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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The IDS(s) has/have been considered and placed in the application file.
Specification
The disclosure is objected to because of the following informalities: In the preliminary amendment, ¶1 states that the Indian Application priority documents were filed May 5, 2022. This should be May 11, 2022. Also, the application numbers are missing numbers. The following correction is suggested, “This application claims priority to International Application No. PCT/EP2023/062648, filed May 11, 2023, which claims priority to Indian Application Nos. 202211027214, 202211027215, 202211027216, 202211027217, and 202211027218, filed May [[5]] 11, 2022, the contents of which are incorporated by reference herein in their entireties.”
Appropriate correction is required.
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 1-9, and 15-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: The claim(s) recite(s) a process, which is one of the four statutory categories.
Claim(s) 1 and 6, and dependent claim 15 recite(s):
“processing sensor data to detect an electrical utility object”
“detecting one or more sub-objects of the electrical utility object”
“determining a connection between the one or more sub-objects and another electrical utility object”
“generating a path for a device to capture sensor data depicting one or more objects of an electricity power delivery network”
“selecting between a drive device, a drone device, or a combination thereof to complete one or more portions of the path to capture the sensor data”
“merging the sensor data from the drive device, the drone device, or a combination thereof on completion of the path”
“determining a first geo-position of an endpoint of the service line”
“determining a second geo-position of an electrical meter”
“determining a distance between the first geo-position of the endpoint and the second geo-position of the electrical meter”
“establishing a connection between the service line and the electrical meter based on the distance”
Step 2A, Prong One: The recited operations fall within the mental processes (concepts performed in the human mind, including observation, evaluation, judgment, and opinion) grouping of abstract ideas, because Claims 1, 2, 6-9, 15-20 recite observations/comparisons/judgments about grid data: detect object/sub-objects and connection, correct topology, generate path, select drive/drone, merge data, output, detect service line, determine geo-positions/distance, establish connection, evaluate business heuristic. None requires a computer. In addition, the distance calculating step of claims 15-20 recites a mathematical concept; the specification describes the mapping platform “can calculate a Euclidean distance (or any other type of distance metric) between the meter and the wire” (¶ 130). Thus, the claims are directed to a mental process and/or a mathematical concept, so Step 2A Prong One grouping is reached.
Claim 18's business heuristic is directed at organizing human activity and commercial interactions. The specification describes using “customer relationship management (CRM) data associated with the consumer associated with the detected electrical meter … Other CRM information as length of service, type of service, past electricity usage, etc.” (¶ 137), and reconciling "whether the detected electricity loss is compatible with four potential electricity users" (¶ 135). Thus claim 18 recites organizing human activity plus mental process.
Step 2A Prong 1: YES, the claim(s) recite(s) an abstract idea (mental processes, mathematical concepts, and human activity).
Step 2A, Prong Two: The claim recites the following additional element(s): the receipt of sensor data and the output of a result. Claims 1-2 recite sensor data processing and “providing the connection as an output.” Claims 15-20's distance comparison and connection declaration are the judicial exception; claims 6-9 recite selecting drive/drone/combination for data capture which is data gathering under MPEP 2106.05(g). Naming device types is not a particular machine under MPEP 2106.05(b). The additional element(s) therefore do not integrate the judicial exception into a practical application.
Claims 10-14 are eligible at Step 2A, Prong 2, because the virtual-GCP chain integrates the exception into a practical application by improving the positioning technology. One surveyed base point seeds a self-propagating layer that corrects device positions without deploying surveyors, and the specification ties it to a concrete technical result.
Step 2A Prong 2: NO for claim(s) 1, 2, 6-9, and 15-20.
Step 2A Prong 2: YES for claim(s) 10-14.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The additional element(s) of the provision of the result as an output is/are shown to be widely prevalent or in common use in the relevant field by (STAHLFELD, ¶10: “providing the generated representation of the electric power grid for display on a user device”), (TARIQ Fig. 1: “passing meta data to both the Geospatial Location Module and the output GIS database”), and
(CHAURASIA Abstract: “put in Attribute table in GIS using ArcGIS9.1 Software”), which establishes that the element is well-understood, routine, and conventional. See MPEP 2106.05(d)(I). The additional elements have been considered both individually and as an ordered combination. Generic data gathering, processing, reporting features perform the abstract mental or mathematical steps; the ordered combination imposes no technical solution, just applies the abstract decision or optimization to sensor data.
Claims 16-20 add no inventive concept in Step 2A Prong Two. Claim 16's consumer indexing uses the distance result; claim 17's comparison is an additional mental/mathematical step; claim 18's business heuristic adds commercial interaction, not technological improvement; claim 19's machine learning feature detector applies the abstract idea using generic computer components under MPEP 2106.05(f); and claim 20 limits field of use to utility infrastructure.
Step 2B: NO.
Claim(s) 3-5 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: The claim(s) recite(s) a process, which is one of the four statutory categories of
invention.
Independent claim(s) 3 recite(s):
“processing sensor data using a machine learning model to generate one or more electrical utility asset detection instances”
“conflating the one or more electrical utility asset detection instances into one or more conflated candidate detections”
“performing a particle swarm optimization on the one or more conflated candidate detections to determine a detected electrical utility asset”
The recited operations fall within the mathematical concepts (mathematical relationships, mathematical formulas or equations, and mathematical calculations) grouping of abstract ideas, because Claim 3 recites particle swarm optimization; the specification calls it “a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality” (¶ 79). Claims 4 and 5 add class detection and training data, but the mathematical concept remains that algorithm.
Step 2A Prong 1: YES, the claim(s) recite(s) an abstract idea (mathematical concepts).
Step 2A, Prong Two: The claim recites the following additional element(s): the machine learning model, the providing of the output, and the generation of a training data set. The machine learning model is recited at the highest level of generality and amounts to no more than an instruction to apply an unspecified model. Generating a training data set is data gathering. Providing the result as an output is insignificant extra-solution activity. These elements, considered individually or in combination, do not integrate the abstract idea into a practical application. The additional element(s) therefore do not integrate the judicial exception into a practical application.
Step 2A Prong 2: NO for claim(s) 3-5.
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. The additional element(s) of the provision of the result as an output is/are shown to be widely prevalent or in common use in the relevant field by (STAHLFELD ¶10, TARIQ Fig. 1 and CHAURASIA Abstract – see quotes above), which establishes that the element is well-understood, routine, and conventional. See MPEP 2106.05(d)(I). The additional elements have been considered both individually and as an ordered combination. Generic data gathering, processing, reporting features perform the abstract mental or mathematical steps; the ordered combination imposes no technical solution, only applies the abstract decision or optimization to sensor data.
Step 2B: NO.
The claim does no more than apply generic machine learning to a new data environment. Claims that “do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.” Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025).
Claim Rejections - 35 USC § 102/103
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.
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.
Claim(s) 1-2 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Stahlfeld et al., US 2021/0141969 A1 (hereinafter “STAHLFELD”), and in the alternative obvious over STAHLFELD in view of Pestun et al., US 2018/0218214 A1 (hereinafter “PESTUN”).
Claim 1.
STAHLFELD discloses a method, comprising:
processing sensor data to detect an electrical utility object (STAHLFELD: “a first predictive
model … to identify utility poles and/or connecting lines from aerial imagery of a geographical region” (¶ 43).);
detecting one or more sub-objects of the electrical utility object (STAHLFELD: “a second predictive model configured to identify the assets (e.g., crossarms, transformers, insulators, switches, reclosers, risers, capacitors, etc.) attached to each of the poles and/or connecting
lines identified by the first predictive model” (¶ 43).);
determining a connection between the one or more sub-objects and another electrical utility object (STAHLFELD: “The final representation can specify the topology of the grid, i.e., respective connections and/or interrelations between these assets included in the grid"
(¶ 47); “configured to identify utility poles and/or connecting lines from aerial imagery of a
geographical region” (¶ 43). This teaches determining a connection between the identified
sub-object assets attached to a pole and another electrical utility object, specifically the
connecting line identified by the first predictive model from aerial imagery.); and
providing the connection as an output (STAHLFELD: “providing the generated representation of the electric power grid for display on a user device” (¶ 10); “The final representation can specify the topology of the grid, i.e., respective connections and/or interrelations between these assets included in the grid” (¶ 47).).
In the alternative, to the extent applicant contends STAHLFELD does not expressly disclose the connection limitation, the claim would have been obvious over STAHLFELD in view of PESTUN. STAHLFELD teaches identifying utility poles “and/or connecting lines” from aerial
imagery and determining the assets attached to those poles and/or connecting lines
(STAHLFELD ¶ 43). STAHLFELD further teaches that its final representation “can specify the
topology of the grid, i.e., respective connections and/or interrelations between these assets
included in the grid” (STAHLFELD ¶ 47). PESTUN teaches creating “tower candidates based on
the most overlapped potential electric lines ridges and tower edges” and then performing “blob detection to obtain connected entity marks that are considered as potential infrastructure entity regions” (PESTUN ¶ 159).
Combining PESTUN's tower-to-electric-line association with STAHLFELD's topology yields would have been obvious to one of ordinary skill in the art before the effective filing date of
the claimed invention. STAHLFELD's grid topology requires specifying connections between assets, and PESTUN's tower-to-electric-line association based on overlapped potential electric line ridges and tower edges identifies those connections. This is combining prior art elements according to known methods to yield predictable results (MPEP 2143(A)). Both references function to identify relationships among grid assets. The combination would predictably produce a grid topology with tower-to-line associations.
Claim 2.
STAHLFELD discloses further comprising: performing a topology correction of the connection, the electrical utility object, the one or more sub-objects, the another electrical utility object, or a combination thereof (STAHLFELD: “adjusting or refining currently determined grid topology” (¶ 81).).
Claim(s) 3-4 is/are rejected under 35 U.S.C. 103 as being unpatentable over PESTUN in view of van den Bergh, “An Analysis of Particle Swarm Optimizers,” (hereinafter "VAN DEN BERGH").
Claim 3.
PESTUN discloses a method, comprising:
processing sensor data using a machine learning model to generate one or more electrical utility asset detection instances (PESTUN: “the entity detector 126 may perform block based classification (e.g., by fully supervised learning framework) based on different features for infrastructure entity candidates” (¶ 161).);
conflating the one or more electrical utility asset detection instances into one or more conflated candidate detections (PESTUN: “If an infrastructure entity is detected, for example, in greater than two separate images, the infrastructure entities that are too far (e.g., based on a given threshold) from the average of GPS positions may be removed, and the average may be re-determined” (¶ 170). This teaches conflating multiple detection instances of the same infrastructure entity from separate images into a single conflated candidate detection by averaging GPS positions and removing outliers.);
(PESTUN ¶ 170); and
providing the detected electrical utility asset as an output (PESTUN: “the entity detector 126 may determine a specified tower position on the image 1334” (¶ 167).).
PESTUN discloses all of the subject matter as described above except for specifically teaching “performing a particle swarm optimization.” However, VAN DEN BERGH in the same field of endeavor teaches this (“The Particle Swarm Optimiser (PSO) is a population-based optimisation method first proposed by Kennedy and Eberhart” (VAN DEN BERGH p. 21), and that “The algorithm maintains a population of particles, where each particle represents a potential solution to an optimisation problem” (VAN DEN BERGH p. 21).).
Therefore, it would have been obvious to one of ordinary skill in the art to combine to modify PESTUN's determination of the final center point to use particle swarm optimization on the conflated candidate detections, as taught by VAN DEN BERGH, to determine the detected electrical utility asset. The motivation for this combination of references would have been to
improve optimization by maintaining “a population of particles, where each particle represents a potential solution to an optimisation problem” (VAN DEN BERGH p. 21) applied to PESTUN's surviving GPS positions which are a population of candidate detections to be optimized. This motivation for the combination of PESTUN and VAN DEN BERGH is supported by using known particle swarm optimization to improve a similar method by refining candidate detections is a rationale under MPEP 2143(C). KSR.
Claim 4.
PESTUN and VAN DEN BERGH teaches further comprising: initiating an electrical utility asset class detection on the one or more conflated candidate detections (PESTUN: “If an infrastructure entity is detected, for example, in greater than two separate images, the infrastructure entities that are too far (e.g., based on a given threshold) from the average of GPS positions may be removed” (¶ 170). The surviving candidate is selected using the largest filtered classification value: “the entity detector 126 may determine the weighted center for the tower candidate that has a largest filtered classification value, as the tower position” (PESTUN ¶ 166).), wherein the particle swarm optimization is further based on the electrical utility asset class detection (VAN DEN BERGH: “The Particle Swarm Optimiser (PSO) is a population-based optimisation method … The algorithm maintains a population of particles, where each particle represents a potential solution to an optimisation problem” (p. 21).; PESTUN: “Weighted classification values may be assigned for each block” (¶ 161).).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to rearrange the class detection of PESTUN so that it is performed on the conflated candidate detections. The rearrangement would have been obvious because PESTUN uses the classification value to choose among candidates, so applying the same class
detection to the surviving conflated set is the same operation on fewer inputs (see MPEP
2144.04(IV)(C), rearrangement of parts). The PESTUN system consolidates and selects by largest filtered classification value, e.g., “the entity detector 126 may determine the weighted center for the tower candidate that has a largest filtered classification value, as the tower position” (PESTUN ¶ 166). Applying class detection to conflated candidates is the same quality-driven selection.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over PESTUN in view of VAN DEN BERGH, further in view of Chaurasia et al. “Consumer Indexing -A GIS Based Approach” (hereinafter “CHAURASIA”).
Claim 5.
PESTUN and VAN DEN BERGH teaches further comprising: generating a training data set comprising one or more electrical utility assets used in a (PESTUN marks positive blocks “for towers detection” (PESTUN ¶ 147), and “The classifiers used by the entity detector 126 may use training data” (PESTUN ¶ 161).).
PESTUN and VAN DEN BERGH disclose all of the subject matter as described above except for specifically teaching “a developing country.” However, CHAURASIA in the same field of endeavor teaches a developing country (CHAURASIA: “Distribution Sector in India is the weakest link among the chain as compared to Generation & Transmission.” (p. 1, Introduction); “Power … is the critical infrastructure on which the economic development of the country depends. The demand of power in India is enormous and is growing steadily” (p. 1, I. Power Sector in India); “consumer related data base like its Feeder number, Transformer number, Circuit number, Pole number, meter type, phase, billing amount, and consumer's location in the form of Easting & Northing have been put in Attribute table in GIS using ArcGIS9.1 Software … Every consumer's location has been
mapped using GPS and high resolution remote sensing satellite images.” (Abstract). ).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention could have combined the developing country electrical distribution mapping taught by CHAURASIA to modify the training data of PESTUN and VAN DEN BERGH as claimed by known methods in order to use a known technique to improve similar devices in the same way (MPEP 2143(C)). PESTUN discloses “The classifiers used by the entity detector 126 may use training data” (¶ 161) and CHAURASIA maps every consumer location in a developing country distribution network using GPS and satellite images. Using CHAURASIA’s developing country data with PESTUN’s classifiers was a predictable application of known techniques.
Claim(s) 6-7 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over
STAHLFELD in view of Tariq et al., “Geo location of Utility Assets Using Omnidirectional Ground-Based Photographic Imagery,” (hereinafter “TARIQ”), further in view of Simon et al., US 2019/0012636 A1 (hereinafter “SIMON”).
Claim 6.
STAHLFELD discloses a method, comprising:
selecting between a drive device, a drone device, or a combination thereof to complete one or more portions of the path to capture the sensor data (STAHLFELD: “the system can select, from multiple localized datasets each including data taken in a different region” (¶ 80); “Each category of LIDAR data can be obtained by a respective LIDAR sensor on-board a moving vehicle, including, for example, car, motorcycle, drone, or airplane” (¶ 62).);
merging the sensor data from the drive device, the drone device, or a combination thereof on completion of the path (STAHLFELD: “aggregating, in accordance with a set of aggregation
rules, the respective partial representations to generate a final representation of the electric
power grid” (¶ 6).); and
providing the merged sensor data as an output (STAHLFELD: “providing the generated representation of the electric power grid for display on a user device” (¶10).).
STAHLFELD disclose all of the subject matter as described above except for specifically teaching the path limitation. However, TARIQ in the same field of endeavor teaches the path limitation (TARIQ: “ideas from route planning, image analysis and recognition, and geolocation are combined” (p. 2). TARIQ further teaches that “The path is then subdivided into a number of steps of a length set by the user (for example, 5 meters), setting a corresponding number of "stops" along the way from which Street View imagery is sought” (p. 2).).
The claim requires selecting between a drive device, a drone device, or a combination thereof to complete one or more portions of the path to capture the sensor data. This is taught by SIMON who is addressing the problem of covering a geographic route with two platform types having different range and access. SIMON determines “a route for each land vehicle that brings the land vehicle within the UAV round-trip range of each destination within the set of destinations … dispatching, for each destination, a UA V carrying the package from the land vehicle at a dispatch location along the respective route” (SIMON ¶ 6). In addition, SIMON discloses that each land vehicle carries a UAV for the final portion of the route, e.g., “Each land vehicle may carry at least one U AV capable of delivering at least one of the number of packages to one of the destinations within the UA V round-trip range” (SIMON ¶ 6).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention could have combined the path elements taught by TARIQ and to allocate portions of the survey path between a land vehicle and a UAV based on their respective ranges and access capabilities as taught by SIMON because STAHLFELD already draws data from “car, motorcycle, drone, or airplane” (STAHLFELD ¶ 62) and merges those captures by “automatically identifying locations of assets within the electric power grid” (STAHLFELD ¶ 75). Applying TARIQ path and SIMON’s allocation of a route suited for each platform type to STAHLFELD’s capture run lets each survey path portion be covered by the platform best suited to it. This is use of a known technique to improve similar devices in the same way (MPEP 2143(C)).
Claim 7.
STAHLFELD, TARIQ, and SIMON teaches wherein the path is generated based on digital map data of a geographic database (TARIQ: “an automated system was built using the Google Maps Javascript application programming interface (API) … Google Maps determines a route between the two points" (TARIQ p. 2).
Claim 9.
STAHLFELD, TARIQ, and SIMON teaches wherein the output is used for a consumer indexing, a network creation (STAHLFELD “generate a final representation of the grid which identifies substantially all assets included in the grid or a portion of the grid, as well as corresponding connections between the assets, i.e., the "topology" of the grid” (¶ 5).), or a combination of the electricity power delivery network.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over
STAHLFELD in view of TARIQ in view of SIMON, and further in view of Liu et al., CN 107729808 A (hereinafter “CN ‘808”).
Claim 8.
STAHLFELD, TARIQ, and SIMON teach the method of claim 6, further comprising: s(STAHLFELD: “high voltage power lines that connect one or more power generators to one or more substations within the electric power transmission networks” (¶ 9). The claim supplies no voltage thresholds and the high voltage and ultra-high voltage transmission lines are within the categories for the recited extra-high-tension and high-tension categories).
CN '808 teaches that “UAVs are applied to daily inspections” and that inspection challenges arise from “the rapid development of ultra-high voltage and ultra-high voltage lines” (CN '808, Background technique).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to select the drone device of the combination for the portions of the path associated with high-tension and extra-high-tension power lines, because CN ‘808 identifies inspection of transmission lines as the task for UAVs and teaches that “As a new type and efficient inspection method, UAVs are applied to daily inspections” (CN ‘808, Background technique). One of ordinary skill would select the drone for those portions because CN ‘808 identifies UAV inspection as efficient for those lines, reducing labor intensity and increasing collection effectiveness. This is use of a known technique to improve similar devices in the same way (MPEP 2143(C)).
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over
STAHLFELD in view of TARIQ in view of SIMON, and further in view of Lawlor et al., US 2021/0089572 A1 (hereinafter “LAWLOR”) and Chen et al., US 2016/0259044 A1 (hereinafter
“CHEN”).
Claim 10.
STAHLFELD, TARIQ, and SIMON teach the method of claim 6.
LAWLOR teaches selecting a known ground control point (GCP) as a base point of a virtual GCP layer (LAWLOR: “the survey point has a known physical location” (LAWLOR ¶ 3). This teaches selecting a known survey point as a base point.); determining a location of the device (LAWLOR: “the meta-data indicates at least a capture location of the sensor system when said each image was captured” (LAWLOR ¶ 3).); calculating an offset of the location based on the base point to generate a virtual GCP of the virtual GCP layer (LAWLOR: “calculating an error between the ray generated for said each image and the known physical location” (LAWLOR ¶ 3).); determining a subsequent location of the device (LAWLOR: “the other images are taken subsequent to the training images” (LAWLOR ¶ 36).) and, calculating a subsequent offset of the subsequent location based on the inputting the base point and the virtual GCP to the machine learning model to generate a subsequent GCP of the virtual GCP layer (LAWLOR applies the trained model across subsequent images: “The trained machine learning model can be used to process a plurality of other images to predict the pose error” (LAWLOR ¶ 35); “the other images are taken subsequent to the training images” (LAWLOR ¶ 36). Repetition of a step for a subsequent location is obvious duplication under MPEP 2144.04(VI)(B).).
Chen teaches surveyed control points were known in the art as virtual ground control points (CHEN: “An image coordinate corresponding to the virtual ground control points is obtained using collinear conditions” (¶ 61).).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use LAWLOR to label the survey point as base point of a virtual GCP layer, generate a virtual GCP from the model-predicted error, and recursively input the base point and virtual GCP to the trained model, because LAWLOR's stated purpose is
“more accurate and reliable mapping products” (LAWLOR ¶ 35). The technique of generating control points was known to improve accuracy, as CHEN teaches that “An image coordinate corresponding to the virtual ground control points is obtained using collinear conditions”(CHEN ¶ 61). The trained model's application to subsequent locations without survey points is the same way, as LAWLOR teaches that "The trained machine learning model can be used to process a plurality of other images to predict the pose error" (LAWLOR ¶ 35).
Claim(s) 12-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over
STAHLFELD in view of TARIQ in view of SIMON in view of LAWLOR in view of CHEN, further in view of Rusli et al., “Accuracy Assessment of DEM from UAV and TanDEM-X Imagery,” (hereinafter “RUSLI”).
Claim 12.
STAHLFELD, TARIQ, SIMON, LAWLOR, and CHEN teach the method of claim 10, except for determining the designated time period based on a target level of positioning accuracy.
However, RUSLI teaches determining the designated time period based on a target level of positioning accuracy (“The method used to observe GCP was by using static method. The observation time for GCP was 30 minutes” (RUSLI p. 2, II. B.). RUSLI also teaches that “Meanwhile, for VP, the data were observed using RTK-VRS method in order to control the network of GCPs” (RUSLI p. 2, II. B.). This shows that a longer static occupation time is assigned to ground control points requiring higher positioning accuracy, while a faster RTK-VRS method is used for verification points).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to select a 30 minute static occupation for control points and to use RTK for subsequent location, as taught by RUSLI because RUSLI teaches “The method used to observe GCP was by using static method. The observation time for GCP was 30 minutes,” reflecting an accuracy-driven method to obtain control point positions accurate enough to seed the layer.
Claim 13.
STAHLFELD, TARIQ, SIMON, LAWLOR, CHEN, and RUSLI teach the method of claim 10, except for teaching wherein the subsequent location is determined using a differential positioning (“Meanwhile, for VP, the data were observed using RTK-VRS method in order to control the network of GCPs” (RUSLI p. 2, II. B.). Where, RTK-VRS uses differential positioning.).
Claim 14.
STAHLFELD, TARIQ, SIMON, LAWLOR, CHEN, and RUSLI teach the method of claim 13, wherein the differential positioning comprises real time kinematic (RTK) (“RTK-VRS method in order to control the network of GCPs” (RUSLI p. 2, II. B.)), post processing kinematic (PPK), or a combination thereof.
Claim(s) 15, 16, 19 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over STAHLFELD in view of TARIQ in view of SIMON, further in view of Jacobsen et al., “Inference of Distribution Grids Based on Crowdsourced Grid Data and Drone Imagery,” (hereinafter “JACOBSEN”), and further in view of CHAURASIA.
Claim 15.
STAHLFELD, TARIQ, and SIMON teach the method of claim 6, further comprising: processing sensor data collected by the device to detect a service line associated with an electricity power delivery network (STAHLFELD: “configured to identify utility poles and/or connecting lines from aerial imagery of a geographical region” (¶ 43).); determining a first geo-position of an endpoint of the service line (STAHLFELD: “the final representation can identify respective geographic locations and classification of the assets of the electric power grid” (¶ 47).); determining a second geo-position of an electrical meter (STAHLFELD: “the final representation can identify respective geographic locations and classification of the assets of the electric power grid” (STAHLFELD ¶ 47); STAHLFELD: “smart meters” (STAHLFELD ¶ 60). This teaches determining a geo-position of an electrical meter because ST AHLFELD identifies geographic locations of grid assets and lists smart meters as grid assets.);
The claim recites determining a distance between the geo-position of the service-line endpoint and the geo-position of the electrical meter and establishing the connection based on that distance. STAHLFELD, TARIQ, SIMON do not teach these steps. JACOBSEN teaches connecting nodes based on proximity, stating that “We connect these isolated nodes by connecting the nodes to the closest cable located within a node's five-meter proximity, and if such a cable does not exist, the node is removed from the dataset.” (JACOBSEN p. 555, § 5.2). CHAURASIA teaches mapping every consumer's location using GPS and high resolution remote sensing satellite images: “Every consumer's location has been mapped using GPS and high resolution remote sensing satellite images.” (CHAURASIA p. 1, Abstract). CHAURASIA's consumer record is mapped to meter type, so the mapped consumer location supplies the geo-position of that consumer's electrical meter.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify STAHLFELD's grid representation to apply JACOBSEN's five-meter proximity test to a CHAURASIA GPS-mapped electrical meter and a STAHLFELD detected service line endpoint, because STAHLFELD's final representation “can identify respective geographic locations and classification grid” (STAHLFELD ¶ 47) but does not have endpoint to meter distances. JACOBSEN's distance-threshold connection technique was
known to improve grid model generation by “connecting the nodes to the closest cable located
within a node's five-meter proximity” (JACOBSEN p. 555). The technique is applicable in the
same way because CHAURASIA's mapping of “Every consumer's location has been mapped
using GPS and high resolution remote sensing satellite images” (CHAURASIA p. 1) provides
the meter geolocation to which the same five-meter cable-proximity test is applied.
Claim 16.
STAHLFELD, TARIQ, SIMON, JACOBSEN, and CHAURASIA discloses performing a consumer indexing of the electricity power delivery network based on the connection (CHAURASIA: “By Consumer Indexing (CI) we can find exact location of the consumer through which feeder, or transformer, or circuit number and or pole consumer is being supplied” (CHAURASIA p. 1 ).).
Claim 19.
The method of claim 15, wherein the processing of the sensor data comprises using a machine learning feature detector to classify the service line into a service line type, and wherein the establishing of the connection is based on the service line type (STAHLFELD: “the data processing engine can implement an object classification neural network” (¶ 40). STAHLFELD teaches that the classification output can be a service line type because “The classification can be, for example, a type, a class, a group, a category, or an operating condition” (STAHLFELD ¶ 47).).
Claim 20.
The method of claim 19, wherein the service line type includes a low-tension line, a high-tension line, an extra high-tension line, or a combination thereof (STAHLFELD: “high voltage power lines that connect one or more power generators to one or more substations within the electric power transmission networks” (¶ 9).).
Allowable Subject Matter
Claim 11 is objected to as being dependent upon a rejected base claim, but would be
allowable if rewritten in independent form including all of the limitations of the base claim and
any intervening claims.
Claims 17-18 are objected to as being dependent upon a rejected base claim, but would be allowable if the § 101 rejection is overcome, and the claims are rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
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/Ross Varndell/Primary Examiner, Art Unit 2674
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