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 .
Status of Claims
Claims 1-20 are currently pending in application 19/050,064.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/03/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 112 (b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims3 and 19 are rejected under 35 U.S.C. 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 3 recites “the set of pipe data” which contains insufficient antecedent basis. Correction for proper antecedent basis is requested to “the improved set of pipe data”.
Claim 19 recites “the first plurality of separate environmental raster datasets” which contains insufficient antecedent basis. Correction for proper antecedent basis is requested to “the first plurality of separate environmental raster form datasets”.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO internet Web site contains terminal disclaimer forms which may be used. Please visit http://www.uspto.gov/forms/. The filing date of the application will determine what form should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-28 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,223,396. Although the claims at issue are not identical, they are not patentably distinct from each other because both inventions disclose equivalent elements for data pre-processing (i.e. imputation) and data analysis (i.e. predicting pipe failure).
19/050,064
US 12,223,396
1. A method for improving pipe data relating to networks of underground pipes or carrying a fluid to consumers, the method comprising:
receiving by a computer system a set of pipe data that includes a plurality of pipe attributes for one or more of said networks of underground pipes;
identifying with the computer system one or more pipes in at least one of said networks for which one or more pipe attributes is missing or incorrect;
automatically generating imputed data for the one or more pipe attributes resulting in an improved set of pipe data; and
managing one or more aspects of one or more of said networks of underground pipes based on the improved set of pipe data.
2. A method according to claim 1 where said automatically generating imputed data includes a geolocation transfer process that uses one or more sources selected from a group consisting of: GPS, address, and geocode.
3. A method according to claim 2 wherein the set of pipe data includes pipe break data at least some of which includes locations for pipe breaks but not specific pipe sections for said breaks, and said geolocation transfer process assigns pipe sections to at least some of said pipe breaks that are not the closest pipe sections to the respective locations for the pipe breaks.
4. A method according to claim 1 wherein said automatically generating imputed data includes using parcel data relating to a geographic position that corresponds to said one or more pipes.
5. A method according to claim 1 wherein said one or more pipe attributes is missing or incorrect includes year of installation for a plurality of underground pipe sections, and said automatically generating imputed data includes correction and/or imputation of said missing or incorrect installation years for at least some of the underground pipe sections.
6. A method according to claim 1 wherein said one or more pipe attributes is missing or incorrect pipe material for a plurality of underground pipe sections, and said automatically generating imputed data includes correction and/or imputation of said missing or incorrect pipe material for at least some of the underground pipe sections.
7. A method according to claim 1 wherein said one or more pipe attributes is missing or incorrect pipe diameter for a plurality of underground pipe sections, and said automatically generating imputed data includes correction and/or imputation of said missing or incorrect pipe diameter for at least some of the underground pipe sections.
8. A method according to claim 1 wherein said automatically generating imputed data includes uses machine learning to correct and/or impute for at least some of the missing or incorrect pipe attributes.
9. A method according to claim 1 further comprising: predicting a likelihood of failure of one or more pipe sections based on said improved set of pipe data.
10. A method according to claim 9 wherein said predicting is based at least in part on a model built with machine learning using at least some of said improved set of pipe data.
11. A method according to claim 9 wherein said managing includes planning pipe section replacement jobs based at least in part on said predicted likelihood of failure of said one or more pipe sections.
12. A method according to claim 1 wherein said imputed data includes imputed material and diameter data of one or more pipe sections, and the method further includes estimating cost of repair and replacement based on said imputed material and diameter data of one or more pipe sections.
13. A system for improving pipe data relating to networks of underground pipes for carrying fluid to consumers, the system comprising:
a database that stores a set of pipe data that includes a plurality of pipe attributes for one or more of said networks of underground pipes; and
a processing system configured to identify one or more pipes in at least on of said networks for which one or more pipe attributes is missing or incorrect, generate imputed data for the one or more pipe attributes resulting in an improved set of pipe data, wherein one or more of aspects of one or more of said networks of underground pipes can be managed based at least in part on the improved set of pipe data.
14. The system according to claim 13 further comprising a front end system that includes an uploader for receiving said set of pipe data from a customer who manages the one or more aspects of at least one of said networks of underground pipes, and a viewer that can display at least some of said improved set of pipe data which facilitates said management.
15. The system according to claim 13 wherein the processing system is further configured to predict likelihood of pipe segments in the said networks leaking, and said one or more of aspects of one or more of said networks of underground pipes being managed includes replacing pipe sections based at least in part on said predicted likelihood of leaking.
16. The system according to claim 15 wherein said processing system predicts the likelihood based at least in part on a model built with machine learning using at least some of said improved set of pipe data.
17. The system according to claim 13 wherein the set of pipe data includes pipe break data at least some of which includes locations for pipe breaks but not specific pipe sections for said breaks, and the imputed data is generated using a geolocation transfer process configured to assign pipe sections to at least some of said pipe breaks that are not the closest pipe sections to the respective locations for the pipe breaks.
18. A method of creating a standardized database containing environmental data, the method comprising:
accessing a first plurality of separate environmental raster form datasets;
merging at least some of the first plurality of separate environmental raster form datasets to generate a second plurality of larger environmental form datasets;
vectorizing at least some of second plurality of larger environmental form datasets to generate a third plurality of larger vector datasets;
merging at least some of the third plurality of larger vector datasets to form a merged vector dataset; and imputing one or more missing attributes in the merged vector dataset to generate a standardized database.
19. A method according to claim 18 wherein at least some of the first plurality of separate environmental raster datasets are not overlapping with each other or are not coextensive with each other.
20. A method according to claim 18 wherein separate environmental raster form datasets include one or more types of data selected from a group consisting of soil properties, population, climate, elevation, slope and soil.
21. A method according to claim 18 further comprising removing outlier data from said standardized database based on falling outside one or more predetermined limits.
22. A method according to claim 18 wherein said standardized database includes one or more new environmental variables that were not included in the first plurality of separate environmental raster form datasets.
23. A method according to claim 22 wherein the one or more new environmental variables include one or more selected from a group consisting of: soil density, population density, national slope, break density and proximity maps of shorelines, railways, subways, roads, rivers, creeks, streams, ponds and/or lakes.
24. A method according to claim 18 wherein said first plurality of separate environmental raster form datasets includes zoning data and the method further comprises categorizing a portion of said zoning data.
25. A method according to claim 18 further comprising approximating non-square-shaped data is to square-shaped data for more efficient integration into said database.
26. A method according to claim 18 wherein said standardized database is used by a plurality of customers to provide computational cost savings to each of said customers compared to using a custom generated database.
27. A method according to claim 18 further comprising predicting likelihood of pipe segments leaking in a network of underground pipes for carrying fluid to consumers based at least in part on said standardized database.
28. A method according to claim 27 further comprising building a model using machine learning based at least in part on said standardized database, and wherein said predicting likelihood of pipe segments leaking is based on the model.
1. A method for improving pipe networks of underground pipes for carrying a fluid to consumers, the method comprising:
receiving by a computer system a set of pipe data that includes a plurality of pipe attributes for one or more of said networks of underground pipes;
identifying with the computer system one or more pipes in at least one of said networks for which one or more pipe attributes is incorrect;
automatically generating imputed data for the one or more pipe attributes resulting in an improved set of pipe data; wherein said generating of imputed data comprises replacing said incorrect pipe attributes with correct attributes that include two or more of: pipe material, diameter, installation year, and surface area; and
managing one or more aspects of one or more of said networks of underground pipes based on the improved set of pipe data, including: generating maintenance and replacement plans for said networks of underground pipes utilizing computer processing of environmental data in vector form and predicted likelihood of future failure of individual pipes; providing job planning; and replacing pipe sections accordingly.
2. A method according to claim 1 wherein the set of pipe data includes pipe break data at least some of which includes locations for pipe breaks but not specific pipe sections for said breaks, and said geolocation transfer process assigns pipe sections to at least some of said pipe breaks that are not the closest pipe sections to the respective locations for the pipe breaks.
3. A method according to claim 1 wherein said automatically generating imputed data includes using parcel data relating to a geographic position that corresponds to said one or more pipes.
4. A method according to claim 1 wherein said one or more pipe attributes is incorrect includes year of installation for a plurality of underground pipe sections, and said automatically generating imputed data includes correction and/or imputation of said incorrect installation years for at least some of the underground pipe sections.
5. A method according to claim 1 wherein said one or more pipe attributes is incorrect pipe material for a plurality of underground pipe sections, and said automatically generating imputed data includes correction and/or imputation of said incorrect pipe material for at least some of the underground pipe sections.
6. A method according to claim 1 wherein said one or more pipe attributes is incorrect pipe diameter for a plurality of underground pipe sections, and said automatically generating imputed data includes correction and/or imputation of said incorrect pipe diameter for at least some of the underground pipe sections.
7. A method according to claim 1 wherein said automatically generating imputed data includes uses machine learning to correct and/or impute for at least some of the incorrect pipe attributes.
8. A method according to claim 1 further comprising: predicting a likelihood of failure of a pipe section that has never leaked based on said improved set of pipe data.
9. A method according to claim 8 wherein said predicting is based at least in part on a model built with machine learning using at least some of said improved set of pipe data.
10. A method according to claim 8 wherein said managing includes planning pipe section replacement jobs based at least in part on said predicted likelihood of failure of said one or more pipe sections.
11. A system for improving pipe networks of underground pipes for carrying fluid to consumers, the system comprising:
a database that stores a set of pipe data that includes a plurality of pipe attributes for one or more of said networks of underground pipes;
a processing system configured to identify one or more pipes in at least on of said networks for which one or more pipe attributes is incorrect, generate imputed data for the two or more pipe attributes for pipe material, diameter, installation year, and surface area resulting in an improved set of pipe data, wherein one or more of aspects of one or more of said networks of underground pipes can be managed based at least in part on the improved set of pipe data; wherein the processing system is further configured to predict likelihood of pipe segments in the said networks leaking, including of an individual pipe that has never leaked; and said one or more of aspects of one or more of said networks of underground pipes being managed includes replacing pipe sections based at least in part on said predicted likelihood of leaking.
12. The system according to claim 11 further comprising a front end system that includes an uploader for receiving said set of pipe data from a customer who manages the one or more aspects of at least one of said networks of underground pipes, and a viewer that can display at least some of said improved set of pipe data which facilitates said management and wherein said processing system predicts the likelihood based at least in part on a model built with machine learning using at least some of said improved set of pipe data.
13. A method of improving networks of underground pipes, the method comprising:
accessing a first plurality of separate environmental raster form datasets;
merging at least some of the first plurality of separate environmental raster form datasets to generate a second plurality of larger environmental form datasets;
vectorizing at least some of second plurality of larger environmental form datasets to generate a third plurality of larger vector datasets;
merging at least some of the third plurality of larger vector datasets to form a merged vector dataset and imputing attributes of individual pipe segments that are incorrect in or missing from the separate environmental raster form databases, wherein the imputed attributes include pipe material and pipe diameter; and replacing pipe sections based in part on said imputed missing attributes in the merged vector dataset;
wherein said replacing comprises selecting pipe sections for replacement based at least on predicting likelihood of failure of individual pipe segments, including of an individual pipe segment that has never leaked, and building a model using machine learning based at least in part on said standardized database, and wherein said predicting likelihood of individual pipe segments leaking is based on the model.
14. A method according to claim 13 wherein at least some of the first plurality of separate environmental raster datasets are not overlapping with each other or are not coextensive with each other.
15. A method according to claim 13 wherein separate environmental raster form datasets include one or more types of data selected from a group consisting of soil properties, population, climate, elevation, slope and soil.
16. A method according to claim 13 further comprising removing outlier data from said standardized database based on falling outside one or more predetermined limits.
17. A method according to claim 13 wherein said standardized database includes one or more new environmental variables that were not included in the first plurality of separate environmental raster form datasets.
18. A method according to claim 17 wherein the one or more new environmental variables include one or more selected from a group consisting of: soil density, population density, national slope, break density and proximity maps of shorelines, railways, subways, roads, rivers, creeks, streams, ponds and/or lakes.
19. A method according to claim 13 wherein said first plurality of separate environmental raster form datasets includes zoning data and the method further comprises categorizing a portion of said zoning data.
20. A method according to claim 13 further comprising approximating non-square-shaped data is to square-shaped data for more efficient integration into said database.
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 1-28 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter, specifically an abstract idea.
Claims 1-28 are directed to a judicial exception (i.e., abstract idea), without providing a practical application, and without providing significantly more.
Under the 35 U.S.C. §101 subject matter eligibility two-part analysis, Step 1 addresses whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. See MPEP §2106.03. If the claim does fall within one of the statutory categories, it must then be determined in Step 2A [prong 1] whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea). See MPEP §2106.04. If the claim is directed toward a judicial exception, it must then be determined in Step 2A [prong 2] whether the judicial exception is integrated into a practical application. See MPEP §2106.04(d). Finally, if the judicial exception is not integrated into a practical application, it must additionally be determined in Step 2B whether the claim recites "significantly more" than the abstract idea. See MPEP §2106.05.
Examiner note: The Office’s 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG) is currently found in the Ninth Edition, Revision 10.2019 (revised June 2020) of the Manual of Patent Examination Procedure (MPEP), specifically incorporated in MPEP §2106.03 through MPEP §2106.07(c).
Regarding Step 1,
Claims 1-12 and 18-28 are directed toward a process (method). Claims 13-17 are directed toward an apparatus (system). Thus, all claims fall within one of the four statutory categories as required by Step 1.
Regarding Step 2A [prong 1],
Claims 1-28 are directed toward the judicial exception of an abstract idea. Independent claims 1, 13 and 18 are directed specifically to the abstract idea of data-processing/ data analysis.
Regarding independent claim 1, the underlined limitations emphasized below correspond to the abstract ideas of the claimed invention:
A method for improving pipe data relating to networks of underground pipes or carrying a fluid to consumers, the method comprising:
receiving by a computer system a set of pipe data that includes a plurality of pipe attributes for one or more of said networks of underground pipes; [Mathematical concept/ Mental process/ Certain methods of organizing human activity - Collecting, organizing, or comparing information]
identifying with the computer system one or more pipes in at least one of said networks for which one or more pipe attributes is missing or incorrect; [Mental process - Human logic or the comparison of information]
automatically generating imputed data for the one or more pipe attributes resulting in an improved set of pipe data; and [Mathematical concepts - Mathematical algorithm or statistical formula used for data processing; Mental processes - Estimating, guessing, or determining missing data mathematically or logically - Can be performed mentally using pen and paper]
managing one or more aspects of one or more of said networks of underground pipes based on the improved set of pipe data.[Certain methods of organizing human activity - Mitigating risk/ commercial or legal interactions]
As the underlined claim limitations above demonstrate, independent claim 1 is directed to the abstract idea of Mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations); Mental processes (concepts performed in the human mind (including an observation, evaluation, judgment, or opinion)); and Certain methods of organizing human activity (fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations)).
Regarding independent claim 13, the underlined limitations emphasized below correspond to the abstract ideas of the claimed invention:
A system for improving pipe data relating to networks of underground pipes for carrying fluid to consumers, the system comprising:
a database that stores a set of pipe data that includes a plurality of pipe attributes for one or more of said networks of underground pipes; and [Mathematical concept/ Mental process/ Certain methods of organizing human activity - Collecting, organizing, or comparing information]
a processing system configured to identify one or more pipes in at least one of said networks for which one or more pipe attributes is missing or incorrect, generate imputed data for the one or more pipe attributes resulting in an improved set of pipe data, [Mathematical concepts - Mathematical algorithm or statistical formula used for data processing; Mental processes - Estimating, guessing, or determining missing data mathematically or logically - Can be performed mentally using pen and paper]
wherein one or more of aspects of one or more of said networks of underground pipes can be managed based at least in part on the improved set of pipe data. [Certain methods of organizing human activity - Mitigating risk/ commercial or legal interactions]
As the underlined claim limitations above demonstrate, independent claim 13 is directed to the abstract idea of Mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations); Mental processes (concepts performed in the human mind (including an observation, evaluation, judgment, or opinion)); and Certain methods of organizing human activity (fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)).
Regarding independent claim 18, the underlined limitations emphasized below correspond to the abstract ideas of the claimed invention:
A method of creating a standardized database containing environmental data, the method comprising:
accessing a first plurality of separate environmental raster form datasets; merging at least some of the first plurality of separate environmental raster form datasets to generate a second plurality of larger environmental form datasets; [Mathematical concept/ Mental process/ Certain methods of organizing human activity - Collecting, organizing, or comparing information]
vectorizing at least some of second plurality of larger environmental form datasets to generate a third plurality of larger vector datasets; merging at least some of the third plurality of larger vector datasets to form a merged vector dataset; and [Mathematical concepts - Converting pixel-based data into geometric vectors - involves underlying geometric or spatial mathematical concepts]
imputing one or more missing attributes in the merged vector dataset to generate a standardized database. [Mathematical concepts - Mathematical algorithm or statistical formula used for data processing; Mental processes - Estimating, guessing, or determining missing data mathematically or logically - Can be performed mentally using pen and paper]
As the underlined claim limitations above demonstrate, independent claim 18 is directed to the abstract idea of Mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations); Mental processes (concepts performed in the human mind (including an observation, evaluation, judgment, or opinion)); and Certain methods of organizing human activity (fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations)).
Dependent claims 2-12, 14-17, and 19-28 provide further details to the abstract idea of claims 1, 13, and 18 regarding the received data, therefore, these claims include mathematical concepts, mental processes, and certain methods of organizing human activities for similar reasons provided above for claims 1, 13, and 18.
After considering all claim elements, both individually and in combination and in ordered combination, it has been determined that the claims do not amount to significantly more than the abstract idea itself.
Regarding Step 2A [prong 2],
Claims 1-28 fail to integrate the recited judicial exception into any practical application. The claims recite additional limitations which are hardware or software elements or particular technological environment, such as a “computer system”, a “system”, a “database”, and a “processing system”. However, these limitations are not enough to qualify as “practical application” being recited in the claims along with the abstract idea since these limitations are merely invoked as a tool to perform instruction of an abstract idea in a particular technological environment and/or are generally linking the use of the abstract idea to a particular technological environment or field of use, and merely applying and abstract idea in a particular technological environment and merely limiting use of an abstract idea to a particular field or a technological environment do not provide practical application for an abstract idea (MPEP 2106.05 (f) & (h)). The claims do not amount to "practical application" for the abstract idea because they neither (1) recite any improvements to another technology or technical field; (2) recite any improvements to the functioning of the computer itself; (3) apply the judicial exception with, or by use of, a particular machine; (4) effect a transformation or reduction of a particular article to a different state or thing; (5) provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment.
The presence of a machine learning algorithm (Dependent Claims 8, 10, 16, and 28) or computer implementations do not necessarily restrict the claim from reciting an abstract idea. The machine learning algorithm and computer limitations claimed herein are simply used as a tool to apply the abstract idea without transforming the underlying abstract idea into patent eligible subject matter. As claimed, the machine learning algorithm is not an improvement to the accuracy of the model itself, it merely processes data to achieve a business decision objectives based on received input. Examiner notes that the additional limitations of machine learning and computer processing do not result in computer functionality or technical/technology improvement and hence do not result in a practical application. The machine learning algorithm and the computer limitation simply process the data through inputting and outputting data. Processing data is 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) or speeding up a loan application process by enabling borrowers to avoid physically going to or calling each lender and filling out a loan application, Lending Tree, LLLC v. Zillow, Inc., 656 Fed. App'x 991, 996-97 (Fed. Cir. 2019)(non-precedential). Thus, the additional limitations of machine learning algorithm and computer limitations do not transform the abstract idea into a practical application.
The relevant question under Step 2A [prong 2] is not whether the claimed invention itself is a practical application, instead, the question is whether the claimed invention includes additional elements beyond the judicial exception that integrate the judicial exception into a practical application by imposing a meaningful limit on the judicial exception. This is not the case with Applicant’s claimed invention. Automating the recited claimed features as a combination of computer instructions implemented by computer hardware and/or software elements as recited above does not qualify an otherwise unpatentable abstract idea as patent eligible. Examples where the Courts have found selecting a particular data source or type of data to be manipulated to be insignificant extra-solution activity include selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016); Applicant’s limitations as recited above do nothing more than supplement the abstract idea using additional hardware/software computer components as a tool to perform the abstract idea and generally link the use of the abstract idea to a technological environment, which is not sufficient to integrate the judicial exception into a practical application since they do not impose any meaningful limits. Dependent claims 2-12, 14-17, and 19-28 merely incorporate the additional elements recited above, along with further embellishments of the abstract idea of independent claims respectively, but these features only serve to further limit the abstract idea of independent claims. Therefore, the additional elements recited in the claimed invention individually, and in combination fail to integrate the recited judicial exception into any practical application.
Regarding Step 2B,
Claims 1-28 fail to amount to “significantly more” than an abstract idea. The claims recite additional limitations which are hardware or software elements or particular technological environment, such as a “computer system”, a “system”, a “database”, and a “processing system”. However, these limitations are not enough to qualify as “significantly more” being recited in the claims along with the abstract idea since these limitations are merely invoked as a tool to perform instruction of Abstract idea in a particular technological environment and/or are generally linking the use of the abstract idea to a particular technological environment or field of use, and merely applying and abstract idea in a particular technological environment and merely limiting use of an abstract idea to a particular field or a technological environment do not provide significantly more to an abstract idea (MPEP 2106.05(f) & (h)). The claims do not amount to "significantly more" than the abstract idea because they neither (1) recite any improvements to another technology or technical field; (2) recite any improvements to the functioning of the computer itself; (3) apply the judicial exception with, or by use of, a particular machine; (4) effect a transformation or reduction of a particular article to a different state or thing; (5) add a specific limitation other than what is well-understood, routine and conventional in the field; (6) add unconventional steps that confine the claim to a particular useful application; nor (7) provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment.
Dependent claims 2-12, 14-17, and 19-28 merely recite further additional embellishments of the abstract idea of independent claims 1, 13, and 18 respectively, but these features only serve to further limit the abstract idea of independent claims 1, 13, and 18; however, none of the dependent claims recite an improvement to a technology or technical field or provide any meaningful limits. The addition of another abstract concept to the limitations of the claims does not render the claim other than abstract. Under the Interim Guidance on Patent Subject Matter Eligibility (PEG 2019), it specifically states that narrowing an abstract idea of claims do not resolve the claims of being "significantly more" than the abstract idea. Thus, the additional elements in the dependent claims only serve to further limit the abstract idea utilizing the computer components as a tool and/or generally link the use of the abstract idea to a particular technological environment.
Therefore, since there are no limitations in the claims 1-28 that transform the exception into a patent eligible application such that the claims amount to significantly more than the exception itself, and looking at the limitations as a combination and as an ordered combination adds nothing that is not already present when looking at the elements taken individually, claims 1-28 are rejected under 35 USC § 101 as being directed to non-statutory subject matter under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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 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.
Claims 1-28 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Scolnicov et al. (US 2013/0211797 A1).
As per independent Claim 1, Scolnicov discloses a method for improving pipe data relating to networks of underground pipes or carrying a fluid to consumers (See at least Para 0006-0009, 0012, 0026-0027, and 0032-0034), the method comprising:
receiving by a computer system a set of pipe data that includes a plurality of pipe attributes for one or more of said networks of underground pipes (See at least Para 0025-0027, and Para 0030-0033);
identifying with the computer system one or more pipes in at least one of said networks for which one or more pipe attributes is missing or incorrect (See at least Para 0038, Probable connection; Para 0043, Adjusted data – “However, if they do not match, one or both of the pipe end coordinates are adjusted to match based on some criteria, step 315, before being stored as pipe junctions in step 317. Criteria to adjust the coordinates may include, but are not limited to, relative location, asset age and material, pipe diameter, etc.”; See also Figs.3-4, pre-processing asset management and GIS data; Para 0029, Data Updates; Para 0030, “For example, structural analysis system 102 is programmed to merge GIS data from GIS 106 with asset management data 103 and sensor archive data 107 to create the enriched GIS data stored in database 101. Structural analysis system 102 can use combinations of mapped variables from other data sources to build and analyze new variables.”; Para 0041-0043; Para 0053-0055, correct existing consumption equations; and Para 0097-0098);
automatically generating imputed data for the one or more pipe attributes resulting in an improved set of pipe data (See at least Para 0025-0027, Enriched GIS database 101; Para 0030-0033; Para 0038-0043, Generation and storage or probable connections; and Para 0096-0097); and
managing one or more aspects of one or more of said networks of underground pipes based on the improved set of pipe data (See at least Para 0009, 0012, 0025-0027, and 0032).
As per Claim 2, Scolnicov discloses where said automatically generating imputed data includes a geolocation transfer process that uses one or more sources selected from a group consisting of: GPS, address, and geocode (See at least Fig.4, Para 0012, “The method also includes generating one or more mathematical graph elements from the one or more assets and creating probable connections between the one or more mathematical graph elements based on the GIS and asset management data.”; Para 0026, “For example, asset management data may include information such as age, size, shape, length, diameter, material, and other characteristics, concerning pipes, line segments, valves, and meters installed in the network.”; Para 0029 “[0029] At least a portion of the GIS data may be retrieved or derived from asset management data 103 and GIS 106. Data obtained from GIS 106, sensor archive data 107, and asset management data 103 may be non-live/non-real-time data such as data that has been archived or offline data that includes data of typical operating conditions. Non-live data can be static snapshots of the water utility network that may be updated regularly or periodically. It is also noted that this data may be evolutionary data including updates consistent with the evolution of the underlying resource system itself, for example, when new water pipes, connections, meters, etc., are installed or otherwise modified in the system. Furthermore, this data may include updates when the underlying resource system is sampled or measured, for example when existing pipes are inspected for material fatigue or internal constriction by accumulated solid deposits. Any other characteristics of the geography and engineering of the water distribution system may also be utilized, as well as any other data relied on by one skilled in the art.”; Para 0038, “[0038] Probable connections are created between the one or more mathematical graph elements based on the GIS and asset management data, step 205. As previously mentioned, information available from GIS and asset management data generally do not provide connections between assets. Therefore, pre-processing of asset and GIS data is performed. As described further with reference to FIGS. 3-5, pre-processing includes transforming assets and other items from the GIS data into a list of nodes and creating probable connections by analyzing an overlay of various GIS layers. A table is also created storing the probable connections established by the pre-processing including pipe/asset ID, a start point, and end point, diameter of the pipe, age, and other asset management data previously mentioned.”; Para 0041, “To generate the best mathematical graph from the data available, a best-effort type of matching is made to determine the connectivity of nodes and edges. Matching may include looking for edges with ends that are very close or the closest to each other and puzzling or connecting the ends together to identify the most probable connections. The determination of closeness may be bounded by a threshold, e.g., less than x number of feet apart. The threshold may be determined by a user of the GIS analysis system or set to a predefined value in the structural analysis system based on certain parameters and region of the assets. In one embodiment, a user may be prompted to verify assets with questionable connections. The connectivity and associations between the features of the map may be extracted and stored in an index with the edges and nodes, numbers, or fractions, etc.”; See also Para 0027, and Para 0039-0043).
As per Claim 3 (2), Scolnicov discloses wherein the set of pipe data includes pipe break data at least some of which includes locations for pipe breaks but not specific pipe sections for said breaks, and said geolocation transfer process assigns pipe sections to at least some of said pipe breaks that are not the closest pipe sections to the respective locations for the pipe breaks (See at least Para 0009, 0012, 0026-0027, 0032, and 0038-0043).
As per Claim 4, Scolnicov discloses wherein said automatically generating imputed data includes using parcel data relating to a geographic position that corresponds to said one or more pipes (See at least Para 0009, 0012, 0026-0027, 0032, and 0038-0043).
As per Claim 5, Scolnicov discloses wherein said one or more pipe attributes is missing or incorrect includes year of installation for a plurality of underground pipe sections, and said automatically generating imputed data includes correction and/or imputation of said missing or incorrect installation years for at least some of the underground pipe sections (See at least Para 0026-0027, 0032, and 0038-0043, Equivalent attributes disclosed).
As per Claim 6, Scolnicov discloses wherein said one or more pipe attributes is missing or incorrect pipe material for a plurality of underground pipe sections, and said automatically generating imputed data includes correction and/or imputation of said missing or incorrect pipe material for at least some of the underground pipe sections (See at least Para 0026-0027, 0032, and 0038-0043, Equivalent attributes disclosed).
As per Claim 7, Scolnicov discloses wherein said one or more pipe attributes is missing or incorrect pipe diameter for a plurality of underground pipe sections, and said automatically generating imputed data includes correction and/or imputation of said missing or incorrect pipe diameter for at least some of the underground pipe sections (See at least Para 0026-0027, 0032, and 0038-0043, Equivalent attributes disclosed).
As per Claim 8, Scolnicov discloses wherein said automatically generating imputed data includes uses machine learning to correct and/or impute for at least some of the missing or incorrect pipe attributes (See at least Para 0095-0098).
As per Claim 9, Scolnicov discloses predicting a likelihood of failure of one or more pipe sections based on said improved set of pipe data (See at least Para 0026-0027, Para 0038-0043; and Para 0081-0098, GIS data would contain building and structure layer data, and System predicts future leaks based on data from all pipes in network, by incorporating machine learning models trained with historical data (pipe age, length, soil type, number of junctions, etc.).
As per Claim 10 (9), Scolnicov discloses wherein said predicting is based at least in part on a model built with machine learning using at least some of said improved set of pipe data (See at least Para 0096-0097).
As per Claim 11 (9), Scolnicov discloses wherein said managing includes planning pipe section replacement jobs based at least in part on said predicted likelihood of failure of said one or more pipe sections (See at least Para 0026-0027, 0034, 0095-0098).
As per Claim 12, Scolnicov discloses wherein said imputed data includes imputed material and diameter data of one or more pipe sections (See at least Para 0026, “For example, asset management data may include information such as age, size, shape, length, diameter, material, and other characteristics, concerning pipes, line segments, valves, and meters installed in the network.”), and the method further includes estimating cost of repair and replacement based on said imputed material and diameter data of one or more pipe sections (See at least Para 0014-0017).
As per independent Claim 13, Scolnicov discloses a system for improving pipe data relating to networks of underground pipes for carrying fluid to consumers (See at least Para 0009, 0012, 0026-0027, and 0032), the system comprising:
a database that stores a set of pipe data that includes a plurality of pipe attributes for one or more of said networks of underground pipes (See at least Para 0025-0027, and Para 0030-0033); and
a processing system configured to identify one or more pipes in at least on of said networks for which one or more pipe attributes is missing or incorrect, generate imputed data for the one or more pipe attributes resulting in an improved set of pipe data (See at least Para 0012, “The method also includes generating one or more mathematical graph elements from the one or more assets and creating probable connections between the one or more mathematical graph elements based on the GIS and asset management data.”; Para 0026, “For example, asset management data may include information such as age, size, shape, length, diameter, material, and other characteristics, concerning pipes, line segments, valves, and meters installed in the network.”; Para 0038, “Probable connections are created between the one or more mathematical graph elements based on the GIS and asset management data, step 205. As previously mentioned, information available from GIS and asset management data generally do not provide connections between assets. Therefore, pre-processing of asset and GIS data is performed. As described further with reference to FIGS. 3-5, pre-processing includes transforming assets and other items from the GIS data into a list of nodes and creating probable connections by analyzing an overlay of various GIS layers. A table is also created storing the probable connections established by the pre-processing including pipe/asset ID, a start point, and end point, diameter of the pipe, age, and other asset management data previously mentioned.”; and Para 0043, Adjusted data – “However, if they do not match, one or both of the pipe end coordinates are adjusted to match based on some criteria, step 315, before being stored as pipe junctions in step 317. Criteria to adjust the coordinates may include, but are not limited to, relative location, asset age and material, pipe diameter, etc.”; See also Figs.3-4, pre-processing asset management and GIS data; Para 0029, Data Updates; Para 0030, “For example, structural analysis system 102 is programmed to merge GIS data from GIS 106 with asset management data 103 and sensor archive data 107 to create the enriched GIS data stored in database 101. Structural analysis system 102 can use combinations of mapped variables from other data sources to build and analyze new variables.”; Para 0041-0043; Para 0053-0055, correct existing consumption equations; and Para 0097-0098), wherein one or more of aspects of one or more of said networks of underground pipes can be managed based at least in part on the improved set of pipe data (See at least Para 0009, 0012, 0097).
As per Claim 14, Scolnicov discloses a front end system that includes an uploader for receiving said set of pipe data from a customer who manages the one or more aspects of at least one of said networks of underground pipes, and a viewer that can display at least some of said improved set of pipe data which facilitates said management (See at least Para 0026-0027, 0034, 0081-0098).
As per Claim 15, Scolnicov discloses wherein the processing system is further configured to predict likelihood of pipe segments in the said networks leaking, and said one or more of aspects of one or more of said networks of underground pipes being managed includes replacing pipe sections based at least in part on said predicted likelihood of leaking (See at least Para 0016-0017, Para 0037-0041, Para 0053; and Para 0096-0098).
As per Claim 16 (15), Scolnicov discloses wherein said processing system predicts the likelihood based at least in part on a model built with machine learning using at least some of said improved set of pipe data (See at least Para 0096-0097).
As per Claim 17, Scolnicov discloses wherein the set of pipe data includes pipe break data at least some of which includes locations for pipe breaks but not specific pipe sections for said breaks, and the imputed data is generated using a geolocation transfer process configured to assign pipe sections to at least some of said pipe breaks that are not the closest pipe sections to the respective locations for the pipe breaks (See at least Para 0009, 0012, 0026-0027, 0032, and 0038-0043).
As per independent Claim 18, Scolnicov discloses a method of creating a standardized database containing environmental data (See at least Para 0008-0009, 0012), the method comprising:
accessing a first plurality of separate environmental raster form datasets (See at least Fig.2, Para 0009, 0012, 0025-0027, 0030-0033);
merging at least some of the first plurality of separate environmental raster form datasets to generate a second plurality of larger environmental form datasets (See at least Para 0025-0027; Para 0030-0033; and Para 0096-0097);
vectorizing at least some of second plurality of larger environmental form datasets to generate a third plurality of larger vector datasets (See at least Para 0025-0027, Enriched GIS database 101; Para 0030-0033; and Para 0096-0097);
merging at least some of the third plurality of larger vector datasets to form a merged vector dataset (See at least Para 0030-0033; and Para 0096-0097); and
imputing one or more missing attributes in the merged vector dataset to generate a standardized database (See at least Para 0012, “The method also includes generating one or more mathematical graph elements from the one or more assets and creating probable connections between the one or more mathematical graph elements based on the GIS and asset management data.”; Para 0026, “For example, asset management data may include information such as age, size, shape, length, diameter, material, and other characteristics, concerning pipes, line segments, valves, and meters installed in the network.”; Para 0038, “[0038] Probable connections are created between the one or more mathematical graph elements based on the GIS and asset management data, step 205. As previously mentioned, information available from GIS and asset management data generally do not provide connections between assets. Therefore, pre-processing of asset and GIS data is performed. As described further with reference to FIGS. 3-5, pre-processing includes transforming assets and other items from the GIS data into a list of nodes and creating probable connections by analyzing an overlay of various GIS layers. A table is also created storing the probable connections established by the pre-processing including pipe/asset ID, a start point, and end point, diameter of the pipe, age, and other asset management data previously mentioned.”; Para 0043, Adjusted data – “However, if they do not match, one or both of the pipe end coordinates are adjusted to match based on some criteria, step 315, before being stored as pipe junctions in step 317. Criteria to adjust the coordinates may include, but are not limited to, relative location, asset age and material, pipe diameter, etc.” ; See also Para 0027, and Para 0039-0042).
As per Claim 19, Scolnicov discloses wherein at least some of the first plurality of separate environmental raster datasets are not overlapping with each other or are not coextensive with each other (See at least Para 0025-0027; Para 0030-0033; and Para 0096-0098).
As per Claim 20, Scolnicov discloses wherein separate environmental raster form datasets include one or more types of data selected from a group consisting of soil properties, population, climate, elevation, slope and soil (See at least Para 0025-0027; Para 0030-0033; and Para 0096-0098).
As per Claim 21, Scolnicov discloses removing outlier data from said standardized database based on falling outside one or more predetermined limits (See at least Para 0025-0027; Para 0030-0033; and Para 0096-0098).
As per Claim 22, Scolnicov discloses wherein said standardized database includes one or more new environmental variables that were not included in the first plurality of separate environmental raster form datasets (See at least Para 0025-0027; Para 0030-0033; and Para 0096-0098).
As per Claim 23 (22), Scolnicov discloses wherein the one or more new environmental variables include one or more selected from a group consisting of: soil density, population density, national slope, break density and proximity maps of shorelines, railways, subways, roads, rivers, creeks, streams, ponds and/or lakes (See at least Para 0096-0098).
As per Claim 24, Scolnicov discloses wherein said first plurality of separate environmental raster form datasets includes zoning data and the method further comprises categorizing a portion of said zoning data (See at least Para 0013-0014, and Para 0027).
As per Claim 25, Scolnicov discloses approximating non-square-shaped data is to square-shaped data for more efficient integration into said database (See at least Para 0025-0027; Para 0030-0033; and Para 0096-0097).
As per Claim 26, Scolnicov discloses wherein said standardized database is used by a plurality of customers to provide computational cost savings to each of said customers compared to using a custom generated database (See at least Para 0014-0017).
As per Claim 27, Scolnicov discloses predicting likelihood of pipe segments leaking in a network of underground pipes for carrying fluid to consumers based at least in part on said standardized database (See at least Fig.2, Para 0016-0017, Para 0037-0041, and Para 0053).
As per Claim 28 (27), Scolnicov discloses building a model using machine learning based at least in part on said standardized database, and wherein said predicting likelihood of pipe segments leaking is based on the model (See at least Para 0016-0017, Para 0037-0041, Para 0053; and Para 0081-0098, GIS data would contain building and structure layer data, and System predicts future leaks based on data from all pipes in network, by incorporating machine learning models trained with historical data (pipe age, length, soil type, number of junctions, etc.).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN P OUELLETTE whose telephone number is (571)272-6807. The examiner can normally be reached on M-F 8am-6pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lynda C Jasmin, can be reached at telephone number (571) 272-6782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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July 22, 2026
/JONATHAN P OUELLETTE/Primary Examiner, Art Unit 3629
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