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 .
Notice to Applicant
The following is a Final Office action. In response to Examiner’s Non-Final Rejection of 10/01/2025, Applicant, on 03/02/2026, amended claims and added claims 19-21.. Claims 1-6 and 8-21 are pending in this application and have been rejected below.
Response to Arguments
Applicant's arguments filed 03/02/2026 have been fully considered, but they are not fully persuasive. The updated 35 USC § 102 has been overcome. However, the updated 35 USC § 103 and 101 rejections of claims 1-6 and 8-21 are applied in light of Applicant's amendments.
Applicant’s arguments with respect to the rejection to claims 1 of 35 U.S.C. 103 have been considered but are moot because the arguments do not apply to the current combination of references being used in the current rejection. In light of Applicants amendments and arguments the Examiner updated the search and provided new art to reject the claim limitations.
The Applicant argues “This evidence establishes that it is more likely than not that the claims have numerous practical applications, such as assisting with planning for and anticipating potential outages, enhancing operational preparedness for inclement weather events and effectively communicating with customers to raise overall satisfaction levels and minimize detrimental outage impacts. All pending claims are directed to this practical application. Withdrawal of the rejection is respectfully requested.” (remarks 03/02/2026)
In response, the Examiner respectfully disagrees. The claimed subject matter, is directed to an abstract idea by reciting concepts performed in the human mind (including an observation, evaluation, judgment, opinion), which falls into the “Mental Process” group; and by reciting mathematical relationships, mathematical formulas or equations, mathematical calculations which falls into the “Mathematical concepts” within the enumerated groupings of abstract ideas. The mere nominal recitation of a generic computer does not take the claim limitation out of mathematical concepts or the mental processes grouping. Thus, the claim recites a mental process for performing certain mathematical concepts.
A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.
The claimed subject matter is merely claims a method for receiving and analyzing information regarding outage data. Although it may be intended to be performed in a digital environment, the claimed subject matter (as currently claimed in the independent claim) speaks to the calculating and analyzing data. Such steps are not tied to the technological realm, but rather utilizing technology to perform the abstract ideas (mathematical concepts). Additionally, the claimed subject matter can also be categorized as a Mental Process as it recites concepts performed in the human mind (observation and evaluation). The steps of calculating data, training/updating models, and generating a model/trend line can be performed by a human (mental process/pen and paper). The practice of calculating information and constructing models with set parameters and timelines can be performed without computers, and thus are not tied to technology nor improving technology.
The solution mentioned in the amended limitation is not implemented/integrated into technology and thus not an improvement to the technical field. Further, there is no integration into a practical application as the claims can be interpreted as humans per se, as the claims fail to tie the steps to technology; insignificant extra solution activities (which are merely calculating and/or analyzing data).
The steps relied upon by the Applicant as recited does not improve upon another technology, the functioning of the computer itself, or allow the computer to perform a function not previously performable by a computer. The claims do not mention to any use of a specialized computer and/or processor. The Applicant is using generic computing components (processors) to perform in a generic/expected way (obtaining and analyzing data).The abstract idea is not particular to a technological environment, but is merely being applied to a computer realm. The process of calculating and analyzing data specifically for purchase requisitions , and performing additional analysis can be done without a computer, and thus the claims are not “necessarily rooted", but rather they are utilizing computer technology to perform the abstract idea. The Examiner does not recognize any elements of the Applicant's claims and/or specification that would improve or allow the computer to perform a function(s) not previously performable by the computer, or improve the functioning of the computer itself. It is insufficient to indicate that the claims are novel and non-obvious, and thus contain “something more.” Just because the components may perform a specialized function does not mean that that the computer components are specialized. As such the application of the abstract idea of collecting and analyzing data regarding purchase information, and performing correlation analysis is insufficient to demonstrate an improvement to the technology.
The additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (generic computing environment). See MPEP 2106.05(f) and 2106.05(h).
The use of Artificial Intelligence (AI), machine learning (ML) models, and/or artificial neural networks (ANN) fall within the realm of abstract ideas. They are, at their core, mathematical algorithms implemented on a computer. As highlighted in Examples 47-49 of the 2024 Patent Subject Matter Eligibility Guidance, the USPTO has consistently viewed claims directed to such models as being drawn to abstract ideas. These examples illustrate claims that, while couched in the language of specific applications, ultimately boil down to mathematical relationships and calculations.
For instance, Applicant claims "wherein the probability value is generated by executing a neural network." While this claim appears to have a practical application, a closer examination reveals that the core of the invention is the underlying mathematical model and its training process. Furthermore, even if the claim recites specific steps related to data collection, preprocessing, or post-processing, these steps often represent well-understood, conventional activities. As demonstrated in Examples 47-49, adding such conventional elements to a claim directed to an abstract idea does not necessarily transform it into a patent-eligible application. These examples illustrate situations where the additional steps were deemed insufficient to provide an "inventive concept" that meaningfully narrowed the scope of the abstract idea. In the context of machine learning, simply collecting and preparing data for input into a model, or applying the model's output to a particular problem, falls into this category of conventional activity. The Applicant has not created a new learning algorithm, but rather optimizing existing algorithm(s) or the application of known techniques to a new dataset. Such incremental advancements, while potentially valuable for business, do not automatically confer patent eligibility or a technological improvement. As highlighted in the Alice framework, the mere recitation of known components or processes does not necessarily amount to an inventive concept.
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 1-6 and 8-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
Claims 1-6 and 8-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the method (claims 10-18) and system (claims 1-6, 8, and 19-21) are directed to potentially eligible categories of subject matter (i.e., process, machine, and article of manufacture respectively). Thus, Step 1 is satisfied.
With respect to Step 2, and in particular Step 2A Prong One of 2019 PEG, it is next noted that the claims recite an abstract idea by reciting concepts performed in the human mind (including an observation, evaluation, judgment, opinion), which falls into the “Mental Process” group; and by reciting mathematical relationships, mathematical formulas or equations, mathematical calculations which falls into the “Mathematical concepts” group within the enumerated groupings of abstract ideas.
The mere nominal recitation of a generic computer does not take the claim limitation out of mathematical concepts or the mental processes grouping. Thus, the claim recites a mental process for performing certain methods of organizing human activity.
The limitations reciting the abstract idea(s) (Mental process and mathematical concepts), as set forth in exemplary claim 10, are: receiving uncorrelated outage data, weather data and graph data at a correlator system…; generating geographically correlated outage data, weather data and graph data using the correlator system; receiving the geographically correlated outage data, weather data and graph data with forecast data…; generating prediction data …; receiving the prediction data at a state of risk system operating …and generating state of risk data; and receiving the state of risk data at a customer notification system operating ….and generating customer notifications as a function of the state of risk. Independent claim 1 recites the system for performing the method of independent claim 10without adding significantly more. Thus, the same rationale/analysis is applied.
With respect to Step 2A Prong Two of the, the judicial exception is not integrated into a practical application. The additional elements are directed to operating on a processor… at a machine learning system operating on the processor…; a correlator system operating on a processor… a machine learning system operating on a processor… a state of risk system operating on a processor… a customer notification system operating on a processor…; (as recited in claims 1 and 10). However, these elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitation(s) is/are directed to: operating on a processor… at a machine learning system operating on the processor…; a correlator system operating on a processor… a machine learning system operating on a processor… a state of risk system operating on a processor… a customer notification system operating on a processor…; (as recited in claims 1 and 10) for implementing the claim steps/functions. These elements have been considered, but merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation), though at a very high level of generality and without imposing meaningful limitation on the scope of the claim.
The additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (generic computing environment). See MPEP 2106.05(f) and 2106.05(h). Even if the acquiring steps are considered as additional elements, these steps at most amount to insignificant extra-solution activity accomplished via receiving/transmitting data, which is not enough to amount to a practical application. See MPEP 2106.05(g).
In addition, Applicant’s Specification (paragraph [0030]) describes generic off-the-shelf computer-based elements for implementing the claimed invention, and which does not amount to significantly more than the abstract idea, which is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. See, e.g., Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself. Further, the courts have found the presentation of data to be a well-understood, routine, conventional activity, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93 (see MPEP 2106.05(d)).
The dependent claims (2-9, and 11-18) are directed to the same abstract idea as recited in the independent claims, and merely incorporate additional details that narrow the abstract idea via additional details of the abstract idea. For example claims 11-21 “trains a data model using the correlated outage data, weather data and graph data to modify an outage duration prediction; utilizes continuous integration and continuous delivery to process new data for the outage data, weather data and graph data; iteratively trains a data model using different combinations of feeder lines in the geographic data; wherein the state of risk data comprises map data having a plurality of geographic zones, wherein each zone has an associated risk that can be different from an associated risk of other zones; wherein the correlator system receives the uncorrelated outage data, weather data and graph data and generates the geographically correlated outage data, weather data and graph data by adjusting coordinates of the uncorrelated outage data, weather data and graph data to match a predetermined set of coordinates having a closest fit; wherein the customer notification system is configured to receive customer response data and to modify the outage data in response to the customer response data; a maintenance scheduling system configured to receive the state of risk data and to generate maintenance scheduling data in response to the state of risk data; further comprising a maintenance scheduling system configured to receive the state of risk data and customer notification data to generate maintenance scheduling data in response to the state of risk data and the customer notification data; wherein the customer notification system assigns each customer a utility function that represents a customer's perceived value of being correctly or falsely notified about an outage that will eventually happen in a predefined time interval and uses the utility function to generate the customer notifications; wherein the customer notification system assigns each customer a utility function that represents a customer's perceived value of being correctly or falsely notified about an outage that will eventually happen in a predefined time interval, in terms of a cost of a false positive and a false negative and a reward of true positive (TP) and true negative (TN) signals provided by a prediction model, and uses the TP and TN signals to generate the customer notifications; assigning each customer a utility function that represents a customer's perceived value of being correctly or falsely notified about an outage that will eventually happen in a predefined time interval, in terms of a cost of a false positive and a false negative and a reward of true positive (TP) and true negative (TN) signals provided by a prediction model; receiving customer response data; and modifying the outage data in response to the customer response data”, without additional elements that integrate the abstract idea into a practical application and without additional elements that amount to significantly more to the claims. The remaining dependent claims (2-9) recite the system for performing the method of claims 11-18. Thus, the same rationale/analysis is applied. Thus, all dependent claims have been fully considered, however, these claims are similarly directed to the abstract idea itself, without integrating it into a practical application and with, at most, a general purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims.
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 103
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.”
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 1-5, 8-14, and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent 11144835 (hereinafter “Anag”) et al., in view of U.S. PGPub 20120109545 to (hereinafter “Meynardi”) et al.
As per claim 1, Anag teaches A system for customer notification based on outage state of risk prediction, comprising:
a correlator system operating on a processor and configured to receive uncorrelated outage data, weather data and graph data and to generate geographically correlated outage data, weather data and graph data; a machine learning system operating on a processor and configured to receive geographically correlated outage data, weather data and graph data and forecast data and to generate prediction data; a state of risk system operating on a processor and configured to receive the prediction data and to generate state of risk data; and a customer notification system operating on a processor and configured to receive the state of risk data and to generate customer notifications as a function of the state of risk; Anag 002-011: “One aspect of the present disclosure provides a method for outage prediction for electrical distribution utilities using high-resolution weather forecasts, coupled with geographic data (e.g., land use and vegetation around overhead-lines) and utility infrastructure data (e.g., transformer fuses, etc.) to predict distributed outage occurrences (e.g., number of outages over a 2-km gridded map) in advance of a storm… the invention provides a method of predicting a utility outage. The method comprises retrieving, with an electronic processor, dynamic weather data from a database, retrieving, with an electronic processor, geographic information data from a database, the geographic information data including data related to location of utility overhead lines in a predefined region, combining, with an electronic processor, the dynamic weather data with the geographic information data to generate a file identifying parameters of a forthcoming weather event, applying a plurality of model forcings and a plurality of machine learning models to the file, generating a plurality of visual output values based on the application of the model forcings and the machine learning models to the file, each of the output values providing a prediction of an outage of one or more of the overhead lines…049: FIG. 2 is a flow chart showing aspects related to a outage prediction model that predicts outages, in accordance with some embodiments. Initially, dynamic weather input data is generated within a raw parameter file. The raw parameter file includes more than 18,000 rows having all centroids within a 2 km grid. The flowchart in FIG. 2 shows how the data is prepared for modeling, how the models are fit, how the output is generated and displayed, and as to how the calibration dataset is updated (contingent upon receiving actual outage data and a weather forecast analysis simulation). In some embodiments, the outage prediction model shown in FIG. 2 provides for three model forcings along with five machine learning models. In an example, historical training data related to more than 150 storms and associated GIS data is provided to the dynamic prediction model…0153: BART has been widely used in risk analysis and the prediction of natural hazards. described by Guikema et al. in 2010 entitled “Prestorm estimation of hurricane damage to electric power distribution systems,” Risk Analysis, 30(12): 1744-1752, incorporated herein by reference, conducted a comparison of multiple models for estimating the number of damaged poles during storms, and concluded that BART and an ensemble model with BART outperformed other parametric regression methods. Nateghi et al. in 2011 entitled “Comparison and validation of statistical methods for predicting power outage durations in the event of hurricanes,” Risk analysis, 31(12):1897-1906, incorporated herein by reference, compared BART with traditional survival models in predicting power outage durations in Hurricane Ivan, 2004, and concluded that BART had better performance over parametric survival models. Blattenberger et al. in 2014 entitled “Avalanche forecasting: using Bayesian additive regression trees (BART).”Note: Matching, uncorrelated with historical data.
Anag may not explicitly teach the following. However, Meynardi teaches:
wherein the customer notification system is configured to receive customer response data and to modify the outage data in response to the customer response data; Meynardi 0012: “The outage event may be remedied when a quantity of restoration alerts, received from customer premises equipment, indicates that the quantity of outages has decreased to a level that is less than the threshold…0021-0025: NMS 130 may provide outage updates and/or identify when an outage and/or outage event has been remedied. For example, NMS 130 may receive a restoration alert from network terminal 120 when power has been restored to network terminal 120. O&R application may process the restoration alert and may provide a restoration notification to O&RS 140 indicating that an outage, associated with network terminal 120 (e.g., at a particular location, address, etc.), has been remedied. In another example, the O&R application may provide an outage event update notification that identifies a quantity of outages and/or restorations, locations of outages and/or restorations, and/or trends associated with outages and/or restorations (e.g., whether the quantity of outages are increasing, decreasing, etc.). The O&R application may send a customer notification to network terminal 120 indicating that the outage alert has been received and/or that the power has been restored, etc… O&RS 140 may update an outage status associated with the power grid and/or determine a rate at which outages are increasing and/or decreasing based on the restoration notification.”
Anag and Meynardi are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Anag with the aforementioned teachings from Meynardi with a reasonable expectation of success, by adding steps that allow the software to receive data with the motivation to more efficiently and accurately communicate and analyze information [Meynardi 0021].
As per claim 2, Anag and Meynardi teach all the limitations of claim 1.
In addition, Anag teaches:
wherein the machine learning system further comprises a machine learning algorithm operating on the processor that iteratively trains a data model using the correlated outage data, weather data and graph data to modify an outage duration prediction; Anag 004: “the invention provides an outage prediction system comprising an electronic processor configured to receive, from a database, dynamic weather data and geographic data from data storage, combine the dynamic weather data with the geographic information to generate a file identifying parameters of a forthcoming weather event, define a plurality of model forcings, each model forcing including a predetermined set of weather variables, define a plurality of machine learning models for each model forcing, each machine learning model calibrated with a dataset of variables of past weather events, apply the plurality of model forcings and the plurality of machine learning models to the identified parameters in the file, and generate a visual output value for each model forcing and each machine learning model, each output value predicting the likelihood of a utility outage in a particular location…049: “FIG. 2 is a flow chart showing aspects related to a outage prediction model that predicts outages, in accordance with some embodiments. Initially, dynamic weather input data is generated within a raw parameter file. The raw parameter file includes more than 18,000 rows having all centroids within a 2 km grid. The flowchart in FIG. 2 shows how the data is prepared for modeling, how the models are fit, how the output is generated and displayed, and as to how the calibration dataset is updated (contingent upon receiving actual outage data and a weather forecast analysis simulation). In some embodiments, the outage prediction model shown in FIG. 2 provides for three model forcings along with five machine learning models. In an example, historical training data related to more than 150 storms and associated GIS data is provided to the dynamic prediction model.”
As per claim 3, Anag and Meynardi teach all the limitations of claim 2.
In addition, Anag teaches:
wherein the machine learning algorithm utilizes continuous integration and continuous delivery to process new data for the outage data, weather data and graph data; Anag 004: “Enormous amounts of data are gathered that describe the process, and the machine learning algorithms described above find the underlying relationship between outages per grid cell and the geographic, environmental and infrastructure data. For example, in some embodiments, the outage prediction models provided herein were calibrated based on storms from 2005-2013, but the system is adaptive in that recent storms (2013-2016) have been simulated and added to the calibration database. As storms arise, the data is added to enrich the database. This allows the outage prediction model to have a dynamic, updated picture of what occurred on the grid during these storms, which can be applied to future storms that will impact the utility distribution grid. The model also accounts for changes in the system such as enhancements of the infrastructure and vegetation management activities…054: It is noted that this data may be updated and that the current updated data would be used by the system 10. The electronic processor 12 then obtains a time/data stamp from the forthcoming storm and calculates a climatological value of leaf area index give the data per the 2 km centroid…FIG. 8 is a flow chart showing various steps in updating the calibration dataset in accordance with some embodiments. The calibration dataset is updated by the electronic processor 12 running a WRF analysis for recently triggered storms and generating a parameter file. The electronic processor 12 receives or accesses a database to retrieve utility outage data from a particular utility via a network (e.g., FTP connection). The electronic processor 12 then reads outage records into a spatial join/recalibration script and merges the outages per grid cell with the WRF analysis. Next, the electronic processor 12 or user verifies that all processes have run correctly and the training database for model fitting is updated.”
As per claim 4, Anag and Meynardi teach all the limitations of claim 1.
In addition, Anag teaches:
wherein the correlator system further comprises a machine learning algorithm operating on the processor that iteratively trains a data model using different combinations of feeder lines in the geographic data; Anag 004: “Enormous amounts of data are gathered that describe the process, and the machine learning algorithms described above find the underlying relationship between outages per grid cell and the geographic, environmental and infrastructure data. For example, in some embodiments, the outage prediction models provided herein were calibrated based on storms from 2005-2013, but the system is adaptive in that recent storms (2013-2016) have been simulated and added to the calibration database. As storms arise, the data is added to enrich the database. This allows the outage prediction model to have a dynamic, updated picture of what occurred on the grid during these storms, which can be applied to future storms that will impact the utility distribution grid. The model also accounts for changes in the system such as enhancements of the infrastructure and vegetation management activities…054: It is noted that this data may be updated and that the current updated data would be used by the system 10. The electronic processor 12 then obtains a time/data stamp from the forthcoming storm and calculates a climatological value of leaf area index give the data per the 2 km centroid…FIG. 8 is a flow chart showing various steps in updating the calibration dataset in accordance with some embodiments. The calibration dataset is updated by the electronic processor 12 running a WRF analysis for recently triggered storms and generating a parameter file. The electronic processor 12 receives or accesses a database to retrieve utility outage data from a particular utility via a network (e.g., FTP connection). The electronic processor 12 then reads outage records into a spatial join/recalibration script and merges the outages per grid cell with the WRF analysis. Next, the electronic processor 12 or user verifies that all processes have run correctly and the training database for model fitting is updated.”
As per claim 5, Anag and Meynardi teach all the limitations of claim 1.
In addition, Anag teaches:
wherein the state of risk data comprises map data having a plurality of geographic zones, wherein each zone has an associated risk that can be different from an associated risk of other zones; Anag 010: “data displayed can include static snapshots of maps of predicted outages at regular time intervals. In other embodiments, the system may display a map that overlays one or more of the following types of data or graphical information: weather radar information, satellite information, weather related or geographical related measurement data, forecast maps, geographical network schematics, a graphical representation of events and their location, and a graphical representation of operation parameters at various locations on the electric distribution network…. FIG. 28 is a map showing spatial resolutions: 2 km grid cell, town, division and territory with grid cells without infrastructure or outside the service territory excluded from the map… FIG. 29 is a map showing weather research and forecasting model nested domains in 18 km, 6 km and 2 km grids used for storm events simulation.”
As per claim 8, Anag and Meynardi teach all the limitations of claim 1.
In addition, Anag teaches:
further comprising a maintenance scheduling system configured to receive the state of risk data and to generate maintenance scheduling data in response to the state of risk data; Anag 054-055: “The electronic processor 12 receives or accesses a database to retries the MODIS Leaf Area Index Data from 2000-2015. It is noted that this data may be updated and that the current updated data would be used by the system 10. The electronic processor 12 then obtains a time/data stamp from the forthcoming storm and calculates a climatological value of leaf area index give the data per the 2 km centroid. FIG. 7B is a flow chart detailing how different tree trimming data (standard maintenance trimming, SMT) and enhanced tree trimming (ETT) are processed on the 2-km grid. A sixth source of the geographic data is related to standard maintenance trimming. The electronic processor 12 receives or accesses a database to retrieve the dissolved overhead line shapefile (referenced above) and a 2 km grid relevant to a particular location. The electronic processor 12 references a SMT polyline shapefile and creates a 30 m buffer around the polyline. Next, the electronic processor 12 clips the overhead line at the extent of the buffer and joins the clipped lines to the 2-km grid. The electronic processor 12 sums the length per grid cell. A seventh source of the geographic data is related to enhanced tree trimming. The electronic processor 12 receives or accesses a database to retrieve the dissolved overhead line shapefile (referenced above) and a 2 km grid relevant to a particular location. The electronic processor 12 references an ETT polygon shapefile and clips the overhead line at the extent of the ETT polygon. The electronic processor 12 then joins the clipped lines to the 2-km grid, and sums the length per grid cell.”
As per claim 9, Anag and Meynardi teach all the limitations of claim 1.
In addition, Anag teaches:
further comprising a maintenance scheduling system configured to receive the state of risk data and customer notification data and to generate maintenance scheduling data in response to the state of risk data and the customer notification data; Anag 054-055: “The electronic processor 12 receives or accesses a database to retries the MODIS Leaf Area Index Data from 2000-2015. It is noted that this data may be updated and that the current updated data would be used by the system 10. The electronic processor 12 then obtains a time/data stamp from the forthcoming storm and calculates a climatological value of leaf area index give the data per the 2 km centroid. FIG. 7B is a flow chart detailing how different tree trimming data (standard maintenance trimming, SMT) and enhanced tree trimming (ETT) are processed on the 2-km grid. A sixth source of the geographic data is related to standard maintenance trimming. The electronic processor 12 receives or accesses a database to retrieve the dissolved overhead line shapefile (referenced above) and a 2 km grid relevant to a particular location. The electronic processor 12 references a SMT polyline shapefile and creates a 30 m buffer around the polyline. Next, the electronic processor 12 clips the overhead line at the extent of the buffer and joins the clipped lines to the 2-km grid. The electronic processor 12 sums the length per grid cell. A seventh source of the geographic data is related to enhanced tree trimming. The electronic processor 12 receives or accesses a database to retrieve the dissolved overhead line shapefile (referenced above) and a 2 km grid relevant to a particular location. The electronic processor 12 references an ETT polygon shapefile and clips the overhead line at the extent of the ETT polygon. The electronic processor 12 then joins the clipped lines to the 2-km grid, and sums the length per grid cell.”
Claims 10-14 and 16-18 are directed to the method for performing the system of claims 1-5 and 7-9 above. Since Anag and Meynardi teach the method, the same art and rationale apply.
Claims 6 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent 11144835 (hereinafter “Anag”) et al., in view of U.S. PGPub 20120109545 to (hereinafter “Meynardi”) et al., and in view of U.S. PGPub 20200036588 to (hereinafter “Porter”) et al.
As per claim 6, Anag and Meynardi teach all the limitations of claim 1.
Anag and Meynardi may not explicitly teach the following. However, Porter teaches:
wherein the correlator system receives the uncorrelated outage data, weather data and graph data and generates the geographically correlated outage data, weather data and graph data by adjusting coordinates of the uncorrelated outage data, weather data and graph data to match a predetermined set of coordinates having a closest fit;Porter 0079: “FIG. 4 depicts data structure 400 in accordance with embodiments of the present disclosure. In one embodiment, one or more data structures 400 may be accessed, such as by a processor executing test 104, to determine if a detected event is a candidate event and/or if an event is a sufficient match to a historic event, (e.g., step 106-110). Data structure 400 may include more records, such as indicated by ellipses 414, or fewer records. Records of data structure 400 may include event identifier 402, such as a unique number or other identifier of the particular data structure 400; type identifier 404 to identify a category, subcategory, or other attribute of the event (e.g., natural disaster, terrorist attack, weather, etc.); severity 406 may be utilized to indicate the severity of a particular event (e.g., category 5 hurricane, earthquake of 1.6 magnitude, minor weather incident, etc.); type of impact 408 may be utilize to categorize the type of impact (e.g., all travel, air travel, all operations, international travel, beachfront hotels, etc.); location of impact 410 may be utilized to categorize the location or area of the event (e.g., widespread power outage, flooding along the river, nation-wide rail strike, etc.); alternatives 412 may be utilized to indicate mitigating or enhancing factors (e.g., minor flooding but a major roadway is underwater, flights to the Canary Islands cancelled but additional passenger ships available from southern Morocco, etc.).”
Anag, Meynardi, and Porter are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Anag and Meynardi with the aforementioned teachings from Porter with a reasonable expectation of success, by adding steps that allow the software to associate data with the motivation to more efficiently and accurately organize and analyze information [Porter 0079].
Claim 15 is directed to the method for performing the system of claim 6 above. Since Anag, Meynardi, and Porter teach the method, the same art and rationale apply.
Claims 19-21 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent 11144835 (hereinafter “Anag”) et al., in view of U.S. PGPub 20120109545 to (hereinafter “Meynardi”) et al., and in view of U.S. Patent 10872339 to (hereinafter “Kramme”) et al.
As per claim 19, Anag and Meynardi teach all the limitations of claim 1.
Anag and Meynardi may not explicitly teach the following. However, Kramme teaches:
wherein the customer notification system assigns each customer a utility function that represents a customer's perceived value of being correctly or falsely notified about an outage that will eventually happen in a predefined time interval and uses the utility function to generate the customer notifications;Kramme 0184-0199: “FIG. 7 illustrates a computer-implemented method 340 of reducing false positive fraud alerts based upon customer feedback…a number of false positive fraud alerts generated in the future by the rules-based model or engine may be reduced, and the customer experience may be enhanced… The financial activity data retrieved at block 364 may be used to identify one or more spending patterns specific to a first cardholder of the plurality of cardholders identified at block 362 (block 366). Various types of spending patterns may be identified, in accordance with the embodiment and/or scenario (e.g., depending upon the amounts, dates and/or other details of the transactions represented by the financial activity data)… The financial activity data retrieved at block 364 may also be used to identify one or more spending patterns specific to a second cardholder of the plurality of cardholders identified at block 362 (block 368). The spending pattern(s) identified for the second cardholder may include some or all of the same types of patterns identified for the first cardholder, and/or may include one or more different types of spending patterns…claim 1: determine a reason why the false positive was generated, and (ii) then modify the rules-based engine to account for the reason why the false positive was generated”
Anag, Meynardi, and Kramme are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Anag and Meynardi with the aforementioned teachings from Kramme with a reasonable expectation of success, by adding steps that allow the software to associate data with the motivation to more efficiently and accurately organize and analyze information [Kramme 0189].
As per claim 20, Anag and Meynardi teach all the limitations of claim 1.
Anag and Meynardi may not explicitly teach the following. However, Kramme teaches:
wherein the customer notification system assigns each customer a utility function that represents a customer's perceived value of being correctly or falsely notified about an outage that will eventually happen in a predefined time interval, in terms of a cost of a false positive and a false negative and a reward of true positive (TP) and true negative (TN) signals provided by a prediction model, and uses the TP and TN signals to generate the customer notifications;Kramme 044, 0185-0188: “notification unit 56 may generate a notification message stating that a fraud alert associated with a particular transaction is a false positive, and cause network interface 32 to send the message to a computer terminal or to FAMS 14 for display to a system user… a number of false positive fraud alerts generated in the future by the rules-based model or engine may be reduced, and the customer experience may be enhanced… The machine learning program may modify the rules-based engine to no longer generate fraud alerts when financial transaction data includes a same or similar fact pattern as the data detailing the financial transaction, the same or similar fact pattern being determined from a same or similar type of credit card, type of other card, card issuer, credit card number, card holder name, merchant, merchant location, transaction location, customer location, product or service, amount of transaction, and/or type of transaction.”
Anag, Meynardi, and Kramme are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Anag and Meynardi with the aforementioned teachings from Kramme with a reasonable expectation of success, by adding steps that allow the software to associate data with the motivation to more efficiently and accurately organize and analyze information [Kramme 0189].
As per claim 21, Anag and Meynardi teach all the limitations of claim 1.
In addition, Meynardi teaches:
receiving customer response data; and modifying the outage data in response to the customer response data;Meynardi 0012: “The outage event may be remedied when a quantity of restoration alerts, received from customer premises equipment, indicates that the quantity of outages has decreased to a level that is less than the threshold…0021-0025: NMS 130 may provide outage updates and/or identify when an outage and/or outage event has been remedied. For example, NMS 130 may receive a restoration alert from network terminal 120 when power has been restored to network terminal 120. O&R application may process the restoration alert and may provide a restoration notification to O&RS 140 indicating that an outage, associated with network terminal 120 (e.g., at a particular location, address, etc.), has been remedied. In another example, the O&R application may provide an outage event update notification that identifies a quantity of outages and/or restorations, locations of outages and/or restorations, and/or trends associated with outages and/or restorations (e.g., whether the quantity of outages are increasing, decreasing, etc.). The O&R application may send a customer notification to network terminal 120 indicating that the outage alert has been received and/or that the power has been restored, etc… O&RS 140 may update an outage status associated with the power grid and/or determine a rate at which outages are increasing and/or decreasing based on the restoration notification.”
Anag and Meynardi are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Anag with the aforementioned teachings from Meynardi with a reasonable expectation of success, by adding steps that allow the software to receive data with the motivation to more efficiently and accurately communicate and analyze information [Meynardi 0021].
Anag and Meynardi may not explicitly teach the following. However, Kramme teaches:
assigning each customer a utility function that represents a customer's perceived value of being correctly or falsely notified about an outage that will eventually happen in a predefined time interval, in terms of a cost of a false positive and a false negative and a reward of true positive (TP) and true negative (TN) signals provided by a prediction model;
Kramme 0184-0185 and 0198: “ FIG. 7 illustrates a computer-implemented method 340 of reducing false positive fraud alerts based upon customer feedback… a number of false positive fraud alerts generated in the future by the rules-based model or engine may be reduced, and the customer experience may be enhanced… The financial activity data retrieved at block 364 may be used to identify one or more spending patterns specific to a first cardholder of the plurality of cardholders identified at block 362 (block 366)…claim 1: a machine learning program that is trained to (i) determine a reason why the false positive was generated, and (ii) then modify the rules-based engine to account for the reason why the false positive was generated, and to no longer generate electronic fraud alerts.”
Anag, Meynardi, and Kramme are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Anag and Meynardi with the aforementioned teachings from Kramme with a reasonable expectation of success, by adding steps that allow the software to associate data with the motivation to more efficiently and accurately organize and analyze information [Kramme 0189].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Zaifman; Art. Systems And Methods For Optimizing A Network Based On Weather Events, .U.S. Patent 11799568 (1) Weather events (e.g., tropical storms, hurricanes, tornados, blizzards, high winds, lightning, and/or the like) may cause power outages, downed utility poles, failed communication equipment, etc., in a geographical location. Many consumers in the geographical location, subject to extreme weather events, may utilize Internet service provider (ISP) routers to access both voice and data networks (e.g., the Internet), but weather events that cause problems, such as power outages, may prevent access to such ISP routers and the respective networks.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. THIS ACTION IS MADE FINAL. 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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Arif Ullah, whose telephone number is (571) 270-0161. The examiner can normally be reached from Monday to Friday between 9 AM and 5:30 PM.
If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Beth Boswell, can be reached at (571) 272-6737. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”).
/Arif Ullah/
Primary Examiner, Art Unit 3625
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