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
The Applicant’s Amendment filed on 03/17/2026 in which claims 1, 5, 9, 11, 13-14, 16, and 19 have been amended and entered of record.
Claims 1 and 5 have been amended herein to correct the informalities. Based on the amended claims, the objections to the claims are withdrawn.
Claim 16 has been amended herein to overcome the rejections under 35 U.S.C 112(b). Therefore, the rejections 35 U.S.C 112(b) are withdrawn.
Claims 1-20 are presented for examination.
Information Disclosure Statement
Information Disclosure Statements (IDS) filed on 03/17/2026 was considered.
Response to Argument
Applicant's arguments filed on 03/17/2026 with respect to the amended independent claim 1 have been considered but are not persuasive. Applicant argues that Cherian fails to disclose a "distribution zonal controller configured to: .. . determine a distribution zonal orchestration index for the assigned distribution zone based on the distribution zonal operational data, the distribution zonal orchestration index defining a measure of operation of the assigned distribution zone relative to baseline operational data for the assigned distribution zone; ... [and] determine an adaptive control action for one or more distribution control devices in the assigned distribution zone based on the distribution zonal orchestration index for the assigned distribution zone and a distribution zonal orchestration index for another one of the plurality of distribution zones . . ." as is recited in claim 1.
The arguments have been considered but are not persuasive. The Examiner respectfully disagrees. Per Applicant disclosure, the distribution zonal orchestration index could be a machine learning model and may receive at least one of load data, control device settings, distributed energy resource (DER) generation profile data, or intermittency profile data for a distribution zone as input. The baseline machine learning model may then determine or identify one more baseline operational parameters or control device settings for the distribution zone as output. The baseline machine learning model may be trained on at least one of historical load data for the distribution zone [0102]. Cherian clearly discloses measuring load data and DER generation data and historical data [0061]-[0069] ([0061] “the regional control module 225 is also aware of energy consumption and demand by residential loads 250 and commercial loads 260” baseline is the power consumption of the loads vs power generated, [0080] further explains the power consumption/production of each regional control module based on historical data and sharing request/response with one another through enterprise control module 275).
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Cherian et al., US Patent Publication 20110106321; hereinafter “Cherian”.
Regarding claim 1, Cherian discloses a power grid system (Fig. 2-9) comprising:
an advanced distribution management system (ADMS) (enterprise control module 275) and
a distribution zonal measurement device (225, “manages power production, distribution, and consumption” [0059]) configured to measure at least one zonal distribution operational parameter for an assigned distribution zone (125) [0059]-[0063] of a plurality of distribution zones in a power grid (Fig. 2), each of the plurality of distribution zones having an associated distribution zonal measurement device (225); and
a distribution zonal controller (225) associated with the assigned distribution zone (125) and in communication with the distribution zonal measurement device [0059]-[0063] and the advanced distribution management system [0059]-[0063], each of the plurality of distribution zones having an associated zonal controller (225), the distribution zonal controller configured to:
receive distribution zonal operational data for the assigned distribution zone from the distribution zonal measurement device [0059];
determine a distribution zonal orchestration index for the assigned distribution zone based on the distribution zonal operational data [0059]-[0063] , the distribution zonal orchestration index defining a measure of operation of the assigned distribution zone relative to baseline operational data for the assigned distribution zone [0061]-[0069] ([0061] “the regional control module 225 is also aware of energy consumption and demand by residential loads 250 and commercial loads 260” baseline is the power consumption of the loads vs power generated, [0080] further explains the power consumption/production of each regional control module based on historical data and sharing request/response with one another through enterprise control module 275);
communicate the distribution zonal orchestration index to the ADMS [0059]-[0063] [0080];
determine an adaptive control action for one or more distribution control devices in the assigned distribution zone based on the distribution zonal orchestration index for the assigned distribution zone [0059]-[0063] [0066]-[0067] and a distribution zonal orchestration index for another one of the plurality of distribution zones (each regional having its own regional control module 225); and
communicate a command to the one or more distribution control devices based on the adaptive control action [0059]-[0063] [0066]-[0067] [0080].
Regarding claim 2, Cherian discloses the power grid system of claim 1 above, Cherian also discloses the ADMS is configured to dynamically classify the power grid into the plurality of distribution zones based on at least one of a power grid topology or a predefined logic [0023] [0059]-[0061].
Regarding claim 3, Cherian discloses the power grid system of claim 1 above, Cherian also discloses each of the plurality of distribution zones includes one or more distribution system assets (Fig. 2); and wherein the one or more distribution system assets include at least one of substation equipment (125), a distribution radial feeder line, or an outgoing line.
Regarding claim 4, Cherian discloses the power grid system of claim 1 above, Cherian also discloses each of the plurality of distribution zones includes one or more interconnections with another one of the plurality of distribution zones (205).
Regarding claim 5, Cherian discloses the power grid system of claim 1 above, Cherian also discloses each of the plurality of distribution zones transmits distribution operational data to the ADMS [0059]-[0063] [0066]-[0067].
Regarding claim 6, Cherian discloses the power grid system of claim 1 above, Cherian also discloses the distribution zonal operational data includes at least one of voltage, power factor, active/reactive power, load per node, battery based storage system (BESS) capacity, distributed energy resource (DER) generation, renewable energy resource (REN) energy source generation, frequency, electric vehicle (EV) load, micro grid generation, micro grid load, feeder voltage, feeder current, feeder load imbalance, or power quality data [0059]-[0063] [0066]-[0067].
Regarding claim 7, Cherian discloses the power grid system of claim 1 above, Cherian also discloses the command is configured to cause the one or more distribution control devices to perform the adaptive control action [0059]-[0063] [0066]-[0067].
Regarding claim 8, Cherian discloses the power grid system of claim 1 above, Cherian also discloses the command is configured to control at least one of an electric vehicle load (Fig. 2 shows vehicle loads connected to residential load250) or a passive load or an active load, and wherein the passive load is an industrial, commercial, or residential load that can be switched on or off and cannot be controlled for partial usage.
Regarding claim 9, Cherian discloses the power grid system of claim 1 above, Cherian also discloses at least one of the plurality of distribution zones is further divided into a plurality of sub-zones (215s) based on one or more sub-zoning rules or policies defined in the ADMS [0058]-[0060].
Regarding claim 10, Cherian discloses the power grid system of claim 9 above, Cherian also discloses the distribution zonal controller is further configured to: receive distribution operational data from at least one of the plurality of sub-zones [0058]-[0063].
Regarding claim 11, Cherian discloses the power grid system of claim 9 above, Cherian also discloses at least one of the plurality of distribution zones is further divided into a plurality of load clusters (215, 250, 260 and 210 of each 225) based on one or more clustering rules or policies defined in the ADMS [0058]-[0063].
Regarding claim 12, Cherian discloses the power grid system of claim 11 above, Cherian also discloses the assigned distribution zone includes a plurality of feeders [0058], and wherein the one or more clustering rules instructs the distribution zonal controller to divide each feeder into a sub-zone and to divide each load connection point via a distribution transformer in the sub-zone into a cluster [0060].
Regarding claim 13, Cherian discloses the power grid system of claim 1 above, Cherian also discloses the distribution zonal controller is further configured to:
communicate the distribution zonal orchestration index for the assigned distribution zone to another one of the plurality of distribution zones [0059]-[0063] [0066]-[0067] [0080].
Regarding claim 14, Cherian discloses the power grid system of claim 1 above, Cherian also discloses the baseline operational data includes one or more of a baseline load for the assigned distribution zone, a baseline setting for the one or more distribution control devices, a baseline distributed energy resource (DER) generation profile for the assigned distribution zone, or a baseline intermittency profile for the assigned distribution zone [0061]-[0069].
Regarding claim 15, Cherian discloses the power grid system of claim 13 above, Cherian also discloses the distribution zonal controller is configured to determine the baseline operational data via a baseline machine learning model that is trained on at least one of historical load data for the assigned distribution zone, simulated load data for the assigned distribution zone, historical settings for the one or more distribution control devices, historical distributed energy resource (DER) generation profile data for the assigned distribution zone, simulated distributed energy resource (DER) generation profile data for the assigned distribution zone, simulated intermittency profile data for the assigned distribution zone, or historical intermittency profile data for the assigned distribution zone (“prediction” indicates machine learning based on data collections and analytics [0077]-[0090] [0104]).
Regarding claim 16, Cherian discloses the power grid system of claim 1 above, Cherian also discloses the distribution zonal controller is further configured to:
identify distribution zonal operational violation based on the distribution zonal operational data and a distribution zonal policy [0019] [0020] [0061]-[0069], wherein distribution zonal policy includes at least one of adaptive control sources [0080], information on how to map adaptive control sources to violations, predictive control sources, information on how to map predictive control sources to violations, information on how to operate the plurality of distribution zones, information on how to form sub-zones or clusters based on power grid topology, machine learning objectives for the plurality of distribution zones, computing indices related to operation of the plurality of distribution zones, or information on coordinating between the plurality of distribution zones.
Regarding claim 17, Cherian discloses the power grid system of claim 1 above, Cherian also discloses the distribution zonal orchestration index is indicative of at least one of operational or non-operational violations in the assigned distribution zone [0058]-[0069].
Regarding claim 18, Cherian discloses the power grid system of claim 1 above, Cherian also discloses the adaptive control action is determined based on an adaptive control machine learning model that is trained on at least one of historical distribution zonal operational parameters, simulated distribution zonal operational parameters, historical distribution control measures, simulated distribution control measures, simulated distribution zonal adaptive policies, or historical distribution zonal adaptive policies [0060]-[0069] [0077]-[0090] [0104].
Regarding claim 19, Cherian discloses the power grid system of claim 1 above, Cherian also discloses the distribution zonal controller is further configured to:
receive distribution forecast data from at least one of the ADMS, a third-party engine, a server, a device [0019] [0104], or a website;
receive a distribution zonal predictive policy from the ADMS [0061]-[0063] [0090] [0104]; and
determine a predictive control action based on the distribution zonal operational data and the distribution zonal predictive policy [0061]-[0063] [0090] [0104].
Regarding claim 20, Cherian discloses the power grid system of claim 19 above, Cherian also discloses the predictive control action is determined based on a predictive control machine learning model that is trained on at least one of historical distribution zonal operational parameters [0061]-[0063] [0090] [0104], simulated distribution zonal operational parameters, historical distribution control measures, simulated distribution control measures, simulated distribution forecast data, or historical distribution forecast data.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THAI H TRAN whose telephone number is (571)270-0668. The examiner can normally be reached M - F 8:30 - 5:00.
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, Rexford Barney can be reached at 571-272-7492. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/THAI H TRAN/Examiner, Art Unit 2836
/REXFORD N BARNIE/Supervisory Patent Examiner, Art Unit 2836
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