of load calculated by the DR layer to be able to be shed without exceeding parameters defined by the system or user), load profile forecasts, and the like.”)
Accordingly, Examiner finds 1) the prior art contained a ‘base’ device (method, or product) upon which the claimed invention can be seen as an “improvement”– the system (method, etc.) of Chen, upon which the incorporation of proactive load curtailment features can be regarded as an “improvement”; 2) the prior art contained a “comparable” device (method, or product, that is not the same as the base device) that has been improved in the same way as the claimed invention; - the teachings of Wenzel, which includes a load consumer which may include PHEV assets that has been improved by the incorporation of proactive load curtailment analysis which may be reported back to the grid or used to adjust charging of PHEVs; 3) one of ordinary skill in the art before the effective filing date of the application could have applied the known “improvement” technique in the same way to be the “base” device (method or product) and the results would have been predictable to one of ordinary skill in the art because Wenzel teaches the generation and use of curtailable load information to support the grid during peak demand ([0055] “if energy is predicted to be expensive during a time when the DR layer 112 determines it can shed extra load or perhaps even enter a net-positive energy state using energy generated by solar arrays, or other energy sources of the building or campus, the DR layer 112 may offer units of energy during that period for sale back to the smart grid” [0056] “(e.g., decides that electrical storage should be conducted instead to help with upcoming anticipated peak demand). When such a decision is made, the DR layer 112 may pre-charge the vehicle… during such periods, the smart building manager 106 or the DR layer 112 may be configured to cause energy to be drawn from plugged-in connected vehicles to supplement or to provide back-up power to grid energy.”)
Regarding Claims 5 and 13, Chen in view of Wenzel teaches all of the limitations of parent claims 2 and 10, respectively;
Wenzel further teaches:
Wherein the recommended load curtailment information is provided to the power flow analyzer ([0056] “the DR layer 112 may also be configured to support a "Grid Aware" plug-in hybrid electric vehicle (PHEV)/electric vehicle charging system” nb. Wenzel also teaches provided curtailable load information to the grid operator (see [0051]; and Chen teaches the server 120 (the power flow analyzer of Chen) may be operated by, variously the grid operator or a third party, see Chen [0030])
And Chen further teaches:
Wherein determining new power flow information of the power grid for the subsequent time period is based on the recommended load curtailment information; (Chen teaches constraints including maximum delivered real power (P) and reactive power (Q); [0024] “That is, optimization of the total demand may be subject to system constraints at the PQ buses where the real power |P| and the reactive power |Q| are specified”)
And wherein the generating, by the charging controller, of the new charging control information associated with the subsequent time period is based on the new power flow information. ([0041] “Particular embodiments may optimize total demand of the power system further based on fairness among electric vehicle recharging customers. For example, assume that the charging load for a particular electric vehicle is reduced for time step t as at 305 of the example method of FIG. 3. Particular embodiments may compensate (or penalize less) the particular electric vehicle for the next time step t+1 to avoid service starvation for the particular electric vehicle.”)
Accordingly, Examiner finds 1) the prior art contained a ‘base’ device (method, or product) upon which the claimed invention can be seen as an “improvement”– the system (method, etc.) of Chen, upon which the incorporation of proactive load curtailment features can be regarded as an “improvement”; 2) the prior art contained a “comparable” device (method, or product, that is not the same as the base device) that has been improved in the same way as the claimed invention; - the teachings of Wenzel, which includes a load consumer which may include PHEV assets that has been improved by the incorporation of proactive load curtailment analysis which may be reported back to the grid or used to adjust charging of PHEVs; 3) one of ordinary skill in the art before the effective filing date of the application could have applied the known “improvement” technique in the same way to be the “base” device (method or product) and the results would have been predictable to one of ordinary skill in the art because Wenzel teaches the generation and use of curtailable load information to support the grid during peak demand ([0055] “if energy is predicted to be expensive during a time when the DR layer 112 determines it can shed extra load or perhaps even enter a net-positive energy state using energy generated by solar arrays, or other energy sources of the building or campus, the DR layer 112 may offer units of energy during that period for sale back to the smart grid” [0056] “(e.g., decides that electrical storage should be conducted instead to help with upcoming anticipated peak demand). When such a decision is made, the DR layer 112 may pre-charge the vehicle… during such periods, the smart building manager 106 or the DR layer 112 may be configured to cause energy to be drawn from plugged-in connected vehicles to supplement or to provide back-up power to grid energy.”)
Regarding Claims 6 and 14, Chen in view of Wenzel teaches all of the limitations of parent claims 1 and 9, respectively;
Chen further discloses:
(Claim 6 representative) wherein the generating charging control information is further based on predicted load information of the power grid, wherein the predicted load information includes a predicted electric load in a section of the power grid in a future time period. ([0019] “wherein H(t) is the base demand profile (e.g., in kW) for all households of the power system at the particular time step t (without electric vehicle recharging). H(t) may be a measured number or a predicted number based on previous energy consumption.”)
Regarding Claims 7, 15 and 19, Chen in view of Wenzel teaches all of the limitations of parent claims 1, 9, and 17, respectively;
Chen further discloses:
wherein the technical constraint information of the power grid comprises operational limit information of one or more distribution transformers in the power grid. ([0024] “Meanwhile, optimization of the total demand may be subject to system constraints at components such that the components (e.g., power lines, transformers, switches, etc.) should be operated within the maximum-rated capacity (e.g., maximum-allowable current level).”)
Regarding Claims 8, 16, and 20, Chen in view of Wenzel teaches all of the limitations of parent claims 1, 9, and 17, respectively;
Chen further discloses:
wherein the generating charging control information is based at least in part on an optimization for minimizing violations of the technical constraint information in the power grid. ([0037] “In some embodiments, if there are multiple system constraint violations, charging loads for electric vehicles identified with the most significant system constraint violations may be adjusted first.” [0038] “After adjusting recharging schedules for identified electric vehicles at the current time step t, server 120 may determine system outputs for the current time step (at 302), and determine whether any system constraint violation exists (at 303).”)
Conclusion
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.
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/J.T.S./Examiner, Art Unit 2119
/ZIAUL KARIM/Primary Examiner, Art Unit 2119
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