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 the Application
This final office action is in response to the amendment filed on 1/21/2026. Claim 1 has been amended. Claims 4-7 are cancelled. Claims 8-24 have been added. Claims 1-3 and 8-24 are currently pending and have been examined below.
Claim Rejections – 35 U.S.C. 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 1-3 and 8-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Per step 1 of the eligibility analysis set forth in MPEP § 2106, subsection III, the claims are directed towards a process, machine, or manufacture.
Per step 2A Prong One, Claim 1 recites specific limitations which fall within at least one of the groupings of abstract ideas enumerated in MPEP 2106.04(a)(2) as follows:
receiving known attribute information for each component of a network system, the components of the network system including a first plurality of pipes, a second plurality of pipes different from the first plurality of pipes, and a plurality of supply-side structures, wherein (mental process)
at least one component of the network system is connected to another component of the network system;
the network system comprises physical infrastructure for distribution of a utility resource;
the known attribute information for the first plurality of pipes includes, for each respective pipe in the first plurality of pipes;
respective location information for the respective pipe; and
a respective value for a first characteristic for the first respective pipe, wherein the first characteristic is a characteristic other than location, wherein the first characteristic describes a physical property of the respective pipe; and
the known attribute information for the second plurality of pipes includes, for each respective pipe in the second plurality of pipes, respective location information for the respective pipe;
the known attribute information for the second plurality of pipes does not include values for the first characteristic for pipes in the second plurality of pipes;
the known attribute information for the plurality of supply-side structures includes, for each respective supply-side structure of the plurality of supply-side structures, respective location information for the respective supply-side structure;
analyzing a network structure by determining adjacency for network components, including:
calculating distances between physical locations of network components based on the location information (mental process and mathematical concept)
associating adjacent supply-side structures to adjacent pipes based at least in part on the known attribute information for the plurality of supply-side structures and location information for pipes (mental process)
determining, for each respective pipe of the second plurality of pipes, unknown attribute information, (mental process) including:
determining, based at least in part on the known attribute information for the plurality of supply-side structures and location information for the respective pipe of the second plurality of pipes, a first adjacent pipe, wherein the first adjacent pipe is a pipe of the first plurality of pipes or the second plurality of pipes, and wherein the first adjacent pipe is physically connected to the respective pipe of the second plurality of pipes; (mental process) and
determining, based at least in part on a respective value for the first characteristic for the first adjacent pipe, a first value for the first characteristic for the respective pipe of the second plurality of pipes (mental process)
calculating a probability for each of a plurality of possible values for the first characteristic using a Gaussian mixture model based on known values of the first characteristic for network components, wherein the Gaussian mixture model includes a plurality of component distributions, each component distribution corresponding to a different time period of installation, and wherein a mixing proportion for each component distribution is based on a proportion of network components installed during the corresponding time period, and wherein the Gaussian mixture model calculates the probability based on a plurality of attributes including at least a first imputed attribute and a second imputed attribute for the respective pipe, wherein the first imputed attribute and the second imputed attribute are determined prior to calculating the probability (mathematical concept);
selecting the first value as the possible value having a highest calculated probability (mental process and mathematical concept).
As noted above, these limitations fall within at least one of the groupings of abstract ideas enumerated in the MPEP 2106.04(a)(2). Specifically, these limitations (with the possible exception of calculating a probability for each of a plurality of possible values for the first characteristic using a Gaussian mixture model) fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. That is – the limitations describe a method of determining unknown attribute information for pipes based on received known attribute information for adjacent pipes. At the level of generality claimed, a human being could perform the claimed mental process of determining unknown pipe attributes based on the received information in the human mind or using a pen and paper. For example, a human being can mentally (or with pen and paper) calculate distances between physical locations of network components based on location information; associate adjacent supply side structures to adjacent pipes based at least in part on the known attribute information and determine unknown attribute information.
With respect to the amended limitation reciting calculating probabilities using a Gaussian mixture model, Examiner notes that even if the calculations are too extensive to be practically performed by a human with pen and paper, a Gaussian mixture model is a mathematical formula (see applicant’s published specification paragraph [0050] reciting:
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Calculating a probability from a mixture of Gaussian distributions is a mathematical calculation regardless of the complexity of the formula or whether a person could practically perform the calculation in the human mind or with pen and paper. This is analogous to Example 47 of the USPTO’s 2024 AI guidance finding that a backpropagation algorithm and a gradient descent algorithm to perform the training of an artificial neural network encompasses mathematical concepts. Accordingly claim 1 recites an abstract idea.
Per step 2A Prong 2, the Examiner finds that the judicial exception is not integrated into a practical application. Claim 1 recites the additional limitations of:
[performing the steps of the method] at an electronic device with an input mechanism and a display;
storing the determined first value for the first characteristic for the respective pipe of the second plurality of pipes in a database, wherein the determined first value enables assessment of a physical condition of the respective.
The additional limitations when viewed individually and when viewed as an ordered combination, and pursuant to the broadest reasonable interpretation, do not integrate the abstract idea into a practical application because each of the additional elements are recited at high level of generality implementing the abstract idea on a computer (i.e. apply it) or generally linking the use of the judicial exception to a particular technological environment. Specifically, the recitation of a generic electronic device with a generic input mechanism and display merely generally links the abstract idea to a particular technological environment (i.e., a generic device to receive input and display information) or merely applies the abstract idea on a generic computer. Similarly, the recitation of a generic database to store a determined value is recited at a high level of generality and merely generally links the abstract idea to a particular technological environment (i.e., a generic database to store information) or merely applies the abstract idea on a generic computer.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are recited at high level of generality and only generally link the use of the judicial exception to a particular technological environment. The same analysis applies here in 2B, i.e., mere instructions to apply an exception in a particular technological environment cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Dependent claims 2-3 and 8-24 are rejected on a similar rational to the claims upon which they depend and merely generally link the abstract idea to a particular technological environment (i.e., a generic device to receive input and display information in dependent claims 8, 9, and 21-23; a generic color display to display information in claim 15) or further narrow the abstract idea by specifying additional details pertaining to the received information (dependent claims 2-3, 10-20, and 24).
Response to Arguments
35 U.S.C. 101
Applicant's arguments, see 9-11, filed 6/11/2026, with respect to the rejection(s) of claims 1-3 and 8-24 under 35 U.S.C. 101 have been fully considered but are not persuasive.
First, Applicant argues that:
Claim 1, as amended, now recites specific technical features that cannot be performed in the human mind. Specifically, claim 1 as amended recites "calculating a probability for each of a plurality of possible values for the first characteristic using a Gaussian mixture model based on known values of the first characteristic for network components" and "selecting the first value as the possible value having a highest calculated probability." These features recite a specific machine learning and statistical technique that requires iterative computation across multiple probability distributions and cannot practically be performed in the human mind or with pen and paper. As described in the specification, the method uses "a Gaussian mixture model used, based on installation year and diameter" to calculate the probability of a respective pipe being a respective material. (Specification as filed, paragraph [0050]). The specification further describes that "the computer system automatically selects the material with the highest probability value." (Specification as filed, paragraph [0057]) (remarks page 9).
Examiner respectfully disagrees. First, Examiner notes that for a sufficiently small set of inputs, a human being with pen and paper could calculate a probability with a Gaussian mixture model per the formula described in paragraph [0050] of Applicant’s specification. However, even if the inputs are large enough that performing the calculation by hand is not practical, a Gaussian mixture model is a mathematical formula (see applicant’s published specification paragraph [0050] reciting:
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Calculating a probability from a mixture of Gaussian distributions is a mathematical calculation regardless of the complexity of the formula or whether a person could practically perform the calculation in the human mind or with pen and paper. This is analogous to Example 47 of the USPTO’s 2024 AI guidance finding that a backpropagation algorithm and a gradient descent algorithm to perform the training of an artificial neural network encompasses mathematical concepts. Accordingly claim 1 recites an abstract idea.
Second, Applicant argues that:
The amended claim is consistent with the PTAB Appeals Review Panel's recent decision in Ex parte Desjardins (Appeal 2024-000567, ARP Decision Sept. 26, 2025), which vacated a § 101 rejection of machine learning claims . . . Like the claims in Desjardins, amended claim 1 recites a specific computational technique, a Gaussian mixture model, applied to solve a specific technical problem (i.e., imputing unknown physical properties of infrastructure components), not merely the generic application of machine learning to a new data environment (remarks page 10).
Examiner respectfully disagrees. In Desjardins, the claimed invention recited an improvement to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” encountered in continual learning systems which was explicitly identified in the specification. By contrast, the presently amended claim recites the use of a generic Gaussian mixture model used to input unknown physical properties of infrastructure components. There is no recited improvement as to how the model itself operates. Rather, a known model is being applied to a new data environment. Further, Examiner notes that Applicant’s own specification paragraph [0042] notes that “FIGS. 4A-4B, and the accompanying descriptions, identify one method of imputing unknown attribute information, but one skilled in the art would recognize that other statistical models can be adapted to impute unknown attribute information in analogous ways. Using a generic statistical model such as a Gaussian mixture model to input unknown physical properties is the application of generic machine learning to a new data environment without disclosing improvement to the machine learning models being applied.
Similarly, Applicant argues that
the amended claim recites a GMM that is specifically configured for infrastructure analysis: its component distributions are structured around installation time periods, its mixing proportions reflect actual infrastructure construction patterns, and it uses previously imputed attributes as inputs in a multistage computational pipeline. Rather than a generic model applied to a new field, this is a model whose internal structure is tailored to the domain (remarks page 10).
Examiner respectfully disagrees. There is no recited improvement as to how the model itself operates. Rather, a well-known model statistical model (see e.g., US Patent Application Publication Number 20220121732 (“ROELSE”) paragraph 0086 “A Gaussian Mixture Model (GMM) is a well-known type of model”) is being applied to a new data environment.
Finally, Applicant argues that:
The multi-attribute interdependent imputation creates a computational dependency chain - the system must first impute certain attributes (such as installation year and diameter), then feed those imputed values into the GMM to determine additional attributes (such as material) - that is inherently computational and cannot be performed mentally (remarks pages 10-11).
Examiner respectfully replies that even if the amended GMM limitation is analyzed as an additional element (rather than a mathematical calculation), the additional element merely recites applying a known GMM model to a new data environment and therefore does not integrate the abstract idea into a practical application. However, as noted above, calculating a probability from a mixture of Gaussian distributions is a mathematical calculation that is part of the abstract idea and does not need to be analyzed under Step 2A, prong 2 or Step 2B.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Patent Application Publication Number 20220057367 (“Claudio”) discloses taking samples of pipe sections, inspecting and scoring the samples, training a model on pipe conditions based on the scores, and estimating the pipe conditions of pipes that have not been inspected based on the model
US Patent Application Publication Number 20200124494 (“Solomon”) discloses estimating the pipe condition of various different sections of a pipe network using collected failure rate records of other pipes
US Patent Publication Number 9183527 (“Close”) discloses performing a statistical estimate of pipe conditions for a network of pipes based on statistical sampling
US Patent Application Publication Number 20190303791 (“Yoshikawa”) discloses a method of predicting a likelihood of pipe segments leaking in an underground pipe network based on a model
However, the prior art fails to teach each and every limitation as claimed, and would involve hindsight reasoning to arrive at the claimed invention. Therefore, the claims are considered allowable over the prior art.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALLAN J WOODWORTH, II whose telephone number is (571)272-6904. The examiner can normally be reached Mon-Fri 9:00-5:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ilana Spar can be reached on (571) 270-7537. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALLAN J WOODWORTH, II/Primary Examiner, Art Unit 3622
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