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 .
In response to the restriction requirement, Applicant elected claims 1, 4-7, 10, 13-14 with traverse for further examination, and canceled all non-elected claims.
Claim Objections
Regarding to claims 1 and 10: The phrase “and/or” renders the claim indefinite because the claim does not clearly set forth the metes and bounds of the claimed invention, thereby rendering the scope of the claim unascertainable.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 and 10 are rejected under 35 U.S.C. 101 because:
Eligibility Step 1 (MPEP 2106. 3, subsection II): The claims, after reviewing the entire application disclosure, considered as a whole, are determined to be directed to one of the statutory category (processes, machines, manufactures, and compositions of matter): A method/apparatus/system.
Eligibility Step 2A (MPEP 2106. 4, subsection II):
Prong One: The claims recite the limitation of calculating a grid impact score to determine the criticality of the power distribution grid. This limitation is analyzed including concepts directed to “mathematical concepts” and “mental process” without the aid of paper and pen, or other tools; That falls within at least one of the groupings of abstract idea (MPEP 2106.04(a) Abstract Ideas: The enumerated groupings of abstract ideas: Mathematical concepts, Certain methods of organizing human activity, Mental processes). As a result, the claims recite a judicial exception.
Prong Two: The additional steps/actions/elements recited in the claims:
- providing a machine learning model and using such model for the score calculation (Insignificant pre solution activity (MPEP 2106.05(g))
When viewed in combination of as a whole, the recited additional steps/actions/elements do no more than add insignificant extra-solution to the judicial exception. As a result, these additional steps/actions/elements do not integrate the judicial exception into a practical application because they do not impose any meaningful limits on practicing the abstract idea. These claims are therefore directed to an abstract idea.
Eligibility Step 2B (MPEP 2106. 5: Whether a claim amounts to significantly more):
The additional steps/actions/elements recited in the claims, providing a machine learning model and using such model for the score calculation, is well known in the field as evidenced by the rejection below, do not add an inventive concept to the claim because they do is no more than adding insignificant pre-solution and post-solution activities to the judicial exception. Furthermore, using a machine learning model for the calculation as claimed is directed to the abstract idea of using a generic machine learning model/technique in a particular/new environment or to a particular field of use, with no invention concept (Recentive Analytics, Inc v. Fox Corp (Fed Cir, 2023-2437, 4/18/2025)).
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, 4-7, 10, and 13-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Applicant’s admission as prior art (Background section (US 2024/0402227)) in view of Tella et al. (US 2019/0228481) and Berry et al. (WO 2020/260003).
Applicant’s admission as prior art in Background section (US 2024/0402227) teaches a method for determining a criticality of an electrical power distribution grid by calculating a grid impact score representing the grid state of the power distribution grid based on power limit violations and/or based on voltage limit violations; and evaluating the calculated grid impact score to determine the criticality of the electrical power distribution grid (paragraphs [0003], [0008]-[0009]).
Applicant’s admission as prior art in Background section (US 2024/0402227), however does not teach using a machine learning model to calculate the grid impact score, and wherein the machine learning model of the power distribution grid fitted to smart meter data representing a power consumption of customers connected via a distribution transformer to conducting equipment of a substation of the power distribution grid and fitted to data representing voltages at selected conducting equipment of the power distribution grid and at the customers’ service delivery points.
Tella et al. discloses a method for managing electricity demand on a distribution grid comprising training/providing a machine learning model (FIG. 5, step 49: Train machine learning model) fitted to a power consumption of customers connected via a distribution transformer to conducting equipment of a substation of the power distribution grid (FIG. 5, step 48: Electricity usage data) and using the machine learning model to calculate a score for identifying peaks in projected electricity usage (FIG. 5, steps 45-46).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to modify the prior method as admitted by Applicant to include training and providing a machine learning model to determine the critically of the distribution grid by projecting the electricity usage to identify peak demand periods to manage the distribution grid accordantly as taught by Tella et al. (Abstract).
In addition, Berry discloses a method for training a machine learning model for monitoring an electric power grid, wherein the training data comprises voltage measurement data on the electric power grid (FIG. 7: Training data (708) for building correlation model (710) as a machine learning model for fault level across electric grid (FIG. 6, step 412)).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to modify the prior method as admitted by Applicant to include measuring voltage on the distribution grid and providing the measured voltage to train a model to determine the state of the grid such as fault level across the distribution grid as taught by Berry et al. (FIG. 7: Training data (708) for building correlation model (710) as a machine learning model for fault level across electric grid (FIG. 6, step 412)).
Regarding to claims 4: wherein the machine learning model is stored in a model memory and describes voltage changes of the voltages at the selected conducting equipment of the power distribution grid and at the customers' service delivery points in response to power changes of injected electrical power consumed by customers connected via the distribution transformer to conducting equipment within the substation of the power distribution grid (Berry et al.: The voltage is measured on a plurality locations on the power grid and provided to train the machine learning model).
Regarding to claims 5-6, 13: wherein the machine learning model comprises a linear model for a radial power distribution grid, wherein the machine learning model comprises a neural network model for a meshed power distribution grid (Berry et al.: Building the correlation model (FIG. 7, element 710) by using a machine learning algorithm employing a neural network. In addition, it is well known in the art that a machine learning model can be based on a linear model and the optimization technique can include linear programming or a linear optimization technique to increase efficiency. Please see Spalt et al. (EP 4057467), paragraph [0003]).
Regarding to claims 7, 14: wherein the grid impact score comprises a historic grid impact score representing the grid state of the power distribution grid for a historical past observation period and/or comprises a projected grid impact score representing an expected grid state of the power distribution grid for a future target observation period (Tella et al.: FIG. 5, steps 42 and 44: Past demand data reads on the claimed historic grid impact score).
CONTACT INFORMATION
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LAM S NGUYEN whose telephone number is (571)272-2151.
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, DOUGLAS RODRIGUEZ, can be reached on 571-431-0716. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LAM S NGUYEN/ Primary Examiner, Art Unit 2853
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