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 § 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 1-18 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 1-18 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. The eligibility analysis in support of these findings is provided below, in accordance with the “2019 Revised Patent Subject Matter Eligibility Guidance” (published on 1/7/2019 in Fed. Register, Vol. 84, No. 4 at pgs. 50-57, hereinafter referred to as the “2019 PEG”).
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 1-9) 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 set forth in the 2019 PEG.
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 2019 PEG, 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.
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 11-18 “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”, 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 § 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 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) 1-5, 7-14, and 16-18 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Patent 11144835 (hereinafter “Anag”) et al.
It is noted that any citation[[s]] to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. [[See, MPEP 2123]]
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.
As per claim 2, Anag teaches 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 teaches 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 teaches 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 teaches 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 7, Anag teaches all the limitations of claim 1.
In addition, Anag 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; Anag 077: “Utilities can provide detailed records of outages outputted from their Outage Management System (OMS) for each of the storms to be simulated. The OMS records included geographic coordinates, nearest substation, customers affected, town, regional operating center, date, time, outage length and circuit affected. In general, analysts should use caution when working with OMS data, as much as the data inputted by lineman can be erroneous; in an effort to save time, the lineman may enter the first entry of a dropdown list into a data collection system, even if incorrect. Duplicate records and records with “cause codes” not related to storm damages (i.e., damage caused by animals or vandalism) were removed. The utility may not track outages at individual metered locations; instead they rely on its customers to notify them of outages. After that, predictive algorithms automatically approximate the location of the damage to the nearest isolating device (i.e., transformers, fuses, reclosers, switches). Once the possible outage is recorded into the OMS, a crew is dispatched to find and repair the damage, and closes out the outage record once restoration is complete.”
As per claim 8, Anag teaches 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 teaches 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 teaches the method, the same art and rationale apply.
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 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 20200036588 to (hereinafter “Porter”) et al.
It is noted that any citation[[s]] to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. [[See, MPEP 2123]]
As per claim 6, Anag teaches all the limitations of claim 1.
Anag 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 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 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 and Porter teach the method, the same art and rationale apply.
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.
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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