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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on June 12, 2026 has been entered.
Notice to Applicant
The following is a Non-Final Office Action for Application Serial Number: 18/153,213, filed on November 11, 2023. In response to Examiner's Final Office Action dated December 12, 2025, Applicant on June 12, 2026, amended claims 1, 6, 7, 12, 17, 18 and 20 and canceled claims 21 and 22. Claims 1, 2, 4-13 and 15-20 are pending in this application and have been rejected below.
Response to Amendment
Applicant's amendments are acknowledged.
Regarding the 35 U.S.C. 101 rejection, Applicants arguments and amendments have been considered but are insufficient to overcome the rejection.
Response to Arguments
Applicant's Arguments/Remarks filed June 12, 2026 (hereinafter Applicant Remarks) have been fully considered but are not persuasive. Applicant’s Remarks will be addressed herein below in the order in which they appear in the response filed June 12, 2026.
Regarding the 35 U.S.C. 101 rejection, Applicant states the features "providing the first set of features to a first set of trained decision trees to generate a first predicted trim device for the first span" cannot practically be performed in the human mind. The Office Action acknowledged this point but dismissed it on the ground that such elements were "additional elements" to be addressed under Step 2A, Prong Two, rather than under Step 2A, Prong One. See Office Action at pp. 8 and 9, where these features are not addressed in the Step 2A, Prong One analysis. This reasoning is in error. The question under Prong One is whether the claim as a whole - not piecemeal elements selected by the Office Action - is directed to a judicial exception. The 2019 Revised Patent Subject Matter Eligibility Guidance requires analysis of the claim as a whole, including whether the claim integrates any alleged judicial exception into a practical application. When the claims are so evaluated, they recite a specific and concrete computational process involving the receipt of structured geospatial data - including feeders of electrical power distribution infrastructures, spans electrically connected to those feeders, roads, and buildings - the generation of a specifically defined feature set using spatial relationships (e.g., road type nearest to a span, distance from the span to the road, whether a vector from the span to the road intersects a building), and the input of those features into a trained machine learning model (a first set of trained decision trees) to generate a predicted trim device. This is not a mental process. No human being, using pen and paper or otherwise, could practically perform these steps as recited in the claims.
Moreover, as proposed to be amended, claim 1 additionally recites (see p. 13 Applicant Remarks). These additional features require the computational processing of geometric polygon data - specifically, the determination of polygon areas as a proxy for building footprints - and then using the computed total building area relative to a feeder region's total area to generate a building density feature that forms part of the feature set supplied to the trained decision trees. The manipulation of polygon representations of buildings, the computation of polygon areas across a geographic feeder region, and the derivation of a building density metric from those computations are operations that cannot be performed mentally and require the capabilities of a computer processing structured geospatial data from data sources. The human mind is not equipped to receive polygon datasets from data sources, compute the area of each polygon, compute the total building area, compute a density ratio relative to a geographic region, and provide that derived feature as input to trained decision trees to generate a predicted trim device for a span of an electrical distribution infrastructure. This is a specific, computerized process, not a thought or observation.
The Office Action characterizes the core steps as "observations, evaluations, judgements and/or opinion that can be performed by a combination of the human mind and a human using pen and paper." Office Action at p. 9. However, this characterization does not engage with the substance of what the claims actually recite. The claims do not recite abstract observational or evaluative concepts; they recite specific computational steps applied to specific structured data objects (spans, feeders, roads, buildings represented as polygons, feeder regions, trained decision trees) in a defined sequence. The specification confirms the technical nature of the claimed implementation, describing at paragraph [0053] that in one example the trim device prediction module "utilizes a random forest machine learning algorithm to train a set of decision trees and provides the sets of features to the set of trained decision trees to generate predicted trim devices for trimming vegetation around different electrical assets." A random forest machine learning model - trained on random subsets of data to produce weakly correlated decision trees whose combination yields more reliable predictions - is an inherently computational construct that exists in no meaningful sense in the human mind. The recitation of a first set of trained decision trees in the claims, taken together with the specific feature engineering steps recited, places the claims firmly outside the mental process grouping. Claims 12 and 20 recite at least generally similar features as claim 1 and therefore also do not recite a mental process. Therefore, claims 1, 12, and 20 do not recite an abstract idea.
In response, Examiner respectfully disagrees. The guidance explicitly states, in part:
The revised Step 2A Prong One procedure for determining whether a claim “recites” an abstract idea is to:
identify the specific limitation(s) in the claim under examination that the examiner believes recites an abstract idea; and
determine whether the identified limitation(s) falls within at least one of the groupings of abstract ideas enumerated in the 2019 PEG.
If the identified limitation(s) falls within any of the groupings of abstract ideas enumerated in the 2019 PEG, the analysis should proceed to Prong Two.
This point is also supported in an example outlined in 2106.07(a)(I) as follows:
Sample explanation: The claim recites the step of comparing collected information to a predefined threshold, which is an act of evaluating information that can be practically performed in the human mind. Thus, this step is an abstract idea in the "mental process" grouping.
Examiner notes, the analysis under Step 2A-Prong One went into detail regarding the claim limitations that included the abstract idea (i.e. receiving a first geographic area, the first geographic area including one or more first feeders of one or more first electrical power distribution infrastructures; receiving a first set of roads for the first geographic area, receiving a first set of spans for the first geographic area, a span of the first set of spans electrically connected to a feeder of the one or more first feeders of the one or more first electrical power distribution infrastructures; receiving a first set of buildings for the first geographic area, wherein receiving the first set of buildings includes receiving a first set of polygons from one or more data sources, a first polygon of the first set of polygons representing a first building of the first set of buildings; generating a first set of features for a first span of the first set of spans, the first set of features including a type of a first road of the first set of roads that is nearest to the first span feature, a distance from the first span to the first road feature, and an intersection feature that indicates whether a vector from the first span to the first road intersects a building of the first set of buildings; generate a first predicted trim device for the first span; and generating and providing a first report, the first report including the first predicted trim device for the first span, wherein the first set of features further includes a building density feature, and further comprising: determining an area of a feeder region that includes a first feeder of the one or more first feeders to which the first span is electrically connected, the feeder region further including multiple buildings, a building having an area; obtaining a total building area based on the areas of the multiple buildings in the feeder region, by determining multiple areas of multiple polygons representing the multiple buildings; and generating the building density feature based on the total building area and the area of the feeder region) and what type of abstract ideas under mental processes were recited (i.e., methods based on observations, evaluations, judgements and/or opinion that can be performed by a combination of the human mind and a human using pen and paper). Additionally, the rejection under Step 2A-Prong Two outlined each additional element used as tools to apply the instructions of the abstract idea; see MPEP 2106.05(f). Thus, Examiner maintains the rejection has met the prima facie case of ineligibility under the substantive law.
Furthermore, Examiner finds Applicants remarks are directed to limitations that recite both abstract and additional elements (i.e. trained decision trees to generate a first predicted trim device for the first span), and therefore were not all analyzed under Step 2A-Prong One. Examiner notes the decision trees recited in Applicant’s independent claims are considered additional elements and therefore were analyzed under Step 2A-Prong Two of the Two-Part Analysis. Examiner maintains the decision trees of the claims are used as tools to perform the instructions of predicting trim devices and building density for geographical areas and are insignificant in taking the claims out of the mental processes grouping. Examiner respectfully reminds Applicant, regardless of the complexity and/or granularity computational steps without meaningful limitations within the claims that amount to significantly more than the abstract idea itself is a judicial exception (i.e. abstract idea). Examiner finds the present claims are directed to the data analysis of predicting trim devices and generating building density for geographical areas. Even in a computer environment, these limitations are still considered abstract by reciting limitations that mimic human thought processes of observation, evaluations, judgement and opinion, that can feasibly be performed with pen and paper, where the data interpretation is perceptible in the human mind. Claims can recite a mental process even if they are claimed as being performed on a computer; see MPEP 2106.04(a)(2)(III)(C). Applicant has not identified any limitations in the claimed invention that shows or submits that the technology (i.e., decision trees) used is being improved or there was a problem in the technology that the claimed invention solves.
Regarding the 35 U.S.C. 101 rejection, Applicant states claims do not merely apply an abstract idea to the business of vegetation management. Rather, the claims are directed to a specific technical solution to the technical problem of how to accurately and automatically predict a trim device for a span of an electrical power distribution infrastructure using geospatial data structures and machine learning. As disclosed in the specification at paragraph [0003], manual examination of electrical assets and the vegetation proximate thereto is impractical. The claims solve this technical problem by defining a specific technical process: (1) receiving structured geospatial datasets for a geographic area including feeders, spans, roads, and buildings; (2) generating a precisely defined feature set for each span using geometric computations including road type lookup, distance computation, and ray-casting (determining whether a vector from a span to a road intersects a building); (3) enriching that feature set with a building density feature computed by aggregating polygon areas within a defined feeder region; and (4) providing the resulting feature vector to a trained set of decision trees to generate a predicted trim device. The output - a predicted trim device - is then reported as an actionable result enabling utility personnel to ensure that appropriate equipment is delivered to a job site, thereby also reducing the risk of fires. See specification at paragraphs [0034] through [0037].
The practical application here is not the abstract idea of "predicting trim devices," but the specific computerized method of generating geospatially-grounded, machine-learning-based trim device predictions tied to the physical configuration of a feeder region - including its road network, span connectivity, and building density as reflected in polygon data. The integration of polygon-based building representation, feeder-region-scoped area computation, building density feature generation, and trained decision tree inference into a unified predictive method constitutes a specific and meaningful feature beyond merely applying an abstract idea in a technological environment. Contrary to the Office Action's assertion that the machine learning technique "does not provide a meaningful limitation to transform the abstract idea into a practical application" (Office Action at p. 10), the integration of a specifically trained set of decision trees as the inference engine - taking as input a carefully engineered feature vector derived from geospatial data - is not the generic use of a machine learning technique. The claimed combination of feature engineering steps and the application of trained decision trees is a specific technical implementation producing a specific and practical result in the field of electrical infrastructure vegetation management.
For at least these reasons, independent claims 1, 12, and 20 are not directed to a judicial exception under Pathway B and thus qualify as eligible subject matter under 35 U.S.C. § 101.
In response, Examiner respectfully disagrees. Examiner finds Applicant is attempting to say the Step 2A-Prong One elements, the abstract idea, is what makes the claim eligible. Applicant’s abovementioned remarks are not technological in nature and merely confines the abstract idea to a particular technological environment or field of use; see MPEP 2106.05(h). Examiner notes claims can recite a mental process even if they are claimed as being performed on a computer; see MPEP 2106.04(a)(2)(III)(C). Examiner finds the pending claims recite similar limitations to claims the courts have indicated may not be sufficient in showing an improvement in computer-functionality, such as mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017), A commonplace business method being applied on a general purpose computer, Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48; see MPEP 2106.05(a)(I) and MPEP 2106.05(a)(II). Examiner maintains Applicants arguments improves an existing business process and not a technology, technological field or computer related technology. The claims are directed to a judicial exception.
Regarding the 35 U.S.C. 101 rejection, Applicant states the claimed additional elements include not merely a generic recitation of a computer or a processor, but a specifically configured system executing a defined sequence of geospatial computations: receiving polygon representations of buildings from data sources, determining the area of a feeder region, determining the areas of the multiple buildings in the feeder region by determining multiple areas of multiple polygons, obtaining a total building area based on those areas, and generating a building density feature based on the total building area and the area of the feeder region. These steps are not the routine and conventional operation of receiving and storing data. Computing the area of a geographic feeder region, computing the areas of building polygons within that region, computing those areas, and computing a density ratio are specific computational operations requiring geospatial data structures and geometric processing capabilities that go beyond any generic computer function identified in the case law cited by the Office Action (such as receiving data over a network or storing data in memory).
Furthermore, providing the resulting feature set - which includes road type, distance, intersection flag, and building density - to a first set of trained decision trees to generate a predicted trim device is not the generic use of machine learning. As described in the specification at paragraph [0053], the decision trees are trained using a random forest algorithm applied to the specific feature sets described in the claims. The application of a trained random forest model to specifically engineered geospatial features to produce a specific prediction for a specific domain (trim device selection for spans in an electrical distribution network) is a specific and non- generic additional element. The Office Action has not established - and the record does not support - that training decision trees on geospatial features of this specific type to predict trim devices for spans of electrical distribution infrastructures was well-understood, routine, or conventional at the time of filing. See MPEP § 2106.05(d).
Viewed as an ordered combination, the additional elements - polygon-based building data ingestion, feeder-region area computation, building density feature generation, and trained decision tree inference - constitute a specific and non-conventional technical implementation that amounts to significantly more than any alleged abstract idea. The combination produces a practical, actionable output (a predicted trim device) that enables utility personnel to prepare appropriate equipment for a job site and reduces the risk of fires. These are concrete, real-world benefits achieved through a specific technical process, not through the mere application of an abstract idea using routine computer components.
For at least these reasons, independent claims 1, 12, and 20 are not directed to a judicial exception under Pathway C and thus qualify as eligible subject matter under 35 U.S.C. § 101.
In response, Examiner respectfully disagrees. Examiner respectfully notes, the analysis in Step 2B addresses the question on whether an additional element (or combination of additional elements) represents well-understood, routine and/or conventional activities. Examiner finds Applicant is again, attempting to say the Step 2A-Prong One elements, the abstract idea, is what makes the claim eligible. Applicant has provided no detailed explanation to the configuration of the combination of additional elements.
Examiner notes in Ex parte Desjardins, Appeal 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision), the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting”, and that the claims reflected the improvement identified in the specification. The improvements identified in the Desjardins specification included disclosures of the effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation. Examiner finds no similar improvements to take into consideration here. The trained decision trees are recited in the claims at a high level of generality without reflecting and/or submitting that the technology used is being improved, there was a technical problem in the technology that the claimed invention solves. Examiner maintains the additional elements recited in the claims do not perform any unconventional functions that can be considered “significantly more” than the judicial exception.
Regarding the 35 U.S.C. 101, Applicant addresses the eligibility of claims 6, 7, 17, and 18 separately, as these claims recite additional features relating to the training of the decision trees that further support patent eligibility both independently and through their dependency on independent claims (see p. 18-19, Applicant Remarks).
For at least the reasons set forth above, claims 6, 7, 17, and 18 are not directed to a judicial exception, or alternatively integrate any exception into a practical application, and in the further alternative include additional elements that amount to significantly more than any alleged abstract idea.
In response, Examiner respectfully notes that decision tree elements of clams 6, 7, 17 and 18 are considered additional elements and therefore were not included in determining the abstract idea and was analyzed under Step 2A-Prong Two. Examiner maintains the abovementioned features amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. Examiner finds the present claims recite similar limitations that the courts have described as merely indicating a field of use or technological environment in which to apply a judicial exception, including limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016). Examiner respectfully reminds Applicant, regardless of the complexity and/or granularity, the specific data used in training decision trees does not contribute an inventive concept that amounts to significantly more than the abstract idea. Examiner maintains the trained decision trees are used as tools to apply the instructions of the abstract idea because they do not perform any unconventional functions that can be considered “significantly more” than the judicial exception.
Regarding the 35 U.S.C. 101, Applicant states Claims 2, 4, 5, 8, 9, 10, 11, 13, 15, 16, and 19 each depend from independent claims that, as demonstrated above, are patent-eligible. Each of these dependent claims adds further specific features - for example, generating second sets of features for additional spans and providing them to the trained decision trees to generate second predicted trim devices (claims 2 and 13); comparing the building density feature to a threshold to assign a residential feature to the feeder region (claims 4 and 15); applying a function to multiple distance and intersection features to determine a trim type device feature (claims 5 and 16); receiving and normalizing vegetation densities (claims 8 and 19); aggregating normalized vegetation densities by feeders and predicted trim devices (claim 9); computing clearance values and assigning vegetation density categories (claim 10); and assigning a confidence value based on the number of decision trees predicting a particular trim device (claim 11). These dependent claims further narrow the scope of the independent claims and add concrete technical features. The Office Action has not separately established that any of these additional features are themselves abstract or fail to contribute to eligibility. The additional elements in the dependent claims do not perform abstract functions but rather provide specific technical refinements to the claimed geospatial feature engineering and machine learning prediction process.
For at least the reasons set forth above, claims 2, 4, 5, 8, 9, 10, 11, 13, 15, 16, and 19 are not directed to a judicial exception, or alternatively integrate any exception into a practical application, and in the further alternative include additional elements that amount to significantly more than any alleged abstract idea.
In response, Examiner respectfully disagrees. Examiner notes each dependent claim was analyzed and determined to respectively recite a machine learning technique as a tool to perform the instructions of the abstract idea; MPEP 2106.05(f) or further narrowing the abstract idea. No additional elements are disclosed in the dependent claims that were not considered in the independent claims. Examiner maintains the rejection of dependent claims 2, 4-11, 13 and 15-19 is proper and finds they do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself.
Regarding the 35 U.S.C. 101, Applicant states the USPTO memorandum of August 24, 2025 reminds examiners that a rejection under 35 U.S.C. § 101 should be made only where ineligibility is established by a preponderance of the evidence, and not where eligibility is merely uncertain. Applicant submits that the present claims are clearly eligible for the reasons explained above. At minimum, however, the rejection should be withdrawn because the Office Action does not establish by a preponderance of the evidence that the claims are directed to ineligible subject matter. The rejection relies on a high- level characterization of the claims as merely "predicting trim devices for geographical areas" while not adequately addressing the specific claimed geospatial feature generation and feeder- region computations that are used to produce the span-specific trim-device prediction.
For at least the reasons set forth herein, the Applicant respectfully requests that the rejections of claims 1, 2, 4-13, and 15-20 under 35 U.S.C. § 101 be withdrawn. Claims 21 and 22 have been canceled, and accordingly, the 35 U.S.C. § 101 rejections of these claims are now moot.
Examiner respectfully disagrees. Examiner notes the August 4th Memorandum is not new guidance, but a reminder to follow the current guidance. MPEP 2106.04(a), which states, in part, “Examiners should determine whether a claim recites an abstract idea by (1) identifying the specific limitation(s) in the claim under examination that the examiner believes recites an abstract idea, and (2) determining whether the identified limitations(s) fall within at least one of the groupings of abstract ideas. If the identified limitation(s) falls within at least one of the groupings of abstract ideas, it is reasonable to conclude that the claim recites an abstract idea in Step 2A Prong One. Examiner maintains the pending claims constitute methods that mimics human thought processes of observation, evaluations, judgement and opinion, that can feasibly be performed with pen and paper, where the data interpretation is perceptible in the human mind. Examiner maintains the decision trees and associated computer components, both individually and in combination, amount to no more than generic computer technology used as tools to apply the instructions of the abstract idea. The mere fact that a claim applies specific techniques to achieve desired result is never, in itself, justification for the allowance of such a claim”. Merely confining the abstract idea to a particular technological environment does not establish a practical application. See Guidance, 84 Fed. Reg. at 54. “A claim does not cease to be abstract for section 101 purposes simply because the claim confines the abstract idea to a particular technological environment in order to effectuate a real-world benefit.” In re Mohapatra, 842 F. App’x 635, 638 (Fed. Cir. 2021). For at least these reasons the claims remain rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
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.
Step 1: The claimed subject matter falls within the four statutory categories of patentable subject matter.
Claims 1, 2, and 4-11 are directed towards a non-transitory computer-readable medium, claims 12, 13, and 15-19 are directed towards a system and claim 20 is directed towards a method, which are among the statutory categories of invention.
Step 2A – Prong One: The claims recite an abstract idea.
Claims 1, 2, 4-13 and 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite predicting trim devices and generating building density for geographical areas.
Claim 1 recites limitations directed to an abstract idea based on mental processes. Specifically, receiving a first geographic area, the first geographic area including one or more first feeders of one or more first electrical power distribution infrastructures; receiving a first set of roads for the first geographic area, receiving a first set of spans for the first geographic area, a span of the first set of spans electrically connected to a feeder of the one or more first feeders of the one or more first electrical power distribution infrastructures; receiving a first set of buildings for the first geographic area, wherein receiving the first set of buildings includes receiving a first set of polygons from one or more data sources, a first polygon of the first set of polygons representing a first building of the first set of buildings; generating a first set of features for a first span of the first set of spans, the first set of features including a type of a first road of the first set of roads that is nearest to the first span feature, a distance from the first span to the first road feature, and an intersection feature that indicates whether a vector from the first span to the first road intersects a building of the first set of buildings; generate a first predicted trim device for the first span; and generating and providing a first report, the first report including the first predicted trim device for the first span, wherein the first set of features further includes a building density feature, and further comprising: determining an area of a feeder region that includes a first feeder of the one or more first feeders to which the first span is electrically connected, the feeder region further including multiple buildings, a building having an area; obtaining a total building area based on the areas of the multiple buildings in the feeder region, by determining multiple areas of multiple polygons representing the multiple buildings; and generating the building density feature based on the total building area and the area of the feeder region constitutes methods based on observations, evaluations, judgements and/or opinion that can be performed by a combination of the human mind and a human using pen and paper. The recitation of a non-transitory computer-readable medium comprising executable instructions by processors and trained decision trees to does not take the claim out of the mental processes grouping. Thus the claim recites an abstract idea. Claims 12 and 20 recite mental processes for similar reasons as claim 1.
Step 2A – Prong Two: The judicial exception is not integrated into a practical application.
The judicial exception is not integrated into a practical application. In particular, claim 1 recites a non-transitory computer-readable medium comprising executable instructions by processors at a high-level of generality such that it amounts to no more than generic computer components used as tools to apply the instructions of the abstract idea; see MPEP 2106.05(f). Additionally, claim 1 recites providing the first set of features to a first set of trained decision trees to generate a first predicted trim device for the first span. The general use of a machine learning technique does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, providing the first set of features to a first set of trained decision trees is considered to be an insignificant extra-solution activity of collecting and delivering data; see MPEP 2106.05(g) and the set of trained decision trees disclosed in the claims are solely used as a tool to perform the instructions of the abstract idea; see MPEP 2106.05(f). Thus, the additional elements do not integrate the abstract idea into practical application because it does not impose any meaningful limitations on practicing the abstract idea. Claim 1 as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application and therefore is directed to an abstract idea. The system comprising memory containing executable instructions by the at least one processor recited in claim 12 and method in claim 20 also amount to no more than tools to perform the instructions of the abstract idea; see MPEP 2106.05(f). Thus, the additional elements recited in claims 12 and 20 do not integrate the abstract idea into practical application for similar reasons as claim 1.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements in the claims other than the abstract idea per se, including a non-transitory computer-readable medium comprising executable instructions by processors and system comprising memory containing executable instructions by the at least one processor amount to no more than a recitation of generic computer elements utilized to perform generic computer functions, such as receiving or transmitting data over a network, e.g., using the Internet to gather data, 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); and storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; see MPEP 2106.05(d)(II). The machine learning technique recited in the claim (i.e., trained decision trees) are disclosed at a high-level of generality (see par. 0053) and does not amount to significantly more than the abstract idea. Viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Therefore, since there are no limitations in the claim that transform the abstract idea into a patent eligible application such that the claim amounts to significantly more than the abstract idea itself, the claims are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
§ 101 Analysis of the dependent claims.
Regarding the dependent claims, claims 2, 6, 7, 9, 11, 13, 17 and 18 recites recite limitations utilizing a trained sets of decision trees, which solely uses a machine learning technique as a tool to perform the instructions of the abstract idea; MPEP 2106.05(f). Additionally, claims 2, 4-11, 13 and 15-19 recite steps that further narrow the abstract idea. No additional elements are disclosed in the dependent claims that were not considered in the independent claims. Therefore claims 2, 4-11, 13 and 15-19 do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zhang et al. (US 20230298343 A1) – Systems and methods for optimizing asset maintenance protocols by predicting vegetation-driven outages are disclosed. An example method includes determining, by one or more processors, a failure probability for each asset in a set of assets within a designated area. 5 The example method further includes defining, by the one or more processors, an asset risk for each asset in the set of assets based on the failure probability, and clustering, by the one or more processors, vegetation within the designated area to determine a predicted vegetation-driven outage. The example method further includes optimizing, by the one or more processors, a set of asset maintenance protocols corresponding to the set of assets based on the asset risk and the 10 predicted vegetation-driven outage.
Rentz et al. (US 20220044167 A1) – A system for prioritizing trim requests can include an image processor that identifies a location associated with a customer trim request that includes an image of vegetation and industrial equipment and marks the vegetation and the industrial equipment visible in the image associated with the customer trim request. The system can also include a vegetation maintenance analyzer that searches for a match between the identified location associated with the customer trim request with a location associated with one of a past executed service ticket and a pending service ticket. The maintenance analyzer generates a maintenance profile for the customer trim request based on the results of the search and data in the customer trim request. The system can further include a priority engine that assigns priority to the customer trim request based on the maintenance profile for the customer trim request.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Crystol Stewart whose telephone number is (571)272-1691. The examiner can normally be reached 9:00am-5:00pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Patty Munson can be reached on (571)270-5396. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/CRYSTOL STEWART/Primary Examiner, Art Unit 3624
Prosecution Insights