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 .
The following Final Office Action is in response to Applicant’s reply filed on 05/21/2026. Claims 1, 3-11, and 13 have been amended. Claims 1, 3-11, and 13 are pending and reject as follows.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/23/2026 has been considered by the examiner.
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, 3-11, and 13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. A subject matter eligibility analysis is set forth below. See MPEP 2106.
Specifically, representative Claim 1 recites:
A computer-implemented method for optimizing placements of water level gauges, comprising:
a data acquisition step for acquiring respective water level data acquired by respective water level gauges installed in a plurality of manholes in a predetermined region;
a classification step of classifying the plurality of manholes into one or more clusters by clustering the respective water level data; and
a decision step to determine, for each cluster, one or more representative manholes from the plurality of manholes included in the one or more clusters based on proximity of temporal variation of water levels for placement of water level gauges used for predicting water levels of the plurality of manholes in the predetermined region, and
wherein the steps are performed by one or more processors executing instructions stored in a memory.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements.”
Similar limitations recited in Method Claim 1 are directed to the similar abstract idea as System Claim 7 and System Claim 13 and comprises:
a data acquisition step of acquiring past time-series water level data of a representative manhole selected by clustering from a plurality of manholes included in a predetermined region and predicted rainfall data for the predetermined region; and
an output step for outputting predicted information on the water level of one or more other manholes included in the predetermined region at a future point in time by inputting the acquired past time-series water level data of the representative manhole and the predicted future rainfall data into a predetermined engine.
wherein the predicted information is estimated based on a correlation between the representative manhole and the other manholes determined by the clustering, and
wherein the steps are performed by one or more processors executing instructions stored in a memory.
Under Step 1 of the analysis, claim 1 and claim 7 belongs to a statutory category, namely it is a method claim. Likewise, claim 13 is a system claim.
Under Step 2A, prong 1: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
In the instant case, claim 1 is found to recite at least one judicial exception (i.e. abstract idea), that being a Mental Process and a Mathematical Concept. This can be seen in the claim limitations of “a classification step of classifying the plurality of manholes into one or more clusters by clustering the respective water level data”, and “a decision step to determine, for each cluster, one or more representative manholes from the plurality of manholes included in the one or more clusters based on proximity of temporal variation of water levels for placement of water level gauges used for predicting water levels of the plurality of manholes in the predetermined region” which is the judicial exception of a mental process because these limitations are merely data observations, evaluations, and/or judgements in order to collect, analyze, and evaluate the water level data and to make determinations based on the analysis and is capable of being performed mentally and/or with the aid of pen and paper. Additionally, the aforementioned limitations recite mathematical calculations, e.g. see Spec. [0040]-[0045] describing the use of mathematical calculations and optimization techniques, such as regression analysis or machine learning algorithms, to calculate predict water levels based on past water level data and precipitation data.
In addition, claim 7 and claim 13 are found to recite to least one judicial exception (i.e. abstract idea), that being a Mental Process and a Mathematical Concept. This can be seen in the claim limitations of “an output step for outputting predicted information on the water level of one or more other manholes included in the predetermined region at a future point in time by inputting the acquired past time-series water level data of the representative manhole and the predicted future rainfall data into a predetermined engine” and “wherein the predicted information is estimated based on a correlation between the representative manhole and the other manholes determined by the clustering” which is the judicial exception of a mental process because these limitations are merely data observations, evaluations, and/or judgements in order to process and evaluate the past time series water level data and he predicted rainfall data, and to generate conclusions based on the evaluation and is capable of being performed mentally and/or with the aid of pen and paper.
Step 2A, prong 2 of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception(s) into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application.
In addition to the abstract ideas recited in claim 1, the claimed method recites additional elements including “a data acquisition step for acquiring respective water level data acquired by respective water level gauges installed in a plurality of manholes in a predetermined region”, and “wherein the steps are performed by one or more processors executing instructions stored in a memory” however these elements are found to be data gathering and output steps, which are recited at a high level of generality, and thus merely amount to “insignificant extra-solution” activity(ies). See MPEP 2106.05(g) “Insignificant Extra-Solution Activity,”.
The generic data gathering, processing, and output steps, are recited at such a high level of generality that it represents no more than mere instructions to apply the judicial exceptions on a computer. It can also be viewed as nothing more than an attempt to generally link the use of the judicial exceptions to the technological environment of a computer. Noting MPEP 2106.04(d)(I): “It is notable that mere physicality or tangibility of an additional element or elements is not a relevant consideration in Step 2A Prong Two. As the Supreme Court explained in Alice Corp., mere physical or tangible implementation of an exception does not guarantee eligibility. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 224, 110 USPQ2d 1976, 1983-84 (2014) ("The fact that a computer ‘necessarily exist[s] in the physical, rather than purely conceptual, realm,’ is beside the point")”.
Thus, under Step 2A, prong 2 of the analysis, even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception. No specific practical application is associated with the claimed system. For instance, nothing is done with the result of calculating predicted water levels beyond outputting the predicted information, and no further technical action or transformation is recited.
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as described above with respect to Step 2A Prong 2, merely amount to a general purpose computer system that attempts to apply the abstract idea in a technological environment, limiting the abstract idea to a particular field of use, and/or merely performs insignificant extra-solution activit(ies) (claims 1, 7 and 13). Such insignificant extra-solution activity, e.g. data gathering and output, when re-evaluated under Step 2B is further found to be well-understood, routine, and conventional as evidenced by MPEP 2106.05(d)(II) (describing conventional activities that include transmitting and receiving data over a network, electronic recordkeeping, storing and retrieving information from memory, and electronically scanning or extracting data from a physical document).
Therefore, similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that claim 1, as well as claim 7 and 13, amount to significantly more than the abstract idea.
With regards to the dependent claims, claims 3-6 and 8-11, merely further expand upon the algorithm/abstract idea and do not set forth further additional elements that integrate the recited abstract idea into a practical application or amount to significantly more. Therefore, these claims are found ineligible for the reasons described for claims 1, 7 and 13. Specifically:
With respect to the dependent claims 3-6 specifically, these claims merely further expand upon the abstract idea recited in the parent claim by adding additional details regarding the clustering process, time series handling, precipitation data, and constraints on the number of water level gauge placements. These limitations further define how the data is analyzed and organized but do not recite any additional elements that improve the functioning of a computer or any other technology. Rather, these claims merely refine or narrow the underlying abstract idea through additional data analysis and algorithmic steps, and therefore fail to integrate the recited abstract idea into a practical application or amount to significant more.
With respect to dependent claims 8 and 9 specifically, the claims further recite acquiring and using past time series water level data from other manholes and using estimated water level data derived from water level gauges installed in different manholes. These limitations merely add additional data sources and further data evaluation to the abstract idea described above. Such limitations amount to nothing more than additional data gathering and analysis performed on collected information, which constitutes insignificant extra solution activity and does not integrate the abstract idea into a practical application. See MPEP 2106.05 (g)(h).
With respect to dependent claims 8 and 9 specifically, the claims further recite acquiring and using past time series water level data from other manholes and using estimated water level data derived from water level gauges installed in different manholes. These limitations merely add additional data sources and further data evaluation to the abstract idea described above. The collection and use of additional data constitutes insignificant extra solution activity and does not integrate the abstract idea into a practical application and do not amount to significantly more. See MPEP 2106.05(g).
With respect to dependent claims 10 and 11 specifically, the claims further recite the use of a learning model trained using past time series water level data and rainfall data, including data from other nearby manholes. These limitations merely recite mathematical calculations, modeling, and data analysis performed using a learning or prediction model. The use of such learning model constitutes abstract mathematical concepts applied to collected data and does not effect a technological improvement to the computer itself. Accordingly, these claims merely further refine the abstract idea and fail to integrate the abstract idea into a practical application or amount to significantly more.
Accordingly, for the reasons above and those discussed in relation to independent claim 1, 7, and 13, the dependent claims are insufficient to integrate the claimed abstract ideas into a practical application or significant more.
Claim Rejections - 35 USC § 103
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.
Claims 1, 3-6 are rejected under 35 U.S.C. 103 as being unpatentable over US 20110071773 A1, David et al (hereinafter David) in view of US 20250154854 A1, Hussein et al (hereinafter Hussein).
Regarding Claim 1, David discloses water level gauges (David, [0034] An improved device and method for analyzing sewer systems maintains the benefits of traditional sewer analysis systems, while achieving the important objective of providing a low cost, simple, and easy to use device and method for monitoring fluid level and flow rates at multiple locations in a sewer system) and a data acquisition step for acquiring respective water level data (David, [0036] A method according to the present invention comprises installing several monitoring devices into sewer system manholes, recording fluid level and flow with accelerometers and pressure monitors, reading the recorded data, and displaying the data in chart or map form. Data displayed in map form may show topography, street maps, and single or multiple sewer systems. The maps may be created may in two or three dimensional. Data displayed on the maps may include fluid flow rates, fluid levels, derivatives and integrals of flow rates, and differences in fluid levels or flow between monitoring devices) acquired by respective water level gauges installed in a plurality of manholes in a predetermined region (David, [0155] The low cost and ease of use of the present invention facilitates the usage of multiple monitoring devices over a wide area in order to gain a better understanding of the flow rate through the sewer systems. FIG. 58 shows a street map 340 with markers 345 showing the hypothetical placement of multiple monitoring devices placed on a street map. Positioning monitoring devices in close proximity to each other reduces the length of sewer pipe that must be search when a blockage or inflow between the devices is identified);
proximity of temporal variation of water levels (David, [0036] installing several monitoring devices into sewer system manholes, recording fluid level and flow with accelerometers and pressure monitors, reading the recorded data, and displaying the data in chart or map form. Data displayed in map form may show topography, street maps, and single or multiple sewer systems. The maps may be created may in two or three dimensional. Data displayed on the maps may include fluid flow rates, fluid levels, derivatives and integrals of flow rates, and differences in fluid levels or flow between monitoring devices) for placement of water level gauges used for predicting water levels of the plurality of manholes in the predetermined region (David, [0155] The low cost and ease of use of the present invention facilitates the usage of multiple monitoring devices over a wide area in order to gain a better understanding of the flow rate through the sewer systems. FIG. 58 shows a street map 340 with markers 345 showing the hypothetical placement of multiple monitoring devices placed on a street map), and
David does not disclose a computer-implemented method for optimizing placements of water level gauges, comprising:
a classification step of classifying the plurality of manholes into one or more clusters by clustering the respective water level data; and
a decision step to determine, for each cluster, one or more representative
manholes from the plurality of manholes included in the one or more clusters based on proximity of temporal variation of water levels for placement of water level gauges used for predicting water levels of the plurality of manholes in the predetermined region, and
wherein the steps are performed by one or more processors executing instructions stored in a memory
However, Hussein discloses a classification technique of forming clusters where measurements are representative of measurements that would be taken throughout the entire cluster (see paragraphs [0118-0126]).
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine David and Hussein’s teaching because David teaches collecting water level data over time from multiple manholes, while Hussien teaches analyzing data with respect to time, clustering multiple sites based on collected data, and selecting representative sites within each cluster for modeling other sites in the cluster. Applying Hussien’s clustering technique to David’s water level data would allow manholes having similar water level changes over time to be grouped together and representative manholes to be selected for placement of water level gauges. Hussien further teaches reducing the number of instrumented locations while using representative sites to model or predict the other sites in the group. A person of ordinary skill in the art would have been motivated to apply Hussien’s clustering and representative-site technique to David’s monitored manholes in order to reduce the number of water level gauges needed while still allowing the water levels of the other manholes in the cluster to be predicted.
The combination would disclose a computer-implemented method for optimizing placements of water level gauges (Hussein, [0119] As to the foregoing example with 100 sites, consider a clustering approach that can be applied to plans, equipment, operations, data, etc., from such sites to determine a reasonable number of groupings such as, for example, 20 groups that may be represented as 20 clusters. In such an example, a site that is near a centroid of a cluster may be taken as a representative site where additional data may be acquired for purposes of training a model that can be implemented to represent other sites in the cluster. As an example, available data from each site within a cluster may be utilized to generate a model for the cluster (e.g., group) where some sites may have more available data than other sites, as applied of the system of David), comprising:
a classification step of classifying the plurality of manholes into one or more clusters by clustering the respective water level data (Hussein, [0121] a clustering analysis may be utilized for purposes of data augmentation, where such clustering may be based on one or more factors relevant to field operations, energy utilization, emissions, modeling, etc, [0126] a clustering model (e.g., k-means, k-medians, expectation maximization, hierarchical clustering, etc.), as applied of the system of David); and
a decision step to determine, for each cluster, one or more representative
manholes from the plurality of manholes included in the one or more clusters (Hussein, [0119] As to the foregoing example with 100 sites, consider a clustering approach that can be applied to plans, equipment, operations, data, etc., from such sites to determine a reasonable number of groupings such as, for example, 20 groups that may be represented as 20 clusters. In such an example, a site that is near a centroid of a cluster may be taken as a representative site where additional data may be acquired for purposes of training a model that can be implemented to represent other sites in the cluster. As an example, available data from each site within a cluster may be utilized to generate a model for the cluster (e.g., group) where some sites may have more available data than other sites, as applied of the system of David) based on proximity of temporal variation of water levels (Hussien, [0048] a numerical technique such as the finite difference method can include discretizing a differential heat equation for temperature with respect to a spatial coordinate or spatial coordinates to approximate temperature derivatives (e.g., first order, second order, etc.). While temperature is mentioned, the finite difference method can be utilized for one or more of various variables (e.g., pressure, fluid flow, stress, strain, emissions, etc.). Further, where time is of interest, a derivative of a variable or variables with respect to time may be provided [0119] As to the foregoing example with 100 sites, consider a clustering approach that can be applied to plans, equipment, operations, data, etc., from such sites to determine a reasonable number of groupings such as, for example, 20 groups that may be represented as 20 clusters [0120] a clustering process may help to identify a site that is more representative of a group of sites than other sites in the group [0121] a clustering analysis may be utilized for purposes of data augmentation, where such clustering may be based on one or more factors relevant to field operations, energy utilization, emissions, modeling, etc, as applied of the system of David) for placement of water level gauges used for predicting water levels of the plurality of manholes in the predetermined region (Hussien, [0118] A framework can reduce demand for instrumentation and/or performance of site surveys. As explained, through modeling, virtualization can be achieved for at least some sites. For example, consider a field with 100 sites where 20 sites are surveyed to generate data where the data are sufficient for ML model training to model the other 80 sites (e.g., generation of suitable digital twins, etc [0119] a site that is near a centroid of a cluster may be taken as a representative site where additional data may be acquired for purposes of training a model that can be implemented to represent other sites in the cluster [0120] the identified site may be assessed to determine whether it is suitable for implementation of a particular strategy, which may result in further data acquisition and model building for a model that can be representative of the group of sites. Such an approach can expedite prediction of how other sites in the group of sites may perform with respect to production, energy utilization, and emissions. As explained, one or more digital twins may be generated, where, for example, each group of sites has at least one digital twin, as applied of the system of David)
wherein the steps are performed by one or more processors executing instructions stored in a memory (Hussien, [0139] In the example of FIG. 20, a system 2090 includes one or more information storage devices 2091, one or more computers 2092, one or more networks 2095, and instructions 2096. As to the one or more computers 2092, each computer may include one or more processors (e.g., or processing cores) 2093 and a memory 2094 for storing the instructions 2096, for example, executable by at least one of the one or more processors. As an example, a computer may include one or more network interfaces (e.g., wired and/or wireless), one or more graphics cards, a display interface (e.g., wired or wireless), etc, as applied of the system of David)
Regarding Claim 3, David discloses plurality of manholes (David, [0036] A method according to the present invention comprises installing several monitoring devices into sewer system manholes, recording fluid level and flow with accelerometers and pressure monitors, reading the recorded data, and displaying the data in chart or map form. Data displayed in map form may show topography, street maps, and single or multiple sewer systems. The maps may be created may in two or three dimensional. Data displayed on the maps may include fluid flow rates, fluid levels, derivatives and integrals of flow rates, and differences in fluid levels or flow between monitoring devices).
David does not disclose the computer-implemented method for optimizing the placements of water level gauges according to claim 1,
wherein the clustering used in the classification step is hierarchical clustering; and
wherein, in the decision step, the one or more representative manholes are determined for each cluster based on the proximity by hierarchical clustering.
However, Hussein teaches the computer-implemented (Hussien, [0137-0141] the computational framework 2060 can include various components such as, for example, a network interface 2062, a predictor 2064, a pathway generator 2066, and a pathway executor 2068. In such an example, the components 2062, 2064, 2066, and 2068 can be utilized to perform various corresponding actions of the method 2000) method for optimizing the placements of water level gauges according to claim 1,
wherein the clustering used in the classification step is hierarchical clustering (Hussein, [0126] a clustering model (e.g., k-means, k-medians, expectation maximization, hierarchical clustering, etc.)); and
wherein, in the decision step, the one or more representative manholes are determined for each cluster based on the proximity by hierarchical clustering (Hussein, [0119] consider a clustering approach that can be applied to plans, equipment, operations, data, etc., from such sites to determine a reasonable number of groupings such as, for example, 20 groups that may be represented as 20 clusters. In such an example, a site that is near a centroid of a cluster may be taken as a representative site where additional data may be acquired for purposes of training a model that can be implemented to represent other sites in the cluster. As an example, available data from each site within a cluster may be utilized to generate a model for the cluster (e.g., group) where some sites may have more available data than other sites, [0120] a clustering process may help to identify a site that is more representative of a group of sites than other sites in the group. In such an example, the identified site may be assessed to determine whether it is suitable for implementation of a particular strategy, which may result in further data acquisition and model building for a model that can be representative of the group of sites).
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine David and Hussein’s teaching because David already teaches installing water level gauges in multiple manholes to collect monitoring data, while Hussein teaches clustering multiple sites and selecting representative sites based on clustering results, including hierarchical clustering and proximity to a centroid. A person of ordinary skill in the art would have been motivated to apply Hussein’s clustering and representative selection technique to David’s monitored manholes would have predictably optimized placement of water level gauges, reduce redundancy, and limit the number of installations while still maintaining effective monitoring.
Regarding Claim 4, David discloses plurality of manholes and water level gauges (David, [0036] A method according to the present invention comprises installing several monitoring devices into sewer system manholes, recording fluid level and flow with accelerometers and pressure monitors, reading the recorded data, and displaying the data in chart or map form. Data displayed in map form may show topography, street maps, and single or multiple sewer systems. The maps may be created may in two or three dimensional. Data displayed on the maps may include fluid flow rates, fluid levels, derivatives and integrals of flow rates, and differences in fluid levels or flow between monitoring devices).
David does not disclose the computer-implemented method of optimizing the placements of water level gauges according to claim 1,
wherein, in the decision step, the representative manhole is determined based on the result of the clustering and an upper limit setting for the number of placements of water level gauges.
However, Hussein teaches the computer-implemented (Hussien, [0137-0141] computational framework 2060 can include various components such as, for example, a network interface 2062, a predictor 2064, a pathway generator 2066, and a pathway executor 2068. In such an example, the components 2062, 2064, 2066, and 2068 can be utilized to perform various corresponding actions of the method 2000) method of optimizing the placements of water level gauges according to claim 1,
wherein, in the decision step, the representative manhole is determined based on the result of the clustering and an upper limit setting for the number of placements of water level gauges (Hussein, [0090] a data-driven solution can utilize such data for building models that can operate on limited amounts and/or types of data where such models can predict data, behaviors, etc., for wells with lesser instrumentation, lesser survey frequency, etc [0094] Such a framework can supplement and in some instances replace instrument-based detection of emissions at one or more sites that can involve installation of hundreds of sensors such as optical gas imaging technology at different locations to cover all sites, which is costly, time-consuming, and impractical, [0118] A framework can reduce demand for instrumentation and/or performance of site surveys. As explained, through modeling, virtualization can be achieved for at least some sites. For example, consider a field with 100 sites where 20 sites are surveyed to generate data where the data are sufficient for ML model training to model the other 80 sites (e.g., generation of suitable digital twins, etc.). In such an approach, data, which can be expensive to acquire, can be leveraged. A framework can reduce instrumentation and survey demands, while meeting or increasing production and reducing emissions, [0119] As to the foregoing example with 100 sites, consider a clustering approach that can be applied to plans, equipment, operations, data, etc., from such sites to determine a reasonable number of groupings such as, for example, 20 groups that may be represented as 20 clusters).
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine David and Hussein’s teaching because David teaches installing water level gauges in multiple manholes, while Hussien teaches using clustering to select representative sites and reducing the number of locations that require instrumentation. Hussien further teaches an example where 20 sites out of 100 sites are surveyed and the sites are divided into a reasonable number of clusters. Applying Hussien’s technique to Daviid’s monitored manholes would determine representative manholes based on the clustering results while limiting the number of locations where water level gauges are placed. A person of ordinary skill in the art would have been motivated to apply Hussien’s technique to David’s system in order to reduce the number of water level gauges needed while still maintaining useful monitoring and modeling of the other manholes.
Regarding Claim 5, David discloses precipitation data pertaining to the manhole is further acquired (David, [0129] During a "rain event," the fluid depth inside of the storm sewer will increase along with fluid flow velocity. A rain event includes rain, sleet, melting snow, melting ice, and other events that cause the flow rate through a collection network to deviate from a standard amount. The increases in fluid pressure (.about.1 atm per 33 feet fluid) results in the hydrophobic fluid exerting greater forces against the pressure sensor which are recorded by the data acquisition device) and the respective water level data (David, [0036] A method according to the present invention comprises installing several monitoring devices into sewer system manholes, recording fluid level and flow with accelerometers and pressure monitors, reading the recorded data, and displaying the data in chart or map form. Data displayed in map form may show topography, street maps, and single or multiple sewer systems. The maps may be created may in two or three dimensional. Data displayed on the maps may include fluid flow rates, fluid levels, derivatives and integrals of flow rates, and differences in fluid levels or flow between monitoring devices).
David does not disclose the computer-implemented method for optimizing the placements of water level gauges according to claim 1 wherein, in the data acquisition step,
wherein, in the classification step, clustering is performed using the precipitation data and the respective water level data.
However, Hussein teaches the computer-implemented (Hussien, [0137-0141] computational framework 2060 can include various components such as, for example, a network interface 2062, a predictor 2064, a pathway generator 2066, and a pathway executor 2068. In such an example, the components 2062, 2064, 2066, and 2068 can be utilized to perform various corresponding actions of the method 2000) for optimizing the placements of water level gauges according to claim 1 wherein, in the data acquisition step,
wherein, in the classification step, clustering is performed using the precipitation data and the respective water level data (Hussein, [0119] As to the foregoing example with 100 sites, consider a clustering approach that can be applied to plans, equipment, operations, data, etc., from such sites to determine a reasonable number of groupings such as, for example, 20 groups that may be represented as 20 clusters, [0126] a clustering model (e.g., k-means, k-medians, expectation maximization, hierarchical clustering, etc.)).
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine David and Hussein’s teaching because David teaches collecting water level and precipitation data from multiple manholes, while Hussein teaches clustering multiple sites and selecting representative sites to reduce the number of monitored locations. A person of ordinary skill in the art would have been motivated to apply Hussein’s clustering and representative selection technique to David’s monitored manholes to optimize placement of water level gauges, reduce redundancy, and limit the number of installations while still maintaining effective monitoring.
Regarding Claim 6, David discloses water level data (David, [0036] A method according to the present invention comprises installing several monitoring devices into sewer system manholes, recording fluid level and flow with accelerometers and pressure monitors, reading the recorded data, and displaying the data in chart or map form. Data displayed in map form may show topography, street maps, and single or multiple sewer systems. The maps may be created may in two or three dimensional. Data displayed on the maps may include fluid flow rates, fluid levels, derivatives and integrals of flow rates, and differences in fluid levels or flow between monitoring devices)
David does not disclose the computer-implemented method for optimizing the placements of water level gauges according to claim 1,
wherein the water level data is a time series of water level data.
However, Hussein teaches the computer-implemented (Hussien, [0137-0141] computational framework 2060 can include various components such as, for example, a network interface 2062, a predictor 2064, a pathway generator 2066, and a pathway executor 2068. In such an example, the components 2062, 2064, 2066, and 2068 can be utilized to perform various corresponding actions of the method 2000) for optimizing the placements of water level gauges according to claim 1,
wherein the water level data is a time series of water level data (Hussein, [0048] While temperature is mentioned, the finite difference method can be utilized for one or more of various variables (e.g., pressure, fluid flow, stress, strain, emissions, etc.). Further, where time is of interest, a derivative of a variable or variables with respect to time may be provided, [0088] the GUI 600 may provide for access to and use of one or more solution engines (e.g., AI/ML, hybrid models, analytics, uncertainty analysis, performance indicators, etc.) and may provide for access to and use of a data management system, which may be operatively coupled to one or more sources of data such as, for example, historical data, real-time data, etc. Various types of data can include field sensor data as may be present at a site associated with one or more assets and other types of data, which may pertain to weather, availability of energy, atmospheric conditions (e.g., levels of components in air, etc.), emissions (e.g., flaring, plumes, etc.), etc.).
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine David and Hussein’s teaching because David teaches acquiring and analyzing water level data from multiple manholes, and Hussein teaches using time based data and analytical models on site data, including both historical and real time data. A person of ordinary skill in the art would have been motivated to apply Hussein’s time-based data analysis to David’s water level measurement to improve modeling, predictions, and overall monitoring performance of the sewer network.
Claims 7-11 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over US 20110071773 A1, David et al (hereinafter David) in view of US 20250154854 A1, Hussein et al (hereinafter Hussein), in further view of US 20240273358 A1, Vincent et al (hereinafter Vincent)
Regarding Claim 7 and 13, David in view of Hussien teaches a computer-implemented (Hussien, [0137-0141] the computational framework 2060 can include various components such as, for example, a network interface 2062, a predictor 2064, a pathway generator 2066, and a pathway executor 2068. In such an example, the components 2062, 2064, 2066, and 2068 can be utilized to perform various corresponding actions of the method 2000) method for predicting water levels in manholes, comprising:
water level data and plurality of manholes (David, [0036] A method according to the present invention comprises installing several monitoring devices into sewer system manholes, recording fluid level and flow with accelerometers and pressure monitors, reading the recorded data, and displaying the data in chart or map form. Data displayed in map form may show topography, street maps, and single or multiple sewer systems. The maps may be created may in two or three dimensional. Data displayed on the maps may include fluid flow rates, fluid levels, derivatives and integrals of flow rates, and differences in fluid levels or flow between monitoring devices).
a representative manhole selected by clustering from a plurality of manholes included in a predetermined region (Hussien, [0119] As to the foregoing example with 100 sites, consider a clustering approach that can be applied to plans, equipment, operations, data, etc., from such sites to determine a reasonable number of groupings such as, for example, 20 groups that may be represented as 20 clusters [0120] a clustering process may help to identify a site that is more representative of a group of sites than other sites in the group. In such an example, the identified site may be assessed to determine whether it is suitable for implementation of a particular strategy, which may result in further data acquisition and model building for a model that can be representative of the group of sites. Such an approach can expedite prediction of how other sites in the group of sites may perform with respect to production, energy utilization, and emissions)
wherein the predicted information is estimated based on a correlation between the representative manhole and the other manholes determined by the clustering (Hussien, [0119] As to the foregoing example with 100 sites, consider a clustering approach that can be applied to plans, equipment, operations, data, etc., from such sites to determine a reasonable number of groupings such as, for example, 20 groups that may be represented as 20 clusters [0120] a clustering process may help to identify a site that is more representative of a group of sites than other sites in the group. In such an example, the identified site may be assessed to determine whether it is suitable for implementation of a particular strategy, which may result in further data acquisition and model building for a model that can be representative of the group of sites. Such an approach can expedite prediction of how other sites in the group of sites may perform with respect to production, energy utilization, and emissions)
wherein the steps are performed by one or more processors executing instructions stored in a memory (Hussien, [0139] In the example of FIG. 20, a system 2090 includes one or more information storage devices 2091, one or more computers 2092, one or more networks 2095, and instructions 2096. As to the one or more computers 2092, each computer may include one or more processors (e.g., or processing cores) 2093 and a memory 2094 for storing the instructions 2096, for example, executable by at least one of the one or more processors. As an example, a computer may include one or more network interfaces (e.g., wired and/or wireless), one or more graphics cards, a display interface (e.g., wired or wireless), etc)
David in view of Hussien does not disclose a computer-implemented method for predicting water levels in manholes, comprising:
a data acquisition step of acquiring past time-series water level data of representative manhole selected by clustering from a plurality of manholes included in a predetermined region and predicted future rainfall data for the predetermined region; and
an output step for outputting predicted information on the water level of one or more other manholes included in the predetermined region at a future point in time by inputting the acquired past time-series water level data of the representative manhole and the predicted future rainfall data into a predetermined engine.
However, Vincent teaches a past data acquisition step of acquiring past time-series water level data (Vincent, [0058] Historical reservoir data 304 may comprise one or more reservoir parameters for the same one or more reservoirs represented in historical climate data 302. The reservoir parameters may be measured by one or more sensors in the water reservoir, over time, so as to represent a time series of reservoir data for each water reservoir. In an embodiment, the reservoir parameter(s) comprise at least the water level of each water reservoir, [0058] Historical reservoir data 304 may also comprise reservoir metadata, such as identifiers of each water reservoir, the location of each water reservoir (e.g., coordinates in GPS or other GNSS representing the location of each stream-gaging station and/or boundaries of the water reservoir), and/or the like, [0060] a training dataset 315 is generated or otherwise acquired from historical climate data 302 and historical reservoir data 304. Training dataset 315 may comprise a plurality of labeled feature vectors. Each feature vector may contain the value of each of a plurality of features, derived from historical climate data 302 and historical reservoir data 304) and predicted future rainfall data for the predetermined region (Vincent, [0057] the climate parameter(s) comprise at least the temperature (e.g., in degrees Celsius or Fahrenheit) and precipitation (e.g., in millimeters, centimeters, or inches of rainfall, snowfall, etc.) in the geographical vicinity of the water reservoir, [0101] LSTM structure 610 consisted of a single layer of fifty nodes, and densely connected structure 620 consisted of four layers of fifty nodes. All of the layers in LSTM structure 610 and densely connected structure 620 were fully connected. Feature vectors of fourteen tuples, representing fourteen consecutive days of temperature, precipitation, and water level, were used. Machine-learning model 116 was trained to predict the next day's change in water level, given such a feature vector); and
an output step for outputting predicted information on the water level of one or more other manholes included in the predetermined region at a future point in time by inputting the acquired past time-series water level data of the representative manhole (Vincent, [0066] The target with which each feature vector is labeled may comprise the value of at least one target parameter for at least one subsequent time interval (i.e., a future time interval with respect to the time series in the feature vector, but not with respect to the time of training). The target parameter may be any parameter that is useful for water-resource management, [0066] the feature vector consisted of a time series of fourteen days of tuples (i.e., N=14) and the target consisted of the value of each target parameter for one week (i.e., seven days) in the future (i.e., M=7) or two weeks (i.e., fourteen days) in the future (i.e., M=14) from the time series in the feature vector) and the predicted future rainfall data into a predetermined engine (Vincent, [0072] machine-learning model 116 receives the values of a plurality of features representing climate and/or reservoir parameters, and outputs a predicted value of each target parameter. In an embodiment, the plurality of features comprises a time series of N tuples, representing N consecutive time intervals, with each tuple comprising a value of one or more climate parameters (e.g., temperature and precipitation) and one or more reservoir parameters (e.g., water level, water storage, or storage capacity), and the target parameter(s) comprise or consist of the water level, change in water level, water storage, change in water storage, storage capacity, or change in storage capacity for subsequent time interval N+M (e.g., M=1, M=7, M=14, M=30, etc.), [0077] a feature vector, comprising the time series of tuples, may be input to machine-learning model 116. When applied to such a feature vector, machine-learning model 116 may predict the value of the target parameter(s) for a single future time interval (e.g., time interval N+M, in which M≥1, such as M=1, M=7, M=14, M=30, etc.) for the water reservoir of interest. Alternatively, machine-learning model 116 may predict the value of the target parameter(s) for a plurality of future time intervals (e.g., time intervals N+M, N+M+1, . . ., N+M+X, such as X=6, X=13, etc.) for the water reservoir of interest).
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine David in view of Hussien and Vincent’s teaching because David teaches monitoring water levels at a plurality of manholes, Hussien teaches clustering multiple sites and selecting a representative site to represent other sites within the cluster, and Vincent teaches using past time series water level data and rainfall data to predict future water levels. A person of ordinary skill in the art would have been motivated to apply Hussein’s clustering and representative technique and Vincent’s prediction technique to David’s monitored manholes in order to reduce the number of directly monitored locations while still predicting future water levels for the other manholes in the cluster.
Regarding Claim 8, David in view of Hussien teaches the computer-implemented method of predicting the water level of a manhole according to claim 7,
water level data of other manholes in proximity to the one manhole (David, [0036] A method according to the present invention comprises installing several monitoring devices into sewer system manholes, recording fluid level and flow with accelerometers and pressure monitors, reading the recorded data, and displaying the data in chart or map form. Data displayed in map form may show topography, street maps, and single or multiple sewer systems. The maps may be created may in two or three dimensional. Data displayed on the maps may include fluid flow rates, fluid levels, derivatives and integrals of flow rates, and differences in fluid levels or flow between monitoring devices).
David in view of Hussien does not teach wherein, in the data acquisition step, it further acquires past time series of water level data of a different manhole in proximity to the representative manhole; and
wherein, in the output step, the past time-series water level data of the different manhole is input to the predetermined engine.
However, Vincent teaches wherein, in the past data acquisition step, it further acquires past time series of water level data of different manhole in proximity to the representative manhole (Vincent, [0058] Historical reservoir data 304 may comprise one or more reservoir parameters for the same one or more reservoirs represented in historical climate data 302. The reservoir parameters may be measured by one or more sensors in the water reservoir, over time, so as to represent a time series of reservoir data for each water reservoir. In an embodiment, the reservoir parameter(s) comprise at least the water level of each water reservoir, [0061] the plurality of features comprises, for a given water reservoir, a time series of a plurality of tuples, with each tuple comprising a value for each of one or more climate parameters, derived from historical climate data 302, and one or more reservoir parameters, derived from historical reservoir data 304, for a respective water reservoir. For example, each tuple may comprise or consist of a value representing temperature derived from historical climate data 302, a value representing precipitation derived from historical climate data 302, and a value representing water level, water storage, or storage capacity derived from historical reservoir data 304 for the respective water reservoir); and
wherein, in the output step, the past time-series water level data of the other manhole is input to the predetermined engine (Vincent, [0072] the trained machine-learning model 116 may be deployed. In an embodiment, machine-learning model 116 receives the values of a plurality of features representing climate and/or reservoir parameters, and outputs a predicted value of each target parameter. In an embodiment, the plurality of features comprises a time series of N tuples, representing N consecutive time intervals, with each tuple comprising a value of one or more climate parameters (e.g., temperature and precipitation) and one or more reservoir parameters (e.g., water level, water storage, or storage capacity), and the target parameter(s) comprise or consist of the water level, change in water level, water storage, change in water storage, storage capacity, or change in storage capacity for subsequent time interval N+M (e.g., M=1, M=7, M=14, M=30, etc.)).
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine David in view of Hussien and Vincent’s teaching because David teaches acquiring water level data from multiple manholes within a sewer system, Hussien teaches clustering multiple sites and selecting a representative site for the group, and Vincent teaches using past time-series water level data from multiple monitored locations as inputs to a prediction engine. A person of ordinary skill in the art would have been motivated to use Vincent’s time-series water level data from another monitored location together with Hussien’s representative-site clustering technique in David’s sewer system in order to provide additional water level information from a different manhole associated with the representative manhole to improve reliability of prediction.
Regarding Claim 9, David in view of Hussien teaches water level data and plurality of manholes (David, [0036] A method according to the present invention comprises installing several monitoring devices into sewer system manholes, recording fluid level and flow with accelerometers and pressure monitors, reading the recorded data, and displaying the data in chart or map form. Data displayed in map form may show topography, street maps, and single or multiple sewer systems. The maps may be created may in two or three dimensional. Data displayed on the maps may include fluid flow rates, fluid levels, derivatives and integrals of flow rates, and differences in fluid levels or flow between monitoring devices) and the method for predicting water levels in manholes according to claim 7.
David in view of Hussien does not teach wherein the past time series of water level data includes estimated water level data estimated based on water level data acquired by a water level gauge installed in a manhole different from the representative manhole.
However, Vincent teaches wherein the past time series of water level data includes estimated water level data estimated based on water level data acquired (Vincent, [0061] each tuple may comprise or consist of a value representing temperature derived from historical climate data 302, a value representing precipitation derived from historical climate data 302, and a value representing water level, water storage, or storage capacity derived from historical reservoir data 304 for the respective water reservoir) by a water level gauge installed in a manhole different from the representative manhole (Vincent, [0063] In some cases, the locations associated with climate parameters in historical climate data 302 may not correspond precisely to the location of a water reservoir. In this case, the value of each climate parameter for the water reservoir may be interpolated from the value(s) of each climate parameter for two or more locations within the geographical vicinity (e.g., watershed) of the water reservoir).
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine David in view of Hussien and Vincent’s teaching because David already teaches installing water level gauges in a plurality of manholes and acquiring water level data for monitoring sewer systems, while Vincent teaches using past time-series water level data, precipitation data, and related environmental parameters as inputs to a predictive engine to forecast future water levels. A person of ordinary skill in the art would have been motivated to apply Vincent’s time-series modeling and prediction techniques to David’s monitored manholes in order to predict future water levels, and enhance system management.
Regarding Claim 10, David in view of Hussien teaches plurality of manholes (David, [0036] A method according to the present invention comprises installing several monitoring devices into sewer system manholes, recording fluid level and flow with accelerometers and pressure monitors, reading the recorded data, and displaying the data in chart or map form. Data displayed in map form may show topography, street maps, and single or multiple sewer systems. The maps may be created may in two or three dimensional. Data displayed on the maps may include fluid flow rates, fluid levels, derivatives and integrals of flow rates, and differences in fluid levels or flow between monitoring devices) and the method of predicting the water level of a manhole according to claim 7.
David in view of Hussien does not teaches wherein the predetermined engine is an engine based on a learning model obtained by learning using at least the past time series of water level data for the representative manhole and the past rainfall data for the predetermined region .
However, Vincent teaches wherein the predetermined engine is an engine based on a learning model (Vincent, [0054] a machine-learning model 116, which may comprise an artificial neural network, such as a recurrent neural network, with long short-term memory, is disclosed. Machine-learning model 116 may be trained to predict storage capacities of water reservoirs from historical climate parameters about the watersheds of water reservoirs and from historical reservoir parameters of those water reservoirs) obtained by learning using at least the past time series of water level data for the one manhole and the past rainfall data for the target area (Vincent, [0061] each tuple may comprise or consist of a value representing temperature derived from historical climate data 302, a value representing precipitation derived from historical climate data 302, and a value representing water level, water storage, or storage capacity derived from historical reservoir data 304 for the respective water reservoir).
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine David in view of Hussien and Vincent’s teaching because David already teaches collecting water level data from multiple manholes and displaying the data for analysis, while Vincent teaches using a machine learning model trained on past time series water level data and rainfall data to predict future water levels. A person of ordinary skill in the art would have recognized that incorporating Vincent’s learning prediction engine into David’s sewer monitoring system would have predictably improved the system by enabling more accurate forecasting of future water levels based on historical trends and rainfall patterns.
Regarding Claim 11, David in view of Hussien teaches plurality of manholes (David, [0036] A method according to the present invention comprises installing several monitoring devices into sewer system manholes, recording fluid level and flow with accelerometers and pressure monitors, reading the recorded data, and displaying the data in chart or map form. Data displayed in map form may show topography, street maps, and single or multiple sewer systems. The maps may be created may in two or three dimensional. Data displayed on the maps may include fluid flow rates, fluid levels, derivatives and integrals of flow rates, and differences in fluid levels or flow between monitoring devices) and the method for predicting the water level of a manhole according to claim 10,
David in view of Hussien does not teaches wherein the predetermined engine is further obtained by learning using past time series of water level data of a different manhole in proximity to the representative manhole.
However, Vincent teaches wherein the predetermined engine is further obtained by learning using past time series of water level data of different manhole in close proximity to the representative manhole (Vincent, [0063] For each water reservoir, the climate parameter(s) for that water reservoir may be correlated to the reservoir parameter(s) for that water reservoir based on the location at which the value(s) of the climate parameter(s) were measured (e.g., as specified in the metadata of historical climate data 302) and the location of the water reservoir (e.g., as specified in the metadata of historical reservoir data 304). In some cases, the locations associated with climate parameters in historical climate data 302 may not correspond precisely to the location of a water reservoir. In this case, the value of each climate parameter for the water reservoir may be interpolated from the value(s) of each climate parameter for two or more locations within the geographical vicinity (e.g., watershed) of the water reservoir).
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine David in view of Hussien and Vincent’s teaching because David already teaches acquiring water level data from multiple manholes and monitoring fluid levels within a sewer system, while Vincent teaches training a learning model using time series data from multiple related locations in close proximity to improve prediction accuracy. A person of ordinary skill in the art would have recognized that using past time series water level data from nearby manholes, as taught by Vincent, to train the prediction engine in David would have predictably improved the accuracy and reliability of forecasting water levels for a target manhole.
Response to Arguments
35 USC§ 101
Applicant’s argument with respect to Claims 1, 3-11, and 13 of the 35 U.S.C. 101 Rejection has been considered but are not persuasive. The amendments to claims 1, 7, and 13 further define the data being analyzed and the way the representative manholes and predicted water levels are determined. However, the claims still recite acquiring data, clustering or correlating the data, determining representative manholes, and outputting predicted information.
The recited water level gauges, manholes, processors, memory, and predetermined engine do not change the nature of the claimed analysis. Claim 1 does not require actually placing or controlling a water level gauge based on the determination and claim 7 and 13 does not require any physical action based on the predicted water level. Therefore, the amendments do not integrate the abstract idea into a practical application, and the rejection under 35 U.S.C. 101 is maintained.
35 USC§ 103
Applicant’s argument with respect to Claims 1, 3-11, and 13 of the 35 U.S.C. 101 Rejection has been considered but are not persuasive.
Regarding claim 1, David teaches collecting water level data from multiple manholes, while Hussien teaches clustering sites based on collected data, selecting sites, and using the representative sites to model or predict other sites within the cluster. Therefore, applying Hussein’s clustering technique to David’s water level data would have resulted in selecting representative manholes based on similar water level behavior while reducing the number of water level gauges required.
Regarding claim 7 and 13, David teaches monitoring multiple manholes, Hussien teaches clustering sites and selecting a representative site for other sites in the cluster, and Vincent teaches past time series water level data and rainfall data to predict future water levels.
Applicant’s arguments address the references individually, while the rejection is based on their combined teachings. Accordingly this argument is unpersuasive because of the cited portions of (David in view of Hussien in further view of Vincent). For at least these reasons, Applicant’s arguments are unpersuasive and Claims 1, 3-11, and 13 are rejected under 35 U.S.C. 103. See rejection above for further detail.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IBRAHIM NAGI SHOHATEE whose telephone number is (571)272-6612. The examiner can normally be reached 8am-5pm.
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/IBRAHIM NAGI SHOHATEE/Examiner, Art Unit 2857
/KYLE R QUIGLEY/Primary Examiner, Art Unit 2857
