DETAILED ACTION
Claims 1-2, 6-7, 9-15, and 17-20 are presented for examination.
This Office Action is in response to submission of documents on August 13, 2025.
Rejection of claims 1-2, 6-7, 9-15, and 17-20 under 35 U.S.C. 101 for being directed to unpatentable subject matter is maintained.
Rejection of claims 1, 2, 7, 9, 13-15, and 18-20 under 35 U.S.C. 102(a)(1) as being anticipated by Saha is withdrawn.
Rejection of claims 6 and 17 under 35 U.S.C. 103 as being obvious over Saha in view of Anderson is withdrawn.
Rejection of claim 10 under 35 U.S.C. 103 as being obvious over Saha in view of Angevine is withdrawn.
Rejection of claims 11 and 12 under 35 U.S.C. 103 as being obvious over Saha in view of Agouridis is withdrawn.
Rejection of claims 1, 2, 7, 11-12, 14-15, and 18-20 under 35 U.S.C. 103 as being obvious over Rudin in view of Nielsen.
Rejection of claims 9 and 13 under 35 U.S.C. 103 as being obvious over Rudin in view of Nielsen and Saha.
Rejection of claims 6 and 17 under 35 U.S.C. 103 as being obvious over Rudin and Nielsen in view of Anderson.
Rejection of claim 10 under 35 U.S.C. 103 as being obvious over Rudin in view of Nielsen and Angevine.
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 .
Information Disclosure Statement
The information disclosure statement filed January 12, 2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Regarding rejection of claims 1-2, 6-7, 9-15, and 17-20 under 35 U.S.C. 101, Examiner has considered Applicant’s arguments and is unpersuaded for the following reasons:
Applicant analogizes the presently amended claims to both Enfish and Desjardins. However, in both instances, Examiner disagrees with the analogies.
As correctly stated by Applicant, “[i]n Enfish, the court found that a self-referential data structure enhanced computer functionality…” Response at pg. 8. However, regarding the present claims, the recitation of the machine learning model does not recite an improvement in the functioning of a data structure nor other computing technology. Instead, the claim recites what data is used and what is outputted by the model. Although what is claimed may improve the field of infrastructure identification, the claims do not reflect how the machine learning model operates to improve the functioning of other models and/or computer performance.
As also correctly stated by the Applicant, “in Desjardins, the ability to train an ML model to address ‘catastrophic forgetting’ was found to be a technological improvement rather than an abstract idea.” Response at pp. 8-9. Again, the claim recited an improvement that, although facilitated by a machine learning model, also improved the operation of models suffering from “catastrophic forgetting.” Further, as indicated by the upcoming amendments to the MPEP provided in memorandum of December 5, 2025 by Charles Kim, “See, e.g., Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), in which 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.” Kim Memo at pp. 2-3, referring to upcoming changes to MPEP 2106.04(d)(I).
For the present application, neither of these cases is analogous to what is presented in the present claims. The specification does not disclose an improvement in the performance and/or operation of machine learning models or computing technology and, even if so, the claims do not reflect such an improvement over other machine learning models.
Accordingly, the rejection under 35 U.S.C. 101 is maintained.
Regarding the rejections of the claims under 35 U.S.C. 112(a) and 35 U.S.C. 112(b), Examiner agrees that the amended claims address the rejections. Accordingly, rejection of claims 1-2, 6-7, 9-15, and 17-20 under 35 U.S.C. 112(a) and/or 35 U.S.C. 112(b) are withdrawn.
Regarding the rejection of claims 1, 7-9, 11-14, and 18-20 under 35 U.S.C. 102 as being anticipated by Saha under 35 U.S.C. 103 as being obvious over Saha in view of one or more other references, Examiner agrees that all of the limitations of the present claims are not anticipated, taught, nor suggested by at least Saha. Accordingly, rejection of claims 1, 7-9, 11-14, and 18-20 as being anticipated by and/or obvious over Saha are withdrawn.
However, this Office Action includes new rejections of the claims necessitated by the amendments to the claims. Accordingly, the claims are newly rejected as further asserted herein.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exceptions without significantly more. The claims recite mathematical concepts (e.g., calculations) and mental processes. This judicial exception is not integrated into a practical application because the additional elements that are recited in the claims are extra-solution activities that do not integrate the judicial exceptions into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because courts have found that recitations of generic computer components and steps of data gathering are not significantly more than a judicial exception.
Claim 1
Step 1: The claim is directed to a process, falling under one of the four statutory categories of invention.
Step 2A, Prong 1: The claim 1 limitations include (bolded for abstract idea identification):
Claim 1
Mapping Under Step 2A Prong 1
A device, comprising: a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, the operations comprising:
ingesting facilities infrastructure dat
training a machine-learning (ML) model from the facilities infrastructure data, wherein the ML model is trained using at least textual, geographic, and visual information as input features, and is configured to output both a predicted location of an existing service line and a reliability score associated with the existing service line, wherein the ML model infers features absent in the facilities infrastructure data, resulting in inferred features, wherein the inferred features include information indicating gaps in coverage associated with the facilities infrastructure data;
receiving a query from a user, wherein the query includes a request to identify a service line of interest in connection with a planned excavation or digging activity at a specified location in a region;
identifying, using the ML model, a service line provided in the region that corresponds to the service line of interest, resulting in an identified service line, and a reliability score associated with the identified service line;
generating a map of the identified service line for the region using the ML model, wherein the map includes virtual markers showing the location and shape of the identified service line
providing a visualization interface to the user including the map, the reliability score associated with the identified service line, and inferred features associated with the identified service line, wherein the visualization interface provides a navigation path that avoids the identified service line for the purposes of excavating or digging in the region.
Abstract Idea: Mathematical Calculations
A machine learning model is comprised of mathematical functions and the process of training the machine learning model includes providing training data to the model, which then performs one or more mathematical operations to result in a trained model. See MPEP § 2106.04(a)(2), Subsection I.
Abstract Idea: Mental Process
Identifying a service line is a process that can be performed by a human using pencil and paper. For example, a user can review a map of a geographic area and select locations on the map that correspond to service lines. See e.g., MPEP 2106.04(a)(2), Subsection III.
Abstract Idea: Mathematical Calculations
Using a machine learning model to generate output is a mathematical concept that includes providing input to the model, which then performs one or more mathematical calculations to generate the output. See MPSP 2106.04(a)(2), Subsection I.
Abstract Idea: Mental Process
Providing a visualization is a mental process that can be performed by a human, such as by drawing a map and providing the score that was generated by the model. Using a generic computer to aid in generating and/or providing the visualization as an “interface” is still a mental process See MPEP 2106.04(a)(2), Subsection III(C).
Step 2A, Prong 2: The claim 1 limitations recite (bolded for additional element identification):
Claim 1
Mapping Under Step 2A Prong 2
A device, comprising: a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, the operations comprising:
ingesting facilities infrastructure dat
training a machine-learning (ML) model from the facilities infrastructure data, wherein the ML model is trained using at least textual, geographic, and visual information as input features, and is configured to output both a predicted location of an existing service line and a reliability score associated with the existing service line, wherein the ML model infers features absent in the facilities infrastructure data, resulting in inferred features, wherein the inferred features include information indicating gaps in coverage associated with the facilities infrastructure data;
receiving a query from a user, wherein the query includes a request to identify a service line of interest in connection with a planned excavation or digging activity at a specified location in a region;
identifying, using the ML model, a service line provided in the region that corresponds to the service line of interest, resulting in an identified service line, and a reliability score associated with the identified service line;
generating a map of the identified service line for the region using the ML model, wherein the map includes virtual markers showing the location and shape of the identified service line
providing a visualization interface to the user including the map, the reliability score associated with the identified service line, and inferred features associated with the identified service line, wherein the visualization interface provides a navigation path that avoids the identified service line for the purposes of excavating or digging in the region.
The limitation recites generic computer components, which do not integrate the judicial exception into a practical application. See MPEP 2106.05(f)(2).
“ingesting…data” is the extra-solution activity of data gathering, which does not integrate the judicial exceptions into a practical application. See MPEP 2106.05(g)(3).
Receiving data from a user is the extra-solution activity of data gathering, which does not integrate the judicial exceptions into a practical application. See MPEP 2106.05(g)(3).
Step 2B: Regarding Step 2B, the inquiry is whether any of the additional elements (i.e., the elements that are not the judicial exception) amount to significantly more than the recited judicial exception. Reciting generic computer components is equivalent to reciting a judicial exception and further reciting “apply it,” which courts have found does not amount to “significantly more” than the judicial exception. See MPEP 2106.05 (“Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984…”). See also MPEP § 2106.05(d). Further, mere data gathering and storage are additional elements that courts have found do not amount to significantly more. See MPEP 2106.05 (“Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011)…). See also MPEP § 2106.05(g).
Accordingly, claim 1 is rejected for being directed to unpatentable subject matter.
Claim 2
Claim 2 recites wherein the ML model infers features absent in the facilities infrastructure data from additional textual, geographic and visual information. The claim merely recites additional limitations on the machine learning model, which is identified as the judicial exception of mathematical concepts. The claim does not include additional elements and therefore does not include any limitations that integrate the judicial exception into a practical application or are significantly more than the recited exceptions.
Accordingly, claim 2 is rejected for being directed to unpatentable subject matter.
Claim 6
Claim 6 recites wherein the facilities infrastructure data includes installation data indicating a time of installation, last service, material type and location. The claim merely further specifies the types of data that is “ingested” in claim 1. The claim does not include additional elements and therefore does not include any limitations that integrate the judicial exception into a practical application or are significantly more than the recited exceptions.
Accordingly, claim 6 is rejected for being directed to unpatentable subject matter.
Claim 7
Claim 7 recites wherein the facilities infrastructure data includes service level data comprising throughput of service provided by the at least one service line. The claim merely further specifies the types of data that is “ingested” in claim 1. The claim does not include additional elements and therefore does not include any limitations that integrate the judicial exception into a practical application or are significantly more than the recited exceptions.
Accordingly, claim 7 is rejected for being directed to unpatentable subject matter.
Claim 9
Claim 9 recites wherein the score of the identified service is presented as a heat map on the map. The claim merely recites specific types of scores that can be presented with the visualization. A heat map is a mental process (e.g., selecting a color for a data point from a palette of colors) that can be performed with pencil and paper or with the aid of a generic computer.
Accordingly, claim 9 is directed to unpatentable subject matter.
Claim 10
Claim 10 recites wherein the visualization interface comprises augmented reality. The recited limitation is directed to how a visualization is presented. “Augmented reality” is a well-understood, routine, and convention method of presenting a visualization. See, e.g., Angevine et al. (U.S. Patent Pub. No. 2022/0207846) at [0005]: “The present invention is intended to overcome this issue by streamlining and automating the data collection and analysis process, allowing users to make selections of parameters, and the present invention uses an augmented reality approach to forming and utilizing a visualization of an area, a building, or even an apartment;” Santarone, et al. (U.S. Patent Pub. No. 2020/0242282): “One or more pieces of equipment that will be deployed in the property may be included into the augmented virtual model 201, equipment may include, for example: machinery 211; building support items 212, and utilities support 213.”
Accordingly, claim 10 is directed to unpatentable subject matter.
Claim 11
Claim 11 recites wherein the visualization interface comprises a two- dimensional overlay map. The limitation is a type of visualization that is a mental process that can be performed by a human using a computer as an aid (e.g., inputting map information into a program and providing the map as interface output).
Accordingly, claim 11 is directed to unpatentable subject matter.
Claim 12
Claim 12 recites wherein the visualization interface comprises a virtual anchor and wherein the operations further comprise receiving additional facilities infrastructure data provided by the user. A “virtual anchor” can be a selected location on a map to utilize as a reference point for other objects on the map. Thus, the virtual anchor location can be selected by a human upon visual inspection of a map. Further, “receiving additional…data” is an additional element of data gathering, which courts have found does not amount to significantly more than the recited judicial exception. See MPEP 2106.05 (“Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011)…). See also MPEP § 2106.05(g).
Accordingly, claim 12 is directed to unpatentable subject matter.
Claim 13
Claim 13 recites wherein the visualization interface provides repeating and time-delayed responses to the user query. Providing a visualization is a mental process that can be performed by a human, such as by drawing a map that includes the score that was generated by the model. Using a generic computer to aid in generating and/or providing the visualization as an “interface” is still a mental process See MPEP 2106.04(a)(2), Subsection III(C). Further, providing “repeating and time-delayed responses” to a user query is the extra-solution activity of transmitting data, which courts have found does not integrate the judicial exception into a practical application and is not significantly more than the judicial exception. See Intellectual Ventures I v. 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).
Accordingly, claim 13 is directed to unpatentable subject matter.
Claim 14
Claim 14 recites A non-transitory, machine-readable medium, comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations, the operations comprising steps that are substantially the same as the steps recited in claim 1. The limitations of a processor and memory are generic computing components and amount to no more than reciting a judicial exception and further reciting “apply it.” Accordingly, for at least the same reasons as claim 1, claim 14 is rejected under 35 U.S.C. 101 for being directed to unpatentable subject matter.
Claims 15 and 17
Claims 15 and 17 recite substantially the same imitations as claims 2 and 6. Accordingly, for at least the same reasons as claims 2 and 6, claims 15 and 17 are rejected under 35 U.S.C. 101 for being directed to unpatentable subject matter.
Claim 18
Claim 18 recites wherein the processing system comprises a plurality of processors operating in a distributed computing environment. The claim merely specifies additional generic computer components, which courts have found to be additional elements that are not significantly more than the recited judicial exceptions. See MPEP 2106.05 (“Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984…”). See also MPEP § 2106.05(d).
Accordingly, claim 18 is rejected for being directed to unpatentable subject matter.
Claim 19
Claim 19 recites a method that includes steps that are substantially similar to steps recited in claim 1. Accordingly, for at least the same reasons as claim 1, claim 19 is rejected under 35 U.S.C. 101 for being directed to unpatentable subject matter.
Claim 20
Claim 20 recites substantially the same imitations as claim 12. Accordingly, for at least the same reasons as claim 12, claim 20 id rejected under 35 U.S.C. 101 for being directed to unpatentable subject matter.
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 (i.e., changing from AIA to pre-AIA ) 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.
The factual inquiries 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 2, 7, 11-12, 14-15, and 18-20 are rejected under 35 U.S.C. 103 as being obvious over Rudin, et al. (“Machine Learning for the New York City Power Grid,” hereinafter “Rudin”) in view of Nielsen, et al., (U.S. Pat. Pub. No. 2010/0201690, hereinafter “Nielsen”).
Claim 1
Rudin discloses:
1
ingesting facilities infrastructure data, wherein the data includes locational information of at least one service line, and at least one quality of service metric associated with the at least one service line,
The general knowledge discovery process for power grid data is shown in Fig. 5. The data can be structured text or categorical data, numerical data, or unstructured text documents. The data are first cleaned and integrated into a single database that can be accurately queried. Rudin at pg. 332, col. 1.
The data used by the machine learning algorithms include past events (failures, replacements, repairs, tests, loading, power quality events, etc.) and asset features (type of equipment, environmental conditions, manufacturer, specifications, components connected to it, borough and network where it is installed, date of installation, etc.). Rudin at pg. 329, col. 2.
wherein the at least one service line includes a power distribution line, a telecommunications line, a cable line, a water line, a sewer line, or a combination thereof;
We introduce a general process for transforming historical electrical grid data into models that aim to predict the risk of failures for components and systems. These models can be used directly by power companies to assist with prioritization of maintenance and repair work. Rudin at Abstract.
training a machine-learning (ML) model from the facilities infrastructure data, wherein the ML model is trained using at least textual, geographic, and
The data used by the machine learning algorithms include past events (failures, replacements, repairs, tests, loading, power quality events, etc.) and asset features (type of equipment, environmental conditions, manufacturer, specifications, components connected to it, borough and network where it is installed, date of installation, etc.). Rudin at pg. 329, col. 2.
“Asset features” are textual data and “borough and network where it is installed” are geographic data.
is configured to output both a predicted location of an existing service line and a reliability score associated with the existing service line,
The trouble tickets are unstructured text documents, so a representation of the ticket had to be defined for the learning problem. This representation encodes information about the time, location, and nature (degree of seriousness) of the event. Rudin at pg. 337, col. 2.
if it is known that a manhole event will occur within 60 days after a prior event, it is almost impossible to predict when within those 60 days it will happen. In fact, insulation breakdown, which causes manhole events, can be a slow process, taking place over months or years. A prediction period of one year was chosen for the machine learning ranking task, as illustrated in Fig. 6. Rudin at pg. 338, col. 2.
The “machine learning ranking task” is analogous to a reliability score.
wherein the ML model infers features absent in the facilities infrastructure data, resulting in inferred features, wherein the inferred features include information indicating gaps in coverage associated with the facilities infrastructure data;
Machine learning techniques can be used to estimate MTBF. Fig. 11 shows the application of one of these techniques [26] to predicting survival times of PILC sections in Queens. Rudin at pg. 338, col. 2.
The “predicted survival times” is data that is not present in the analog to “facilities infrastructure data” (see above), so it constitutes a “gap in the infrastructure data.”
identifying, using the ML model, a service line provided in the region that corresponds to the service line of interest, resulting in an identified service line,
We also developed a visualization tool (discussed in [28]) that uses Google Earth as a backdrop to display the locations of events, manholes, and cables. Fig. 22 displays two screen shots from the visualization tool. Rudin at pg. 342, col. 2.
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and a reliability score associated with the identified service line;
Fig. 13 shows the results of a blind test for predicting feeder failures in Crown Heights, Brooklyn, with prediction period from May 2008 to January 2009. Fig. 12 shows results of various tests on the individual components. At each point ðx;yÞ on the plot, x gives a position on the ranked list and y is the percent of failures that are ranked at or above x in the list. Rudin at pg. 339, col. 2.
providing a visualization interface to the user including
See FIG. 20, illustrating an interface that includes the ranking of the facility, analogous to a “reliability score.”
inferred features associated with the identified service line,
We also developed a visualization tool (discussed in [28]) that uses Google Earth as a backdrop to display the locations of events, manholes, and cables. Fig. 22 displays two screen shots from the visualization tool. Rudin at pg. 342, col. 2.
Rudin does not appear to disclose:
A device, comprising: a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, the operations comprising:
visual information as input features
receiving a query from a user, wherein the query includes a request to identify a service line of interest in connection with a planned excavation or digging activity at a specified location in a region;
generating a map of the identified service line for the region , wherein the map includes virtual markers showing the location and shape of the identified service line; and
providing a visualization interface to the user including the map,
Nielsen, which is analogous art, discloses:
A device, comprising: a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, the operations comprising:
A further embodiment comprises an apparatus for facilitating detection of a presence or an absence of at least one underground facility within a dig area, wherein at least a portion of the dig area may be excavated or disturbed during excavation activities. The apparatus comprises: a communication interface; a display device; a user input device; a memory to store processor-executable instructions; and a processing unit coupled to the communication interface, the display device, the user input device, and the memory, wherein upon execution of the processor-executable instructions by the processing unit. Nielsen at [0010].
For example, to create such an electronic record, one or more input images relating to the geographic area including the dig area may be utilized. For example, source data representing one or more input images of a geographic area including the dig area is received and/or processed. Nielsen at [0006].
Visual images are provided as input to the system, which is processed by the system.
receiving a query from a user, wherein the query includes a request to identify a service line of interest in connection with a planned excavation or digging activity at a specified location in a region;
electronically receiving source data representing at least one input image of a geographic area including the dig area; Neilsen at [0008].
generating a map of the identified service line for the region
Facility maps illustrating installed underground facilities, such as gas, power, telephone, cable, fiber optics, water, sewer, drainage, etc. Facility maps may also indicate street-level features (streets, buildings, public facilities, etc.) in relation to the depicted underground facilities. Examples of facility maps include CAD drawings that may be created and viewed with a GIS to include geo-encoded information (e.g., metadata) that provides location information (e.g., infrastructure vectors) for represented items on the facility map. An exemplary facility map 800 is shown in FIG. 8; Nielsen at [0036].
See also FIG. 3.
providing a visualization interface to the user including the map,
providing, via the user input device, a description of a path to be followed during the locate operation with reference to the at least one reference indicator; and E) electronically transmitting and/or electronically storing information relating to the marked-up digital image together with information relating to the description of the path so as to facilitate the detection of the presence or the absence of the at least one underground facility within the dig area. Nielsen at [0008].
Nielsen is analogous art to the claimed invention because both are associated with locating and providing data related to buried facility lines. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to combine Rudin and Nielsen to result in a system that uses a machine learning model to predict locations of buried electrical lines because the system would allow for identification of lines that may otherwise be missed using Nielsen alone. Motivation to combine includes increased accuracy of the system, thus improving warning before digging in a location, which, if performed incorrectly, would incur significant costs to repair.
Claim 2
Rudin discloses:
wherein the ML model infers features absent in the facilities infrastructure data from additional textual, geographic and visual information.
The process in its most general form can handle diverse, noisy, sources that are historical (static), semi-real-time, or real time, incorporates state-of-the-art machine learning algorithms for prioritization (supervised ranking or MTBF), and includes an evaluation of results via cross-validation and blind test. Rudin at Abstract.
Claim 7
Rudin discloses:
wherein the facilities infrastructure data includes service level data comprising throughput of service provided by the at least one service line and an error rate associated with the at least one service line.
The data used by the machine learning algorithms include past events (failures, replacements, repairs, tests, loading, power quality events, etc.) and asset features (type of equipment, environmental conditions, manufacturer, specifications, components connected to it, borough and network where it is installed, date of installation, etc.). Rudin at pg. 329, col. 2.
“Power quality events” is analogous to “throughput of service” and “failures” is analogous to “error rate.”
Claim 11
Rudin does not appear to disclose:
wherein the visualization interface comprises a two- dimensional overlay map.
Nielsen discloses:
wherein the visualization interface comprises a two- dimensional overlay map.
FIG. 3 illustrates another example of a virtual white lines image, which shows more details of virtual white lines for indicating a point, line, and/or path of planned excavation, according to the present disclosure; Nielsen at [0018].
Claim 12
Rudin discloses:
wherein the visualization interface comprises a virtual anchor and
FIG. 22 illustrates locations of events, which act as “virtual anchors” in the image to associate a location with an event.
wherein the operations further comprise receiving additional facilities infrastructure data provided by the user.
The trouble tickets are unstructured text documents, so a representation of the ticket had to be defined for the learning problem. This representation encodes information about the time, location, and nature (degree of seriousness) of the event. The timestamps on the ticket are directly used, but the location and seriousness must be inferred (and/or learned). The locations of events were inferred using several sources of location information present in the trouble tickets, including a street address (possibly mis spelled or abbreviated, e.g., 325 GREENWHICH ST), structure names typed within the text of the ticket (S/B 153267), and structure names sometimes included in the structured fields of three tables (ECS, ELIN, or ESR_ENE). Rudin at pg. 337, col. 2.
“Trouble tickets” are submitted by users.
Claim 14
Nielsen discloses:
A non-transitory, machine-readable medium, comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations, the operations comprising:
The claim further recites a method that is substantially the same as the method recited in claim 1. Accordingly, for at least the same reasons as claim 1 and based on the same prior art, claim 14 is rejected under 35 U.S.C. 103 as being obvious over Rudin in view of Nielsen.
Claim 15
Claim 15 recites substantially the same limitations as claim 2. Accordingly, for at least the same reasons and based on the same prior art as claim 2, claim 15 is rejected under 35 U.S.C. 103 as being obvious over Saha.
Claim 18
Nielsen discloses:
wherein the processing system comprises a plurality of processors operating in a distributed computing environment.
Claim 19
Claim 19 recites a method that includes steps that are substantially similar to steps recited in claim 1. Accordingly, for at least the same reasons as claim 1, claim 19 is rejected under 35 U.S.C. 103 as being obvious over Rudin in view of Nielsen.
Claim 20
Claim 20 recites limitations that are substantially the same as limitations recited in claim 12. Accordingly, for at least the same reasons and based on the same prior art as claim 12, claim 20 is rejected under 35 U.S.C. 103 as being obvious over Rudin in view of Nielsen.
Claims 9 and 13 are rejected under 35 U.S.C. 103 as being obvious over Rudin in view of Nielsen and Saha, et al., (U.S. Pat. Pub No. 2021/0073692, hereinafter “Saha”).
Claim 9
Rudin and Nielsen do not appear to disclose:
wherein the score is presented as a heat map.
Saha, which is analogous art, discloses:
wherein the score is presented as a heat map.
For the probability mapping (heat mapping) estimation task, the wind effect detection module 1604 is configured to, using a machine learning model consistent with the deep learning model 1600, assign a continuous variable to each individual pixel in the condition data (e.g., an image or video, etc.) that correlates with the likelihood of a problem. Saha at [0131].
Saha is analogous art to the claimed invention because both are associated with identifying and preventing potential damage to electrical lines. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to combine the heat maps of Saha to illustrate the rankings of Rudin because the heat maps would provide the user with additional visualizations of the issues with the facilities. Motivation to combine includes providing a more effective presentation of the data, thus allowing a user to identify potential issues with improved speed and accuracy.
Claim 13
Rudin and Nielsen do not appear to disclose:
wherein the visualization interface provides repeating and time-delayed responses to a user query.
Saha discloses:
wherein the visualization interface provides repeating and time-delayed responses to a user query.
Whenever a vegetation proximity reaches a predefined critical or potential hazard threshold distance to a power line, the system will issue the appropriate VPA and VRA for that particular location based on spatial situation of the georeferenced sensor. The end users (vegetation management or control crews) can take necessary actions based on the automatic advisory issued by this invention. Saha at [0011].
It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to combine the delayed and repeated responses of Saha to the provided interfaces of Rudin because the user may be provided with data that accurately reflects the current state of the facilities. Motivation to combine includes providing a more up-to-date presentation of the data, thus allowing a user to identify potential issues with improved speed and accuracy.
Claims 6 and 17 are rejected under 35 U.S.C. 103 as being obvious over Rudin and Nielsen in view of Anderson (U.S. Patent Pub. No. 2013/0080205, hereinafter “Anderson”).
Claim 6
Rudin and Nielsen do not appear to disclose:
wherein the facilities infrastructure data includes installation data indicating a time of installation, last service, material type and location.
Anderson, which is analogous art, discloses:
wherein the facilities infrastructure data includes installation data indicating a time of installation,
See FIG. 13B, illustrating infrastructure components and age of the components.
last service,
See FIG. 16B, illustrating “Last Sch Wk Date” (i.e., “Last Scheduled Work Date”) to indicate the most recent date on which the component was serviced.
material type and
See FIG. 13B, illustrating a manufacturer of each component (e.g., “Westinghouse,” Elastimold”).
location.
See FIG. 10, illustrating a map of the “components in the feeder.”
Anderson is analogous art to the claimed invention because both disclose generating a map that includes infrastructure information that is determined by machine learning. It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the application, to combine Rudin and Nielsen with Anderson to include the additional information identified in Anderson as input to the machine learning model. Motivation to do so includes generating a more accurate map of the facility region that includes additional information that can be relayed to the mode, such as database entries and/or user-created text, thus improving user experience by making more data available for analysis.
Claim 17
Claim 17 recites substantially the same limitations as recited in claim 6. Accordingly, for at least the same reasons and based on the same prior art as claim 6, claim 17 is rejected under 35 U.S.C. 103 as being obvious over Rudin in view of Nielsen and Anderson.
Claim 10 is rejected under 35 U.S.C. 103 as being obvious over Rudin in view of Nielsen and Angevine, et al. (U.S. Patent Pub. No. 2022/0207846, hereinafter “Angevine”).
Claim 10
Rudin and Nielsen do not appear to disclose:
wherein the visualization interface comprises augmented reality.
Angevine, which is analogous art, discloses:
wherein the visualization interface comprises augmented reality.
The present invention is intended to overcome this issue by streamlining and automating the data collection and analysis process, allowing users to make selections of parameters, and the present invention uses an augmented reality approach to forming and utilizing a visualization of an area, a building, or even an apartment. Angevine at [0005].
Angevine is analogous art to the claimed invention because both are related to generating a mapping of a region with scoring information for one or more structures included with the mapping. It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the application, to combine the visualization techniques of Angevine with the utility infrastructure feature inference system Rudin and Nielsen to result in a system that provides mapping information to a user that queries a region of interest as augmented reality. Motivation to combine includes reducing time and effort required for the user to identify an appropriate location in a region, thereby improving the user experience.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Jagerson, U.S. Pat. App. No. 2018/0218525
Duff, et al., U.S. Pat. App. No. 2020/0065433
Nielsen, et al., U.S. Pat. No. 8,861,794
Dunbadin, et al., U.S. Pat. No. 8,315,789
Nielsen, et al., U.S. Pat. No. 8,549,084
Communication
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JOSEPH MORRIS
Examiner
Art Unit 2188
/JOSEPH P MORRIS/Examiner, Art Unit 2188
/RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188
1 Not explicitly disclosed by Rudin, but references to tools and/or programs that perform the disclosed embodiments are implicitly performed on a computer having memory and a processor. Thus, the explicit disclosure of Nielsen is relied upon to teach this limitation.