DETAILED ACTION
This Final Office Action is in response to Application 18/430,797. In response to Examiner’s action mail dated July 30, 2025, Applicant submitted arguments mail dated October 27, 2025. Applicant did not submit amendments to the claims. The claims 1-12 are examined below and are pending in this application.
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 the event the determination if 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. Or any correction of the statutory basis for the rejection will not be considered in a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Information Disclosure Statement
Applicant did not submit an information disclosure statement (IDS) for consideration by the examiner.
Response to Amendments
Applicant submitted arguments. Applicant did not submit amendments.
Regarding the 35 U.S.C. 101, the Applicant’s amendments are not persuasive.
Regarding the 35 U.S.C 102 rejection. Applicant did not submit amendments therefore, the rejection is the same as the prior rejection. See Examiner’s response to Applicant’s prior art arguments.
Regarding the 35 U.S.C 103 rejection. Applicant did not submit amendments therefore, the rejection is the same as the prior rejection. See Examiner’s response to Applicant’s prior art arguments.
Applicant is encouraged to request an interview.
Response to Arguments
Applicant’s arguments filed on October 27, 2025 have been fully considered but they are not persuasive and/or are moot in view of the revised rejections. Applicant’s arguments will be addressed herein below.
35 U.S.C. 101 Rejection
On pages 6-8 of the Applicant’s 35 U.S.C. 101 arguments, the Applicant traverses, Examiner’s rejection. Applicant submits claim 1, is/are directed to a method for the automated planning of vegetation trimming for maintenance of an electrical power distribution system, while independent claim 7 is directed to a counterpart apparatus. Applicant states the claims provide improvements in the technology for planning and scheduling physical maintenance activities with respect to an electrical grid. Applicant traverses, “satellite imagery”, “proxy index data”, and “machine learning”. Applicant submits the claims combine various mathematical operations, applied to real-world data pertaining to a real-world system, to yield an improvement to an existing technology, they integrate any “abstract idea” contained therein into a “practical application; of those abstract ideas. Applicant request the rejection under 35 U.S.C. 101 be withdrawn.
Examiner respectfully disagrees with Applicant’s 35 U.S.C. 101 arguments. The Applicant claims recite correlating normalized difference vegetation index (NDVI) data with electrical system data, which is a mathematical concept, and thus is an abstract idea at Step 2A prong one. The claims recite additional elements including, “ … the one or more computing devices comprising processing circuity and memory operatively coupled to the processing circuitry and storing program instructions for execution by the processing circuitry, whereby the processing circuitry among the one or more computing devices is configured to: correlate normalized difference vegetation index (NDVI) data extracted from satellite imagery with electrical system outage data …”, where the computer is used to correlate data (e.g., satellite imagery, electrical system data).
Regarding the additional elements of a heatmap and a neural network, the claims are applying data to these technologies. Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea are not indicative of integration into a practical application. -See MPEP 2106.05 (f).
Regarding improvement, the claims are using the data to complete a correlation to make a prediction, and thus, the claims do not recite an improvement in technology. The Applicant is encouraged to integrate the technologies into the judicial exception at Step 2A Prong two. Additionally, Applicant is pointed to Subject Matter Eligibility Guidance example 47 and Example 39 for consideration. At Step 2B, as previously discussed the claims are apply it. – MPEP 2106.05 (f). The claims are not rooted in an improvement to technology. The claims are not patent eligible.
Applicant is encouraged to point to specification [028], [033]-[037] where technologies are used to identify prioritized vegetation (pixels and neural network) and illustrate data (heatmap). Applicant should identity the integration of the technologies into the prediction models to improve the system reliability. Applicant is encouraged to request an interview.
35 U.S.C. 102 Rejection
On pages 8-10 of the Applicant’s 35 U.S.C. 102 arguments, the Applicant traverses Examiner’s 35 U.S.C. 102 rejection. Applicant request reconsideration of claims 1, 2, 4-8 and 10-12. Applicant asserts the claims are directed to a method for planning vegetation trimming for maintenance of an electric power distribution system as recited in the independent claim 1 and claim 7. Applicant submits an anticipation rejection requires that all of the elements of the invention be disclosed by a reference, expressly or inherently. Applicant request the rejection of claims 1 and claim 7 are withdrawn. The rejection of dependent claims 2, 4-6, 8, and 10-12 should be withdrawn for at least the same reasons.
Applicant argues Abi-Rached fails to disclose that vegetation-related outage events and/or number of customers affected are predicted by device protective zone nor does art discuss electrical system outage data mapped to power distribution line segments.
Examiner respectfully disagrees with Applicant’s arguments. Examiner submits, Abi-Rached teaches the elements that are claimed. However, in-light of the Applicant’s arguments, Examiner reviewed the art and maintains Abi-Rached teaches the claimed limitations and elements.
Abi-Rached is relied on to reject claim 1 and claim 7. Specifically, Abi-Rached teaches vegetation management, where decision making regarding the proximity of the vegetation and how to manage it may be important when it comes to minimizing the number of outages due to vegetation, preventing forest fires and disruption of the power., See Abi-Rached [Column 1 lines 30-44].; Abi-Rached is a computerized system for vegetation management that provides location-based trim scheduled and improved forecasting of vegetation trimmings, which may reduce costs for upcoming cycle trims on the same resource conduits (“feeders”). See Abi-Rached [Column 2 lines 20-25]. Abi- Rached teaches feeder which is referred to as an electrical/ distribution line. Abi-Rached teaches Feed 1 has two feeder segments. Abi-Rached [column 5 lines 10-40]. Abi-Rached discloses one or more embodiments provide for the generation of a risk score for each feeder, where the risk score may indicate the likelihood, the feeder will experience an outage in a given amount of time., See Abi-Rached [Column 2 lines 25-30].
Abi-Rached teaches non-exhaustive examples of data sources include asset and asset-related information, weather data (e.g., extreme wind conditions, wind direction, wind pressure, wind gust, snow, rain temperatures, etc.), multiple satellite inputs (e.g., Normalized Difference Vegetation Index (NDVI)), satellite data-based vegetation analytics, work orders (e.g., last trimming date and next trimming date), number of customers affected, and historical data of vegetation-related outages. Therefore, Abi-Rached conditions weather and the number of customers affected., See Abi-Rached [Column 4 lines 49-55].
Examiner submits Abi-Rached teaches the elements and concepts of claim 1 and claim 7. Examiner submits, the elements and concepts of the claimed limitations are anticipated by Abi-Rached. Applicant’s arguments are not persuasive.
The dependent claims 2, 4-6, 8, and 10-12 rely on the independent claims, and remain rejected at least the same reasons as the independent claims. Dependent claims 2-6 further narrow the abstract idea of independent claims 1. Dependent claims 8-12 further narrow the abstract idea of independent claim 7.
Examiner maintains the 35 U.S.C. 102 rejection for claims 1, 2, 4-8 and 10-12.
35 U.S.C. 103 Rejection
On page 10 of the Applicant’s 35 U.S.C. 103 arguments, the Applicant traverses, dependent claim 3 and 9 are rejected as obvious over Abi-Rached in view of Bhola. Applicant traverses Bhola ‘s disclosure does not cure any of the deficiencies in Abi-Rached described in parent claim 1 and 7. The rejections of claims 3 and 9 should be withdrawn for the same reasons as the parents claims.
Examiner respectfully disagrees with Applicant’s 35 U.S.C 103 arguments. The argument is a general allegations that is dependent on the persuasiveness of the independent claim 1 and claim 7 arguments.
As disclosed in the response to the Applicant’s 35 U.S.C. 102 arguments to clarify the context of the prior art used in the rejection, the Applicant is pointed to: Abi-Rached [Column 1 lines 30-44] - a computerized system for vegetation management that provides location-based trim scheduled and improved. Abi-Rached [Column 2 lines 20-30] - generation of a risk score for each feeder, where the risk score may indicate the likelihood, the feeder will experience an outage in a given amount of time. Abi-Rached [Column 4 lines 49-55] – weather and number of customers. Abi-Rached teaches the limitations of the independent claims.
Examiner notes “[a] general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references does not comply with the requirements of this section.” 37 CFR 1.111(b). Applicant’s assertions regarding claims 3 and 9 are not persuasive because they are general allegations, rather than arguments specifically pointing out how the language of the claims patently distinguishes them from Abi-Rached in view of Bhola.
Abi-Rached considers vegetation management using image data. Bhola discloses spectral-spatial methods to detect the power lines. It would have been obvious to combine before the effective filing date, determine the proximity of the vegetation to the power lines, as taught by Abi-Rached, with using clustering algorithms to detect powerline images taken by a UAV, as taught by Bhola, to decrease operations and maintenance costs., Bhola [abstract]
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 are process.
Claims 7-12 are machine.
Claims 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims (claim 1) recite, “… correlating normalized difference vegetation index (NDVI) data extracted from satellite imagery with electrical system outage data mapped to power distribution system line segments, to generate vegetation proxy index data spatially associated with said power distribution line segments; predicting vegetation related outage events and/or numbers of customers affected by device protective zone, based on the vegetation proxy index data; and identifying prioritized areas for vegetation management based on the predicted outage events and/or numbers of affected customers.”; Claim 7 recites, “ … correlate normalized difference vegetation index (NDVI) data extracted from satellite imagery with electrical system outage data mapped to power distribution system line segments, to generate vegetation proxy index data spatially associated with said power distribution line segments; predict vegetation related outage events and/or numbers of customers affected by device protective zone, based on the vegetation proxy index data; and identify prioritized areas for vegetation management based on the predicted outage events and/or numbers of affected customers…” . Claims 1-12, in view of the claim limitations recite the abstract idea of, … identify the prioritized areas for vegetation trimming based on an economic model that estimates vegetation trimming costs.., and these recite concepts performed in the human mind (including an observation, evaluation, judgement, opinion), and thus, the claims are related to mental process, and thus, the claims are related to an abstract idea under the first prong of Step 2A.
This judicial exception are not integrated into a practical application under the second prong of Step 2A. In particular, the claims recite the additional elements are integrated beyond the recited abstract idea of, “A method for planning vegetation trimming for maintenance of an electric power distribution system, the method comprising”, are disclosed in claim 1; “One or more computing devices, configured for planning vegetation trimming for maintenance of an electric power distribution system, each of the one or more computing devices comprising processing circuity and memory operatively coupled to the processing circuitry and storing program instructions for execution by the processing circuitry, whereby the processing circuitry among the one or more computing devices is configured to, in claim 7; 20however, when viewed as an ordered combination, and pursuant to the broadest reasonable interpretation, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea on a computer (i.e. apply it), and thus, are no more than applying the abstract idea. Adding the words “apply it” (or equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - See MPEP 2106.05 (f)
The dependent claims recite the following additional elements:
Claim 2: “a model linking historical system reliability and resilience data for mapped electrical system components and historical vegetation index data”
Claim 3: “Gaussian kernel filtering”, “said model”
Claim 4: “said model is … a neural network model”, “a combination of a linear model and a neural network model”
Claim 6: “a heatmap”
Claim 8: “The one or more computing devices of claim 7, wherein the processing circuitry among the one or more computing devices is configured to”
Claim 9: “The one or more computing devices of claim 8, wherein the processing circuitry among the one or more computing devices is configured to perform Gaussian kernel filtering”, “said model”
Claim 10: “said model is … a neural network model, or a combination of a linear model and a neural network model.”
Claim 11: “The one or more computing devices of claim 7, wherein the processing circuitry among the one or more computing devices is configured to”
Claim 12: “The one or more computing devices of claim 7, wherein the processing circuitry among the one or more computing devices is configured to”, “ a heatmap”
Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on the abstract idea. The claims also fail to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, and/ or an additional element applies or used the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than drafting effort designed to monopolize the exception.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, because the additional elements when considered both individually and as an ordered combination do not amount to significantly more. (See MPEP 2106.05 (f) Mere Instruction to Apply an Exception – Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct at 235).
At Step 2B, it is MPEP 2106.05 (d) – Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).
Examiner concludes that the additional elements in combination fail to amount to significantly more than the abstract idea based on findings that each element merely performs the same function (s) in combination as each element performs separately. The claim is not patent eligible. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified exception (the abstract idea). Looking at the limitation as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Dependent claims 2-6 further narrow the abstract idea of independent claims 1. Dependent claims 8-12 further narrow the abstract idea of independent claim 7.
Claims 1-12 are not patent eligible under 35 U.S.C. 101.
Claim Rejections - 35 USC § 102
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-2, 4-8, 10-12 is/are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Abi-Rached (US 11527025 B2).
Regarding Claim 1, (Original)
A method for planning vegetation trimming for maintenance of an electric power distribution system, the method comprising: correlating normalized difference vegetation index (NDVI) data extracted from satellite imagery with electrical system outage data mapped to power distribution system line segments, to generate vegetation proxy index data spatially associated with said power distribution line segments;
Abi-Rached discloses vegetation management, GIS visualization and interaction tool for vegetation management. Data sources include multiple satellite inputs (e.g., Normalized Difference Vegetation Index (NDVI)) … The vegetation management module may use one or more visualization tools … to interact with data displayed on a map., Abi-Rached [column 4 lines 25-63]
Abi-Rached discloses the vegetation management module may suggest the feeder with the highest risk score be trimmed before feeders with lower risk scores. As used herein, “feeder” may refer to an electrical/distribution line or medium that runs along some geographical location to deliver electricity or other resources to one or more houses, businesses or other structures. A feeder may include one or more segments. The segments may be separated by poles (e.g., Feeder 1 has two feeder segments: segment 1 between pole 1 and pole 2, and segment 2 between pole 2 and pole 3)., Abi-Rached [column 5 lines 10-40]
Abi-Rached discloses … if a tree that falls on a segment of the feeder, and the segment is broken, all of the houses/businesses on that feeder are affected by that current interruption. In other words, a feeder may be like a closed circuit in that if at any point the feeder is disrupted, all of the houses/businesses on that feeder are disrupted., Abi-Rached [column 5 lines 40-50]
predicting vegetation related outage events and/or numbers of customers affected by device protective zone, based on the vegetation proxy index data;
See above, Abi-Rached [column 5 lines 40-50] teaches a disruption (e.g., tree fall).
and identifying prioritized areas for vegetation management based on the predicted outage events and/or numbers of affected customers.
Abi-Rached teaches when a team is sent out to trim vegetation, the team is instructed to trim every feeder connected to a substation (i.e., electrical generation, transmission, and distribution system where multiple feeders originate) so that everything nearby a given feeder and associated with the substation is trimmed, whether it needs to be trimmed or not. This conventional process avoids the team (and the associated machinery etc.) having to be sent to this area again. This conventional process may waste time and resources trimming feeders that are low risk. One or more embodiments provide for the feeders in a cluster to be trimmed because they are at a same risk level (e.g., high) and are relatively close together. As such, embodiments provide for all of the high risk feeders to be trimmed in an area, instead of the conventional process of trimming feeders in an area attached to a substation that may not need to be trimmed., Abi-Rached [column 5 lines 40-66]
In light of the Applicant’s arguments, to clarify the context of the prior art used in the rejection, the Applicant is pointed to:
Abi-Rached [Column 1 lines 30-44] - a computerized system for vegetation management that provides location-based trim scheduled and improved.
See Abi-Rached [Column 2 lines 20-30] - generation of a risk score for each feeder, where the risk score may indicate the likelihood the feeder will experience an outage in a given amount of time.
See Abi-Rached [Column 4 lines 49-55] – weather and number of customers.
Regarding Claim 7, (Original)
One or more computing devices, configured for planning vegetation trimming for maintenance of an electric power distribution system, each of the one or more computing devices comprising processing circuity and memory operatively coupled to the processing circuitry and storing program instructions for execution by the processing circuitry, whereby the processing circuitry among the one or more computing devices is configured to: correlate normalized difference vegetation index (NDVI) data extracted from satellite imagery with electrical system outage data mapped to power distribution system line segments, to generate vegetation proxy index data spatially associated with said power distribution line segments; predict vegetation related outage events and/or numbers of customers affected by device protective zone, based on the vegetation proxy index data; and identify prioritized areas for vegetation management based on the predicted outage events and/or numbers of affected customers.
Claim 7 is similar to Claim 1, and thus, Claim 7 is rejected for similar reasons as Claim 1. See Abi-Rached [column 4 lines 25-63] and Abi-Rached [column 5 lines 10-66]
In light of the Applicant’s arguments, to clarify the context of the prior art used in the rejection, the Applicant is pointed to:
Abi-Rached [Column 1 lines 30-44] - a computerized system for vegetation management that provides location-based trim scheduled and improved.
See Abi-Rached [Column 2 lines 20-30] - generation of a risk score for each feeder, where the risk score may indicate the likelihood the feeder will experience an outage in a given amount of time.
See Abi-Rached [Column 4 lines 49-55] – weather and number of customers.
Regarding Claim 8, [and similarly claim 2] (Original)
The one or more computing devices of claim 7, wherein the processing circuitry among the one or more computing devices is configured to predict the outage events and/or numbers of customers affected by outage events based further on a model linking historical system reliability and resilience data for mapped electrical system components and historical vegetation index data.
Abi-Rached discloses a heat map component to indicate predictions of risk scores indicating the risk of utility outage due to vegetation, so that a user may prioritize vegetation trimming. In one or more embodiments, the vegetation management module may use machine learning (ML) techniques to incorporate information on the location of vegetation from satellites, the number of outages that have occurred, the duration of the outages, and how the feeder experienced the outage, to generate a risk score associated with each feeder., Abi-Rached [column 6 lines 32-50] and
Abi-Rached discloses the ML module 204 may use a Machine Learning (ML) technique called “survival modeling” to compute a risk score 206. Other suitable techniques may be used. As described above, the ML module 204 may operate in two stages: 1. Training and 2. Evaluating. With training, the ML module 204 may collect certain historical attribute data for each feeder (e.g., for a given outage—the number of customers impacted and the duration of the outage, in addition to the number of customers impacted the previous year by outages, the population density, estimation of outages in the previous year, time since last trimming etc.) … estimation of outages, trimming, and impacts to customers (e.g., customers impacted, # outages. etc)., Abi-Rached [column 7 lines 2-30]
Regarding Claim 10, [and similarly claim 4] (Original)
The one or more computing devices of claim 8, wherein said model is a linear model or a neural network model, or a combination of a linear model and a neural network model.
See above - Abi-Rached [column 6 lines 32-50] and Abi-Rached [column 7 lines 2-30] teach machine learning.
Abi-Rached teaches During the evaluation phase of the classification approach, the output produced by the trained model is the risk score. In the above, f.sub.β is a regression model with parameter β, and g.sub.α is a classification model with parameter α. Standard machine learning methods like linear regression, neural networks or logistic regression can be used to determine the function as well as the unknown parameters., - Abi-Rached [column 11 lines 42-52]
Regarding Claim 11, [and similarly claim 5] (Original)
The one or more computing devices of claim 7, wherein the processing model
circuitry among the one or more computing devices is configured to identify the prioritized areas based further on an economic model that estimates vegetation trimming costs based on suggested trimming areas and trimming frequencies.
See claim 1 and claim 7 in which this claim depends and
Abi-Rached discloses the operator may select any dot 706 on the map to view additional information about the outage. The additional outage data 708 may include, but is not limited to: cause of outage, customer Interrupted; date of outage; date of last trim; outage duration, CMI, etc., ., Abi-Rached [column 16 lines 12 - 16].;
Abi-Rached considers the cost of trimming in combinations of trim schedule, equipment costs, and geographical location…. To resolve … scheduling inefficiency by combining the feeder-level risk scores 206 along with geographical proximity to determine geospatial clusters 232 and then these geospatial clusters 232 may be used to determine which set of feeders across different regions should get priority trimming over the others., Abi-Rached [column 12 lines 61-67] - [column 13 lines 1-5].
Abi-Rached discloses the vegetation management module 202 may allow a real time interactive visualization that runs, as a non-exhaustive example, 30 frames per second, as the operator changes his filter selection in any of the elements. It is noted that as the operator is selecting the lower part of the number of customer axis in the graph 806 in FIG. 8, where the selection is indicated by the box 805 a heatmap is visualized on the map showing that one of the feeders holds more customers than the other. This interactivity and instantaneous visualization leads the user to make decisions, such as prioritizing trimming and vegetation management to the feeders that hold more customers, and this may be more impactful cost-wise, in case an outage occurs. … . A non-exhaustive example of an output of the vegetation management module 202 is a training schedule 228. Examples of executable analysis include but are not limited to: scheduling and optimization algorithms, mobility applications, utility outage risk modeling/predictions, fire risk modeling, etc., may be executed., Abi-Rached [column 17 lines 65-67] - [column 18 lines 1-24].
Examiner submits the model considers the risk outage and history of CHI (Customer Hours of Interruption) and thus, the model considers economics., Abi-Rached [column 15 lines 50-55 and column 16 lines 6-11]
Regarding Claim 12, [and similarly claim 6] (Original)
The one or more computing devices of claim 7, wherein the processing circuitry among the one or more computing devices is configured to identify the prioritized areas for vegetation management by generating a heatmap, wherein colors and/or intensities of the heatmap are indicative of prioritized areas for vegetation management.
Abi-Rached discloses vegetation management module map provide a map ... heat maps, Abi-Rached [column 5 lines 4 -30].; Abi-Rached discloses the map 704 may then automatically navigate to the feeder 302 in the highlighted selection 702. The color (or other visual indicator) of the feeder lines on the map may reflect the heatmap value of the risk score of the selected feeder., Abi-Rached [column 15 lines 57 -67].;
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 cl-aimed 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 3 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable overAbi-Rached (US 11527025 B2) in view of Bhola (2018, Detection of the power lines in UAV remote sensed images using spectral-spatial methods.)
Regarding Claim 9, [and similarly claim 3] (Original)
The one or more computing devices of claim 8, wherein the processing circuitry among the one or more computing devices is configured to perform …. of pixel-level NDVI data to generate power distribution line segment-level NDVI data and/or feeder-level NDVI data for input to said model.
Abi-Rached discloses As part of vegetation management, the utility company may determine the proximity of the vegetation to the power lines. Decision making regarding the proximity of the vegetation and how to manage it may be important when it comes to minimizing the number of outages due to vegetation, preventing forest fires and disruption of the power., Abi-Rached, [abstract]
Abi-Rached discloses the vegetation management module 202 may include a ML module 204. Prior to the start of the process 100, the ML module 204 may train one or more machine learning models with vegetation modeling analytics and satellite/aerial/other data to: automatically identify each pixel in an image as being a tree vs not-tree; generate a height map showing the height of vegetation; identify whether the vegetation is healthy vs not healthy; generate one or more tree Key Performance Indicators (KPI) (e.g., feeder length, area, volume); determine which species the vegetation belongs to (e.g., if hyperspectral data is available, for example); and determine a risk score for the feeder. Other suitable vegetation modeling analytics may be used to train the ML models., Abi-Rached [column 6 lines 32-45] and Abi-Rached discloses …. [Abi-Rached column 5 lines 8 -45].
Abi-Rached does not teach:
… Gaussian kernel filtering …
Bhola teaches:
… Gaussian kernel filtering of pixel-level NDVI data to generate power distribution line
Bhola discloses powerline inspection using Unmanned Aerial Vehicle (UAV), images., Bhola [abstract] and
Bhola considers a mixture of Gaussian distributions, proportion to the number of clusters computed using Davies-Bouldin Index (DBI) , where the k value corresponding to the lowest DBI is the optimum number and is used in spectral clustering methods. The k clusters are agglomerated into two classes, namely, power line and non-power line., Bhola [p. 1223 column 1 paragraph 2-3]
Abi-Rached considers vegetation management using image data. Bhola discloses spectral-spatial methods to detect the power lines. It would have been obvious to combine before the effective filing date, determine the proximity of the vegetation to the power lines, as taught by Abi-Rached, with using clustering algorithms to detect powerline images taken by a UAV, as taught by Bhola, to decrease operations and maintenance costs., Bhola [abstract]
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Anagnostou (WO 2018/013148 A1) teaches outage prediction, considers weather and vegetation, discusses specific customer outage model.
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 nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/THEA LABOGIN/Examiner, Art Unit 3624 /PATRICIA H MUNSON/Supervisory Patent Examiner, Art Unit 3624