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 .
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.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ratnayake et al. (US Pub 2022/0115867).
As of claims 1, 14 and 20, Ratnayake discloses a system, comprising:
a data processing system, comprising one or more processors coupled with memory, located remote from a plurality of edge devices located at a plurality of sites in an electricity distribution grid (via grid planning system 201 located remote from a plurality of nodes/assets located at a plurality of sites in an electricity distribution grid; see fig. 2; also see paragraph [0074]), the data processing system to:
trigger, based on a characteristic of electricity related to the electricity distribution grid, a control function (see paragraph [0134], “communication may be provided to elements within the power distribution grid that adjust and/or throttle loads connected to the network and/or enable/disable ESS (energy storage system) resources connected to the network. An automated demand response mechanism may involve turning down or off certain appliances or loads (and, when demand is unexpectedly low, potentially increasing usage”);
ping, using a natural language-based protocol compatible with a generative machine learning model, the plurality of edge devices for data related to consumption of electricity via the electricity distribution grid (via extracting asset data; see paragraph [0172] and [01887]);
receive, from the plurality of edge devices, information in response to the ping, the information comprising one or more data structures generated via the generative machine learning model executed by each of the plurality of edge devices (via receiving meter data and sensor data; see fig. 13; see paragraphs [0170], [0172], [0196]-[0197]);
generate, based on the information and a layout of the electricity distribution grid, responsive to the trigger of the control function, guidance configured to impact the characteristic of electricity at the plurality of sites (via generating recommendations; see fig. 13; find and recommend database 1347; also see paragraph [0199]); and
transmit, to the plurality of edge devices, the guidance to impact the characteristic of electricity at the plurality of sites (via transmitting commands to impact the characteristic of electricity at plurality of sites 1415; see fig. 14; also see paragraph [0199]).
As of claims 2 and 15, Ratnayake discloses that the data processing system is further configured to: detect a peak in demand of electricity on the electricity distribution grid; and trigger, based on the detection of the peak, the control function to flatten the demand (via detecting an increase in demand and reducing the power consumption; see paragraph [0199]).
As of claims 3 and 16, Ratnayake discloses the data processing system is further configured to: ping, prior to triggering the control function, the plurality of edge devices for a prediction related to consumption of electricity over a time interval; detect a peak in demand of electricity during the time interval based on receipt of the prediction responsive to the ping; and trigger the control function responsive to detection of the peak in the demand during the time interval (via predicting/forecasting the power consumption and control the assets according to the predications; see paragraphs [0136]) and [0197]).
As of claim 4 and 17, Ratnayake discloses that the data processing system is further configured to: construct, using the natural language-based protocol, a prompt defining a format for the information generated by the plurality of edge devices; ping the plurality of edge devices using the prompt; and receive the information in accordance with the format defined in the prompt (via system communicating with the nodes autonomously using machine learning, hence the agents will be exchanging structured messages that carry tasks, data, constraints and results, so the receiver knows what to do with the instructions; see paragraph [0124]).
As of claims 5 and 18, Ratnayake discloses that the data processing system is further configured to: determine, based on the information, to perform a second ping, using the natural language-based protocol, of the plurality of edge devices prior to generation of the guidance; receive, from the plurality of edge devices, second information responsive to the second ping; and generate the guidance based on the second information (via periodically receiving information from the nodes; see paragraph [0079]).
As of claims 6-8 and 19, Ratnayake discloses the data processing system is further configured to: generate, based on the information and the layout of the electricity distribution grid, responsive to the trigger of the control function, preliminary guidance configured to impact consumption of electricity at the plurality of sites; construct, using the natural language-based protocol, a prompt comprising the preliminary guidance and a request for second information related to consumption of electricity in accordance with the preliminary guidance; transmit the prompt to the plurality of edge devices; receive, from the plurality of edge devices, preliminary information in response to the prompt comprising the preliminary guidance; determine, based on the preliminary information, that the preliminary guidance satisfies a condition related to the characteristic of electricity; and generate, responsive to the determination, the guidance based on the preliminary guidance (Ratnayake discloses that the system performs dynamic adjustment to power distribution grid, so the first occurrence of controlling the asset/node is interpreted as preliminary guidance and subsequent occurrences as the guidance; see paragraph [0199]).
As of claims 9 and 10, Ratnayake discloses determine, based on a type of control function, to generate a prompt related to quantitative analysis; and transmit the prompt related to quantitative analysis to the plurality of edge devices to cause the plurality of edge devices to generate, using the generative machine learning model executed by the plurality of edge devices, processor-executable instructions to perform the quantitative analysis in accordance with the prompt, wherein the data processing system is further configured to: receive the information in response to the plurality of edge devices executing the generated processor-executable instructions to perform the quantitative analysis in accordance with the prompt (via performing the power flow analysis; see paragraph [0153]).
As of claim 11 and 12, Ratnayake discloses that the natural language-based protocol is constructed dynamically on-the-fly using the generative machine learning model and a knowledge base related to the electricity distribution grid, wherein the knowledge base comprises a topology of the electricity distribution grid (via constructing a model of power distribution grid; see paragraph [0074]).
As of claim 13, Ratnayake discloses generate the guidance in accordance with a constraint established for the electricity distribution grid (see abstract).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NABIL H SYED whose telephone number is (571)270-3028. The examiner can normally be reached 8:00-5:00 M-F.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Davetta W Goins can be reached at (571) 272-2957. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NABIL H SYED/ Primary Examiner, Art Unit 2689
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